system

Abstract

An object of a system according to an embodiment is to support an ADHD user to efficiently perform task management.SOLUTION: A system according to an embodiment includes a personalized task management designing unit, an advice providing unit, and an analytical review unit. The personalized task management designing unit designs a personalized AI using the generated task management. The advice providing unit provides appropriate advice using the generated AI. The analysis-review unit uses the generated AI to analyze and review actions and progresses.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for users with ADHD to efficiently manage tasks in business situations.

[0005] The system according to the embodiment aims to support users with ADHD in efficiently managing tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a personalized task management design unit, an advice providing unit, and an analysis and review unit. The personalized task management design unit uses a generation AI to design personalized task management. The advice providing unit uses the generation AI to provide appropriate advice. The analysis and review unit uses the generation AI to analyze and review actions and progress. [Effects of the Invention]

[0007] The system according to the embodiment can assist a user with ADHD in efficiently managing tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI task coaching system according to an embodiment of the present invention is a system for enabling people with ADHD to efficiently manage tasks in business situations. In this system, a generative AI provides personalized task management design, appropriate advice, and an analytical review of behavior and progress. This allows the AI ​​task coaching system to enable people with ADHD to efficiently manage tasks in business situations.

[0029] An AI task coaching system according to an embodiment includes a personalized task management design unit, an advice providing unit, and an analysis and review unit. The personalized task management design unit uses a generation AI to design personalized task management. For example, the generation AI proposes an efficient task scheduling method based on a user's characteristics and past behavioral data. The generation AI can also create schedules taking into account time periods when the user is likely to concentrate and task priorities. The generation AI receives prompts including the user's characteristics and past behavioral data as input and generates an optimal scheduling method. The advice providing unit uses the generation AI to provide appropriate advice. For example, the generation AI provides advice regarding the user's task management. The generation AI receives prompts including the user's current task status and meeting schedule as input and generates appropriate advice. The generation AI can provide advice on specific methods to reduce meeting time and revisit task priorities. The analysis and review unit uses the generation AI to analyze and review behavior and progress. For example, the generation AI analyzes the user's behavior and task progress, identifies issues, and proposes improvement measures. The generation AI receives prompts including the user's behavioral data and task progress as input and generates an analysis and improvement measures. If a user is spending too much time on a particular task, the generative AI can identify the cause and suggest more efficient methods. This allows the AI ​​task coaching system according to the embodiment to help people with ADHD manage their tasks efficiently in business situations. For example, by placing important tasks during times when users are most likely to concentrate, task completion rates can be improved. Furthermore, by reducing meeting time, users can spend more time focusing on their actual work. Furthermore, through behavior and progress analysis, issues users face can be identified early and appropriate improvement measures can be implemented.

[0030] The personalized task management design unit collects the user's physiological data and proposes an optimal task schedule based on that data. The personalized task management design unit collects the user's physiological data (heart rate, sleep patterns, etc.) and proposes an optimal task schedule based on that data. For example, it collects the user's heart rate data and places tasks that require concentration during times when the heart rate is stable. It analyzes the user's sleep patterns and places important tasks during times after the user has had enough sleep. Based on the physiological data, it places more difficult tasks when the user is feeling well and easier tasks when the user is feeling unwell. This makes it possible to propose an optimal task schedule based on the user's physiological data.

[0031] The personalized task management design unit analyzes the user's past task completion data, extracts success patterns, and generates a schedule based on them. The personalized task management design unit analyzes the user's past task completion data, extracts success patterns, and generates a schedule based on them. For example, it analyzes the user's past task completion data, extracts successful task patterns, and generates a new task schedule based on them. Based on the past task completion data, it identifies the time periods and task orders in which the user can work most efficiently, and proposes a schedule based on them. To extract success patterns, it analyzes the user's task completion times and task types, and generates an optimal schedule based on them. In this way, it is possible to extract success patterns based on the user's past task completion data, and generate an optimal schedule.

[0032] The Personalized Task Management Design Department will also be applied to task management within the home, optimizing the schedules of all family members. The Personalized Task Management Design Department will also be applied to task management within the home, developing a system that optimizes the schedules of all family members. For example, by collecting the schedules of all family members and optimally allocating each member's tasks, it will streamline task management within the home. Based on the schedules of all family members, it will adjust common tasks and events and optimize the schedule so that everyone can participate. It will analyze the task completion data of all family members, extract success patterns, and generate a task schedule within the home based on that. This will streamline task management within the home and optimize the schedules of all family members.

[0033] The Personalized Task Management Design Department is applied to assignment management in schools and educational institutions, providing each student with the optimal study schedule. The Personalized Task Management Design Department is applied to assignment management in schools and educational institutions, providing each student with the optimal study schedule. For example, we will develop a system that collects student learning data and generates the optimal study schedule for each student. We will analyze students' learning patterns and propose the optimal schedule based on successful learning methods. We will allocate the optimal study tasks for each student based on student learning progress data, supporting efficient learning. This will make it possible to provide each student with the optimal study schedule.

[0034] The advice providing unit extracts the most effective advice pattern based on the user's past task management data and provides individually customized advice. The advice providing unit extracts the most effective advice pattern based on the user's past task management data and provides individually customized advice. For example, the advice providing unit analyzes the user's past task management data, extracts the most effective advice pattern, and provides individually customized advice. Based on the past task management data, the advice providing unit identifies a method by which the user can most effectively complete a task and provides advice based on that. The advice providing unit analyzes the user's past data and provides individually customized advice based on successful task management methods. In this way, the advice providing unit can extract the most effective advice pattern based on the user's past task management data and provide individually customized advice.

[0035] The advice providing unit monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. The advice providing unit monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. For example, a system is constructed that monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. Based on the behavioral patterns, a method by which the user can most effectively complete a task is identified and advice is dynamically updated based on that. User behavioral data is analyzed in real time and advice is dynamically updated as needed. For example, advice is adjusted according to the progress of the task. This makes it possible to monitor the user's behavioral patterns in real time and dynamically update advice as needed.

[0036] The advice providing unit is also applied to health management and fitness advice, providing specific advice based on the user's health condition. The advice providing unit is also applied to health management and fitness advice, providing specific advice based on the user's health condition. For example, it provides specific health management and fitness advice based on the user's health data. It analyzes the health condition, identifies how the user can most effectively maintain their health, and provides advice based on that. It monitors the user's health data in real time, and dynamically updates health management and fitness advice as needed. This makes it possible to provide specific advice based on the user's health condition.

[0037] The advice providing unit is applied to financial management, analyzing the user's spending patterns and providing savings and investment advice. The advice providing unit is applied to financial management, analyzing the user's spending patterns and providing savings and investment advice. For example, it analyzes the user's spending patterns and provides specific savings and investment advice. Based on the spending data, it identifies how the user can most effectively manage their assets and provides advice based on that. It monitors the user's spending patterns in real time and dynamically updates savings and investment advice as needed. This makes it possible to analyze the user's spending patterns and provide savings and investment advice.

[0038] The analysis and review unit analyzes the user's behavioral data in detail, identifies factors that affect task progress, and proposes improvement measures. The analysis and review unit analyzes the user's behavioral data in detail, identifies factors that affect task progress, and proposes improvement measures. For example, the analysis and review unit analyzes the user's behavioral data in detail, identifies factors that affect task progress, and proposes improvement measures based on the factors. Based on the behavioral data, the unit identifies how the user can most effectively complete the task, and proposes improvement measures based on the factors. The analysis and review unit monitors the user's behavioral data in real time, identifies factors that affect task progress, and proposes improvement measures based on the factors. This makes it possible to analyze the user's behavioral data in detail, identify factors that affect task progress, and propose improvement measures.

[0039] The analysis and review unit analyzes long-term performance trends based on the user's task completion rate and provides insights that are useful for future task management. The analysis and review unit analyzes long-term performance trends based on the user's task completion rate and provides insights that are useful for future task management. For example, the analysis and review unit analyzes the user's task completion rate, identifies long-term performance trends, and provides insights that are useful for future task management based on the trends. Based on the task completion rate, the time period and method in which the user can most effectively complete the task are identified, and future task management is suggested based on the results. The analysis and review unit analyzes the user's task completion data over the long term, identifies performance trends, and provides insights that are useful for future task management based on the trends. This makes it possible to analyze long-term performance trends based on the user's task completion rate and provide insights that are useful for future task management.

[0040] The analysis and review department analyzes task progress for the entire team and provides feedback to promote cooperation between team members. The analysis and review department analyzes task progress for the entire team and provides feedback to promote cooperation between team members. For example, it analyzes task progress data for the entire team, identifies tasks and members that require cooperation, and provides feedback based on that. It monitors task progress between team members in real time and provides feedback when cooperation is required. It provides specific feedback to promote cooperation based on task progress data for the entire team. For example, it emphasizes the importance of cooperation on a specific task. This makes it possible to analyze task progress for the entire team and provide feedback to promote cooperation between team members.

[0041] The Analysis and Review Department is applied to project management, monitoring the progress of the entire project in real time and making adjustments as necessary. The Analysis and Review Department is applied to project management, monitoring the progress of the entire project in real time and making adjustments as necessary. For example, it monitors the progress data of the entire project in real time and rearranges tasks or adjusts the schedule as necessary. It analyzes the progress of the project, identifies tasks that are delayed or resource shortages, and makes adjustments based on that. It makes adjustments to achieve efficient project management based on the progress data of the entire project. For example, it proposes the rearrangement of resources or changes in task priorities. This allows the progress of the entire project to be monitored in real time and adjustments to be made as necessary.

[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 personalized task management design unit can also propose a task schedule that takes into account the user's hobbies and interests. For example, if the user likes listening to music, music listening time can be scheduled before and after tasks that require concentration. If the user likes exercise, important tasks can be scheduled for the time after exercise when concentration is at its highest. Furthermore, if the user enjoys reading, reading time can be incorporated into the schedule as time for relaxation. This makes it possible to propose a task schedule that takes into account the user's hobbies and interests.

[0044] The Personalized Task Management Design Department collects the user's physiological data and proposes the optimal task schedule based on it. For example, it collects the user's heart rate data and arranges tasks that require concentration during times when the heart rate is stable. It analyzes the user's sleep patterns and arranges important tasks for times after a sufficient sleep. Based on the physiological data, it arranges more difficult tasks when the user is feeling well and easier tasks when they are feeling unwell. This makes it possible to propose the optimal task schedule based on the user's physiological data.

[0045] The personalized task management design unit analyzes a user's past task completion data, extracts success patterns, and generates a schedule based on them. For example, it analyzes a user's past task completion data, extracts successful task patterns, and generates a new task schedule based on them. Based on the past task completion data, it identifies the time periods and task orders in which the user can work most efficiently, and proposes a schedule based on them. To extract success patterns, it analyzes the user's task completion times and task types, and generates an optimal schedule based on them. This makes it possible to extract success patterns based on a user's past task completion data and generate an optimal schedule.

[0046] The Personalized Task Management Design Department will develop a system that will also be applied to household task management, optimizing the schedules of all family members. For example, by collecting the schedules of all family members and optimally allocating each member's tasks, household task management will be made more efficient. Based on the schedules of all family members, common tasks and events will be adjusted and the schedule optimized so that everyone can participate. Task completion data for all family members will be analyzed, success patterns will be extracted, and a household task schedule will be generated based on that. This will make household task management more efficient and optimize the schedules of all family members.

[0047] The Personalized Task Management Design Department applies this technology to assignment management in schools and educational institutions, providing each student with the optimal study schedule. For example, we will develop a system that collects student learning data and generates the optimal study schedule for each student. We will analyze students' learning patterns and propose the optimal schedule based on successful learning methods. Based on students' learning progress data, we will allocate the optimal study tasks for each student, supporting efficient learning. This will enable us to provide the optimal study schedule for each student.

[0048] The advice providing unit extracts the most effective advice pattern based on the user's past task management data and provides individually customized advice. For example, the advice providing unit analyzes the user's past task management data, extracts the most effective advice pattern, and provides individually customized advice. For example, the advice providing unit identifies a method by which the user can most effectively complete a task based on the past task management data and provides advice based on that. For example, the advice providing unit analyzes the user's past data and provides individually customized advice based on successful task management methods. In this way, the advice providing unit can extract the most effective advice pattern based on the user's past task management data and provide individually customized advice.

[0049] The advice providing unit monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. For example, a system is constructed that monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. Based on the behavioral patterns, the method by which the user can complete a task most effectively is identified and the advice is dynamically updated based on that. The user's behavioral data is analyzed in real time and the advice is dynamically updated as needed. For example, the advice is adjusted according to the progress of the task. This makes it possible to monitor the user's behavioral patterns in real time and dynamically update the advice as needed.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The Personalized Task Management Design Department uses a generation AI to design personalized task management. The generation AI proposes an efficient task scheduling method based on the user's characteristics and past behavioral data. For example, it can create a schedule that takes into account the time periods when the user is most likely to concentrate and task priorities. The generation AI receives prompts containing the user's characteristics and past behavioral data as input and generates the optimal scheduling method. Step 2: The advice provider uses the generation AI to provide appropriate advice. The generation AI provides advice on the user's task management. For example, it receives prompts including the user's current task status and meeting schedule as input and generates appropriate advice. The generation AI can provide advice on specific ways to reduce meeting time or to review task priorities. Step 3: The analysis and review department uses the generation AI to analyze and review behavior and progress. The generation AI analyzes user behavior and task progress, identifies issues, and proposes improvement measures. For example, it receives prompts containing user behavior data and task progress as input and generates an analysis and improvement measures. If too much time is being spent on a particular task, the generation AI can identify the cause and propose more efficient methods.

[0052] (Example 2) An AI task coaching system according to an embodiment of the present invention is a system for enabling people with ADHD to efficiently manage tasks in business situations. In this system, a generative AI provides personalized task management design, appropriate advice, and an analytical review of behavior and progress. This allows the AI ​​task coaching system to enable people with ADHD to efficiently manage tasks in business situations.

[0053] An AI task coaching system according to an embodiment includes a personalized task management design unit, an advice providing unit, and an analysis and review unit. The personalized task management design unit uses a generation AI to design personalized task management. For example, the generation AI proposes an efficient task scheduling method based on a user's characteristics and past behavioral data. The generation AI can also create schedules taking into account time periods when the user is likely to concentrate and task priorities. The generation AI receives prompts including the user's characteristics and past behavioral data as input and generates an optimal scheduling method. The advice providing unit uses the generation AI to provide appropriate advice. For example, the generation AI provides advice regarding the user's task management. The generation AI receives prompts including the user's current task status and meeting schedule as input and generates appropriate advice. The generation AI can provide advice on specific methods to reduce meeting time and revisit task priorities. The analysis and review unit uses the generation AI to analyze and review behavior and progress. For example, the generation AI analyzes the user's behavior and task progress, identifies issues, and proposes improvement measures. The generation AI receives prompts including the user's behavioral data and task progress as input and generates an analysis and improvement measures. If a user is spending too much time on a particular task, the generative AI can identify the cause and suggest more efficient methods. This allows the AI ​​task coaching system according to the embodiment to help people with ADHD manage their tasks efficiently in business situations. For example, by placing important tasks during times when users are most likely to concentrate, task completion rates can be improved. Furthermore, by reducing meeting time, users can spend more time focusing on their actual work. Furthermore, through behavior and progress analysis, issues users face can be identified early and appropriate improvement measures can be implemented.

[0054] The personalized task management design unit monitors the user's emotional state in real time and dynamically adjusts the task schedule according to the emotional state. The personalized task management design unit uses generative AI to monitor the user's emotional state in real time and dynamically adjusts the task schedule according to the emotion. For example, the system monitors the user's emotional state in real time and assigns tasks that require concentration when emotions are high and assigns lighter tasks when emotions are low. To monitor the emotional state, the system analyzes the user's facial expressions and voice and dynamically adjusts the task schedule according to changes in emotion. Based on the emotional state data, it identifies the time periods when the user can work most efficiently and assigns important tasks to those time periods. This allows the system to dynamically adjust the task schedule according to the user's emotional state.

[0055] The personalized task management design unit collects the user's physiological data and proposes an optimal task schedule based on that data. The personalized task management design unit collects the user's physiological data (heart rate, sleep patterns, etc.) and proposes an optimal task schedule based on that data. For example, it collects the user's heart rate data and places tasks that require concentration during times when the heart rate is stable. It analyzes the user's sleep patterns and places important tasks during times after the user has had enough sleep. Based on the physiological data, it places more difficult tasks when the user is feeling well and easier tasks when the user is feeling unwell. This makes it possible to propose an optimal task schedule based on the user's physiological data.

[0056] The personalized task management design unit analyzes the user's past task completion data, extracts success patterns, and generates a schedule based on them. The personalized task management design unit analyzes the user's past task completion data, extracts success patterns, and generates a schedule based on them. For example, it analyzes the user's past task completion data, extracts successful task patterns, and generates a new task schedule based on them. Based on the past task completion data, it identifies the time periods and task orders in which the user can work most efficiently, and proposes a schedule based on them. To extract success patterns, it analyzes the user's task completion times and task types, and generates an optimal schedule based on them. In this way, it is possible to extract success patterns based on the user's past task completion data, and generate an optimal schedule.

[0057] The Personalized Task Management Design Department will also be applied to task management within the home, optimizing the schedules of all family members. The Personalized Task Management Design Department will also be applied to task management within the home, developing a system that optimizes the schedules of all family members. For example, by collecting the schedules of all family members and optimally allocating each member's tasks, it will streamline task management within the home. Based on the schedules of all family members, it will adjust common tasks and events and optimize the schedule so that everyone can participate. It will analyze the task completion data of all family members, extract success patterns, and generate a task schedule within the home based on that. This will streamline task management within the home and optimize the schedules of all family members.

[0058] The Personalized Task Management Design Department is applied to assignment management in schools and educational institutions, providing each student with the optimal study schedule. The Personalized Task Management Design Department is applied to assignment management in schools and educational institutions, providing each student with the optimal study schedule. For example, we will develop a system that collects student learning data and generates the optimal study schedule for each student. We will analyze students' learning patterns and propose the optimal schedule based on successful learning methods. We will allocate the optimal study tasks for each student based on student learning progress data, supporting efficient learning. This will make it possible to provide each student with the optimal study schedule.

[0059] The personalized task management design unit uses the emotion estimation function to provide feedback to enhance the sense of accomplishment when the user completes a task. The personalized task management design unit uses the emotion estimation function to provide feedback to enhance the sense of accomplishment when the user completes a task. For example, when a user completes a task, the emotion estimation function is used to provide feedback to enhance the sense of accomplishment. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotional state and generate feedback to enhance the sense of accomplishment. For example, it displays positive messages or rewards. The emotion estimation function is used to analyze the user's emotional state when they complete a task and provides feedback to enhance the sense of accomplishment. Based on the emotion estimation data, if the emotion score when the user completes the task is high, words of praise are displayed. This makes it possible to provide feedback to enhance the sense of accomplishment when the user completes the task.

[0060] The advice providing unit uses a generation AI to analyze the user's emotional state and provide specific advice to reduce stress. The advice providing unit uses a generation AI to analyze the user's emotional state and provide specific advice to reduce stress. For example, it analyzes the user's emotional state in real time and provides specific advice to relax if stress is increasing. The emotion estimation function uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's emotional state and generate advice to reduce stress. Based on the emotional state, it suggests specific ways for the user to reduce stress. For example, it suggests taking a short break or trying relaxation techniques. The user's emotional data is analyzed and specific advice to reduce stress is provided. For example, it suggests deep breathing or meditation. In this way, it is possible to analyze the user's emotional state and provide specific advice to reduce stress.

[0061] The advice providing unit extracts the most effective advice pattern based on the user's past task management data and provides individually customized advice. The advice providing unit extracts the most effective advice pattern based on the user's past task management data and provides individually customized advice. For example, the advice providing unit analyzes the user's past task management data, extracts the most effective advice pattern, and provides individually customized advice. Based on the past task management data, the advice providing unit identifies a method by which the user can most effectively complete a task and provides advice based on that. The advice providing unit analyzes the user's past data and provides individually customized advice based on successful task management methods. In this way, the advice providing unit can extract the most effective advice pattern based on the user's past task management data and provide individually customized advice.

[0062] The advice providing unit monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. The advice providing unit monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. For example, a system is constructed that monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. Based on the behavioral patterns, a method by which the user can most effectively complete a task is identified and advice is dynamically updated based on that. User behavioral data is analyzed in real time and advice is dynamically updated as needed. For example, advice is adjusted according to the progress of the task. This makes it possible to monitor the user's behavioral patterns in real time and dynamically update advice as needed.

[0063] The advice providing unit is also applied to health management and fitness advice, providing specific advice based on the user's health condition. The advice providing unit is also applied to health management and fitness advice, providing specific advice based on the user's health condition. For example, it provides specific health management and fitness advice based on the user's health data. It analyzes the health condition, identifies how the user can most effectively maintain their health, and provides advice based on that. It monitors the user's health data in real time, and dynamically updates health management and fitness advice as needed. This makes it possible to provide specific advice based on the user's health condition.

[0064] The advice providing unit is applied to financial management, analyzing the user's spending patterns and providing savings and investment advice. The advice providing unit is applied to financial management, analyzing the user's spending patterns and providing savings and investment advice. For example, it analyzes the user's spending patterns and provides specific savings and investment advice. Based on the spending data, it identifies how the user can most effectively manage their assets and provides advice based on that. It monitors the user's spending patterns in real time and dynamically updates savings and investment advice as needed. This makes it possible to analyze the user's spending patterns and provide savings and investment advice.

[0065] The advice providing unit uses the emotion estimation function to suggest an environment in which the user can be most relaxed, thereby improving work efficiency. The advice providing unit uses the emotion estimation function to suggest an environment in which the user can be most relaxed, thereby improving work efficiency. For example, the emotion estimation function is used to identify an environment in which the user can be most relaxed and suggest that environment. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotional state and suggest an environment in which the user can be most relaxed. The emotion estimation function analyzes the user's emotional state and suggests an environment in which the user can be most relaxed. For example, it suggests working in a quiet place or in nature. Based on the emotion estimation data, a system is built that dynamically adjusts the environment in which the user can be most relaxed. For example, it suggests an environment in which the user can be most relaxed if the emotion score is low. This suggests an environment in which the user can be most relaxed, thereby improving work efficiency.

[0066] The analysis and review unit uses generation AI to analyze the user's emotional state and provide a progress review that corresponds to changes in emotion. The analysis and review unit uses generation AI to analyze the user's emotional state and provide a progress review that corresponds to changes in emotion. For example, it analyzes the user's emotional state in real time and provides a progress review that corresponds to changes in emotion. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotional state and generate a progress review that corresponds to changes in emotion. Based on the emotional state, it identifies how the user can most effectively complete the task and provides a progress review based on that. It analyzes the user's emotional data and provides a progress review that corresponds to changes in emotion. For example, it provides positive feedback when emotions are high. This makes it possible to provide a progress review that corresponds to the user's emotional state.

[0067] The analysis and review unit analyzes the user's behavioral data in detail, identifies factors that affect task progress, and proposes improvement measures. The analysis and review unit analyzes the user's behavioral data in detail, identifies factors that affect task progress, and proposes improvement measures. For example, the analysis and review unit analyzes the user's behavioral data in detail, identifies factors that affect task progress, and proposes improvement measures based on the factors. Based on the behavioral data, the unit identifies how the user can most effectively complete the task, and proposes improvement measures based on the factors. The analysis and review unit monitors the user's behavioral data in real time, identifies factors that affect task progress, and proposes improvement measures based on the factors. This makes it possible to analyze the user's behavioral data in detail, identify factors that affect task progress, and propose improvement measures.

[0068] The analysis and review unit analyzes long-term performance trends based on the user's task completion rate and provides insights that are useful for future task management. The analysis and review unit analyzes long-term performance trends based on the user's task completion rate and provides insights that are useful for future task management. For example, the analysis and review unit analyzes the user's task completion rate, identifies long-term performance trends, and provides insights that are useful for future task management based on the trends. Based on the task completion rate, the time period and method in which the user can most effectively complete the task are identified, and future task management is suggested based on the results. The analysis and review unit analyzes the user's task completion data over the long term, identifies performance trends, and provides insights that are useful for future task management based on the trends. This makes it possible to analyze long-term performance trends based on the user's task completion rate and provide insights that are useful for future task management.

[0069] The analysis and review department analyzes task progress for the entire team and provides feedback to promote cooperation between team members. The analysis and review department analyzes task progress for the entire team and provides feedback to promote cooperation between team members. For example, it analyzes task progress data for the entire team, identifies tasks and members that require cooperation, and provides feedback based on that. It monitors task progress between team members in real time and provides feedback when cooperation is required. It provides specific feedback to promote cooperation based on task progress data for the entire team. For example, it emphasizes the importance of cooperation on a specific task. This makes it possible to analyze task progress for the entire team and provide feedback to promote cooperation between team members.

[0070] The Analysis and Review Department is applied to project management, monitoring the progress of the entire project in real time and making adjustments as necessary. The Analysis and Review Department is applied to project management, monitoring the progress of the entire project in real time and making adjustments as necessary. For example, it monitors the progress data of the entire project in real time and rearranges tasks or adjusts the schedule as necessary. It analyzes the progress of the project, identifies tasks that are delayed or resource shortages, and makes adjustments based on that. It makes adjustments to achieve efficient project management based on the progress data of the entire project. For example, it proposes the rearrangement of resources or changes in task priorities. This allows the progress of the entire project to be monitored in real time and adjustments to be made as necessary.

[0071] The analysis and review unit uses the emotion estimation function to identify the task that motivates the user most and provides support to help the user focus on that task. The analysis and review unit uses the emotion estimation function to identify the task that motivates the user most and provides support to help the user focus on that task. For example, the emotion estimation function is used to identify the task that motivates the user most and adjust the schedule so that the user can focus on that task. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotional state and identify tasks that will increase motivation. The user's emotion data is analyzed to identify tasks that will increase motivation and optimize task placement based on that. The emotion estimation data is used to identify the task that motivates the user most and provide feedback to help the user focus on that task. This allows the analysis and review unit to identify the task that motivates the user most and provide support to help the user focus on that task.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The personalized task management design unit can also propose a task schedule that takes into account the user's hobbies and interests. For example, if the user likes listening to music, music listening time can be scheduled before and after tasks that require concentration. If the user likes exercise, important tasks can be scheduled for the time after exercise when concentration is at its highest. Furthermore, if the user enjoys reading, reading time can be incorporated into the schedule as time for relaxation. This makes it possible to propose a task schedule that takes into account the user's hobbies and interests.

[0074] The personalized task management design unit monitors the user's emotional state in real time and dynamically adjusts the task schedule according to the emotional state. For example, it monitors the user's emotional state in real time and assigns tasks that require concentration when emotions are high, and assigns lighter tasks when emotions are low. To monitor the emotional state, it analyzes the user's facial expressions and voice and dynamically adjusts the task schedule according to changes in emotions. Based on the emotional state data, it identifies the time periods when the user can work most efficiently and assigns important tasks to those time periods. This allows the task schedule to be dynamically adjusted according to the user's emotional state.

[0075] The Personalized Task Management Design Department collects the user's physiological data and proposes the optimal task schedule based on it. For example, it collects the user's heart rate data and arranges tasks that require concentration during times when the heart rate is stable. It analyzes the user's sleep patterns and arranges important tasks for times after a sufficient sleep. Based on the physiological data, it arranges more difficult tasks when the user is feeling well and easier tasks when they are feeling unwell. This makes it possible to propose the optimal task schedule based on the user's physiological data.

[0076] The personalized task management design unit analyzes a user's past task completion data, extracts success patterns, and generates a schedule based on them. For example, it analyzes a user's past task completion data, extracts successful task patterns, and generates a new task schedule based on them. Based on the past task completion data, it identifies the time periods and task orders in which the user can work most efficiently, and proposes a schedule based on them. To extract success patterns, it analyzes the user's task completion times and task types, and generates an optimal schedule based on them. This makes it possible to extract success patterns based on a user's past task completion data and generate an optimal schedule.

[0077] The Personalized Task Management Design Department will develop a system that will also be applied to household task management, optimizing the schedules of all family members. For example, by collecting the schedules of all family members and optimally allocating each member's tasks, household task management will be made more efficient. Based on the schedules of all family members, common tasks and events will be adjusted and the schedule optimized so that everyone can participate. Task completion data for all family members will be analyzed, success patterns will be extracted, and a household task schedule will be generated based on that. This will make household task management more efficient and optimize the schedules of all family members.

[0078] The Personalized Task Management Design Department applies this technology to assignment management in schools and educational institutions, providing each student with the optimal study schedule. For example, we will develop a system that collects student learning data and generates the optimal study schedule for each student. We will analyze students' learning patterns and propose the optimal schedule based on successful learning methods. Based on students' learning progress data, we will allocate the optimal study tasks for each student, supporting efficient learning. This will enable us to provide the optimal study schedule for each student.

[0079] The personalized task management design unit uses the emotion estimation function to provide feedback to enhance the sense of accomplishment when a user completes a task. For example, when a user completes a task, the emotion estimation function is used to provide feedback to enhance the sense of accomplishment. The emotion estimation function uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's emotional state and generate feedback to enhance the sense of accomplishment. For example, it displays positive messages or rewards. The emotion estimation function is used to analyze the user's emotional state when they complete a task and provides feedback to enhance the sense of accomplishment. Based on the emotion estimation data, if the emotional score when the user completes the task is high, words of praise are displayed. This makes it possible to provide feedback to enhance the sense of accomplishment when the user completes the task.

[0080] The advice providing unit uses a generation AI to analyze the user's emotional state and provide specific advice to reduce stress. For example, it analyzes the user's emotional state in real time and provides specific advice to relax if stress is rising. The emotion estimation function uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's emotional state and generate advice to reduce stress. Based on the emotional state, it suggests specific ways for the user to reduce stress. For example, it suggests taking a short break or trying relaxation techniques. It analyzes the user's emotional data and provides specific advice to reduce stress. For example, it suggests deep breathing or meditation. This makes it possible to analyze the user's emotional state and provide specific advice to reduce stress.

[0081] The advice providing unit extracts the most effective advice pattern based on the user's past task management data and provides individually customized advice. For example, the advice providing unit analyzes the user's past task management data, extracts the most effective advice pattern, and provides individually customized advice. For example, the advice providing unit identifies a method by which the user can most effectively complete a task based on the past task management data and provides advice based on that. For example, the advice providing unit analyzes the user's past data and provides individually customized advice based on successful task management methods. In this way, the advice providing unit can extract the most effective advice pattern based on the user's past task management data and provide individually customized advice.

[0082] The advice providing unit monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. For example, a system is constructed that monitors the user's behavioral patterns in real time and dynamically updates the advice as needed. Based on the behavioral patterns, the method by which the user can complete a task most effectively is identified and the advice is dynamically updated based on that. The user's behavioral data is analyzed in real time and the advice is dynamically updated as needed. For example, the advice is adjusted according to the progress of the task. This makes it possible to monitor the user's behavioral patterns in real time and dynamically update the advice as needed.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The Personalized Task Management Design Department uses a generation AI to design personalized task management. The generation AI proposes an efficient task scheduling method based on the user's characteristics and past behavioral data. For example, it can create a schedule that takes into account the time periods when the user is most likely to concentrate and task priorities. The generation AI receives prompts containing the user's characteristics and past behavioral data as input and generates the optimal scheduling method. Step 2: The advice provider uses the generation AI to provide appropriate advice. The generation AI provides advice on the user's task management. For example, it receives prompts including the user's current task status and meeting schedule as input and generates appropriate advice. The generation AI can provide advice on specific ways to reduce meeting time or to review task priorities. Step 3: The analysis and review department uses the generation AI to analyze and review behavior and progress. The generation AI analyzes user behavior and task progress, identifies issues, and proposes improvement measures. For example, it receives prompts containing user behavior data and task progress as input and generates an analysis and improvement measures. If too much time is being spent on a particular task, the generation AI can identify the cause and propose more efficient methods.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 personalized task management design department that uses generative AI to design personalized task management; an advice providing unit that provides appropriate advice using the generation AI; An analysis and review unit that analyzes and reviews behavior and progress using the generation AI. A system characterized by:

2. The personalized task management design unit Monitor the user's emotional state in real time and dynamically adjust the task schedule according to the emotional state.

2. The system of claim 1.

3. The personalized task management design unit It can also be applied to household task management, optimizing the schedules of all family members.

2. The system of claim 1.

4. The advice providing unit The generative AI is used to analyze the user's emotional state and provide specific advice to reduce stress.

2. The system of claim 1.

5. The advice providing unit Monitor user behavior patterns in real time and dynamically update the advice as needed.

2. The system of claim 1.

6. The analysis and review unit The generative AI is used to analyze the user's emotional state and provide progress reviews according to changes in emotions.

2. The system of claim 1.

7. The analysis and review unit Analyze task progress across the team and provide feedback to promote collaboration among team members 2. The system of claim 1.

8. The analysis and review unit Identify the tasks that motivate users most and provide support to help them focus on those tasks 2. The system of claim 1.

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