System

Abstract

An object of a system according to an embodiment is to improve the efficiency of schedule reminding, schedule arrangement, and progress management in project management.SOLUTION: A system according to an embodiment includes a reminder unit, a schedule creation unit, an organization unit, a Gantt chart creation unit, and a progress management unit. The reminder unit reminds the user of a plan set in the calendar. The schedule creation unit creates a new schedule item. The organization unit organizes the schedule. The Gantt chart creation unit creates a Gantt chart. The progress management unit manages a progress status.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] In conventional project management, schedule reminders, organization, and progress management are done manually, which is inefficient and leaves room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of schedule reminders, organization, and progress management in project management. [Means for solving the problem]

[0006] The system according to the embodiment includes a reminder unit, a schedule creation unit, an organizing unit, a Gantt chart creation unit, and a progress management unit. The reminder unit reminds users of schedules set in a calendar. The schedule creation unit creates new schedules. The organizing unit organizes schedules. The Gantt chart creation unit creates Gantt charts. The progress management unit manages progress. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of schedule reminders, organization, and progress management in project management. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 project management support system according to an embodiment of the present invention utilizes a generation AI as an auxiliary tool to automate schedule reminders and creation, as well as project progress management in conjunction with a calendar. This allows the project management support system to accurately grasp the progress of a project and manage it efficiently.

[0029] A project management support system according to an embodiment includes a reminder unit, a schedule creation unit, an organizing unit, a Gantt chart creation unit, and a progress management unit. The reminder unit reminds users of events set in a calendar. For example, when a user instructs the reminder unit to "remind me of tomorrow's meeting," the reminder unit checks the calendar and reminds them of the relevant meeting. Furthermore, when a user instructs the reminder unit to "schedule a meeting for next Monday," the reminder unit adds a new event to the calendar. The schedule creation unit creates a new event. For example, when a user instructs the schedule creation unit to "schedule a meeting for next Monday," the schedule creation unit adds a new event to the calendar. The organizing unit organizes events. For example, when a calendar event is set as "Project A progress meeting," the organizing unit associates the event with Project A. The Gantt chart creation unit creates a Gantt chart. For example, when the start and end dates of each task in Project A are set in a calendar, the Gantt chart creation unit generates a Gantt chart based on that information. The progress management unit manages progress. For example, the progress management department can input the progress of each task into a calendar, automatically updating the Gantt chart and understanding the progress of the entire project. This allows the project management support system to accurately understand the progress of the project and manage it efficiently.

[0030] The reminder unit can learn the user's past behavioral patterns and automatically suggest reminder timing. For example, the generation AI in the reminder unit analyzes the user's past behavioral patterns and suggests the optimal reminder timing. For example, it identifies the time periods when the user tends to forget meetings in the past and sets reminders for those time periods. The reminder unit also learns the optimal reminder timing based on the user's calendar history. For example, it sets reminders to avoid time periods when the user frequently cancels meetings. The reminder unit also learns the user's behavioral patterns and adjusts the frequency and timing of reminders. For example, it refrains from sending reminders when the user is busy and sets reminders when the user has more time. This makes it possible to suggest the optimal reminder timing based on the user's behavioral patterns.

[0031] The schedule creation unit can analyze the user's voice instructions in real time and instantly create a schedule using natural language processing. For example, when the user commands the schedule creation unit to "schedule a meeting for tomorrow at 10:00," the generation AI analyzes the voice in real time and adds the schedule to the calendar. In addition, when the schedule creation unit analyzes the user's voice instructions using natural language processing technology, the generation AI instantly creates a schedule. For example, when the user commands the unit to "schedule a project meeting for next Friday," the generation AI adds the schedule to the calendar. In addition, when the user commands the unit to create a schedule by voice, the generation AI analyzes the command and instantly reflects it in the calendar. For example, when the user commands the unit to "schedule a meeting with a client for 2:00 p.m." the generation AI adds the schedule to the calendar. This allows schedules to be instantly created based on the user's voice instructions.

[0032] The reminder unit can adjust the reminder timing by referring to the user's health data. For example, the generation AI in the reminder unit analyzes the user's sleep patterns and sets the optimal reminder timing. For example, a reminder is sent after the user has had enough sleep. The reminder unit also refers to the user's stress level and the generation AI adjusts the reminder timing. For example, a reminder is set to avoid times when the user is feeling stressed. The reminder unit also optimizes the reminder timing by using the generation AI based on the user's health data. For example, a reminder is sent during times when the user is relaxed. This makes it possible to adjust the optimal reminder timing based on the user's health data.

[0033] The reminder unit works in conjunction with a digital assistant to provide schedule reminders on a variety of devices. For example, the generation AI in the reminder unit works in conjunction with a smart home device to provide schedule reminders. For example, reminders are provided through a smart speaker. The reminder unit also works in conjunction with other digital assistants to provide schedule reminders on a variety of devices. For example, reminders are provided through a smartphone or tablet. The reminder unit also works in conjunction with a digital assistant to provide schedule reminders on a variety of devices. For example, reminders are provided through a smart watch. This allows schedule reminders to be provided on a variety of devices.

[0034] The organizing unit can automatically evaluate the priority or urgency of projects and organize schedules in the optimal order. In the organizing unit, for example, the generation AI analyzes the priority of each project and organizes schedules in the optimal order. For example, it prioritizes tasks with high urgency in the schedule. The organizing unit also evaluates the urgency of a project and the generation AI automatically organizes schedules. For example, it prioritizes important meetings and tasks with upcoming deadlines in the schedule. The organizing unit also organizes schedules in the optimal order based on the project priority. For example, it prioritizes tasks that the user considers most important in the schedule. This makes it possible to organize schedules in the optimal order based on the project priority and urgency.

[0035] The organizing unit can analyze the user's past project data and organize the schedule based on the success patterns of similar projects. In the organizing unit, for example, the generation AI analyzes past project data and organizes the schedule based on success patterns. For example, it refers to the schedules of projects that have been successful in the past. The organizing unit also analyzes the success patterns of similar projects and the generation AI optimizes the schedule. For example, it assembles a schedule based on past success cases. In addition, the organizing unit, the generation AI extracts success patterns based on the user's past project data and organizes the schedule. For example, it refers to the schedules of successful projects. This makes it possible to optimize the schedule based on past project data.

[0036] The organizing unit can work in conjunction with other project management tools to centralize the organization of schedules. For example, the generation AI of the organizing unit works in conjunction with a task management app to centralize the organization of schedules. For example, data from the task management app is integrated into a calendar. The organizing unit can also work in conjunction with other project management tools to centralize the organization of schedules. For example, data from project management software is integrated into a calendar. The organizing unit can also work in conjunction with other project management tools to centralize the organization of schedules. For example, data from a task management app or project management software is integrated into a calendar. This allows for centralized organization of schedules.

[0037] The organizing unit can also consider the schedules of the user's team members and propose the optimal meeting time. In the organizing unit, for example, the generation AI analyzes the schedules of the team members and proposes the optimal meeting time. For example, it automatically selects a time period when everyone can participate. In addition, the organizing unit can also consider the schedules of the team members and propose the optimal meeting time. For example, it selects the optimal time period based on everyone's schedules. In addition, the organizing unit can also consider the schedules of the user's team members and propose the optimal meeting time. For example, it automatically selects a time period when everyone can participate. This makes it possible to propose the optimal meeting time in consideration of the schedules of the team members.

[0038] The Gantt chart creation unit can automatically analyze the risk factors of a project and create a Gantt chart that incorporates risk management. In the Gantt chart creation unit, for example, a generation AI analyzes the risk factors of a project and creates a Gantt chart that incorporates risk management. For example, it highlights high-risk tasks. In addition, the Gantt chart creation unit automatically analyzes the risk factors of a project and a generation AI creates a Gantt chart that incorporates risk management. For example, it displays countermeasures for high-risk tasks. In addition, the Gantt chart creation unit analyzes the risk factors of a project and creates a Gantt chart that incorporates risk management. For example, it prioritizes high-risk tasks in the schedule. In this way, a Gantt chart that incorporates risk management can be created.

[0039] The Gantt chart creation unit can collect project progress data in real time and dynamically update the Gantt chart. In the Gantt chart creation unit, for example, a generation AI collects project progress data in real time and dynamically updates the Gantt chart. For example, it automatically reflects the completion status of tasks. In addition, the Gantt chart creation unit collects project progress data in real time and a generation AI dynamically updates the Gantt chart. For example, it automatically reflects the progress status of tasks. In addition, the Gantt chart creation unit collects project progress data in real time and a generation AI dynamically updates the Gantt chart. For example, it automatically reflects the progress status of tasks. This allows the Gantt chart to be dynamically updated based on the progress data.

[0040] The Gantt chart creation unit can work in conjunction with other project management tools to automatically update the Gantt chart. In the Gantt chart creation unit, for example, the generation AI works in conjunction with a spreadsheet to automatically update the Gantt chart. For example, the Gantt chart is updated based on data from the spreadsheet. The Gantt chart creation unit also works in conjunction with other project management tools, and the generation AI automatically updates the Gantt chart. For example, the Gantt chart is updated based on data from the project management software. The Gantt chart creation unit also works in conjunction with a database, and the generation AI automatically updates the Gantt chart. For example, the Gantt chart is updated based on information from the database. This allows the Gantt chart to be automatically updated in conjunction with other project management tools.

[0041] The Gantt chart creation unit visualizes the progress of the project and can provide a dashboard that the user can intuitively understand. In the Gantt chart creation unit, for example, the generation AI visualizes the progress of the project and provides a dashboard that the user can intuitively understand. For example, the progress is displayed in a graph or chart. In addition, the Gantt chart creation unit visualizes the progress of the project and provides a dashboard that the user can intuitively understand. For example, the progress is displayed in a graph or chart. In addition, the Gantt chart creation unit visualizes the progress of the project and provides a dashboard that the user can intuitively understand. For example, the progress is displayed in a graph or chart. In this way, the progress of the project can be visualized and a dashboard that the user can intuitively understand can be provided.

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

[0043] The project management support system further includes a prediction unit. The prediction unit can analyze past project data and predict future project progress. For example, the prediction unit can predict whether the progress of a current project is behind schedule based on the progress data of past projects. The prediction unit can also analyze past project data and identify future risk factors. For example, the prediction unit can identify tasks where problems occurred in the past and predict the possibility of a similar problem occurring in the current project. The prediction unit can also predict future resource shortages based on past project data. For example, the prediction unit can identify times when resources were scarce in the past and predict the possibility of a similar resource shortage occurring in the current project. This makes it possible to predict project progress and avoid risks in advance.

[0044] The project management support system further includes a notification unit. The notification unit can notify the user of important project events and deadlines. For example, the notification unit sends a notification to the user when a project deadline is approaching. The notification unit can also send a notification to the user when an important project event occurs. For example, the notification unit sends a notification when an important project milestone is achieved. The notification unit can also send a notification to the user when project progress is behind schedule. For example, the notification unit sends a notification when task progress is behind schedule. This makes it easier for the user to understand important project events and deadlines.

[0045] The project management support system further includes a feedback unit. The feedback unit can collect user feedback on the progress of the project and use it to improve the system. For example, the feedback unit provides an interface that allows users to input comments on the progress of the project. The feedback unit can also analyze user feedback and identify areas for improvement in the system. For example, if a user is dissatisfied with the reminder function, the reminder function can be improved based on that feedback. The feedback unit can also suggest new functions based on user feedback. For example, if a user requests a specific function, the new function can be added based on that feedback. In this way, the system can be continuously improved by utilizing user feedback.

[0046] The project management support system further includes a learning unit. The learning unit can learn the user's operation history and improve the operability of the system. For example, the learning unit can identify functions that the user uses frequently and display those functions preferentially. The learning unit can also analyze the user's operation patterns and simplify operation procedures. For example, if the user repeatedly performs a specific operation, the learning unit can automate that operation. The learning unit can also suggest system customization based on the user's operation history. For example, if the user frequently changes a specific setting, the learning unit can suggest setting that setting to the default. In this way, the user's operation history can be utilized to improve the operability of the system.

[0047] The project management support system further includes an alert unit. The alert unit can send an alert to the user when an abnormality occurs in the progress of the project. For example, the alert unit sends an alert when task progress is behind schedule. The alert unit can also send an alert to the user when there is a shortage of resources for the project. For example, an alert is sent when resource utilization rates increase. The alert unit can also send an alert to the user when project risks are increasing. For example, an alert is sent when risk factors increase. This allows the user to respond quickly when an abnormality occurs in the progress of the project.

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

[0049] Step 1: The reminder unit reminds you of events set in the calendar. For example, if the user instructs "Remind me about tomorrow's meeting," it checks the calendar and reminds you of the relevant meeting. Also, if the user instructs "Set up a meeting for next Monday," it adds a new event to the calendar. Step 2: The schedule creation unit creates a new schedule. For example, when the user instructs "Schedule a meeting for next Monday," the new schedule is added to the calendar. Step 3: The organizer organizes the schedule. For example, if there is a schedule set as "Progress meeting for Project A" on the calendar, the schedule is associated with Project A. Step 4: The Gantt chart creation unit creates a Gantt chart. For example, if the start and end dates of each task in Project A are set on a calendar, a Gantt chart is generated based on that information. Step 5: The progress management department manages the progress status. For example, by entering the progress status of each task into a calendar, the Gantt chart is automatically updated and the progress status of the entire project is grasped.

[0050] (Example 2) The project management support system according to an embodiment of the present invention utilizes a generation AI as an auxiliary tool to automate schedule reminders and creation, as well as project progress management in conjunction with a calendar. This allows the project management support system to accurately grasp the progress of a project and manage it efficiently.

[0051] A project management support system according to an embodiment includes a reminder unit, a schedule creation unit, an organizing unit, a Gantt chart creation unit, and a progress management unit. The reminder unit reminds users of events set in a calendar. For example, when a user instructs the reminder unit to "remind me of tomorrow's meeting," the reminder unit checks the calendar and reminds them of the relevant meeting. Furthermore, when a user instructs the reminder unit to "schedule a meeting for next Monday," the reminder unit adds a new event to the calendar. The schedule creation unit creates a new event. For example, when a user instructs the schedule creation unit to "schedule a meeting for next Monday," the schedule creation unit adds a new event to the calendar. The organizing unit organizes events. For example, when a calendar event is set as "Project A progress meeting," the organizing unit associates the event with Project A. The Gantt chart creation unit creates a Gantt chart. For example, when the start and end dates of each task in Project A are set in a calendar, the Gantt chart creation unit generates a Gantt chart based on that information. The progress management unit manages progress. For example, the progress management department can input the progress of each task into a calendar, automatically updating the Gantt chart and understanding the progress of the entire project. This allows the project management support system to accurately understand the progress of the project and manage it efficiently.

[0052] The reminder unit can learn the user's past behavioral patterns and automatically suggest reminder timing. For example, the generation AI in the reminder unit analyzes the user's past behavioral patterns and suggests the optimal reminder timing. For example, it identifies the time periods when the user tends to forget meetings in the past and sets reminders for those time periods. The reminder unit also learns the optimal reminder timing based on the user's calendar history. For example, it sets reminders to avoid time periods when the user frequently cancels meetings. The reminder unit also learns the user's behavioral patterns and adjusts the frequency and timing of reminders. For example, it refrains from sending reminders when the user is busy and sets reminders when the user has more time. This makes it possible to suggest the optimal reminder timing based on the user's behavioral patterns.

[0053] The schedule creation unit can analyze the user's voice instructions in real time and instantly create a schedule using natural language processing. For example, when the user commands the schedule creation unit to "schedule a meeting for tomorrow at 10:00," the generation AI analyzes the voice in real time and adds the schedule to the calendar. In addition, when the schedule creation unit analyzes the user's voice instructions using natural language processing technology, the generation AI instantly creates a schedule. For example, when the user commands the unit to "schedule a project meeting for next Friday," the generation AI adds the schedule to the calendar. In addition, when the user commands the unit to create a schedule by voice, the generation AI analyzes the command and instantly reflects it in the calendar. For example, when the user commands the unit to "schedule a meeting with a client for 2:00 p.m." the generation AI adds the schedule to the calendar. This allows schedules to be instantly created based on the user's voice instructions.

[0054] The reminder unit can use the emotion estimation function to select a reminder method according to the user's emotional state. For example, the generation AI in the reminder unit analyzes the user's emotional state and selects a reminder method. For example, if the user is feeling stressed, the reminder is sent in a gentle tone. The reminder unit also uses the emotion estimation function to select a reminder method according to the user's emotional state. For example, if the user is tired, the reminder is sent with encouraging words. The reminder unit also analyzes the user's emotional state in real time and adjusts the reminder method. For example, if the user is feeling down, the reminder is sent with an encouraging message. This makes it possible to select a reminder method according to the user's emotional state.

[0055] The reminder unit can adjust the reminder timing by referring to the user's health data. For example, the generation AI in the reminder unit analyzes the user's sleep patterns and sets the optimal reminder timing. For example, a reminder is sent after the user has had enough sleep. The reminder unit also refers to the user's stress level and the generation AI adjusts the reminder timing. For example, a reminder is set to avoid times when the user is feeling stressed. The reminder unit also optimizes the reminder timing by using the generation AI based on the user's health data. For example, a reminder is sent during times when the user is relaxed. This makes it possible to adjust the optimal reminder timing based on the user's health data.

[0056] The reminder unit works in conjunction with a digital assistant to provide schedule reminders on a variety of devices. For example, the generation AI in the reminder unit works in conjunction with a smart home device to provide schedule reminders. For example, reminders are provided through a smart speaker. The reminder unit also works in conjunction with other digital assistants to provide schedule reminders on a variety of devices. For example, reminders are provided through a smartphone or tablet. The reminder unit also works in conjunction with a digital assistant to provide schedule reminders on a variety of devices. For example, reminders are provided through a smart watch. This allows schedule reminders to be provided on a variety of devices.

[0057] The reminding unit uses the emotion estimation function to analyze the user's emotional response when receiving a reminder, and can continuously improve the reminder method. In the reminding unit, for example, the generation AI analyzes the user's emotional response to reminders and improves the reminder method. For example, the reminder unit preferentially uses reminder methods to which the user has a positive response. The reminding unit also uses the emotion estimation function to analyze the user's emotional response to reminders and adjusts the reminder method. For example, if the user has a negative response, the reminder method is changed. In the reminding unit, the generation AI also continuously monitors the user's emotional response to reminders and optimizes the reminder method. For example, the reminder method is improved based on the user's emotional data. This makes it possible to continuously improve the reminder method based on the user's emotional response.

[0058] The organizing unit can automatically evaluate the priority or urgency of projects and organize schedules in the optimal order. In the organizing unit, for example, the generation AI analyzes the priority of each project and organizes schedules in the optimal order. For example, it prioritizes tasks with high urgency in the schedule. The organizing unit also evaluates the urgency of a project and the generation AI automatically organizes schedules. For example, it prioritizes important meetings and tasks with upcoming deadlines in the schedule. The organizing unit also organizes schedules in the optimal order based on the project priority. For example, it prioritizes tasks that the user considers most important in the schedule. This makes it possible to organize schedules in the optimal order based on the project priority and urgency.

[0059] The organizing unit can analyze the user's past project data and organize the schedule based on the success patterns of similar projects. In the organizing unit, for example, the generation AI analyzes past project data and organizes the schedule based on success patterns. For example, it refers to the schedules of projects that have been successful in the past. The organizing unit also analyzes the success patterns of similar projects and the generation AI optimizes the schedule. For example, it assembles a schedule based on past success cases. In addition, the organizing unit, the generation AI extracts success patterns based on the user's past project data and organizes the schedule. For example, it refers to the schedules of successful projects. This makes it possible to optimize the schedule based on past project data.

[0060] The organizing unit can use the emotion estimation function to suggest project priorities according to the user's emotional state. For example, the generation AI in the organizing unit analyzes the user's emotional state and suggests project priorities. For example, if the user is feeling stressed, it prioritizes less burdensome tasks. The organizing unit also uses the emotion estimation function to suggest project priorities according to the user's emotional state. For example, if the user is relaxed, it prioritizes important tasks. The organizing unit also uses the generation AI to analyze the user's emotional state in real time and adjust project priorities. For example, if the user is tired, it prioritizes easier tasks. This makes it possible to suggest project priorities based on the user's emotional state.

[0061] The organizing unit can work in conjunction with other project management tools to centralize the organization of schedules. For example, the generation AI of the organizing unit works in conjunction with a task management app to centralize the organization of schedules. For example, data from the task management app is integrated into a calendar. The organizing unit can also work in conjunction with other project management tools to centralize the organization of schedules. For example, data from project management software is integrated into a calendar. The organizing unit can also work in conjunction with other project management tools to centralize the organization of schedules. For example, data from a task management app or project management software is integrated into a calendar. This allows for centralized organization of schedules.

[0062] The organizing unit can also consider the schedules of the user's team members and propose the optimal meeting time. In the organizing unit, for example, the generation AI analyzes the schedules of the team members and proposes the optimal meeting time. For example, it automatically selects a time period when everyone can participate. In addition, the organizing unit can also consider the schedules of the team members and propose the optimal meeting time. For example, it selects the optimal time period based on everyone's schedules. In addition, the organizing unit can also consider the schedules of the user's team members and propose the optimal meeting time. For example, it automatically selects a time period when everyone can participate. This makes it possible to propose the optimal meeting time in consideration of the schedules of the team members.

[0063] The organizing unit can use the emotion estimation function to analyze the emotional state of the entire team and propose the optimal project progress method. In the organizing unit, for example, the generation AI analyzes the emotional state of the entire team and proposes the optimal project progress method. For example, if the team is feeling stressed, it prioritizes less burdensome tasks. The organizing unit also uses the emotion estimation function to analyze the emotional state of the entire team and adjust the project progress method. For example, if the team is relaxed, it prioritizes important tasks. In the organizing unit, the generation AI analyzes the emotional state of the entire team in real time and optimizes the project progress method. For example, if the team is tired, it prioritizes easier tasks. This makes it possible to propose the optimal project progress method based on the emotional state of the entire team.

[0064] The Gantt chart creation unit can automatically analyze the risk factors of a project and create a Gantt chart that incorporates risk management. In the Gantt chart creation unit, for example, a generation AI analyzes the risk factors of a project and creates a Gantt chart that incorporates risk management. For example, it highlights high-risk tasks. In addition, the Gantt chart creation unit automatically analyzes the risk factors of a project and a generation AI creates a Gantt chart that incorporates risk management. For example, it displays countermeasures for high-risk tasks. In addition, the Gantt chart creation unit analyzes the risk factors of a project and creates a Gantt chart that incorporates risk management. For example, it prioritizes high-risk tasks in the schedule. In this way, a Gantt chart that incorporates risk management can be created.

[0065] The Gantt chart creation unit can collect project progress data in real time and dynamically update the Gantt chart. In the Gantt chart creation unit, for example, a generation AI collects project progress data in real time and dynamically updates the Gantt chart. For example, it automatically reflects the completion status of tasks. In addition, the Gantt chart creation unit collects project progress data in real time and a generation AI dynamically updates the Gantt chart. For example, it automatically reflects the progress status of tasks. In addition, the Gantt chart creation unit collects project progress data in real time and a generation AI dynamically updates the Gantt chart. For example, it automatically reflects the progress status of tasks. This allows the Gantt chart to be dynamically updated based on the progress data.

[0066] The Gantt chart creation unit uses the emotion estimation function to create a Gantt chart that reflects the emotional states of the project members, thereby making it possible to visualize the team's motivation. In the Gantt chart creation unit, for example, the generation AI analyzes the emotional states of the project members and reflects this in the Gantt chart. For example, tasks that motivate members highly are highlighted. The Gantt chart creation unit also uses the emotion estimation function to create a Gantt chart that reflects the emotional states of the project members. For example, task priorities are adjusted according to the members' emotional states. The Gantt chart creation unit also uses the generation AI to analyze the emotional states of the project members in real time and reflects this in the Gantt chart. For example, tasks that motivate members low are highlighted. This makes it possible to create a Gantt chart that reflects the emotional states of the project members and visualize the team's motivation.

[0067] The Gantt chart creation unit can work in conjunction with other project management tools to automatically update the Gantt chart. In the Gantt chart creation unit, for example, the generation AI works in conjunction with a spreadsheet to automatically update the Gantt chart. For example, the Gantt chart is updated based on data from the spreadsheet. The Gantt chart creation unit also works in conjunction with other project management tools, and the generation AI automatically updates the Gantt chart. For example, the Gantt chart is updated based on data from the project management software. The Gantt chart creation unit also works in conjunction with a database, and the generation AI automatically updates the Gantt chart. For example, the Gantt chart is updated based on information from the database. This allows the Gantt chart to be automatically updated in conjunction with other project management tools.

[0068] The Gantt chart creation unit visualizes the progress of the project and can provide a dashboard that the user can intuitively understand. In the Gantt chart creation unit, for example, the generation AI visualizes the progress of the project and provides a dashboard that the user can intuitively understand. For example, the progress is displayed in a graph or chart. In addition, the Gantt chart creation unit visualizes the progress of the project and provides a dashboard that the user can intuitively understand. For example, the progress is displayed in a graph or chart. In addition, the Gantt chart creation unit visualizes the progress of the project and provides a dashboard that the user can intuitively understand. For example, the progress is displayed in a graph or chart. In this way, the progress of the project can be visualized and a dashboard that the user can intuitively understand can be provided.

[0069] The Gantt chart creation unit can use the emotion estimation function to analyze the emotional states of project members and propose optimal countermeasures for tasks that are behind schedule. In the Gantt chart creation unit, for example, the generation AI analyzes the emotional states of project members and proposes optimal countermeasures for tasks that are behind schedule. For example, it proposes actions to increase members' motivation. In addition, the Gantt chart creation unit can use the emotion estimation function to analyze the emotional states of project members and propose optimal countermeasures for tasks that are behind schedule. For example, it proposes actions to reduce members' stress. In addition, the Gantt chart creation unit can use the generation AI to analyze the emotional states of project members in real time and propose optimal countermeasures for tasks that are behind schedule. For example, it proposes countermeasures based on the emotional states of members. In this way, it is possible to propose optimal countermeasures for tasks that are behind schedule based on the emotional states of project members.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The project management support system further includes a prediction unit. The prediction unit can analyze past project data and predict future project progress. For example, the prediction unit can predict whether the progress of a current project is behind schedule based on the progress data of past projects. The prediction unit can also analyze past project data and identify future risk factors. For example, the prediction unit can identify tasks where problems occurred in the past and predict the possibility of a similar problem occurring in the current project. The prediction unit can also predict future resource shortages based on past project data. For example, the prediction unit can identify times when resources were scarce in the past and predict the possibility of a similar resource shortage occurring in the current project. This makes it possible to predict project progress and avoid risks in advance.

[0072] The project management support system further includes a notification unit. The notification unit can notify the user of important project events and deadlines. For example, the notification unit sends a notification to the user when a project deadline is approaching. The notification unit can also send a notification to the user when an important project event occurs. For example, the notification unit sends a notification when an important project milestone is achieved. The notification unit can also send a notification to the user when project progress is behind schedule. For example, the notification unit sends a notification when task progress is behind schedule. This makes it easier for the user to understand important project events and deadlines.

[0073] The project management support system further includes a feedback unit. The feedback unit can collect user feedback on the progress of the project and use it to improve the system. For example, the feedback unit provides an interface that allows users to input comments on the progress of the project. The feedback unit can also analyze user feedback and identify areas for improvement in the system. For example, if a user is dissatisfied with the reminder function, the reminder function can be improved based on that feedback. The feedback unit can also suggest new functions based on user feedback. For example, if a user requests a specific function, the new function can be added based on that feedback. In this way, the system can be continuously improved by utilizing user feedback.

[0074] The project management support system further includes a learning unit. The learning unit can learn the user's operation history and improve the operability of the system. For example, the learning unit can identify functions that the user uses frequently and display those functions preferentially. The learning unit can also analyze the user's operation patterns and simplify operation procedures. For example, if the user repeatedly performs a specific operation, the learning unit can automate that operation. The learning unit can also suggest system customization based on the user's operation history. For example, if the user frequently changes a specific setting, the learning unit can suggest setting that setting to the default. In this way, the user's operation history can be utilized to improve the operability of the system.

[0075] The project management support system further includes an alert unit. The alert unit can send an alert to the user when an abnormality occurs in the progress of the project. For example, the alert unit sends an alert when task progress is behind schedule. The alert unit can also send an alert to the user when there is a shortage of resources for the project. For example, an alert is sent when resource utilization rates increase. The alert unit can also send an alert to the user when project risks are increasing. For example, an alert is sent when risk factors increase. This allows the user to respond quickly when an abnormality occurs in the progress of the project.

[0076] The project management support system can further include an evaluation unit that uses an emotion estimation function to evaluate the progress of the project based on the emotional state of the user. For example, if the user is feeling stressed, the evaluation unit evaluates that the progress of the project is delayed. The evaluation unit also uses the emotion estimation function to evaluate the progress of the project based on the emotional state of the user. For example, if the user is relaxed, the evaluation unit evaluates that the progress of the project is going smoothly. The evaluation unit also evaluates the progress of the project by having the generation AI analyze the emotional state of the user in real time. For example, if the user is tired, the evaluation unit evaluates that the progress of the project is delayed. This makes it possible to evaluate the progress of the project based on the emotional state of the user.

[0077] The project management support system may further include a customization unit that customizes the content of the reminder based on the user's emotional state using an emotion estimation function. For example, if the user is feeling stressed, the customization unit may simplify the content of the reminder. The customization unit also customizes the content of the reminder based on the user's emotional state using the emotion estimation function. For example, if the user is relaxed, the content of the reminder may be more detailed. The customization unit also customizes the content of the reminder by having the generation AI analyze the user's emotional state in real time. For example, if the user is tired, the content of the reminder may be simplified. This allows the content of the reminder to be customized based on the user's emotional state.

[0078] The project management support system may further include a priority adjustment unit that adjusts project priorities based on the user's emotional state using an emotion estimation function. For example, if the user is feeling stressed, the priority adjustment unit prioritizes less burdensome tasks. The priority adjustment unit also adjusts project priorities based on the user's emotional state using the emotion estimation function. For example, if the user is relaxed, it prioritizes important tasks. The priority adjustment unit also adjusts project priorities using a generation AI that analyzes the user's emotional state in real time. For example, if the user is tired, it prioritizes easier tasks. This makes it possible to adjust project priorities based on the user's emotional state.

[0079] The project management support system can further include a progress method suggestion unit that uses an emotion estimation function to suggest a project progress method based on the user's emotional state. For example, if the user is feeling stressed, the progress method suggestion unit prioritizes less burdensome tasks. The progress method suggestion unit also uses the emotion estimation function to suggest a project progress method based on the user's emotional state. For example, if the user is relaxed, it prioritizes important tasks. The progress method suggestion unit also uses the generation AI to analyze the user's emotional state in real time and suggest a project progress method. For example, if the user is tired, it prioritizes easier tasks. This makes it possible to suggest a project progress method based on the user's emotional state.

[0080] The project management support system can further include an evaluation unit that uses an emotion estimation function to evaluate the progress of the project based on the emotional state of the user. For example, if the user is feeling stressed, the evaluation unit evaluates that the progress of the project is delayed. The evaluation unit also uses the emotion estimation function to evaluate the progress of the project based on the emotional state of the user. For example, if the user is relaxed, the evaluation unit evaluates that the progress of the project is going smoothly. The evaluation unit also evaluates the progress of the project by having the generation AI analyze the emotional state of the user in real time. For example, if the user is tired, the evaluation unit evaluates that the progress of the project is delayed. This makes it possible to evaluate the progress of the project based on the emotional state of the user.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The reminder unit reminds you of events set in the calendar. For example, if the user instructs "Remind me about tomorrow's meeting," it checks the calendar and reminds you of the relevant meeting. Also, if the user instructs "Set up a meeting for next Monday," it adds a new event to the calendar. Step 2: The schedule creation unit creates a new schedule. For example, when the user instructs "Schedule a meeting for next Monday," the new schedule is added to the calendar. Step 3: The organizer organizes the schedule. For example, if there is a schedule set as "Progress meeting for Project A" on the calendar, the schedule is associated with Project A. Step 4: The Gantt chart creation unit creates a Gantt chart. For example, if the start and end dates of each task in Project A are set on a calendar, a Gantt chart is generated based on that information. Step 5: The progress management department manages the progress status. For example, by entering the progress status of each task into a calendar, the Gantt chart is automatically updated and the progress status of the entire project is grasped.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 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. A reminder section that reminds you of events set in the calendar, a schedule creation unit for creating a new schedule; An organizing department that organizes schedules; a Gantt chart creation unit that creates a Gantt chart; A progress management unit that manages the progress status. A system characterized by:

2. The reminding unit Learns the user's past behavioral patterns and automatically suggests reminder timing 2. The system of claim 1.

3. The schedule creation unit Analyzes user voice commands in real time and creates schedules instantly using natural language processing 2. The system of claim 1.

4. The reminding unit Selecting a reminder method according to the user's emotional state 2. The system of claim 1.

5. The reminding unit Refer to the user's health data and adjust the timing of reminders 2. The system of claim 1.

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