System

Abstract

An object of a system according to an embodiment is to automatically create a schedule of tasks and reduce communication costs.SOLUTION: A system according to an embodiment includes a task analysis unit, a resource analysis unit, a schedule generation unit, and a notification unit. The task analysis unit analyzes the content and priority of the task. The resource analysis unit analyzes a use state of a resource. The schedule generation part generates an optimum schedule on the basis of information analyzed by the task analysis part and the resource analysis part. The notification unit notifies the schedule generated by the schedule generation 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] In conventional techniques, task scheduling is done manually, which can lead to high communication costs.

[0005] The system according to the embodiment aims to automatically create a task schedule and reduce communication costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a task analysis unit, a resource analysis unit, a schedule generation unit, and a notification unit. The task analysis unit analyzes the content and priority of tasks. The resource analysis unit analyzes resource usage. The schedule generation unit generates an optimal schedule based on the information analyzed by the task analysis unit and the resource analysis unit. The notification unit notifies the schedule generated by the schedule generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create a schedule of tasks and reduce communication costs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A schedule creation system according to an embodiment of the present invention is a system that automatically creates schedules for tasks that do not have schedules set, reducing communication costs. This allows the schedule creation system to improve the efficiency of task management and significantly reduce communication costs between the parties involved.

[0029] A schedule creation system according to an embodiment includes a task analysis unit, a resource analysis unit, a schedule generation unit, and a notification unit. The task analysis unit analyzes the content and priority of a task. For example, the generation AI analyzes the content and priority of a task input by a user. For example, when a task such as "Create materials for next week's meeting" is input, the generation AI analyzes the importance and deadline of the task and compares its priority with other tasks. The resource analysis unit analyzes resource usage. For example, the generation AI refers to the user's calendar and past work history to determine which resources are available at what time periods. The generation AI analyzes the usage of the user's resources (time, skills, tools, etc.). The schedule generation unit generates an optimal schedule based on the information analyzed by the task analysis unit and the resource analysis unit. For example, the generation AI generates an optimal schedule based on the analyzed task content, priority, and resource usage. The generation AI proposes a specific schedule, such as "Create materials for the meeting next Monday morning and review them on Tuesday afternoon." The notification unit notifies the user of the schedule generated by the schedule generation unit. For example, the generation AI notifies the user of the generated schedule and shares it with relevant parties. For example, the generation AI sends a notification to the user such as "I plan to create meeting materials next Monday morning," and also sends a similar notification to relevant parties. In this way, the schedule creation system according to the embodiment can automatically create schedules for tasks that do not have a schedule set, thereby reducing communication costs.

[0030] When analyzing the content of a task, the task analysis unit refers to the success and failure rates of past similar tasks and dynamically adjusts the priority. For example, the generation AI analyzes data on past similar tasks and sets priorities based on the success and failure rates. For example, it schedules tasks with a high success rate first. For example, the generation AI refers to data on past projects or similar tasks and calculates the success and failure rates. This makes it possible to dynamically adjust task priorities based on past data.

[0031] When analyzing the contents of tasks, the task analysis unit automatically detects task dependencies and sets priorities based on those dependencies. For example, the task analysis unit uses a generation AI to analyze task dependencies and prioritize scheduling of tasks with strong dependencies. For example, it prioritizes tasks that cannot proceed until prerequisite tasks are completed. For example, the generation AI analyzes the order and dependency between tasks to detect dependencies. This allows priorities to be set based on task dependencies.

[0032] When analyzing resource usage, the resource analysis unit refers to past performance data and selects the optimal resource. For example, the generation AI analyzes the past performance data of resources and selects the optimal resource. For example, it prioritizes resources with a high past success rate. For example, the generation AI analyzes the work speed and quality evaluation of resources and calculates performance data. This makes it possible to select the optimal resource based on past performance data.

[0033] When generating a schedule, the schedule generation unit refers to the success and failure rates of past schedules and dynamically adjusts the optimal schedule. For example, the generation AI analyzes past schedule data and generates an optimal schedule based on the success and failure rates. For example, important tasks are assigned to time periods with a high success rate. For example, the generation AI analyzes the completion rate and achievement level of past schedules and calculates the success and failure rates. This makes it possible to dynamically adjust the optimal schedule based on past data.

[0034] When generating a schedule, the schedule generation unit automatically detects task dependencies and sets the schedule based on those dependencies. For example, the schedule generation unit uses a generation AI to analyze task dependencies and prioritize scheduling of tasks with strong dependencies. For example, it prioritizes tasks that cannot proceed until prerequisite tasks are completed. For example, the generation AI analyzes the order and dependency between tasks to detect dependencies. This allows it to set a schedule based on task dependencies.

[0035] When notifying and sharing schedules, the notification unit customizes the content of the notification based on the user's past reaction data. For example, the generation AI analyzes the user's past reaction data and customizes the content of the notification. For example, the notification is sent in a format preferred by the user. For example, the generation AI analyzes the user's click rate and feedback and calculates reaction data. This makes it possible to customize the content of the notification based on the user's past reaction data.

[0036] When notifying a schedule, the notification unit automatically sets the priority of notifications and prioritizes sending important notifications. For example, the generation AI automatically sets the priority of notifications and prioritizes sending important notifications. For example, it sends notifications of important tasks with the highest priority. For example, the generation AI analyzes the urgency and importance of notifications and sets the priority of notifications. This allows the priority of notifications to be automatically set and important notifications to be sent with priority.

[0037] When adjusting the schedule, the schedule generation unit automatically detects task dependencies and reconfigures the schedule based on those dependencies. For example, the schedule generation unit uses a generation AI to analyze task dependencies and prioritize scheduling of tasks with strong dependencies. For example, it prioritizes tasks that cannot proceed until prerequisite tasks are completed. For example, the generation AI analyzes the order and dependency between tasks to detect dependencies. This allows the schedule to be reconfigured based on task dependencies.

[0038] When adjusting and updating the schedule, the schedule generation unit incorporates schedule management methods from different industries and fields to diversify the schedule adjustment methods. For example, the schedule generation unit incorporates schedule management methods from different industries to diversify the schedule adjustment methods. For example, it incorporates manufacturing industry methods to adjust the schedule. The generation AI references schedule management methods such as agile and waterfall to select the optimal adjustment method. In this way, by incorporating schedule management methods from different industries and fields, it is possible to diversify the schedule adjustment methods.

[0039] When adjusting and updating the schedule, the schedule generation unit incorporates schedule management methods from different industries and fields to diversify the schedule adjustment methods. For example, the schedule generation unit incorporates schedule management methods from different industries to diversify the schedule adjustment methods. For example, it incorporates manufacturing industry methods to adjust the schedule. The generation AI references schedule management methods such as agile and waterfall to select the optimal adjustment method. In this way, by incorporating schedule management methods from different industries and fields, it is possible to diversify the schedule adjustment methods.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The schedule creation system can also include a progress monitoring unit that monitors the progress of tasks in real time. The progress monitoring unit, for example, monitors the progress of a task in real time as the user works on it, and checks whether the task is progressing as planned. If the progress is behind schedule, the notification unit can send a reminder to the user, and the schedule generation unit can readjust the schedule as necessary. This allows the user to always be aware of the progress of the task and prevent delays.

[0042] The schedule creation system may further include a health management unit that collects the user's health data and adjusts the schedule based on the user's health condition. The health management unit may, for example, analyze the user's sleep data and exercise data and assign important tasks to times when the user's health condition is good. For example, an important meeting may be scheduled for the day after the user has had a sufficient amount of sleep. This allows the system to generate an optimal schedule based on the user's health condition.

[0043] The schedule creation system may further include a hobby analysis unit that adjusts the schedule taking into account the user's hobbies and interests. The hobby analysis unit, for example, analyzes the amount of time the user spends on hobbies and adjusts the schedule to ensure time for hobbies. For example, the hobby analysis unit may concentrate tasks on weekdays to ensure the user has time to enjoy sports on the weekends. This can improve the user's quality of life.

[0044] The schedule creation system may further include a social analysis unit that adjusts the schedule taking into account the user's social activities. The social analysis unit, for example, analyzes the frequency and importance of the user's social activities and prioritizes incorporating social activities into the schedule. For example, the social analysis unit may reflect community events that the user regularly participates in in the schedule. This supports the user's social activities and provides a balanced schedule.

[0045] The schedule creation system may further include a learning analysis unit that adjusts the schedule taking into account the user's learning activities. The learning analysis unit, for example, analyzes the amount of time the user spends studying and their learning progress, and adjusts the schedule to ensure time for studying. For example, the learning analysis unit adjusts other tasks to ensure the user has time to study for a qualification exam. This supports the user's learning activities and provides an efficient schedule.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The task analysis unit analyzes the content and priority of the task. For example, if the task input by the user is "Create materials for next week's meeting," the generation AI analyzes the importance and deadline of that task and compares its priority with other tasks. Step 2: The resource analysis unit analyzes resource usage. For example, the generation AI refers to the user's calendar and past work history to determine which resources are available at what time. The generation AI analyzes the user's resource usage (time, skills, tools, etc.). Step 3: The schedule generation unit generates an optimal schedule based on the information analyzed by the task analysis unit and resource analysis unit. For example, the generation AI generates an optimal schedule based on the analyzed task content, priority, and resource usage status, and proposes a specific schedule such as "Prepare meeting materials next Monday morning and review them on Tuesday afternoon." Step 4: The notification unit notifies the user of the schedule generated by the schedule generation unit. For example, the generation AI notifies the user of the generated schedule and shares it with relevant parties. It sends a notification to the user such as "We plan to create meeting materials next Monday morning," and also sends a similar notification to relevant parties.

[0048] (Example 2) A schedule creation system according to an embodiment of the present invention is a system that automatically creates schedules for tasks that do not have schedules set, reducing communication costs. This allows the schedule creation system to improve the efficiency of task management and significantly reduce communication costs between the parties involved.

[0049] A schedule creation system according to an embodiment includes a task analysis unit, a resource analysis unit, a schedule generation unit, and a notification unit. The task analysis unit analyzes the content and priority of a task. For example, the generation AI analyzes the content and priority of a task input by a user. For example, when a task such as "Create materials for next week's meeting" is input, the generation AI analyzes the importance and deadline of the task and compares its priority with other tasks. The resource analysis unit analyzes resource usage. For example, the generation AI refers to the user's calendar and past work history to determine which resources are available at what time periods. The generation AI analyzes the usage of the user's resources (time, skills, tools, etc.). The schedule generation unit generates an optimal schedule based on the information analyzed by the task analysis unit and the resource analysis unit. For example, the generation AI generates an optimal schedule based on the analyzed task content, priority, and resource usage. The generation AI proposes a specific schedule, such as "Create materials for the meeting next Monday morning and review them on Tuesday afternoon." The notification unit notifies the user of the schedule generated by the schedule generation unit. For example, the generation AI notifies the user of the generated schedule and shares it with relevant parties. For example, the generation AI sends a notification to the user such as "I plan to create meeting materials next Monday morning," and also sends a similar notification to relevant parties. In this way, the schedule creation system according to the embodiment can automatically create schedules for tasks that do not have a schedule set, thereby reducing communication costs.

[0050] The task analysis unit collects user emotional data when analyzing the content of a task and quantifies the emotional importance. For example, when the generation AI analyzes the content of a task, the task analysis unit collects user emotional data and quantifies the emotional importance. For example, it lowers the priority of a task that causes stress to the user. The generation AI collects emotional data using, for example, facial expression recognition or voice analysis of the user and calculates an emotional score. This makes it possible to adjust the priority of a task based on the user's emotions.

[0051] When analyzing the content of a task, the task analysis unit refers to the success and failure rates of past similar tasks and dynamically adjusts the priority. For example, the generation AI analyzes data on past similar tasks and sets priorities based on the success and failure rates. For example, it schedules tasks with a high success rate first. For example, the generation AI refers to data on past projects or similar tasks and calculates the success and failure rates. This makes it possible to dynamically adjust task priorities based on past data.

[0052] When analyzing the contents of tasks, the task analysis unit automatically detects task dependencies and sets priorities based on those dependencies. For example, the task analysis unit uses a generation AI to analyze task dependencies and prioritize scheduling of tasks with strong dependencies. For example, it prioritizes tasks that cannot proceed until prerequisite tasks are completed. For example, the generation AI analyzes the order and dependency between tasks to detect dependencies. This allows priorities to be set based on task dependencies.

[0053] When analyzing resource usage, the resource analysis unit takes into account the emotional load of the resource and prioritizes allocating resources with less load. For example, the resource analysis unit uses a generation AI to analyze the emotional load of resources and prioritizes allocating resources with less load. For example, it selects time periods with less stress. For example, the generation AI analyzes the stress level and fatigue level of resources and calculates the emotional load. This allows resources with less emotional load to be prioritized for allocation.

[0054] When analyzing resource usage, the resource analysis unit refers to past performance data and selects the optimal resource. For example, the generation AI analyzes the past performance data of resources and selects the optimal resource. For example, it prioritizes resources with a high past success rate. For example, the generation AI analyzes the work speed and quality evaluation of resources and calculates performance data. This makes it possible to select the optimal resource based on past performance data.

[0055] When generating a schedule, the schedule generation unit references the user's emotional data and generates an emotionally positive schedule. For example, the schedule generation unit uses a generation AI to analyze the user's emotional data and generate a schedule with a strong positive emotional impact. For example, important tasks are assigned to times when the user is relaxed. The generation AI generates a schedule based on the user's emotional score, for example. This makes it possible to generate a positive schedule based on the user's emotions.

[0056] When generating a schedule, the schedule generation unit refers to the success and failure rates of past schedules and dynamically adjusts the optimal schedule. For example, the generation AI analyzes past schedule data and generates an optimal schedule based on the success and failure rates. For example, important tasks are assigned to time periods with a high success rate. For example, the generation AI analyzes the completion rate and achievement level of past schedules and calculates the success and failure rates. This makes it possible to dynamically adjust the optimal schedule based on past data.

[0057] When generating a schedule, the schedule generation unit automatically detects task dependencies and sets the schedule based on those dependencies. For example, the schedule generation unit uses a generation AI to analyze task dependencies and prioritize scheduling of tasks with strong dependencies. For example, it prioritizes tasks that cannot proceed until prerequisite tasks are completed. For example, the generation AI analyzes the order and dependency between tasks to detect dependencies. This allows it to set a schedule based on task dependencies.

[0058] When notifying the user of the schedule, the notification unit refers to the user's emotional data and notifies the user at a timing that is emotionally positive. For example, the generation AI analyzes the user's emotional data and notifies the user of the schedule at a timing when positive emotions are strongest. For example, the notification unit sends notifications when the user is relaxed. For example, the generation AI sets the timing of notifications based on the user's emotional score. This allows the schedule to be notified at a positive timing based on the user's emotions.

[0059] When notifying and sharing schedules, the notification unit customizes the content of the notification based on the user's past reaction data. For example, the generation AI analyzes the user's past reaction data and customizes the content of the notification. For example, the notification is sent in a format preferred by the user. For example, the generation AI analyzes the user's click rate and feedback and calculates reaction data. This makes it possible to customize the content of the notification based on the user's past reaction data.

[0060] When notifying a schedule, the notification unit automatically sets the priority of notifications and prioritizes sending important notifications. For example, the generation AI automatically sets the priority of notifications and prioritizes sending important notifications. For example, it sends notifications of important tasks with the highest priority. For example, the generation AI analyzes the urgency and importance of notifications and sets the priority of notifications. This allows the priority of notifications to be automatically set and important notifications to be sent with priority.

[0061] When adjusting the schedule, the schedule generation unit refers to the user's emotional data and recreates an emotionally positive schedule. For example, the schedule generation unit uses a generation AI to analyze the user's emotional data and recreate a schedule with a strong positive emotional impact. For example, important tasks are assigned to times when the user is relaxed. The generation AI recreates the schedule based on the user's emotional score, for example. This allows a positive schedule to be recreated based on the user's emotions.

[0062] When adjusting the schedule, the schedule generation unit automatically detects task dependencies and reconfigures the schedule based on those dependencies. For example, the schedule generation unit uses a generation AI to analyze task dependencies and prioritize scheduling of tasks with strong dependencies. For example, it prioritizes tasks that cannot proceed until prerequisite tasks are completed. For example, the generation AI analyzes the order and dependency between tasks to detect dependencies. This allows the schedule to be reconfigured based on task dependencies.

[0063] When adjusting and updating the schedule, the schedule generation unit incorporates schedule management methods from different industries and fields to diversify the schedule adjustment methods. For example, the schedule generation unit incorporates schedule management methods from different industries to diversify the schedule adjustment methods. For example, it incorporates manufacturing industry methods to adjust the schedule. The generation AI references schedule management methods such as agile and waterfall to select the optimal adjustment method. In this way, by incorporating schedule management methods from different industries and fields, it is possible to diversify the schedule adjustment methods.

[0064] When adjusting and updating the schedule, the schedule generation unit uses the emotion estimation function to recreate an emotionally positive schedule. For example, the schedule generation unit uses a generation AI to estimate the user's emotions and recreate a schedule with a strong positive emotion. For example, it assigns important tasks to times when the user is relaxed. The generation AI estimates emotions using, for example, facial expression recognition or voice analysis and calculates an emotion score. This allows the emotion estimation function to recreate a positive schedule.

[0065] When adjusting and updating the schedule, the schedule generation unit incorporates schedule management methods from different industries and fields to diversify the schedule adjustment methods. For example, the schedule generation unit incorporates schedule management methods from different industries to diversify the schedule adjustment methods. For example, it incorporates manufacturing industry methods to adjust the schedule. The generation AI references schedule management methods such as agile and waterfall to select the optimal adjustment method. In this way, by incorporating schedule management methods from different industries and fields, it is possible to diversify the schedule adjustment methods.

[0066] When adjusting and updating the schedule, the schedule generation unit uses the emotion estimation function to recreate an emotionally positive schedule. For example, the schedule generation unit uses a generation AI to estimate the user's emotions and recreate a schedule with a strong positive emotion. For example, it assigns important tasks to times when the user is relaxed. The generation AI estimates emotions using, for example, facial expression recognition or voice analysis and calculates an emotion score. This allows the emotion estimation function to recreate a positive schedule.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The schedule creation system can also include a progress monitoring unit that monitors the progress of tasks in real time. The progress monitoring unit, for example, monitors the progress of a task in real time as the user works on it, and checks whether the task is progressing as planned. If the progress is behind schedule, the notification unit can send a reminder to the user, and the schedule generation unit can readjust the schedule as necessary. This allows the user to always be aware of the progress of the task and prevent delays.

[0069] The schedule creation system may further include a health management unit that collects the user's health data and adjusts the schedule based on the user's health condition. The health management unit may, for example, analyze the user's sleep data and exercise data and assign important tasks to times when the user's health condition is good. For example, an important meeting may be scheduled for the day after the user has had a sufficient amount of sleep. This allows the system to generate an optimal schedule based on the user's health condition.

[0070] The schedule creation system may further include a hobby analysis unit that adjusts the schedule taking into account the user's hobbies and interests. The hobby analysis unit, for example, analyzes the amount of time the user spends on hobbies and adjusts the schedule to ensure time for hobbies. For example, the hobby analysis unit may concentrate tasks on weekdays to ensure the user has time to enjoy sports on the weekends. This can improve the user's quality of life.

[0071] The schedule creation system may further include a social analysis unit that adjusts the schedule taking into account the user's social activities. The social analysis unit, for example, analyzes the frequency and importance of the user's social activities and prioritizes incorporating social activities into the schedule. For example, the social analysis unit may reflect community events that the user regularly participates in in the schedule. This supports the user's social activities and provides a balanced schedule.

[0072] The schedule creation system may further include a learning analysis unit that adjusts the schedule taking into account the user's learning activities. The learning analysis unit, for example, analyzes the amount of time the user spends studying and their learning progress, and adjusts the schedule to ensure time for studying. For example, the learning analysis unit adjusts other tasks to ensure the user has time to study for a qualification exam. This supports the user's learning activities and provides an efficient schedule.

[0073] The schedule creation system may further include an emotion priority adjustment unit that estimates the user's emotion and dynamically adjusts task priorities based on the estimated emotion. For example, if the user is feeling stressed, the emotion priority adjustment unit estimates the emotion and lowers task priorities to reduce stress. For example, when the user is tired, tasks with lower importance are assigned preferentially. This allows dynamic adjustment of task priorities based on the user's emotion.

[0074] The schedule creation system may further include an emotion notification customization unit that estimates the user's emotion and customizes the content of notifications based on the estimated emotion. For example, when the user is relaxed, the emotion notification customization unit estimates the user's emotion and changes the notification content to be more easily received when the user is relaxed. For example, when the user is relaxed, the notification is sent in a softer tone. This allows the content of notifications to be customized based on the user's emotion.

[0075] The schedule creation system may further include an emotion schedule adjustment unit that estimates the user's emotion and adjusts the schedule based on the estimated emotion. For example, if the user has a positive emotion, the emotion schedule adjustment unit estimates the emotion and adjusts the schedule to maintain the positive emotion. For example, important tasks are assigned to times when the user is relaxed. This makes it possible to adjust the schedule based on the user's emotion.

[0076] The schedule creation system may further include an emotion resource allocation unit that estimates the user's emotion and allocates resources based on the estimated emotion. For example, if the user is feeling stressed, the emotion resource allocation unit estimates the emotion and adjusts resource allocation to reduce the stress. For example, when the user is tired, resources with a lower load are preferentially allocated. This allows resource allocation based on the user's emotion.

[0077] The schedule creation system may further include an emotion progress monitoring unit that estimates the user's emotion and monitors the task progress based on the estimated emotion. For example, if the user has a positive emotion, the emotion progress monitoring unit estimates the emotion and monitors the task progress to maintain the positive emotion. For example, if the progress is delayed during a time when the user is relaxing, the emotion progress monitoring unit sends a reminder. This makes it possible to monitor the task progress based on the user's emotion.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The task analysis unit analyzes the content and priority of the task. For example, if the task input by the user is "Create materials for next week's meeting," the generation AI analyzes the importance and deadline of that task and compares its priority with other tasks. Step 2: The resource analysis unit analyzes resource usage. For example, the generation AI refers to the user's calendar and past work history to determine which resources are available at what time. The generation AI analyzes the user's resource usage (time, skills, tools, etc.). Step 3: The schedule generation unit generates an optimal schedule based on the information analyzed by the task analysis unit and resource analysis unit. For example, the generation AI generates an optimal schedule based on the analyzed task content, priority, and resource usage status, and proposes a specific schedule such as "Prepare meeting materials next Monday morning and review them on Tuesday afternoon." Step 4: The notification unit notifies the user of the schedule generated by the schedule generation unit. For example, the generation AI notifies the user of the generated schedule and shares it with relevant parties. It sends a notification to the user such as "We plan to create meeting materials next Monday morning," and also sends a similar notification to relevant parties.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 analysis unit that analyzes the content and priority of a task; a resource analysis unit that analyzes resource usage; a schedule generation unit that generates an optimal schedule based on the information analyzed by the task analysis unit and the resource analysis unit; a notification unit that notifies the schedule generated by the schedule generation unit. A system characterized by:

2. The task analysis unit When analyzing the content of the task, the system dynamically adjusts the priority by referring to the success and failure rates of past similar tasks.

2. The system of claim 1.

3. The resource analysis unit When analyzing the utilization status of the resources, the emotional load of the resources is taken into consideration, and resources with less load are preferentially allocated.

2. The system of claim 1.

4. The schedule generation unit When generating the schedule, the system dynamically adjusts the optimal schedule by referring to past success and failure rates.

2. The system of claim 1.

5. The notification unit When notifying the schedule, the user's emotional data is referenced and the schedule is notified at a timing that is emotionally positive.

2. The system of claim 1.

6. The task analysis unit When analyzing the content of the task, the user's emotional data is collected and the emotional importance is quantified.

2. The system of claim 1.

7. The resource analysis unit When analyzing the utilization status of the resources, the emotional load of the resources is taken into consideration, and resources with less load are preferentially allocated.

2. The system of claim 1.

8. The task analysis unit When analyzing the content of the task, the system dynamically adjusts the priority by referring to the success and failure rates of past similar tasks.

2. The system of claim 1.

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