System

Abstract

An object of a system according to an embodiment is to efficiently manage tasks of a user and optimize a schedule.SOLUTION: A system according to an embodiment includes a task collection unit, a task classification unit, a delivery time analysis unit, a schedule optimization unit, a reminder unit, and a feedback learning unit. The task collection unit collects tasks of a user. The task classification unit classifies the tasks collected by the task collection unit. A delivery date analysis part analyzes the delivery date and communication frequency of the task classified by the task classification part. The schedule optimization unit optimizes the schedule on the basis of the result analyzed by the delivery time analysis unit. The reminder unit provides a reminder based on the schedule optimized by the schedule optimization unit. The feedback learning unit learns feedback of the user on the task reminded by the reminder unit.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 techniques have had the problem that it becomes difficult for users to schedule tasks when they are busy.

[0005] The system according to the embodiment aims to efficiently manage a user's tasks and optimize the schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a task collection unit, a task classification unit, a delivery date analysis unit, a schedule optimization unit, a reminder unit, and a feedback learning unit. The task collection unit collects user tasks. The task classification unit classifies the tasks collected by the task collection unit. The delivery date analysis unit analyzes the delivery dates and contact frequency of the tasks classified by the task classification unit. The schedule optimization unit optimizes the schedule based on the results of the analysis by the delivery date analysis unit. The reminder unit issues reminders based on the schedule optimized by the schedule optimization unit. The feedback learning unit learns the user's feedback on the tasks reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the tasks of a user and optimize the schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The secretary AI system according to an embodiment of the present invention automatically collects user tasks, analyzes them using a generation AI, and proposes an optimal schedule. This allows the secretary AI system to efficiently manage the user's tasks and optimize the schedule.

[0029] The secretary AI system according to the embodiment includes a task collection unit, a task classification unit, a delivery date analysis unit, a schedule optimization unit, a reminder unit, and a feedback learning unit. The task collection unit collects user tasks. For example, it automatically collects tasks entered by the user. The task collection unit can also analyze the user's past behavior history and suggest tasks that are easily forgotten. The task collection unit can also analyze voice input and add verbally instructed tasks to a list. For example, if a user says, "Prepare materials for tomorrow's meeting," the task collection unit adds the task to the list. The task classification unit classifies the collected tasks. For example, it classifies the tasks based on the importance and urgency of the tasks. The task classification unit can also use an emotion estimation function to identify tasks that cause stress to the user and suggest that the user prioritize those tasks. The delivery date analysis unit analyzes the delivery dates and contact frequency of the classified tasks. For example, it suggests that tasks with approaching project deadlines or tasks that require frequent contact be prioritized. The delivery date analysis unit can also calculate the expected completion time for each task based on past task completion data and suggest ways to allow for leeway in delivery dates. The schedule optimization unit optimizes the schedule based on the analysis results. For example, it can schedule tasks with high importance in the morning and prioritize tasks with high urgency. The schedule optimization unit can also analyze the user's biometric data (e.g., heart rate and sleep data) to suggest optimal work time slots. The reminder unit provides reminders based on the optimized schedule. For example, it can send notifications to the user when a meeting start time or a project deadline is approaching. The feedback learning unit learns the user's feedback on reminded tasks. For example, if the user inputs feedback such as "I wish I had finished this task earlier," the feedback learning unit learns that feedback and reflects it in future schedules. As a result, the secretary AI system according to the embodiment can efficiently manage the user's tasks and optimize the schedule. For example, the system can efficiently manage tasks and optimize the schedule even during busy periods.In addition, the reminder function helps you remember important meetings and tasks.

[0030] The task collection unit can analyze a user's past activity history and automatically suggest tasks that the user tends to forget. For example, the task collection unit uses AI to analyze a user's past activity history and automatically list tasks that are performed regularly but tend to be forgotten. For example, it suggests tasks such as creating monthly reports and preparing for regular meetings. The task collection unit also learns the user's past activity patterns and predicts and reminds them of tasks that are likely to be forgotten at specific times. For example, it suggests tasks such as quarterly performance reviews and annual budget planning. The task collection unit also uses AI to automatically detect tasks that the user tends to forget based on the user's past task completion history and add them to the list. For example, it suggests regular maintenance work and contract renewal procedures. This automatically suggests tasks that the user tends to forget, preventing tasks from being overlooked.

[0031] The task collection unit can analyze the user's voice input and automatically add verbally instructed tasks to a list. For example, the task collection unit uses AI to analyze tasks in real time that the user instructs verbally and automatically add them to a task list. For example, if the user says, "Prepare materials for tomorrow's meeting," the AI ​​adds the task to the list. The task collection unit also uses voice recognition technology to convert the tasks instructed verbally by the user into text and register them in a task management system. For example, if the user says, "Prepare for next week's presentation," the AI ​​adds the task to the list. The task collection unit also analyzes the user's voice input and automatically adds detailed task information (e.g., deadline and priority) to the list. For example, if the user says, "Send a report to the client by the end of this week," the AI ​​adds the task to the list. This makes task management easier by automatically adding tasks instructed verbally by the user to the list.

[0032] The task collection unit can compare the task management data of other users and suggest efficient task management methods. For example, the task collection unit uses AI to analyze other users' task management data and suggest efficient task management methods. For example, suggestions are made based on how other users in the same industry manage their tasks. The task collection unit also compares the task completion data of other users and extracts and suggests the most efficient task management methods. For example, it suggests methods for prioritizing tasks and allocating time. The task collection unit also uses AI to find common task management patterns based on the task management data of other users and suggest them to the user. For example, it suggests methods for grouping tasks and optimizing schedules. This makes it possible to suggest efficient task management methods by comparing the task management data of other users.

[0033] When collecting tasks, the task collection unit can use the user's location information to automatically add location-specific tasks to the list. For example, the task collection unit automatically adds location-specific tasks to the list based on the user's location information. For example, when the user is in the office, the task collection unit suggests a meeting preparation task. The task collection unit also uses the location information to automatically add tasks to be done in a specific location to the list. For example, when the user is at home, the task collection unit suggests a housework task. The task collection unit also analyzes the user's location information and prioritizes location-specific tasks on the list. For example, tasks for a business trip are automatically added to the list. This makes task management easier by using the user's location information to automatically add location-specific tasks to the list.

[0034] The delivery date analysis unit can calculate the expected completion time of each task based on past task completion data and make suggestions to allow for a margin in the delivery date. In the delivery date analysis unit, for example, AI analyzes past task completion data and calculates the expected completion time of each task. For example, it calculates the average completion time of tasks based on past data and makes suggestions to allow for a margin in the delivery date. The delivery date analysis unit also calculates the expected completion time of each task based on past task completion data and makes suggestions to the user to adjust the delivery date. For example, it suggests starting the task earlier if the completion time of the task is long. In addition, the delivery date analysis unit calculates the expected completion time of each task in real time based on past task completion data and makes suggestions to allow for a margin in the delivery date. For example, it adjusts the delivery date according to the progress of the task. In this way, by calculating the expected completion time of each task and allowing for a margin in the delivery date, it is possible to prevent task delays.

[0035] The delivery date analysis unit can analyze a user's contact history and prioritize communications with people with whom they contact frequently in their schedules. For example, AI can analyze a user's contact history and prioritize communications with people with whom they contact frequently in their schedules. For example, it can prioritize meetings with clients with whom they contact frequently. The delivery date analysis unit also builds a system that incorporates communications with people with whom they contact frequently in their schedules based on the user's contact history. For example, it can suggest regular meetings with important contacts. The delivery date analysis unit also uses AI to analyze contact history and prioritize communications with people with whom they contact frequently in their schedules. For example, it can suggest meetings with superiors or colleagues with whom they contact frequently. In this way, by prioritizing communications with people with whom they contact frequently in their schedules, important communications can be avoided from being missed.

[0036] The delivery date analysis unit can compare delivery date data with other users and propose optimal delivery date settings. For example, AI can analyze delivery date data of other users and propose optimal delivery date settings. For example, suggestions can be made based on delivery dates set by other users in the same industry. The delivery date analysis unit can also compare delivery date data of other users, extract and propose optimal delivery date settings. For example, it can calculate optimal delivery dates based on past data. The delivery date analysis unit can also use AI to find common delivery date setting patterns based on delivery date data of other users and propose them to the user. For example, it can propose delivery date settings according to the type and scale of the project. This makes it possible to propose optimal delivery date settings by comparing with delivery date data of other users.

[0037] When analyzing delivery dates and contact frequency, the delivery date analysis unit works in conjunction with the user's calendar app to make suggestions that take into account the balance with other appointments. For example, AI works in conjunction with the user's calendar app to suggest delivery date settings that take into account the balance with other appointments. For example, it adjusts delivery dates so that they do not overlap with existing appointments. The delivery date analysis unit also builds a system that suggests delivery date settings that take into account the balance with other appointments based on data from the calendar app. For example, it sets delivery dates so that they do not overlap with important meetings or events. The delivery date analysis unit also works in conjunction with the calendar app to suggest delivery date settings that take into account the balance with other appointments. For example, it adjusts delivery dates taking into account the user's vacations and business trip plans. In this way, by working in conjunction with the calendar app, it is possible to suggest delivery date settings that take into account the balance with other appointments.

[0038] The schedule optimization unit can analyze the user's biometric data and suggest optimal work hours. For example, AI can analyze the user's biometric data and suggest optimal work hours. For example, it can suggest the time period when the user can concentrate best based on heart rate and sleep data. The schedule optimization unit can also identify the time period when the user is most efficient based on the biometric data and suggest placing important tasks during that time period. For example, it can select the time period when the heart rate is stable. The schedule optimization unit can also analyze the user's biometric data in real time using AI to dynamically suggest optimal work hours. For example, it can suggest the time period when the user is most refreshed based on sleep data. In this way, it is possible to suggest optimal work hours by analyzing the user's biometric data.

[0039] The schedule optimization unit can propose the most efficient schedule pattern based on the user's past schedule history. For example, the schedule optimization unit uses AI to analyze the user's past schedule history and propose the most efficient schedule pattern. For example, based on past data, it may propose the time period in which the user most efficiently completed tasks. The schedule optimization unit also extracts patterns in which the user's work efficiency was high based on the past schedule history and proposes those patterns. For example, it may propose a pattern in which work is concentrated on specific days of the week or time periods. The schedule optimization unit also uses AI to propose the most efficient schedule pattern in real time based on the user's past schedule history. For example, it may propose the time period in which the user was most productive based on past data. This makes it possible to propose the most efficient schedule pattern based on the user's past schedule history.

[0040] The schedule optimization unit can compare the schedule data of other users and propose the optimal schedule pattern. For example, the schedule optimization unit uses AI to analyze other users' schedule data and propose the optimal schedule pattern. For example, it can make proposals based on how other users in the same industry have created their schedules. The schedule optimization unit also compares other users' schedule data and extracts and proposes the most efficient schedule pattern. For example, it calculates the optimal schedule based on past data. The schedule optimization unit also uses AI to find common schedule patterns based on other users' schedule data and propose them to the user. For example, it can propose a pattern of concentrating work during specific time periods. This makes it possible to propose the optimal schedule pattern by comparing with other users' schedule data.

[0041] When optimizing a schedule, the schedule optimization unit can propose a balanced schedule by taking into account the user's meal and break times. For example, the schedule optimization unit uses AI to consider the user's meal and break times and propose a balanced schedule. For example, it allocates meal and break times appropriately. The schedule optimization unit also builds a system that proposes a balanced schedule based on the user's meal and break times. For example, it suggests appropriate breaks after working for a long period of time. The schedule optimization unit also uses AI to consider the user's meal and break times and propose a balanced schedule in real time. For example, it adjusts break times based on the user's fatigue level. In this way, a balanced schedule can be proposed by taking into account the user's meal and break times.

[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 secretary AI system can also be equipped with a health management unit that collects the user's health data and adjusts task priorities based on their health condition. For example, it can analyze the user's heart rate and blood pressure data and prioritize less demanding tasks if their health condition is deteriorating. The health management unit can also use the user's sleep data to suggest postponing important tasks if the user is sleep deprived. Furthermore, the health management unit can analyze the user's dietary data and suggest increasing break times if their nutrition is not balanced. This enables task management that takes the user's health condition into consideration, contributing to long-term health maintenance.

[0044] The secretary AI system can also include a hobby suggestion unit that suggests tasks for refreshing based on the user's hobbies and interests. For example, if the user likes music, the system can suggest time to listen to music between work tasks. The hobby suggestion unit can also periodically suggest hobby-related events and activities based on the user's past hobby activity data. Furthermore, the hobby suggestion unit can suggest new hobbies and activities based on the user's interests. This allows the user to efficiently manage tasks while refreshing themselves.

[0045] The secretary AI system may also include a social management unit that collects the user's social data and schedules social activities. For example, it may analyze the user's social media data and make suggestions to promote communication with friends and colleagues. The social management unit may also periodically suggest social events based on the user's past social activity data. Furthermore, the social management unit may analyze the user's social network and suggest events and meetings to build new connections. This allows the system to efficiently manage the user's social activities and support a balanced life.

[0046] The secretary AI system can also be equipped with a learning support unit that collects the user's learning data and suggests tasks to improve skills. For example, it can suggest online courses or seminars in areas of interest to the user. The learning support unit can also suggest what the user should learn next based on the user's past learning history. Furthermore, the learning support unit can analyze the user's skill level and suggest learning tasks of appropriate difficulty. This allows the user to improve their skills efficiently.

[0047] The secretary AI system can also be equipped with an environment management unit that collects environmental data about the user and optimizes the work environment. For example, it can analyze the temperature and lighting data of the user's work area and suggest the optimal work environment. The environment management unit can also analyze the noise level in the user's work environment and suggest working in a quieter environment. Furthermore, the environment management unit can analyze air quality data about the user's work environment and suggest using an air purifier. This allows the user to efficiently perform tasks in a comfortable work environment.

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

[0049] Step 1: The task collection unit collects the user's tasks. For example, it automatically collects tasks entered by the user. The task collection unit can also analyze the user's past activity history and suggest tasks that are likely to be forgotten. Furthermore, the task collection unit can analyze voice input and add verbally instructed tasks to the list. For example, if the user says, "Prepare materials for tomorrow's meeting," the task collection unit adds that task to the list. Step 2: The task classification unit classifies the collected tasks. For example, it classifies them based on the importance or urgency of the tasks. The task classification unit can also use emotion estimation to identify tasks that cause stress to the user and suggest that those tasks be prioritized. Step 3: The delivery time analysis unit analyzes the delivery time and frequency of contact for the classified tasks. For example, it suggests that tasks with approaching project deadlines or tasks that require frequent contact be prioritized. The delivery time analysis unit can also calculate the expected completion time for each task based on past task completion data and make suggestions to allow for leeway in delivery times. Step 4: The schedule optimization unit optimizes the schedule based on the analysis results. For example, it may arrange a schedule so that important tasks are placed in the morning and urgent tasks are given priority. The schedule optimization unit can also analyze the user's biometric data (e.g., heart rate and sleep data) to suggest optimal work times. Step 5: The Reminders section will remind you based on your optimized schedule, for example, notifying you when a meeting is about to start or a project deadline is approaching. Step 6: The feedback learning unit learns the user's feedback on the reminded task. For example, if the user enters feedback such as "I wish I had finished this task earlier," the feedback learning unit learns that feedback and reflects it in future schedules.

[0050] (Example 2) The secretary AI system according to an embodiment of the present invention automatically collects user tasks, analyzes them using a generation AI, and proposes an optimal schedule. This allows the secretary AI system to efficiently manage the user's tasks and optimize the schedule.

[0051] The secretary AI system according to the embodiment includes a task collection unit, a task classification unit, a delivery date analysis unit, a schedule optimization unit, a reminder unit, and a feedback learning unit. The task collection unit collects user tasks. For example, it automatically collects tasks entered by the user. The task collection unit can also analyze the user's past behavior history and suggest tasks that are easily forgotten. The task collection unit can also analyze voice input and add verbally instructed tasks to a list. For example, if a user says, "Prepare materials for tomorrow's meeting," the task collection unit adds the task to the list. The task classification unit classifies the collected tasks. For example, it classifies the tasks based on the importance and urgency of the tasks. The task classification unit can also use an emotion estimation function to identify tasks that cause stress to the user and suggest that the user prioritize those tasks. The delivery date analysis unit analyzes the delivery dates and contact frequency of the classified tasks. For example, it suggests that tasks with approaching project deadlines or tasks that require frequent contact be prioritized. The delivery date analysis unit can also calculate the expected completion time for each task based on past task completion data and suggest ways to allow for leeway in delivery dates. The schedule optimization unit optimizes the schedule based on the analysis results. For example, it can schedule tasks with high importance in the morning and prioritize tasks with high urgency. The schedule optimization unit can also analyze the user's biometric data (e.g., heart rate and sleep data) to suggest optimal work time slots. The reminder unit provides reminders based on the optimized schedule. For example, it can send notifications to the user when a meeting start time or a project deadline is approaching. The feedback learning unit learns the user's feedback on reminded tasks. For example, if the user inputs feedback such as "I wish I had finished this task earlier," the feedback learning unit learns that feedback and reflects it in future schedules. As a result, the secretary AI system according to the embodiment can efficiently manage the user's tasks and optimize the schedule. For example, the system can efficiently manage tasks and optimize the schedule even during busy periods.In addition, the reminder function helps you remember important meetings and tasks.

[0052] The task collection unit can analyze a user's past activity history and automatically suggest tasks that the user tends to forget. For example, the task collection unit uses AI to analyze a user's past activity history and automatically list tasks that are performed regularly but tend to be forgotten. For example, it suggests tasks such as creating monthly reports and preparing for regular meetings. The task collection unit also learns the user's past activity patterns and predicts and reminds them of tasks that are likely to be forgotten at specific times. For example, it suggests tasks such as quarterly performance reviews and annual budget planning. The task collection unit also uses AI to automatically detect tasks that the user tends to forget based on the user's past task completion history and add them to the list. For example, it suggests regular maintenance work and contract renewal procedures. This automatically suggests tasks that the user tends to forget, preventing tasks from being overlooked.

[0053] The task collection unit can analyze the user's voice input and automatically add verbally instructed tasks to a list. For example, the task collection unit uses AI to analyze tasks in real time that the user instructs verbally and automatically add them to a task list. For example, if the user says, "Prepare materials for tomorrow's meeting," the AI ​​adds the task to the list. The task collection unit also uses voice recognition technology to convert the tasks instructed verbally by the user into text and register them in a task management system. For example, if the user says, "Prepare for next week's presentation," the AI ​​adds the task to the list. The task collection unit also analyzes the user's voice input and automatically adds detailed task information (e.g., deadline and priority) to the list. For example, if the user says, "Send a report to the client by the end of this week," the AI ​​adds the task to the list. This makes task management easier by automatically adding tasks instructed verbally by the user to the list.

[0054] The task collection unit can use the emotion estimation function to identify tasks that the user is feeling stressed about and suggest that the task be prioritized. The task collection unit, for example, uses the emotion estimation function to identify tasks that the user is feeling stressed about and display those tasks in a list with priority. For example, the task collection unit may suggest that meeting preparations, which the user finds stressful, be prioritized. The task collection unit also analyzes the user's emotion data to identify tasks that the user is feeling stressed about and provide reminders. For example, the task collection unit may suggest that progress on a project, which the user finds pressure, be prioritized. The task collection unit also uses the emotion estimation function to identify tasks that the user is feeling stressed about and automatically increase the priority of those tasks. For example, the task collection unit may suggest that tasks with an approaching deadline, which the user is feeling anxious about, be prioritized. This allows the user's stress to be reduced by prioritizing tasks that the user is feeling stressed about.

[0055] The task collection unit can compare the task management data of other users and suggest efficient task management methods. For example, the task collection unit uses AI to analyze other users' task management data and suggest efficient task management methods. For example, suggestions are made based on how other users in the same industry manage their tasks. The task collection unit also compares the task completion data of other users and extracts and suggests the most efficient task management methods. For example, it suggests methods for prioritizing tasks and allocating time. The task collection unit also uses AI to find common task management patterns based on the task management data of other users and suggest them to the user. For example, it suggests methods for grouping tasks and optimizing schedules. This makes it possible to suggest efficient task management methods by comparing the task management data of other users.

[0056] When collecting tasks, the task collection unit can use the user's location information to automatically add location-specific tasks to the list. For example, the task collection unit automatically adds location-specific tasks to the list based on the user's location information. For example, when the user is in the office, the task collection unit suggests a meeting preparation task. The task collection unit also uses the location information to automatically add tasks to be done in a specific location to the list. For example, when the user is at home, the task collection unit suggests a housework task. The task collection unit also analyzes the user's location information and prioritizes location-specific tasks on the list. For example, tasks for a business trip are automatically added to the list. This makes task management easier by using the user's location information to automatically add location-specific tasks to the list.

[0057] The task collection unit can use the emotion estimation function to analyze the emotions of the user when entering a task and make suggestions to elicit positive emotions. For example, the task collection unit uses the emotion estimation function to analyze the emotions of the user when entering a task in real time and make suggestions to elicit positive emotions. For example, it displays an encouraging message. The task collection unit also provides an interface for eliciting positive emotions when entering a task based on the user's emotion data. For example, it displays success stories and positive feedback. The task collection unit also uses the emotion estimation function to provide advice to elicit positive emotions when the user enters a task. For example, it displays a message that emphasizes the sense of accomplishment of the task. This elicits positive emotions when the user enters a task, thereby increasing the motivation to complete the task.

[0058] The delivery date analysis unit can calculate the expected completion time of each task based on past task completion data and make suggestions to allow for a margin in the delivery date. In the delivery date analysis unit, for example, AI analyzes past task completion data and calculates the expected completion time of each task. For example, it calculates the average completion time of tasks based on past data and makes suggestions to allow for a margin in the delivery date. The delivery date analysis unit also calculates the expected completion time of each task based on past task completion data and makes suggestions to the user to adjust the delivery date. For example, it suggests starting the task earlier if the completion time of the task is long. In addition, the delivery date analysis unit calculates the expected completion time of each task in real time based on past task completion data and makes suggestions to allow for a margin in the delivery date. For example, it adjusts the delivery date according to the progress of the task. In this way, by calculating the expected completion time of each task and allowing for a margin in the delivery date, it is possible to prevent task delays.

[0059] The delivery date analysis unit can analyze a user's contact history and prioritize communications with people with whom they contact frequently in their schedules. For example, AI can analyze a user's contact history and prioritize communications with people with whom they contact frequently in their schedules. For example, it can prioritize meetings with clients with whom they contact frequently. The delivery date analysis unit also builds a system that incorporates communications with people with whom they contact frequently in their schedules based on the user's contact history. For example, it can suggest regular meetings with important contacts. The delivery date analysis unit also uses AI to analyze contact history and prioritize communications with people with whom they contact frequently in their schedules. For example, it can suggest meetings with superiors or colleagues with whom they contact frequently. In this way, by prioritizing communications with people with whom they contact frequently in their schedules, important communications can be avoided from being missed.

[0060] The delivery date analysis unit can use the emotion estimation function to identify tasks for which the user is feeling pressured and make suggestions to adjust the delivery dates of those tasks. The delivery date analysis unit, for example, uses the emotion estimation function to identify tasks for which the user is feeling pressured and make suggestions to adjust the delivery dates of those tasks. For example, it extends the delivery date of a task that causes stress to the user. The delivery date analysis unit also identifies tasks for which the user is feeling pressured based on the user's emotion data and makes suggestions to adjust the delivery dates of those tasks. For example, it lowers the priority of the task to adjust the delivery date. The delivery date analysis unit also uses the emotion estimation function to identify tasks for which the user is feeling pressured and makes suggestions to adjust the delivery dates of those tasks. For example, it suggests dividing the task or adding resources. In this way, by adjusting the delivery date of a task for which the user is feeling pressured, the user's stress can be reduced.

[0061] The delivery date analysis unit can compare delivery date data with other users and propose optimal delivery date settings. For example, AI can analyze delivery date data of other users and propose optimal delivery date settings. For example, suggestions can be made based on delivery dates set by other users in the same industry. The delivery date analysis unit can also compare delivery date data of other users, extract and propose optimal delivery date settings. For example, it can calculate optimal delivery dates based on past data. The delivery date analysis unit can also use AI to find common delivery date setting patterns based on delivery date data of other users and propose them to the user. For example, it can propose delivery date settings according to the type and scale of the project. This makes it possible to propose optimal delivery date settings by comparing with delivery date data of other users.

[0062] When analyzing delivery dates and contact frequency, the delivery date analysis unit works in conjunction with the user's calendar app to make suggestions that take into account the balance with other appointments. For example, AI works in conjunction with the user's calendar app to suggest delivery date settings that take into account the balance with other appointments. For example, it adjusts delivery dates so that they do not overlap with existing appointments. The delivery date analysis unit also builds a system that suggests delivery date settings that take into account the balance with other appointments based on data from the calendar app. For example, it sets delivery dates so that they do not overlap with important meetings or events. The delivery date analysis unit also works in conjunction with the calendar app to suggest delivery date settings that take into account the balance with other appointments. For example, it adjusts delivery dates taking into account the user's vacations and business trip plans. In this way, by working in conjunction with the calendar app, it is possible to suggest delivery date settings that take into account the balance with other appointments.

[0063] The delivery date analysis unit can use the emotion estimation function to make suggestions to reduce the stress the user feels about delivery dates. The delivery date analysis unit, for example, uses the emotion estimation function to make suggestions to reduce the stress the user feels about delivery dates. For example, it makes a suggestion to extend the delivery date. The delivery date analysis unit also makes suggestions to reduce stress about delivery dates based on the user's emotion data. For example, it suggests dividing tasks or adding resources. The delivery date analysis unit also uses the emotion estimation function to make suggestions to reduce the stress the user feels about delivery dates. For example, it lowers the priority of a task to adjust the delivery date. This reduces the stress the user feels about delivery dates, thereby increasing the user's motivation to complete the task.

[0064] The schedule optimization unit can analyze the user's biometric data and suggest optimal work hours. For example, AI can analyze the user's biometric data and suggest optimal work hours. For example, it can suggest the time period when the user can concentrate best based on heart rate and sleep data. The schedule optimization unit can also identify the time period when the user is most efficient based on the biometric data and suggest placing important tasks during that time period. For example, it can select the time period when the heart rate is stable. The schedule optimization unit can also analyze the user's biometric data in real time using AI to dynamically suggest optimal work hours. For example, it can suggest the time period when the user is most refreshed based on sleep data. In this way, it is possible to suggest optimal work hours by analyzing the user's biometric data.

[0065] The schedule optimization unit can propose the most efficient schedule pattern based on the user's past schedule history. For example, the schedule optimization unit uses AI to analyze the user's past schedule history and propose the most efficient schedule pattern. For example, based on past data, it may propose the time period in which the user most efficiently completed tasks. The schedule optimization unit also extracts patterns in which the user's work efficiency was high based on the past schedule history and proposes those patterns. For example, it may propose a pattern in which work is concentrated on specific days of the week or time periods. The schedule optimization unit also uses AI to propose the most efficient schedule pattern in real time based on the user's past schedule history. For example, it may propose the time period in which the user was most productive based on past data. This makes it possible to propose the most efficient schedule pattern based on the user's past schedule history.

[0066] The schedule optimization unit can use the emotion estimation function to identify time periods when the user can concentrate best and suggest allocating important tasks to those time periods. The schedule optimization unit, for example, uses the emotion estimation function to identify time periods when the user can concentrate best and suggest allocating important tasks to those time periods. For example, the schedule optimization unit identifies time periods when the user is most likely to concentrate based on the user's emotion data. The schedule optimization unit also analyzes the user's emotion data to identify time periods when the user can concentrate best and suggest allocating important tasks to those time periods. For example, the schedule optimization unit selects time periods when positive emotions are strongest. The schedule optimization unit also uses the emotion estimation function to identify time periods when the user can concentrate best in real time and suggest allocating important tasks to those time periods. For example, the schedule optimization unit monitors changes in the user's emotions and suggests optimal time periods. This allows work efficiency to be improved by allocating important tasks to time periods when the user can concentrate best.

[0067] The schedule optimization unit can compare the schedule data of other users and propose the optimal schedule pattern. For example, the schedule optimization unit uses AI to analyze other users' schedule data and propose the optimal schedule pattern. For example, it can make proposals based on how other users in the same industry have created their schedules. The schedule optimization unit also compares other users' schedule data and extracts and proposes the most efficient schedule pattern. For example, it calculates the optimal schedule based on past data. The schedule optimization unit also uses AI to find common schedule patterns based on other users' schedule data and propose them to the user. For example, it can propose a pattern of concentrating work during specific time periods. This makes it possible to propose the optimal schedule pattern by comparing with other users' schedule data.

[0068] When optimizing a schedule, the schedule optimization unit can propose a balanced schedule by taking into account the user's meal and break times. For example, the schedule optimization unit uses AI to consider the user's meal and break times and propose a balanced schedule. For example, it allocates meal and break times appropriately. The schedule optimization unit also builds a system that proposes a balanced schedule based on the user's meal and break times. For example, it suggests appropriate breaks after working for a long period of time. The schedule optimization unit also uses AI to consider the user's meal and break times and propose a balanced schedule in real time. For example, it adjusts break times based on the user's fatigue level. In this way, a balanced schedule can be proposed by taking into account the user's meal and break times.

[0069] The schedule optimization unit can use the emotion estimation function to make suggestions to reduce the stress the user feels about the schedule. The schedule optimization unit, for example, uses the emotion estimation function to make suggestions to reduce the stress the user feels about the schedule. For example, it proposes adjusting the schedule or dividing tasks. The schedule optimization unit also makes suggestions to reduce the stress about the schedule based on the user's emotion data. For example, it adjusts the schedule by lowering the priority of a task. The schedule optimization unit also uses the emotion estimation function to make suggestions to reduce the stress the user feels about the schedule. For example, it incorporates time for relaxation into the schedule. This reduces the stress the user feels about the schedule, thereby increasing the motivation to complete tasks.

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

[0071] The secretary AI system can also be equipped with a health management unit that collects the user's health data and adjusts task priorities based on their health condition. For example, it can analyze the user's heart rate and blood pressure data and prioritize less demanding tasks if their health condition is deteriorating. The health management unit can also use the user's sleep data to suggest postponing important tasks if the user is sleep deprived. Furthermore, the health management unit can analyze the user's dietary data and suggest increasing break times if their nutrition is not balanced. This enables task management that takes the user's health condition into consideration, contributing to long-term health maintenance.

[0072] The secretary AI system can also include a hobby suggestion unit that suggests tasks for refreshing based on the user's hobbies and interests. For example, if the user likes music, the system can suggest time to listen to music between work tasks. The hobby suggestion unit can also periodically suggest hobby-related events and activities based on the user's past hobby activity data. Furthermore, the hobby suggestion unit can suggest new hobbies and activities based on the user's interests. This allows the user to efficiently manage tasks while refreshing themselves.

[0073] The secretary AI system may also include a social management unit that collects the user's social data and schedules social activities. For example, it may analyze the user's social media data and make suggestions to promote communication with friends and colleagues. The social management unit may also periodically suggest social events based on the user's past social activity data. Furthermore, the social management unit may analyze the user's social network and suggest events and meetings to build new connections. This allows the system to efficiently manage the user's social activities and support a balanced life.

[0074] The secretary AI system can also be equipped with a learning support unit that collects the user's learning data and suggests tasks to improve skills. For example, it can suggest online courses or seminars in areas of interest to the user. The learning support unit can also suggest what the user should learn next based on the user's past learning history. Furthermore, the learning support unit can analyze the user's skill level and suggest learning tasks of appropriate difficulty. This allows the user to improve their skills efficiently.

[0075] The secretary AI system can also be equipped with an environment management unit that collects environmental data about the user and optimizes the work environment. For example, it can analyze the temperature and lighting data of the user's work area and suggest the optimal work environment. The environment management unit can also analyze the noise level in the user's work environment and suggest working in a quieter environment. Furthermore, the environment management unit can analyze air quality data about the user's work environment and suggest using an air purifier. This allows the user to efficiently perform tasks in a comfortable work environment.

[0076] The secretary AI system can also suggest relaxation tasks to reduce stress based on the user's emotional data. For example, if the user is feeling stressed, it can suggest a short meditation session or deep breathing session. It can also suggest music or videos that will help the user relax based on the emotional data. Furthermore, it can analyze the emotional data and make suggestions to create an environment where the user can relax. This allows the user to efficiently manage tasks while reducing stress.

[0077] The secretary AI system can also suggest tasks to increase the user's motivation based on the user's emotional data. For example, if the user is feeling unmotivated, it can display success stories or encouraging messages. It can also prioritize tasks that give the user a sense of accomplishment based on the emotional data. Furthermore, it can analyze the emotional data and suggest activities that will increase the user's motivation. This allows the user to efficiently manage tasks while increasing their motivation.

[0078] The secretary AI system can also predict emotional fluctuations based on the user's emotional data and suggest tasks at the appropriate time. For example, it can suggest relaxation tasks before the user feels stressed. It can also suggest important tasks based on emotional data when the user is feeling positive. Furthermore, it can analyze emotional data and adjust task priorities according to the user's emotional fluctuations. This allows users to efficiently manage tasks while responding to emotional fluctuations.

[0079] The secretary AI system can also suggest tasks to balance emotions based on the user's emotional data. For example, if the user is feeling negative, it can suggest tasks to bring out positive emotions. It can also suggest relaxation tasks to balance emotions based on the emotional data. Furthermore, it can analyze the emotional data and suggest activities to balance emotions. This allows users to efficiently manage tasks while balancing their emotions.

[0080] The secretary AI system can also use the user's emotional data to send task reminders according to their emotional fluctuations. For example, if the user is feeling stressed, the system will refrain from sending reminders. It can also use the emotional data to send reminders when the user is feeling positive. It can also analyze the emotional data and adjust the timing of reminders according to the user's emotional fluctuations. This allows the user to efficiently manage tasks while responding to emotional fluctuations.

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

[0082] Step 1: The task collection unit collects the user's tasks. For example, it automatically collects tasks entered by the user. The task collection unit can also analyze the user's past activity history and suggest tasks that are likely to be forgotten. Furthermore, the task collection unit can analyze voice input and add verbally instructed tasks to the list. For example, if the user says, "Prepare materials for tomorrow's meeting," the task collection unit adds that task to the list. Step 2: The task classification unit classifies the collected tasks. For example, it classifies them based on the importance or urgency of the tasks. The task classification unit can also use emotion estimation to identify tasks that cause stress to the user and suggest that those tasks be prioritized. Step 3: The delivery time analysis unit analyzes the delivery time and frequency of contact for the classified tasks. For example, it suggests that tasks with approaching project deadlines or tasks that require frequent contact be prioritized. The delivery time analysis unit can also calculate the expected completion time for each task based on past task completion data and make suggestions to allow for leeway in delivery times. Step 4: The schedule optimization unit optimizes the schedule based on the analysis results. For example, it may arrange a schedule so that important tasks are placed in the morning and urgent tasks are given priority. The schedule optimization unit can also analyze the user's biometric data (e.g., heart rate and sleep data) to suggest optimal work times. Step 5: The Reminders section will remind you based on your optimized schedule, for example, notifying you when a meeting is about to start or a project deadline is approaching. Step 6: The feedback learning unit learns the user's feedback on the reminded task. For example, if the user enters feedback such as "I wish I had finished this task earlier," the feedback learning unit learns that feedback and reflects it in future schedules.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a task collection unit that collects tasks of users; a task classification unit that classifies the tasks collected by the task collection unit; a delivery time analysis unit that analyzes delivery times and contact frequencies of the tasks classified by the task classification unit; a schedule optimization unit that optimizes a schedule based on the results of the analysis by the delivery date analysis unit; a reminder unit that provides reminders based on the schedule optimized by the schedule optimization unit; a feedback learning unit that learns a user's feedback regarding the task reminded by the reminder unit. A system characterized by:

2. The task collection unit Analyzing the user's past behavior history and automatically suggesting tasks that the user tends to forget 2. The system of claim 1.

3. The task collection unit Analyzing the user's voice input and automatically adding verbally instructed tasks to the list 2. The system of claim 1.

4. The task collection unit Identifying a task that the user is experiencing stress over and suggesting that the task be prioritized 2. The system of claim 1.

5. The task collection unit Compare task management data with other users and suggest efficient task management methods 2. The system of claim 1.

6. The task collection unit When collecting tasks, the user's location information is used to automatically add location-specific tasks to the list.

2. The system of claim 1.

7. The task collection unit Analyze the emotions of the user when entering tasks and make suggestions to elicit positive emotions 2. The system of claim 1.

8. The delivery date analysis unit Based on past task completion data, calculate the expected completion time for each task and make suggestions to allow for leeway in delivery dates.

2. The system of claim 1.

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