System

Abstract

An object of the system according to the embodiment is to integrate information from a plurality of business communication tools and manage correspondence matters without omission.SOLUTION: A system according to an embodiment includes an information collection unit, an action item extraction unit, a set-up creation unit, and a deadline setting unit. The information collection unit collects information from the business communication tool. The action item extraction unit extracts an action item from the information collected by the information collection unit. The setup creation unit creates a corresponding setup based on the action item extracted by the action item extraction unit. The deadline setter sets the deadline based on the set-up created by the set-up creator.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] Conventional technology has had the problem of making it difficult to integrate information from multiple business communication tools and manage all response items without missing anything.

[0005] The system according to the embodiment aims to integrate information from multiple business communication tools and manage all correspondence matters without omission. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an action item extraction unit, a schedule creation unit, and a deadline setting unit. The information collection unit collects information from a business communication tool. The action item extraction unit extracts action items from the information collected by the information collection unit. The schedule creation unit creates corresponding schedules based on the action items extracted by the action item extraction unit. The deadline setting unit sets a deadline based on the schedule created by the schedule creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate information from multiple business communication tools and manage all response items without omission. [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 business communication support system according to an embodiment of the present invention collects information from various tools used for business communication, and a generation AI extracts action items, automatically creating response procedures and deadlines. This allows the business communication support system to efficiently manage information from business communication tools and prevent oversight of response items.

[0029] A business communication support system according to an embodiment includes an information collection unit, an action item extraction unit, a schedule creation unit, and a deadline setting unit. The information collection unit collects information from business communication tools. For example, the information collection unit collects information from emails, chats, telephone call logs, etc. The information collection unit can also collect information by acquiring data through APIs or using scraping technology. The action item extraction unit extracts action items from the information collected by the information collection unit. For example, a generation AI uses natural language processing technology to analyze email content and chat messages and extract important tasks and to-do lists. The action item extraction unit can also identify follow-up items using keyword extraction technology. The schedule creation unit creates corresponding schedules based on the action items extracted by the action item extraction unit. For example, the generation AI sets the necessary steps for a specific task based on project management best practices. The schedule creation unit can also create step-by-step procedures and allocate necessary resources. The deadline setting unit sets a deadline based on the schedule created by the schedule creation unit. For example, the generation AI sets deadlines for each task, taking into account task priority and resource availability. The deadline setting unit can also monitor task progress in real time and send reminders as needed. This allows the business communication support system to efficiently manage information from business communication tools and prevent oversight of response items.

[0030] The information collection unit collects information from emails, chats, and telephone call records, and the action item extraction unit analyzes the information collected by the information collection unit to extract important action items. The information collection unit, for example, analyzes the content of emails to extract important tasks. The information collection unit can also analyze chat messages to identify follow-up items. The information collection unit can also analyze telephone call records to extract requests from clients. The action item extraction unit, for example, uses a generation AI to analyze the content of emails and chats using natural language processing technology to extract important action items. The action item extraction unit can also identify follow-up items using keyword extraction technology. The action item extraction unit can also analyze telephone call records to extract requests from clients. This ensures the extraction of important action items.

[0031] The setup creation unit can set the necessary steps and deadlines for specific tasks and reflect them in the work schedule. For example, the generation AI in the setup creation unit sets the necessary steps for specific tasks based on project management best practices. The setup creation unit can also create step-by-step procedures and allocate the necessary resources. The generation AI in the setup creation unit can also set deadlines for each task, taking into account task priority and resource availability. This allows for efficient setting of task setups and deadlines.

[0032] The deadline setting unit can monitor the progress of each task in real time and send reminders as necessary. For example, the generation AI can monitor the progress of each task in real time and send reminders if progress is behind schedule. The deadline setting unit can also monitor the progress of tasks in real time and send reminders as necessary. The deadline setting unit can also use an emotion estimation function to analyze the user's emotional state and adjust the priority of action items if it is estimated that stress is high. This makes it possible to monitor the progress of tasks in real time and send reminders.

[0033] The information collection unit can convert telephone conversation content into text in real time using voice recognition technology, and the action item extraction unit can extract action items from the text. In the information collection unit, for example, the generation AI can convert telephone conversation content into text in real time using voice recognition technology. The information collection unit can also convert telephone conversation content into text in real time using voice recognition technology. The information collection unit can also convert telephone conversation content into text in real time using voice recognition technology. The information collection unit can also convert telephone conversation content into text in real time using voice recognition technology. The action item extraction unit can, for example, analyze the textual content converted by the generation AI and extract important action items. The action item extraction unit can also analyze the textual content converted by the generation AI and extract important action items. In this way, telephone conversation content can be converted into text in real time and action items can be extracted.

[0034] The information collection unit analyzes the contents of a whiteboard during a meeting using image recognition technology, and the action item extraction unit can extract action items from the analyzed content. In the information collection unit, for example, the generation AI uses image recognition technology to analyze the contents of a whiteboard during a meeting. The information collection unit can also analyze the contents of a whiteboard during a meeting using image recognition technology. In the information collection unit, the generation AI can also analyze the contents of a whiteboard during a meeting using image recognition technology. The action item extraction unit extracts important action items based on the content analyzed by the generation AI, for example. The action item extraction unit can also extract important action items based on the analyzed content. In this way, the content of a whiteboard during a meeting can be analyzed and action items can be extracted.

[0035] The setup creation unit can analyze the dependency relationships between each task and automatically generate the optimal setup. In the setup creation unit, for example, a generation AI analyzes the dependency relationships between each task and automatically generates the optimal setup. The setup creation unit can also analyze the dependency relationships between each task and automatically generate the optimal setup. In addition, the setup creation unit can also use a generation AI to analyze the dependency relationships between each task and automatically generate the optimal setup. This makes it possible to analyze the dependency relationships between each task and automatically generate the optimal setup.

[0036] The setup creation unit can learn from past project data and propose setups based on the success patterns of similar projects. For example, the setup creation unit has a generation AI that learns from past project data and proposes setups based on the success patterns of similar projects. The setup creation unit can also learn from past project data and propose setups based on the success patterns of similar projects. The setup creation unit can also learn from past project data and propose setups based on the success patterns of similar projects. This makes it possible to learn from past project data and propose setups based on success patterns.

[0037] The deadline setting unit can analyze the resource requirements of each task and propose the optimal resource allocation. For example, the generation AI can analyze the resource requirements of each task and propose the optimal resource allocation for the deadline setting unit. The deadline setting unit can also analyze the resource requirements of each task and propose the optimal resource allocation for the deadline setting unit. The generation AI can also analyze the resource requirements of each task and propose the optimal resource allocation for the deadline setting unit. This makes it possible to analyze the resource requirements of each task and propose the optimal resource allocation.

[0038] The deadline setting unit can monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. For example, the generation AI can monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. The deadline setting unit can also monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. The deadline setting unit can also monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. This makes it possible to monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs.

[0039] The deadline setting unit can monitor the progress of each task in real time and automatically send a reminder if the progress is behind. For example, the generation AI can monitor the progress of each task in real time and automatically send a reminder if the progress is behind. The deadline setting unit can also monitor the progress of each task in real time and automatically send a reminder if the progress is behind. The deadline setting unit can also monitor the progress of each task in real time and automatically send a reminder if the progress is behind. This makes it possible to monitor the progress of tasks in real time and send a reminder if the progress is behind.

[0040] The deadline setting unit can learn past schedule data and propose an optimal schedule pattern. For example, the generation AI of the deadline setting unit learns past schedule data and proposes an optimal schedule pattern. The deadline setting unit can also learn past schedule data and propose an optimal schedule pattern. The generation AI of the deadline setting unit can also learn past schedule data and propose an optimal schedule pattern. In this way, past schedule data can be learned and an optimal schedule pattern can be proposed.

[0041] The deadline setting unit can visualize the progress of each task and provide a dashboard that the user can intuitively understand. For example, the generation AI can visualize the progress of each task in the deadline setting unit and provide a dashboard that the user can intuitively understand. The deadline setting unit can also visualize the progress of each task and provide a dashboard that the user can intuitively understand. The generation AI can also visualize the progress of each task in the deadline setting unit and provide a dashboard that the user can intuitively understand. This makes it possible to visualize the progress of each task and provide a dashboard that the user can intuitively understand.

[0042] The deadline setting unit can share the task progress with other team members to promote cooperation. For example, the deadline setting unit can cause the generation AI to share the task progress with other team members to promote cooperation. The deadline setting unit can also cause the generation AI to share the task progress with other team members to promote cooperation. For example, the deadline setting unit can cause the generation AI to share the task progress with other team members to promote cooperation. This allows the task progress to be shared with other team members to promote cooperation.

[0043] The information collection unit can automatically synchronize data between each tool, thereby realizing centralized management of information. For example, the generation AI of the information collection unit can automatically synchronize data between each tool, thereby realizing centralized management of information. The information collection unit can also automatically synchronize data between each tool, thereby realizing centralized management of information. For example, the generation AI of the information collection unit can automatically synchronize data between each tool, thereby realizing centralized management of information. This allows data between each tool to be automatically synchronized, thereby realizing centralized management of information.

[0044] The information collection unit can analyze the usage status of each tool and propose the optimal combination of tools. For example, the generation AI can analyze the usage status of each tool and propose the optimal combination of tools. The information collection unit can also analyze the usage status of each tool and propose the optimal combination of tools. The information collection unit can also analyze the usage status of each tool and propose the optimal combination of tools. In this way, the usage status of each tool can be analyzed and the optimal combination of tools can be proposed.

[0045] The information collection unit can integrate the data from each tool and visualize the overall business flow. For example, the information collection unit can use a generation AI to integrate the data from each tool and visualize the overall business flow. The information collection unit can also integrate the data from each tool and visualize the overall business flow. For example, the information collection unit can use a generation AI to integrate the data from each tool and visualize the overall business flow. This allows the data from each tool to be integrated and visualize the overall business flow.

[0046] The information collection unit can automatically generate new tasks based on the data from each tool, thereby improving work efficiency. For example, the information collection unit can have a generation AI automatically generate new tasks based on the data from each tool, thereby improving work efficiency. The information collection unit can also automatically generate new tasks based on the data from each tool, thereby improving work efficiency. The information collection unit can also have a generation AI automatically generate new tasks based on the data from each tool, thereby improving work efficiency. This allows new tasks to be automatically generated based on the data from each tool, thereby improving work efficiency.

[0047] The information collecting unit can learn the user's operation history and automatically generate an optimal interface. For example, the information collecting unit allows a generation AI to learn the user's operation history and automatically generate an optimal interface. The information collecting unit can also learn the user's operation history and automatically generate an optimal interface. For example, the information collecting unit allows a generation AI to learn the user's operation history and automatically generate an optimal interface. This makes it possible to learn the user's operation history and automatically generate an optimal interface.

[0048] The information collecting unit can collect user feedback in real time and continuously improve the interface. For example, the information collecting unit can have the generation AI collect user feedback in real time and continuously improve the interface. The information collecting unit can also collect user feedback in real time and continuously improve the interface. The information collecting unit can also have the generation AI collect user feedback in real time and continuously improve the interface. This allows user feedback to be collected in real time and continuously improve the interface.

[0049] The information gathering unit can make the interface multilingual to accommodate international users. For example, the generation AI of the information gathering unit can make the interface multilingual to accommodate international users. The information gathering unit can also make the interface multilingual to accommodate international users. For example, the generation AI of the information gathering unit can make the interface multilingual to accommodate international users. This makes the interface multilingual to accommodate international users.

[0050] The information collecting unit can make the interface customizable, allowing the user to set it to their own preferences. The information collecting unit can, for example, make the generating AI customizable, allowing the user to set it to their own preferences. The information collecting unit can also make the interface customizable, allowing the user to set it to their own preferences. The information collecting unit can also make the generating AI customizable, allowing the user to set it to their own preferences. This makes the interface customizable, allowing the user to set it to their own preferences.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The business communication support system can further include a voice recognition unit. The voice recognition unit can convert the contents of meetings and telephone conversations into text in real time and provide the text to the information collection unit. For example, it can automatically convert statements made during meetings into text and save it in a form that can be searched later. It can also convert the contents of telephone conversations into text in real time to ensure that important information is not overlooked. This makes it possible to efficiently manage voice information and improve the accuracy of business communications.

[0053] The business communication support system can further include a visualization unit. The visualization unit visually displays the progress and dependencies of each task, providing a dashboard that users can intuitively understand. For example, the progress of tasks can be displayed in real time using a Gantt chart or Kanban board. It can also visually show task dependencies and clarify which tasks affect other tasks. This makes it easier for users to grasp the overall picture of tasks, enabling efficient business management.

[0054] The business communication support system can further include a resource optimization unit. The resource optimization unit analyzes the resources required for each task and proposes optimal resource allocation. For example, it reallocates resources taking into account the progress of the project and resource availability. The resource optimization unit can also learn from past project data and optimize resource allocation based on the success patterns of similar projects. This reduces resource waste and enables efficient project management.

[0055] The business communication support system can also be equipped with a data synchronization unit, which automatically synchronizes data between each tool and enables centralized information management. For example, data from email, chat, and project management tools can be integrated, allowing all information to be viewed on a single dashboard. The data synchronization unit can also update data in real time, ensuring that the latest information is always available. This prevents information omissions and enables efficient business management.

[0056] The business communication support system can further include a feedback collection unit. The feedback collection unit collects user operation history and feedback in real time and continuously improves the interface. For example, it analyzes frequently used functions and operation patterns by users to optimize the interface. It can also add new functions and improve existing functions based on user feedback. This makes it possible to provide a flexible interface that meets user needs and improve work efficiency.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information collection department collects information from business communication tools, such as emails, chats, and phone call records. The information collection department can also collect information by acquiring data through APIs or using scraping technology. Step 2: The action item extraction unit extracts action items from the information collected by the information collection unit. For example, the generation AI uses natural language processing technology to analyze email content and chat messages and extract important tasks and to-do lists. The action item extraction unit can also use keyword extraction technology to identify follow-up items. Step 3: The process planning unit creates corresponding process plans based on the action items extracted by the action item extraction unit. For example, the generation AI sets the necessary steps for a specific task based on project management best practices. The process planning unit can also create step-by-step procedures and allocate the necessary resources. Step 4: The deadline setting unit sets deadlines based on the schedule created by the schedule creation unit. For example, the generation AI sets deadlines for each task taking into account task priority and resource availability. The deadline setting unit can also monitor task progress in real time and send reminders as needed.

[0059] (Example 2) The business communication support system according to an embodiment of the present invention collects information from various tools used for business communication, and a generation AI extracts action items, automatically creating response procedures and deadlines. This allows the business communication support system to efficiently manage information from business communication tools and prevent oversight of response items.

[0060] A business communication support system according to an embodiment includes an information collection unit, an action item extraction unit, a schedule creation unit, and a deadline setting unit. The information collection unit collects information from business communication tools. For example, the information collection unit collects information from emails, chats, telephone call logs, etc. The information collection unit can also collect information by acquiring data through APIs or using scraping technology. The action item extraction unit extracts action items from the information collected by the information collection unit. For example, a generation AI uses natural language processing technology to analyze email content and chat messages and extract important tasks and to-do lists. The action item extraction unit can also identify follow-up items using keyword extraction technology. The schedule creation unit creates corresponding schedules based on the action items extracted by the action item extraction unit. For example, the generation AI sets the necessary steps for a specific task based on project management best practices. The schedule creation unit can also create step-by-step procedures and allocate necessary resources. The deadline setting unit sets a deadline based on the schedule created by the schedule creation unit. For example, the generation AI sets deadlines for each task, taking into account task priority and resource availability. The deadline setting unit can also monitor task progress in real time and send reminders as needed. This allows the business communication support system to efficiently manage information from business communication tools and prevent oversight of response items.

[0061] The information collection unit collects information from emails, chats, and telephone call records, and the action item extraction unit analyzes the information collected by the information collection unit to extract important action items. The information collection unit, for example, analyzes the content of emails to extract important tasks. The information collection unit can also analyze chat messages to identify follow-up items. The information collection unit can also analyze telephone call records to extract requests from clients. The action item extraction unit, for example, uses a generation AI to analyze the content of emails and chats using natural language processing technology to extract important action items. The action item extraction unit can also identify follow-up items using keyword extraction technology. The action item extraction unit can also analyze telephone call records to extract requests from clients. This ensures the extraction of important action items.

[0062] The setup creation unit can set the necessary steps and deadlines for specific tasks and reflect them in the work schedule. For example, the generation AI in the setup creation unit sets the necessary steps for specific tasks based on project management best practices. The setup creation unit can also create step-by-step procedures and allocate the necessary resources. The generation AI in the setup creation unit can also set deadlines for each task, taking into account task priority and resource availability. This allows for efficient setting of task setups and deadlines.

[0063] The deadline setting unit can monitor the progress of each task in real time and send reminders as necessary. For example, the generation AI can monitor the progress of each task in real time and send reminders if progress is behind schedule. The deadline setting unit can also monitor the progress of tasks in real time and send reminders as necessary. The deadline setting unit can also use an emotion estimation function to analyze the user's emotional state and adjust the priority of action items if it is estimated that stress is high. This makes it possible to monitor the progress of tasks in real time and send reminders.

[0064] The information collection unit can convert telephone conversation content into text in real time using voice recognition technology, and the action item extraction unit can extract action items from the text. In the information collection unit, for example, the generation AI can convert telephone conversation content into text in real time using voice recognition technology. The information collection unit can also convert telephone conversation content into text in real time using voice recognition technology. The information collection unit can also convert telephone conversation content into text in real time using voice recognition technology. The information collection unit can also convert telephone conversation content into text in real time using voice recognition technology. The action item extraction unit can, for example, analyze the textual content converted by the generation AI and extract important action items. The action item extraction unit can also analyze the textual content converted by the generation AI and extract important action items. In this way, telephone conversation content can be converted into text in real time and action items can be extracted.

[0065] The information collection unit analyzes the contents of a whiteboard during a meeting using image recognition technology, and the action item extraction unit can extract action items from the analyzed content. In the information collection unit, for example, the generation AI uses image recognition technology to analyze the contents of a whiteboard during a meeting. The information collection unit can also analyze the contents of a whiteboard during a meeting using image recognition technology. In the information collection unit, the generation AI can also analyze the contents of a whiteboard during a meeting using image recognition technology. The action item extraction unit extracts important action items based on the content analyzed by the generation AI, for example. The action item extraction unit can also extract important action items based on the analyzed content. In this way, the content of a whiteboard during a meeting can be analyzed and action items can be extracted.

[0066] The information collection unit uses the emotion estimation function to analyze the emotions of participants during the meeting, and the action item extraction unit can extract emotionally significant statements as action items. In the information collection unit, for example, the generation AI uses the emotion estimation function to analyze the emotions of participants during the meeting in real time. The information collection unit can also use the emotion estimation function to analyze the emotions of participants during the meeting in real time. The information collection unit can also use the emotion estimation function to analyze the emotions of participants during the meeting in real time. The action item extraction unit, for example, extracts action items based on emotionally significant statements made by the generation AI. The action item extraction unit can also extract action items based on emotionally significant statements made by the generation AI. In this way, emotionally significant statements during the meeting can be extracted as action items.

[0067] The setup creation unit can analyze the dependency relationships between each task and automatically generate the optimal setup. In the setup creation unit, for example, a generation AI analyzes the dependency relationships between each task and automatically generates the optimal setup. The setup creation unit can also analyze the dependency relationships between each task and automatically generate the optimal setup. In addition, the setup creation unit can also use a generation AI to analyze the dependency relationships between each task and automatically generate the optimal setup. This makes it possible to analyze the dependency relationships between each task and automatically generate the optimal setup.

[0068] The setup creation unit can learn from past project data and propose setups based on the success patterns of similar projects. For example, the setup creation unit has a generation AI that learns from past project data and proposes setups based on the success patterns of similar projects. The setup creation unit can also learn from past project data and propose setups based on the success patterns of similar projects. The setup creation unit can also learn from past project data and propose setups based on the success patterns of similar projects. This makes it possible to learn from past project data and propose setups based on success patterns.

[0069] The schedule creation unit can use the emotion estimation function to take into account the user's emotional state and arrange important tasks in time periods when stress is estimated to be low. In the schedule creation unit, for example, the generation AI uses the emotion estimation function to analyze the user's emotional state in real time and arrange important tasks in time periods when stress is estimated to be low. The schedule creation unit can also use the emotion estimation function to analyze the user's emotional state in real time and arrange important tasks in time periods when stress is estimated to be low. In addition, the schedule creation unit can also use the emotion estimation function to analyze the user's emotional state in real time and arrange important tasks in time periods when stress is estimated to be low. In this way, it is possible to take into account the user's emotional state and arrange important tasks in time periods when stress is low.

[0070] The deadline setting unit can analyze the resource requirements of each task and propose the optimal resource allocation. For example, the generation AI can analyze the resource requirements of each task and propose the optimal resource allocation for the deadline setting unit. The deadline setting unit can also analyze the resource requirements of each task and propose the optimal resource allocation for the deadline setting unit. The generation AI can also analyze the resource requirements of each task and propose the optimal resource allocation for the deadline setting unit. This makes it possible to analyze the resource requirements of each task and propose the optimal resource allocation.

[0071] The deadline setting unit can monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. For example, the generation AI can monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. The deadline setting unit can also monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. The deadline setting unit can also monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs. This makes it possible to monitor the progress of the task in real time, and automatically readjust the setup if a delay occurs.

[0072] The deadline setting unit can use the emotion estimation function to analyze the emotional state of the entire team and concentrate important tasks on periods when motivation is high. For example, the generation AI in the deadline setting unit can use the emotion estimation function to analyze the emotional state of the entire team in real time and concentrate important tasks on periods when motivation is high. The deadline setting unit can also use the emotion estimation function to analyze the emotional state of the entire team in real time and concentrate important tasks on periods when motivation is high. The deadline setting unit can also use the emotion estimation function to analyze the emotional state of the entire team in real time and concentrate important tasks on periods when motivation is high. This allows the emotional state of the entire team to be analyzed and important tasks to be concentrated on periods when motivation is high.

[0073] The deadline setting unit can monitor the progress of each task in real time and automatically send a reminder if the progress is behind. For example, the generation AI can monitor the progress of each task in real time and automatically send a reminder if the progress is behind. The deadline setting unit can also monitor the progress of each task in real time and automatically send a reminder if the progress is behind. The deadline setting unit can also monitor the progress of each task in real time and automatically send a reminder if the progress is behind. This makes it possible to monitor the progress of tasks in real time and send a reminder if the progress is behind.

[0074] The deadline setting unit can learn past schedule data and propose an optimal schedule pattern. For example, the generation AI of the deadline setting unit learns past schedule data and proposes an optimal schedule pattern. The deadline setting unit can also learn past schedule data and propose an optimal schedule pattern. The generation AI of the deadline setting unit can also learn past schedule data and propose an optimal schedule pattern. In this way, past schedule data can be learned and an optimal schedule pattern can be proposed.

[0075] The deadline setting unit can analyze the emotional state of the user using the emotion estimation function, and automatically adjust the schedule if it is estimated that the user is under high stress. For example, the deadline setting unit uses the emotion estimation function to analyze the emotional state of the user in real time, and automatically adjust the schedule if it is estimated that the user is under high stress. The deadline setting unit can also use the emotion estimation function to analyze the emotional state of the user in real time, and automatically adjust the schedule if it is estimated that the user is under high stress. The deadline setting unit can also use the emotion estimation function to analyze the emotional state of the user in real time, and automatically adjust the schedule if it is estimated that the user is under high stress. In this way, the emotional state of the user can be analyzed, and the schedule can be automatically adjusted if the user is under high stress.

[0076] The deadline setting unit can visualize the progress of each task and provide a dashboard that the user can intuitively understand. For example, the generation AI can visualize the progress of each task in the deadline setting unit and provide a dashboard that the user can intuitively understand. The deadline setting unit can also visualize the progress of each task and provide a dashboard that the user can intuitively understand. The generation AI can also visualize the progress of each task in the deadline setting unit and provide a dashboard that the user can intuitively understand. This makes it possible to visualize the progress of each task and provide a dashboard that the user can intuitively understand.

[0077] The deadline setting unit can share the task progress with other team members to promote cooperation. For example, the deadline setting unit can cause the generation AI to share the task progress with other team members to promote cooperation. The deadline setting unit can also cause the generation AI to share the task progress with other team members to promote cooperation. For example, the deadline setting unit can cause the generation AI to share the task progress with other team members to promote cooperation. This allows the task progress to be shared with other team members to promote cooperation.

[0078] The deadline setting unit can use the emotion estimation function to analyze the emotional state of the entire team, and if motivation is low, suggest a break to refresh. For example, the generation AI can use the emotion estimation function to analyze the emotional state of the entire team in real time, and if motivation is low, suggest a break to refresh. The deadline setting unit can also use the emotion estimation function to analyze the emotional state of the entire team in real time, and if motivation is low, suggest a break to refresh. The generation AI can also use the emotion estimation function to analyze the emotional state of the entire team in real time, and if motivation is low, suggest a break to refresh. In this way, the emotional state of the entire team can be analyzed, and if motivation is low, suggest a break to refresh.

[0079] The information collection unit can automatically synchronize data between each tool, thereby realizing centralized management of information. For example, the generation AI of the information collection unit can automatically synchronize data between each tool, thereby realizing centralized management of information. The information collection unit can also automatically synchronize data between each tool, thereby realizing centralized management of information. For example, the generation AI of the information collection unit can automatically synchronize data between each tool, thereby realizing centralized management of information. This allows data between each tool to be automatically synchronized, thereby realizing centralized management of information.

[0080] The information collection unit can analyze the usage status of each tool and propose the optimal combination of tools. For example, the generation AI can analyze the usage status of each tool and propose the optimal combination of tools. The information collection unit can also analyze the usage status of each tool and propose the optimal combination of tools. The information collection unit can also analyze the usage status of each tool and propose the optimal combination of tools. In this way, the usage status of each tool can be analyzed and the optimal combination of tools can be proposed.

[0081] The information collection unit can analyze the user's emotional state using the emotion estimation function, and adjust the frequency of tool use if it is estimated that stress is high. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time, and adjust the frequency of tool use if it is estimated that stress is high. The information collection unit can also analyze the user's emotional state in real time using the emotion estimation function, and adjust the frequency of tool use if it is estimated that stress is high. The information collection unit can also analyze the user's emotional state in real time using the emotion estimation function, and adjust the frequency of tool use if it is estimated that stress is high. In this way, the user's emotional state can be analyzed, and the frequency of tool use can be adjusted if stress is high.

[0082] The information collection unit can integrate the data from each tool and visualize the overall business flow. For example, the information collection unit can use a generation AI to integrate the data from each tool and visualize the overall business flow. The information collection unit can also integrate the data from each tool and visualize the overall business flow. For example, the information collection unit can use a generation AI to integrate the data from each tool and visualize the overall business flow. This allows the data from each tool to be integrated and visualize the overall business flow.

[0083] The information collection unit can automatically generate new tasks based on the data from each tool, thereby improving work efficiency. For example, the information collection unit can have a generation AI automatically generate new tasks based on the data from each tool, thereby improving work efficiency. The information collection unit can also automatically generate new tasks based on the data from each tool, thereby improving work efficiency. The information collection unit can also have a generation AI automatically generate new tasks based on the data from each tool, thereby improving work efficiency. This allows new tasks to be automatically generated based on the data from each tool, thereby improving work efficiency.

[0084] The information collection unit can use the emotion estimation function to analyze tool usage and suggest a tool that the user can use most comfortably. For example, the generation AI can use the emotion estimation function to analyze tool usage in real time and suggest a tool that the user can use most comfortably. The information collection unit can also use the emotion estimation function to analyze tool usage in real time and suggest a tool that the user can use most comfortably. The information collection unit can also use the emotion estimation function to analyze tool usage in real time and suggest a tool that the user can use most comfortably. This makes it possible to analyze tool usage and suggest a tool that the user can use most comfortably.

[0085] The information collecting unit can learn the user's operation history and automatically generate an optimal interface. For example, the information collecting unit allows a generation AI to learn the user's operation history and automatically generate an optimal interface. The information collecting unit can also learn the user's operation history and automatically generate an optimal interface. For example, the information collecting unit allows a generation AI to learn the user's operation history and automatically generate an optimal interface. This makes it possible to learn the user's operation history and automatically generate an optimal interface.

[0086] The information collecting unit can collect user feedback in real time and continuously improve the interface. For example, the information collecting unit can have the generation AI collect user feedback in real time and continuously improve the interface. The information collecting unit can also collect user feedback in real time and continuously improve the interface. The information collecting unit can also have the generation AI collect user feedback in real time and continuously improve the interface. This allows user feedback to be collected in real time and continuously improve the interface.

[0087] The information collection unit can use the emotion estimation function to analyze the user's emotional state and propose an interface design that is estimated to be low-stress. For example, the information collection unit can have the generation AI use the emotion estimation function to analyze the user's emotional state in real time and propose an interface design that is estimated to be low-stress. The information collection unit can also use the emotion estimation function to analyze the user's emotional state in real time and propose an interface design that is estimated to be low-stress. The information collection unit can also use the emotion estimation function to analyze the user's emotional state in real time and propose an interface design that is estimated to be low-stress. In this way, the user's emotional state can be analyzed and an interface design that is estimated to be low-stress can be proposed.

[0088] The information gathering unit can make the interface multilingual to accommodate international users. For example, the generation AI of the information gathering unit can make the interface multilingual to accommodate international users. The information gathering unit can also make the interface multilingual to accommodate international users. For example, the generation AI of the information gathering unit can make the interface multilingual to accommodate international users. This makes the interface multilingual to accommodate international users.

[0089] The information collecting unit can make the interface customizable, allowing the user to set it to their own preferences. The information collecting unit can, for example, make the generating AI customizable, allowing the user to set it to their own preferences. The information collecting unit can also make the interface customizable, allowing the user to set it to their own preferences. The information collecting unit can also make the generating AI customizable, allowing the user to set it to their own preferences. This makes the interface customizable, allowing the user to set it to their own preferences.

[0090] The information collection unit can use the emotion estimation function to analyze the user's emotional state and propose the most comfortable interface to use. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and propose the most comfortable interface to use. The information collection unit can also use the emotion estimation function to analyze the user's emotional state in real time and propose the most comfortable interface to use. The information collection unit can also use the emotion estimation function to analyze the user's emotional state in real time and propose the most comfortable interface to use. In this way, the user's emotional state can be analyzed and the most comfortable interface to use can be proposed.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The business communication support system can further include a voice recognition unit. The voice recognition unit can convert the contents of meetings and telephone conversations into text in real time and provide the text to the information collection unit. For example, it can automatically convert statements made during meetings into text and save it in a form that can be searched later. It can also convert the contents of telephone conversations into text in real time to ensure that important information is not overlooked. This makes it possible to efficiently manage voice information and improve the accuracy of business communications.

[0093] The business communication support system can also use an emotion estimation function to analyze the user's emotional state. For example, it can estimate the user's emotions from the content of emails and chats, and adjust the frequency of reminder sending if it determines that the user is under high stress. The emotion estimation function can also be used to assign important tasks when the user is feeling positive. This allows for flexible responses based on the user's emotional state, improving work efficiency.

[0094] The business communication support system can further include a visualization unit. The visualization unit visually displays the progress and dependencies of each task, providing a dashboard that users can intuitively understand. For example, the progress of tasks can be displayed in real time using a Gantt chart or Kanban board. It can also visually show task dependencies and clarify which tasks affect other tasks. This makes it easier for users to grasp the overall picture of tasks, enabling efficient business management.

[0095] The business communication support system can also use an emotion estimation function to analyze the emotional state of the entire team. For example, it can estimate the emotions of team members from comments made during meetings and chat content, and if it determines that motivation is low, it can suggest a break to refresh. The emotion estimation function can also be used to monitor the emotional state of the entire team in real time, allowing important tasks to be concentrated when motivation is high. This enables flexible work management that takes the emotional state of the entire team into account.

[0096] The business communication support system can further include a resource optimization unit. The resource optimization unit analyzes the resources required for each task and proposes optimal resource allocation. For example, it reallocates resources taking into account the progress of the project and resource availability. The resource optimization unit can also learn from past project data and optimize resource allocation based on the success patterns of similar projects. This reduces resource waste and enables efficient project management.

[0097] The business communication support system can also use the emotion estimation function to provide an interface that takes the user's emotional state into consideration. For example, if it is estimated that the user is feeling stressed, it can change the color tone and layout of the interface to provide a more relaxing environment. Also, if the user is feeling positive, it can simplify the interface to allow the user to work more efficiently. This makes it possible to provide a flexible interface that responds to the user's emotional state and improve work efficiency.

[0098] The business communication support system can also be equipped with a data synchronization unit, which automatically synchronizes data between each tool and enables centralized information management. For example, data from email, chat, and project management tools can be integrated, allowing all information to be viewed on a single dashboard. The data synchronization unit can also update data in real time, ensuring that the latest information is always available. This prevents information omissions and enables efficient business management.

[0099] The business communication support system can also use the emotion estimation function to adjust task priorities based on the user's emotional state. For example, if it estimates that the user is feeling stressed, it can adjust the schedule so that low-priority tasks are processed first. Also, if the user is feeling positive, it can prioritize important tasks. This enables flexible task management according to the user's emotional state, improving work efficiency.

[0100] The business communication support system can further include a feedback collection unit. The feedback collection unit collects user operation history and feedback in real time and continuously improves the interface. For example, it analyzes frequently used functions and operation patterns by users to optimize the interface. It can also add new functions and improve existing functions based on user feedback. This makes it possible to provide a flexible interface that meets user needs and improve work efficiency.

[0101] The business communication support system can also use its emotion estimation function to suggest interface designs based on the user's emotional state. For example, if it estimates that the user is feeling stressed, it can suggest relaxing colors and layouts. Also, if the user is feeling positive, it can suggest simple designs that allow the user to work efficiently. This allows for flexible interface designs that correspond to the user's emotional state, improving work efficiency.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The information collection department collects information from business communication tools, such as emails, chats, and phone call records. The information collection department can also collect information by acquiring data through APIs or using scraping technology. Step 2: The action item extraction unit extracts action items from the information collected by the information collection unit. For example, the generation AI uses natural language processing technology to analyze email content and chat messages and extract important tasks and to-do lists. The action item extraction unit can also use keyword extraction technology to identify follow-up items. Step 3: The process planning unit creates corresponding process plans based on the action items extracted by the action item extraction unit. For example, the generation AI sets the necessary steps for a specific task based on project management best practices. The process planning unit can also create step-by-step procedures and allocate the necessary resources. Step 4: The deadline setting unit sets deadlines based on the schedule created by the schedule creation unit. For example, the generation AI sets deadlines for each task taking into account task priority and resource availability. The deadline setting unit can also monitor task progress in real time and send reminders as needed.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

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

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

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

[0129] Fig. 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.

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

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] 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.

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

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

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

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] 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.

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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, in order to avoid confusion and to 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.

[0170] 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]

[0171] 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. an information gathering unit that gathers information from business communication tools; an action item extraction unit that extracts action items from the information collected by the information collection unit; a setup planning unit that creates corresponding setups based on the action items extracted by the action item extraction unit; a deadline setting unit that sets a deadline based on the setup created by the setup creation unit. A system characterized by:

2. The information collecting unit Collect information from email, chat, and phone call records, The action item extraction unit Analyzing the information collected by the information collection unit and extracting important action items.

2. The system of claim 1.

3. The setup planning unit Set the necessary steps and deadlines for specific tasks and reflect them in your work schedule 2. The system of claim 1.

4. The deadline setting unit Monitor the progress of each task in real time and send reminders as needed 2. The system of claim 1.

5. The information collecting unit Analyze participants' emotions during meetings, The action item extraction unit Extract emotionally significant statements as action items 2. The system of claim 1.

6. The setup planning unit Analyzes the dependencies between tasks and automatically generates optimal schedules 2. The system of claim 1.

7. The deadline setting unit Monitor the progress of each task in real time and automatically send reminders if progress is falling behind 2. The system of claim 1.

8. The information collecting unit Automatically synchronize data between tools to centralize information management 2. The system of claim 1.

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