System

Abstract

An object of a system according to an embodiment is to enable a new employee to efficiently perform work in a remote work.SOLUTION: A system includes an task management part, a matter management part, a lecture part, a query part, and an adjustment part. The task management unit manages personal tasks. The pending-order management unit manages the progress status of the pending order. The lecture part lectures a system operation method. The inquiry unit automatically makes an inquiry to a senior employee. The adjustment unit automatically adjusts the meeting.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing technology does not provide sufficient support for new employees to work efficiently remotely, and there is room for improvement.

[0005] The system according to the embodiment aims to enable new employees to efficiently carry out their work through remote work. [Means for solving the problem]

[0006] The system according to the embodiment includes a task management unit, a case management unit, a lecture unit, an inquiry unit, and a coordination unit. The task management unit manages individual tasks. The case management unit manages the progress of cases. The lecture unit gives lectures on how to operate the system. The inquiry unit automatically makes inquiries to senior employees. The coordination unit automatically arranges meetings. [Effects of the Invention]

[0007] The system according to the embodiment allows new employees to efficiently carry out their work through remote work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The work support system according to an embodiment of the present invention is a system that allows new employees to receive support from AI and various tools when working remotely. This system provides various functions, such as individual task management, case management, and lectures on how to operate the system. This allows the work support system to support new employees in efficiently carrying out their work.

[0029] The work support system according to the embodiment includes a task management unit, a case management unit, a lecture unit, an inquiry unit, and a coordination unit. The task management unit manages individual tasks. For example, the generation AI lists the tasks of new employees and displays them in order of priority. The task management unit can also track the progress of tasks and suggest necessary actions. The case management unit manages the progress of cases. For example, the generation AI tracks the progress of cases and suggests necessary actions. The case management unit can also analyze the progress of cases in detail, identify risk factors, and issue warnings in advance. The lecture unit gives lectures on how to operate the system. For example, the generation AI explains the operation procedures and provides demonstrations as needed. The lecture unit can also provide operation procedures in video format to provide lectures that are visually easy to understand. The inquiry unit automatically makes inquiries to senior employees. For example, the generation AI automatically makes inquiries to senior employees and conveys the answers to the new employee. The inquiry unit can also analyze past inquiry history and automatically suggest answers to similar inquiries. The coordination unit automatically arranges meetings. For example, the generation AI automatically checks the schedules of the relevant parties and proposes the optimal date and time for the meeting. The coordination unit can also automatically organize the purpose and agenda of the meeting and share it with the relevant parties in advance. As a result, the work support system according to the embodiment allows new employees to efficiently carry out their work while working remotely. For example, task management and case management can be carried out smoothly, so work progresses smoothly. In addition, necessary information can be obtained quickly by automating lectures on how to operate the system and inquiries to senior employees. Furthermore, automating meeting arrangements reduces time waste.

[0030] The task management unit uses the generation AI to monitor the progress of tasks in real time and can dynamically change task priorities according to the progress. For example, the task management unit uses the generation AI to monitor the progress of tasks in real time and prioritize tasks that are lagging behind. For example, it moves tasks that are 50% or less in progress to the top of the list. The generation AI also analyzes task progress data and postpones tasks that are progressing quickly. For example, it moves tasks that are 80% or more in progress to the bottom of the list. The generation AI also dynamically changes task priorities based on the task progress. For example, it prioritizes tasks that are lagging behind and postpones tasks that are progressing quickly. This makes it possible to dynamically change priorities according to the progress of tasks.

[0031] The task management unit can use the generative AI to automatically suggest resources and tools based on the content of the task. For example, the generative AI analyzes the content of the task and automatically suggests the necessary resources and tools. For example, for a report creation task, it suggests templates and reference materials. The generative AI also suggests efficient tools based on the content of the task. For example, for a data analysis task, it suggests appropriate analysis software. The generative AI also analyzes the content of the task and automatically suggests the necessary resources. For example, for a presentation creation task, it suggests image and graph materials. This makes it possible to automatically suggest the necessary resources and tools based on the content of the task.

[0032] The project management department can use the generation AI to analyze the progress of projects in detail, identify risk factors, and issue warnings in advance. In the project management department, for example, the generation AI analyzes the progress of projects in detail and identifies risk factors. For example, it detects tasks that are behind schedule or lack of resources. Furthermore, if a risk factor is identified, the generation AI issues a warning in advance. For example, it sends a reminder for tasks that are behind schedule. Furthermore, the generation AI analyzes the progress of projects in detail, identifies risk factors, and issues warnings in advance. For example, it detects lack of resources or schedule delays and issues a warning. In this way, the progress of projects can be analyzed in detail, identified risk factors, and issued warnings in advance.

[0033] The project management department can use the generation AI to automatically propose resource reallocation according to the progress of the project. For example, the generation AI analyzes the progress of the project and automatically proposes the reallocation of necessary resources. For example, it proposes additional resources for tasks that are short of resources. Furthermore, in order to optimize resources, the generation AI proposes resource reallocation according to the progress of the project. For example, it moves resources from tasks that are progressing quickly. Furthermore, the generation AI automatically proposes the reallocation of necessary resources based on the progress of the project, thereby optimizing resources. For example, it detects resource surpluses and shortages and proposes optimal allocations. This makes it possible to automatically propose the reallocation of necessary resources according to the progress of the project.

[0034] The lecture section can use the generation AI to analyze the user's operation history and suggest the most efficient operation procedure. For example, the generation AI analyzes the user's operation history and suggests the most efficient operation procedure. For example, it may present the shortest route based on past operation data. The generation AI also analyzes the user's operation history and uses the data to suggest efficient operation procedures. For example, it may prioritize displaying frequently used functions. The generation AI also analyzes the user's operation history and suggest the most efficient operation procedure. For example, it may prioritize presenting procedures with fewer operation errors. This makes it possible to analyze the user's operation history and suggest the most efficient operation procedure.

[0035] The lecture unit uses the generation AI to provide operation procedures in video format, allowing for visually easy-to-understand lectures. For example, the generation AI may provide operation procedures in video format, allowing for visually easy-to-understand lectures. For example, a video explaining the operation procedures may be generated using screen capture. The generation AI may also provide lectures in video format so that the user can easily understand the operation procedures visually. For example, a video showing the operation procedures in animation may be provided. The generation AI may also provide operation procedures in video format, allowing for visually easy-to-understand lectures. For example, a video explaining the operation procedures step by step may be generated. This allows for operation procedures to be provided in video format, allowing for visually easy-to-understand lectures.

[0036] The inquiry unit can use the generation AI to analyze past inquiry history and automatically suggest answers to similar inquiries. For example, the generation AI analyzes past inquiry history and automatically suggests answers to similar inquiries. For example, it presents the optimal answer based on a database of past answers. In addition, to automatically suggest answers to similar inquiries, the generation AI utilizes past inquiry history. For example, it reuses past answers to the same problem. In addition, the generation AI analyzes past inquiry history and automatically suggests answers to similar inquiries. For example, it analyzes the content of the inquiry and presents the optimal answer. In this way, it is possible to analyze past inquiry history and automatically suggest answers to similar inquiries.

[0037] The inquiry unit uses the generation AI to analyze the inquiry content using natural language processing, and can automatically select the most suitable senior employee to make the inquiry. For example, the generation AI analyzes the inquiry content using natural language processing, and automatically selects the most suitable senior employee. For example, it selects a senior employee with specialized knowledge related to the inquiry content. Furthermore, the generation AI utilizes natural language processing to analyze the inquiry content and automatically select the most suitable senior employee. For example, it selects a senior employee with specialized knowledge related to the inquiry content. Furthermore, the generation AI analyzes the inquiry content using natural language processing, and automatically selects the most suitable senior employee to make the inquiry. For example, it selects the senior employee who is most suitable for the inquiry content. This makes it possible to analyze the inquiry content using natural language processing, and automatically select the most suitable senior employee to make the inquiry.

[0038] The adjustment unit can use the generation AI to monitor the schedules of the parties involved in real time and dynamically adjust the optimal meeting date and time. For example, the generation AI monitors the schedules of the parties involved in real time and dynamically adjusts the optimal meeting date and time. For example, it proposes the optimal date and time based on everyone's free time. In addition, the generation AI analyzes schedule data to monitor the schedules of the parties involved and dynamically adjust the optimal meeting date and time. For example, it avoids schedule overlaps. In addition, the generation AI monitors the schedules of the parties involved in real time and dynamically adjusts the optimal meeting date and time. For example, it proposes the optimal date and time based on everyone's free time. In this way, the schedules of the parties involved can be monitored in real time and the optimal meeting date and time can be dynamically adjusted.

[0039] The coordination unit can use the generation AI to automatically organize the purpose and agenda of a meeting and share it with the relevant parties in advance. For example, the coordination unit uses the generation AI to automatically organize the purpose and agenda of a meeting and share it with the relevant parties in advance. For example, the generation AI can automatically generate a meeting agenda and send it to the relevant parties. In addition, in order to organize the purpose and agenda of a meeting and share it with the relevant parties in advance, the generation AI automatically organizes data. For example, it can set the priority of the agenda. In addition, the generation AI can automatically organize the purpose and agenda of a meeting and share it with the relevant parties in advance. For example, it can automatically generate a meeting agenda and send it to the relevant parties. In this way, the purpose and agenda of a meeting can be automatically organized and shared with the relevant parties in advance.

[0040] The coordination department can use the generation AI to automatically generate meeting minutes and share them with the relevant parties. For example, the coordination department has the generation AI automatically generate meeting minutes and share them with the relevant parties. For example, the content of the meeting is saved as text data and sent to the relevant parties. In addition, in order to automatically generate meeting minutes and share them with the relevant parties, the generation AI analyzes voice data. For example, minutes are created using voice recognition technology. In addition, the generation AI automatically generates meeting minutes and shares them with the relevant parties. For example, the content of the meeting is summarized and sent to the relevant parties. In this way, meeting minutes can be automatically generated and shared with the relevant parties.

[0041] The coordination unit can use the generation AI to automatically list follow-up tasks for meetings and notify the relevant parties. For example, the coordination unit uses the generation AI to automatically list follow-up tasks for meetings and notify the relevant parties. For example, it lists action items decided in a meeting and sends them to the relevant parties. In addition, the generation AI analyzes the content of the meeting to list follow-up tasks and notify the relevant parties. For example, it sets deadlines and responsible persons for tasks. In addition, the generation AI automatically lists follow-up tasks for meetings and notifies the relevant parties. For example, it generates tasks based on the content of the meeting and sends them to the relevant parties. In this way, follow-up tasks for meetings can be automatically listed and notified to the relevant parties.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The work support system can further include a health management unit. The health management unit monitors the user's health condition and provides appropriate health advice. For example, if the generation AI analyzes the user's activity data and detects a lack of exercise, it will suggest appropriate exercise. The health management unit can also analyze the user's food records and suggest nutritionally balanced meals. Furthermore, the health management unit can analyze the user's sleep data and provide advice on improving sleep quality. This allows for comprehensive management of the user's health condition and improves work efficiency.

[0044] The work support system can further include a learning support unit. The learning support unit provides learning content to support the user's skill improvement. For example, the generative AI analyzes the user's work content, identifies the required skills, and suggests learning content based on those skills. The learning support unit can also track the user's learning progress and provide feedback according to the progress. Furthermore, the learning support unit can provide a customized learning plan tailored to the user's learning style. This makes it possible to efficiently support the user's skill improvement.

[0045] The work support system can further include a communication support unit. The communication support unit supports smooth communication between users. For example, the generation AI analyzes the user's communication history and suggests appropriate communication methods. The communication support unit can also provide training content to improve the user's communication skills. Furthermore, the communication support unit can provide forums and discussion boards to promote the exchange of opinions between users. This supports smooth communication between users and strengthens team cooperation.

[0046] The work support system can further include a feedback unit. The feedback unit provides feedback on the user's work performance. For example, the generation AI analyzes the user's work data and evaluates performance. The feedback unit can also identify the user's strengths and areas for improvement and provide specific advice. Furthermore, the feedback unit can track the user's goal achievement and evaluate progress toward the goal. This makes it possible to provide feedback to improve the user's work performance and support their growth.

[0047] The work support system can further include a motivation management unit. The motivation management unit provides support to maintain and improve the user's motivation. For example, the generation AI analyzes the user's work data and detects signs of declining motivation. The motivation management unit can also propose incentives and reward programs to improve the user's motivation. Furthermore, the motivation management unit can support the user in setting goals and provide advice to maintain motivation to achieve those goals. This helps maintain and improve the user's motivation and improve work efficiency.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The task manager manages individual tasks. For example, the generative AI might list and prioritize tasks for a new employee. The task manager can also track task progress and suggest necessary actions. Step 2: The case management department manages the progress of the case. For example, the generative AI tracks the progress of the case and suggests necessary actions. The case management department can also perform detailed analysis of the case's progress, identify risk factors, and issue warnings in advance. Step 3: The lecture department gives a lecture on how to operate the system. For example, the generation AI explains the operation procedure and provides a demonstration as needed. The lecture department can also provide the operation procedure in video format to make the lecture easier to understand visually. Step 4: The inquiry department automatically sends inquiries to senior employees. For example, the generation AI automatically sends inquiries to senior employees and conveys their answers to the new employee. The inquiry department can also analyze past inquiry history and automatically suggest answers to similar inquiries. Step 5: The coordination department automatically arranges meetings. For example, the generation AI automatically checks the schedules of the people involved and suggests the optimal date and time for the meeting. The coordination department can also automatically organize the purpose and agenda of the meeting and share it with the people involved in advance.

[0050] (Example 2) The work support system according to an embodiment of the present invention is a system that allows new employees to receive support from AI and various tools when working remotely. This system provides various functions, such as individual task management, case management, and lectures on how to operate the system. This allows the work support system to support new employees in efficiently carrying out their work.

[0051] The work support system according to the embodiment includes a task management unit, a case management unit, a lecture unit, an inquiry unit, and a coordination unit. The task management unit manages individual tasks. For example, the generation AI lists the tasks of new employees and displays them in order of priority. The task management unit can also track the progress of tasks and suggest necessary actions. The case management unit manages the progress of cases. For example, the generation AI tracks the progress of cases and suggests necessary actions. The case management unit can also analyze the progress of cases in detail, identify risk factors, and issue warnings in advance. The lecture unit gives lectures on how to operate the system. For example, the generation AI explains the operation procedures and provides demonstrations as needed. The lecture unit can also provide operation procedures in video format to provide lectures that are visually easy to understand. The inquiry unit automatically makes inquiries to senior employees. For example, the generation AI automatically makes inquiries to senior employees and conveys the answers to the new employee. The inquiry unit can also analyze past inquiry history and automatically suggest answers to similar inquiries. The coordination unit automatically arranges meetings. For example, the generation AI automatically checks the schedules of the relevant parties and proposes the optimal date and time for the meeting. The coordination unit can also automatically organize the purpose and agenda of the meeting and share it with the relevant parties in advance. As a result, the work support system according to the embodiment allows new employees to efficiently carry out their work while working remotely. For example, task management and case management can be carried out smoothly, so work progresses smoothly. In addition, necessary information can be obtained quickly by automating lectures on how to operate the system and inquiries to senior employees. Furthermore, automating meeting arrangements reduces time waste.

[0052] The task management unit uses the generation AI to monitor the progress of tasks in real time and can dynamically change task priorities according to the progress. For example, the task management unit uses the generation AI to monitor the progress of tasks in real time and prioritize tasks that are lagging behind. For example, it moves tasks that are 50% or less in progress to the top of the list. The generation AI also analyzes task progress data and postpones tasks that are progressing quickly. For example, it moves tasks that are 80% or more in progress to the bottom of the list. The generation AI also dynamically changes task priorities based on the task progress. For example, it prioritizes tasks that are lagging behind and postpones tasks that are progressing quickly. This makes it possible to dynamically change priorities according to the progress of tasks.

[0053] The task management unit can use the generative AI to automatically suggest resources and tools based on the content of the task. For example, the generative AI analyzes the content of the task and automatically suggests the necessary resources and tools. For example, for a report creation task, it suggests templates and reference materials. The generative AI also suggests efficient tools based on the content of the task. For example, for a data analysis task, it suggests appropriate analysis software. The generative AI also analyzes the content of the task and automatically suggests the necessary resources. For example, for a presentation creation task, it suggests image and graph materials. This makes it possible to automatically suggest the necessary resources and tools based on the content of the task.

[0054] The task management unit can use the emotion estimation function to evaluate the user's stress level and make suggestions to reduce the task load if the stress level is high. The task management unit, for example, uses the emotion estimation function to evaluate the user's stress level in real time. For example, it analyzes facial expressions and vocal tone to calculate a stress score. Furthermore, if the user's stress level is high, the generation AI makes suggestions to reduce the task load. For example, it suggests changing task priorities and doing easier tasks first. Furthermore, it uses the emotion estimation function to evaluate the user's stress level and suggests taking a break if stress is high. For example, it notifies the user to take a short break. This makes it possible to make suggestions to reduce the task load according to the user's stress level.

[0055] The project management department can use the generation AI to analyze the progress of projects in detail, identify risk factors, and issue warnings in advance. In the project management department, for example, the generation AI analyzes the progress of projects in detail and identifies risk factors. For example, it detects tasks that are behind schedule or lack of resources. Furthermore, if a risk factor is identified, the generation AI issues a warning in advance. For example, it sends a reminder for tasks that are behind schedule. Furthermore, the generation AI analyzes the progress of projects in detail, identifies risk factors, and issues warnings in advance. For example, it detects lack of resources or schedule delays and issues a warning. In this way, the progress of projects can be analyzed in detail, identified risk factors, and issued warnings in advance.

[0056] The project management department can use the generation AI to automatically propose resource reallocation according to the progress of the project. For example, the generation AI analyzes the progress of the project and automatically proposes the reallocation of necessary resources. For example, it proposes additional resources for tasks that are short of resources. Furthermore, in order to optimize resources, the generation AI proposes resource reallocation according to the progress of the project. For example, it moves resources from tasks that are progressing quickly. Furthermore, the generation AI automatically proposes the reallocation of necessary resources based on the progress of the project, thereby optimizing resources. For example, it detects resource surpluses and shortages and proposes optimal allocations. This makes it possible to automatically propose the reallocation of necessary resources according to the progress of the project.

[0057] The project management department can use the emotion estimation function to monitor the emotional state of members involved in a project and make suggestions to maintain team morale. The project management department, for example, uses the emotion estimation function to monitor the emotional state of members involved in a project in real time. For example, it analyzes facial expressions and voice tone to calculate an emotion score. Furthermore, to maintain team morale, the generation AI makes suggestions based on the emotion estimation data. For example, it sends encouraging messages to members with low emotion scores. Furthermore, it uses the emotion estimation function to monitor the emotional state of members involved in a project and make suggestions to maintain team morale. For example, if the emotion score is low, it suggests taking a break to refresh. In this way, it is possible to monitor the emotional state of members involved in a project and make suggestions to maintain team morale.

[0058] The lecture section can use the generation AI to analyze the user's operation history and suggest the most efficient operation procedure. For example, the generation AI analyzes the user's operation history and suggests the most efficient operation procedure. For example, it may present the shortest route based on past operation data. The generation AI also analyzes the user's operation history and uses the data to suggest efficient operation procedures. For example, it may prioritize displaying frequently used functions. The generation AI also analyzes the user's operation history and suggest the most efficient operation procedure. For example, it may prioritize presenting procedures with fewer operation errors. This makes it possible to analyze the user's operation history and suggest the most efficient operation procedure.

[0059] The lecture unit uses the generation AI to provide operation procedures in video format, allowing for visually easy-to-understand lectures. For example, the generation AI may provide operation procedures in video format, allowing for visually easy-to-understand lectures. For example, a video explaining the operation procedures may be generated using screen capture. The generation AI may also provide lectures in video format so that the user can easily understand the operation procedures visually. For example, a video showing the operation procedures in animation may be provided. The generation AI may also provide operation procedures in video format, allowing for visually easy-to-understand lectures. For example, a video explaining the operation procedures step by step may be generated. This allows for operation procedures to be provided in video format, allowing for visually easy-to-understand lectures.

[0060] The lecture unit uses the emotion estimation function to evaluate the level of difficulty the user feels for an operation, and can provide additional support if the level of difficulty is high. The lecture unit, for example, uses the emotion estimation function to evaluate the level of difficulty the user feels for an operation in real time. For example, it analyzes facial expressions and vocal tone to calculate a difficulty score. Furthermore, if the user feels that the operation is difficult, the generation AI provides additional support. For example, it presents detailed operating procedures and FAQs. Furthermore, it uses the emotion estimation function to evaluate the level of difficulty the user feels for an operation, and can provide additional support if the level of difficulty is high. For example, it can provide a video that explains the operating procedures again. In this way, it is possible to evaluate the level of difficulty the user feels for an operation, and can provide additional support if the level of difficulty is high.

[0061] The inquiry unit can use the generation AI to analyze past inquiry history and automatically suggest answers to similar inquiries. For example, the generation AI analyzes past inquiry history and automatically suggests answers to similar inquiries. For example, it presents the optimal answer based on a database of past answers. In addition, to automatically suggest answers to similar inquiries, the generation AI utilizes past inquiry history. For example, it reuses past answers to the same problem. In addition, the generation AI analyzes past inquiry history and automatically suggests answers to similar inquiries. For example, it analyzes the content of the inquiry and presents the optimal answer. In this way, it is possible to analyze past inquiry history and automatically suggest answers to similar inquiries.

[0062] The inquiry unit uses the generation AI to analyze the inquiry content using natural language processing, and can automatically select the most suitable senior employee to make the inquiry. For example, the generation AI analyzes the inquiry content using natural language processing, and automatically selects the most suitable senior employee. For example, it selects a senior employee with specialized knowledge related to the inquiry content. Furthermore, the generation AI utilizes natural language processing to analyze the inquiry content and automatically select the most suitable senior employee. For example, it selects a senior employee with specialized knowledge related to the inquiry content. Furthermore, the generation AI analyzes the inquiry content using natural language processing, and automatically selects the most suitable senior employee to make the inquiry. For example, it selects the senior employee who is most suitable for the inquiry content. This makes it possible to analyze the inquiry content using natural language processing, and automatically select the most suitable senior employee to make the inquiry.

[0063] The inquiry unit uses an emotion estimation function to evaluate the workload of senior employees, and if the workload is high, it can assign inquiries to other more suitable individuals. The inquiry unit, for example, uses the emotion estimation function to evaluate the workload of senior employees in real time. For example, it analyzes facial expressions and voice tone to calculate a workload score. Furthermore, if the workload of a senior employee is high, the generation AI assigns inquiries to other more suitable individuals. For example, it avoids making inquiries to senior employees with high workload scores. Furthermore, it uses the emotion estimation function to evaluate the workload of senior employees, and if the workload is high, it assigns inquiries to other more suitable individuals. For example, it prioritizes the selection of senior employees with low workload scores. This makes it possible to evaluate the workload of senior employees, and if the workload is high, it assigns inquiries to other more suitable individuals.

[0064] The adjustment unit can use the generation AI to monitor the schedules of the parties involved in real time and dynamically adjust the optimal meeting date and time. For example, the generation AI monitors the schedules of the parties involved in real time and dynamically adjusts the optimal meeting date and time. For example, it proposes the optimal date and time based on everyone's free time. In addition, the generation AI analyzes schedule data to monitor the schedules of the parties involved and dynamically adjust the optimal meeting date and time. For example, it avoids schedule overlaps. In addition, the generation AI monitors the schedules of the parties involved in real time and dynamically adjusts the optimal meeting date and time. For example, it proposes the optimal date and time based on everyone's free time. In this way, the schedules of the parties involved can be monitored in real time and the optimal meeting date and time can be dynamically adjusted.

[0065] The coordination unit can use the generation AI to automatically organize the purpose and agenda of a meeting and share it with the relevant parties in advance. For example, the coordination unit uses the generation AI to automatically organize the purpose and agenda of a meeting and share it with the relevant parties in advance. For example, the generation AI can automatically generate a meeting agenda and send it to the relevant parties. In addition, in order to organize the purpose and agenda of a meeting and share it with the relevant parties in advance, the generation AI automatically organizes data. For example, it can set the priority of the agenda. In addition, the generation AI can automatically organize the purpose and agenda of a meeting and share it with the relevant parties in advance. For example, it can automatically generate a meeting agenda and send it to the relevant parties. In this way, the purpose and agenda of a meeting can be automatically organized and shared with the relevant parties in advance.

[0066] The coordination department can use the generation AI to automatically generate meeting minutes and share them with the relevant parties. For example, the coordination department has the generation AI automatically generate meeting minutes and share them with the relevant parties. For example, the content of the meeting is saved as text data and sent to the relevant parties. In addition, in order to automatically generate meeting minutes and share them with the relevant parties, the generation AI analyzes voice data. For example, minutes are created using voice recognition technology. In addition, the generation AI automatically generates meeting minutes and shares them with the relevant parties. For example, the content of the meeting is summarized and sent to the relevant parties. In this way, meeting minutes can be automatically generated and shared with the relevant parties.

[0067] The coordination unit can use the generation AI to automatically list follow-up tasks for meetings and notify the relevant parties. For example, the coordination unit uses the generation AI to automatically list follow-up tasks for meetings and notify the relevant parties. For example, it lists action items decided in a meeting and sends them to the relevant parties. In addition, the generation AI analyzes the content of the meeting to list follow-up tasks and notify the relevant parties. For example, it sets deadlines and responsible persons for tasks. In addition, the generation AI automatically lists follow-up tasks for meetings and notifies the relevant parties. For example, it generates tasks based on the content of the meeting and sends them to the relevant parties. In this way, follow-up tasks for meetings can be automatically listed and notified to the relevant parties.

[0068] The coordination unit can use the emotion estimation function to analyze participants' emotional reactions to the meeting content and suggest improvements for the next meeting. The coordination unit, for example, uses the emotion estimation function to analyze participants' emotional reactions to the meeting content in real time. For example, it analyzes facial expressions and voice tone to calculate an emotion score. The generative AI also uses the emotion data to analyze participants' emotional reactions and suggest improvements for the next meeting. For example, it identifies areas with low emotion scores and suggests improvements. The emotion estimation function also analyzes participants' emotional reactions to the meeting content and suggests improvements for the next meeting. For example, if the emotion score is low, it suggests changing the agenda or improving the way the meeting is conducted. In this way, it is possible to analyze participants' emotional reactions to the meeting content and suggest improvements for the next meeting.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The work support system can further include a health management unit. The health management unit monitors the user's health condition and provides appropriate health advice. For example, if the generation AI analyzes the user's activity data and detects a lack of exercise, it will suggest appropriate exercise. The health management unit can also analyze the user's food records and suggest nutritionally balanced meals. Furthermore, the health management unit can analyze the user's sleep data and provide advice on improving sleep quality. This allows for comprehensive management of the user's health condition and improves work efficiency.

[0071] The work support system can further include a learning support unit. The learning support unit provides learning content to support the user's skill improvement. For example, the generative AI analyzes the user's work content, identifies the required skills, and suggests learning content based on those skills. The learning support unit can also track the user's learning progress and provide feedback according to the progress. Furthermore, the learning support unit can provide a customized learning plan tailored to the user's learning style. This makes it possible to efficiently support the user's skill improvement.

[0072] The work support system can further include a communication support unit. The communication support unit supports smooth communication between users. For example, the generation AI analyzes the user's communication history and suggests appropriate communication methods. The communication support unit can also provide training content to improve the user's communication skills. Furthermore, the communication support unit can provide forums and discussion boards to promote the exchange of opinions between users. This supports smooth communication between users and strengthens team cooperation.

[0073] The work support system can further include a feedback unit. The feedback unit provides feedback on the user's work performance. For example, the generation AI analyzes the user's work data and evaluates performance. The feedback unit can also identify the user's strengths and areas for improvement and provide specific advice. Furthermore, the feedback unit can track the user's goal achievement and evaluate progress toward the goal. This makes it possible to provide feedback to improve the user's work performance and support their growth.

[0074] The work support system can further include a motivation management unit. The motivation management unit provides support to maintain and improve the user's motivation. For example, the generation AI analyzes the user's work data and detects signs of declining motivation. The motivation management unit can also propose incentives and reward programs to improve the user's motivation. Furthermore, the motivation management unit can support the user in setting goals and provide advice to maintain motivation to achieve those goals. This helps maintain and improve the user's motivation and improve work efficiency.

[0075] The task management unit can use the emotion estimation function to evaluate the user's emotional state and assign tasks according to the emotional state. For example, the emotion estimation function can be used to evaluate the user's emotional state in real time, and if the user is highly stressed, easy tasks can be assigned preferentially. The emotion estimation function can also be used to evaluate the user's emotional state and assign complex tasks if the user is highly focused. Furthermore, by using the emotion estimation function to evaluate the user's emotional state and assign tasks according to the emotional state, the user's work efficiency can be maximized. This makes it possible to assign tasks according to the user's emotional state, thereby improving work efficiency.

[0076] The project management department can use the emotion estimation function to monitor the emotional state of members involved in a project and propose a leadership style that matches their emotional state. For example, the emotion estimation function can be used to evaluate a member's emotional state in real time and propose supportive leadership if stress levels are high. The emotion estimation function can also be used to evaluate a member's emotional state and propose encouraging leadership if motivation levels are low. Furthermore, by using the emotion estimation function to evaluate a member's emotional state and propose a leadership style that matches their emotional state, team performance can be maximized. This makes it possible to propose a leadership style that matches the member's emotional state and maintain and improve team morale.

[0077] The lecture unit can use the emotion estimation function to evaluate the user's level of understanding and provide lecture content according to the level of understanding. For example, the emotion estimation function can be used to evaluate the user's level of understanding in real time, and if the level of understanding is low, detailed explanations can be added. The emotion estimation function can also be used to evaluate the user's level of understanding, and if the level of understanding is high, it can suggest moving on to the next step. Furthermore, by using the emotion estimation function to evaluate the user's level of understanding and providing lecture content according to the level of understanding, the user's learning efficiency can be maximized. This makes it possible to provide lecture content according to the user's level of understanding and improve learning efficiency.

[0078] The inquiry unit can use the emotion estimation function to evaluate the user's emotional state and suggest a response method according to the emotional state. For example, the emotion estimation function can be used to evaluate the user's emotional state in real time and suggest a polite response if the user is under high stress. The emotion estimation function can also be used to evaluate the user's emotional state and suggest a concise response if the user is calm. Furthermore, by using the emotion estimation function to evaluate the user's emotional state and suggest a response method according to the emotional state, user satisfaction can be improved. This makes it possible to suggest a response method according to the user's emotional state and improve the quality of inquiry responses.

[0079] The adjustment unit can use the emotion estimation function to evaluate the emotional state of the meeting participants and propose a method of proceeding with the meeting in accordance with their emotional state. For example, the emotion estimation function can be used to evaluate the emotional state of the participants in real time, and if stress is high, it can propose proceeding with the meeting in a relaxed atmosphere. The emotion estimation function can also be used to evaluate the emotional state of the participants and propose an efficient method of proceeding if they are highly focused. Furthermore, by using the emotion estimation function to evaluate the emotional state of the participants and propose a method of proceeding with the meeting in accordance with their emotional state, the effectiveness of the meeting can be maximized. In this way, a method of proceeding with the meeting in accordance with the emotional state of the participants can be proposed, and the effectiveness of the meeting can be improved.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The task manager manages individual tasks. For example, the generative AI might list and prioritize tasks for a new employee. The task manager can also track task progress and suggest necessary actions. Step 2: The case management department manages the progress of the case. For example, the generative AI tracks the progress of the case and suggests necessary actions. The case management department can also perform detailed analysis of the case's progress, identify risk factors, and issue warnings in advance. Step 3: The lecture department gives a lecture on how to operate the system. For example, the generation AI explains the operation procedure and provides a demonstration as needed. The lecture department can also provide the operation procedure in video format to make the lecture easier to understand visually. Step 4: The inquiry department automatically sends inquiries to senior employees. For example, the generation AI automatically sends inquiries to senior employees and conveys their answers to the new employee. The inquiry department can also analyze past inquiry history and automatically suggest answers to similar inquiries. Step 5: The coordination department automatically arranges meetings. For example, the generation AI automatically checks the schedules of the people involved and suggests the optimal date and time for the meeting. The coordination department can also automatically organize the purpose and agenda of the meeting and share it with the people involved in advance.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. It is a system that allows new employees to receive support from AI and various tools when working remotely. A task management section that manages individual tasks; A project management department that manages the progress of projects; A lecture section that gives lectures on how to operate the system, An inquiry department that automatically makes inquiries to senior employees, A system characterized by comprising: an adjustment unit that automatically adjusts meetings.

2. The task management unit Using generative AI to monitor the progress of the task in real time and dynamically change the priority of the task according to the progress.

2. The system of claim 1.

3. The case management department Using generative AI, the progress of the project is analyzed in detail, risk factors are identified, and warnings are issued in advance.

2. The system of claim 1.

4. The lecture section Analyzes user operation history using generative AI and suggests the most efficient operation procedure 2. The system of claim 1.

5. The inquiry unit Generative AI is used to analyze past inquiries and automatically suggest answers to similar inquiries.

2. The system of claim 1.

6. The adjustment unit Generative AI is used to monitor the schedules of the people involved in the meeting in real time, and dynamically adjust the optimal date and time for the meeting.

2. The system of claim 1.

7. The task management unit Evaluate the user's stress level, and if the stress level is high, suggest reducing the burden of the task.

2. The system of claim 1.

8. The case management department Monitor the emotional state of the members involved in the project and make suggestions to maintain team morale.

2. The system of claim 1.

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