System

Abstract

An object of a system according to an embodiment is to efficiently optimize a schedule of a user.SOLUTION: A system according to an embodiment includes a schedule adjustment unit, a task management unit, a weather report providing unit, a traffic information providing unit, and a real-time information acquiring unit. The schedule adjustment unit adjusts the schedule. The task management unit manages a task. The weather forecast providing unit provides a weather forecast. The traffic information providing unit provides traffic information. The real-time information acquisition unit acquires information in real time and optimizes the schedule.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 technologies require users to manually link multiple apps and devices to optimize their schedules, making efficient schedule management difficult.

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

[0006] The system according to the embodiment includes a schedule adjustment unit, a task management unit, a weather forecast providing unit, a traffic information providing unit, and a real-time information acquisition unit. The schedule adjustment unit adjusts schedules. The task management unit manages tasks. The weather forecast providing unit provides weather forecasts. The traffic information providing unit provides traffic information. The real-time information acquisition unit acquires information in real time and optimizes the schedule. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently optimize a user's 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) An AI platform according to an embodiment of the present invention is a system that optimizes a user's schedule in cooperation with a calendar app and IoT devices. This system adjusts appointments, manages tasks, provides weather forecasts, provides traffic information, obtains real-time information, and optimizes the schedule. This allows the AI ​​platform to optimize the user's schedule and quickly respond to unexpected problems and troubles.

[0029] The AI ​​platform according to the embodiment includes a schedule adjustment unit, a task management unit, a weather forecast providing unit, a traffic information providing unit, and a real-time information acquisition unit. The schedule adjustment unit adjusts schedules. For example, the generation AI works with a user's calendar app to automatically propose a new time when a meeting is rescheduled and notify all relevant parties. The generation AI can also analyze the user's past schedule history to learn and propose optimal schedule adjustment patterns. The generation AI can also consider the user's health data to propose optimal schedules. The task management unit manages tasks. For example, the generation AI can determine the priority of tasks added by the user and set reminders at appropriate times. The generation AI can also analyze the user's past task completion data to learn and propose optimal task management patterns. The generation AI can also consider the user's energy level to propose optimal task schedules. The weather forecast providing unit provides weather forecasts. For example, the generation AI can provide a weather forecast based on the user's schedule and suggest necessary measures if an outdoor event is planned. The generation AI can also analyze past weather data and the user's behavioral patterns to provide optimal weather forecasts. Furthermore, the generation AI can also provide health-conscious weather forecasts by taking into account the user's health data. The traffic information provision unit provides traffic information. For example, the generation AI can provide traffic information about the user's travel and suggest the optimal route if traffic congestion is expected when commuting to work. The generation AI can also analyze past traffic data and the user's travel patterns to suggest the optimal route. Furthermore, the generation AI can suggest the most economical route by taking into account the user's vehicle data. The real-time information acquisition unit acquires information in real time and optimizes the schedule. For example, the generation AI can quickly respond to unexpected problems, such as a sudden meeting cancellation or a delay due to a traffic accident, and propose a new schedule. The generation AI can also acquire the user's real-time biometric data to suggest the optimal schedule.Furthermore, the generation AI can obtain the user's real-time location information and propose optimal travel routes. This allows the AI ​​platform according to the embodiment to optimize the user's schedule and quickly respond to unexpected problems or issues. For example, the output unit notifies the user of schedule changes or proposals. The user can check the schedule optimization in real time through a web application or mobile application. Notifications can also be sent by email, allowing the user to respond quickly.

[0030] The schedule adjustment unit can analyze the user's past schedule history, learn, and suggest optimal schedule adjustment patterns. For example, the generation AI in the schedule adjustment unit analyzes the user's schedule history over the past year and learns patterns of frequent meetings and events. This allows it to suggest the optimal time slot when a similar appointment occurs. The generation AI can also learn the user's tendency to schedule appointments on specific days of the week or at specific times based on the user's past schedule history, and suggest new appointments based on that pattern. For example, it could schedule a meeting every Monday morning. The generation AI can also analyze the user's past schedule history and make adjustments to allow for leeway before and after specific events or meetings. For example, it could reserve breaks before and after important presentations. This allows it to suggest optimal schedules based on the user's past schedule history.

[0031] The schedule adjustment unit can suggest optimal schedules based on the user's health data. For example, the generation AI analyzes sleep data obtained from the user's wearable device and suggests scheduling an important meeting in the morning on a day when the user has had enough sleep. The generation AI also monitors the user's stress level and prioritizes suggestions for relaxing schedules during times of high stress. For example, it suggests taking a vacation to refresh yourself during a week of high stress. The generation AI also suggests scheduling important tasks or meetings based on the user's health data during times when the user has the most energy. For example, it schedules a presentation in the afternoon when energy levels are high. This makes it possible to suggest optimal schedules based on the user's health condition.

[0032] The schedule adjustment unit also works with the schedules of the user's family and friends to optimize everyone's schedules. For example, the generation AI works with the calendar apps of the user's family and friends to integrate everyone's schedules and propose optimal plans. For example, it suggests weekend events that the whole family can attend. The generation AI also takes into account the schedules of the user's friends and family and sets plans for common free time. For example, it adjusts plans for dinner with friends to fit everyone's free time. The generation AI also works with the schedules of family and friends to set reminders to prevent important events and anniversaries from being forgotten. For example, family birthdays and wedding anniversaries are automatically added to the calendar. This makes it possible to optimize everyone's schedules by working with the schedules of the user's family and friends.

[0033] The schedule adjustment unit can automatically insert a schedule for refreshing based on the user's hobbies or interests. In the schedule adjustment unit, for example, the generation AI learns the user's hobbies and interests and automatically inserts a schedule for refreshing. For example, if the user likes movies, it suggests a weekend movie viewing schedule. The generation AI also suggests activities that can refresh the user regularly based on the user's interests. For example, if the user likes the outdoors, it inserts a hiking or camping schedule. The generation AI also takes into account the hobbies and interests and sets a schedule for refreshing during a time when the user can relax. For example, if the user likes music, it suggests a music festival schedule. In this way, it is possible to automatically insert a schedule for refreshing based on the user's hobbies and interests.

[0034] The task management unit can analyze a user's past task completion data, learn, and suggest optimal task management patterns. For example, the task management unit's generation AI analyzes a user's task completion data from the past year and learns patterns of frequently performed tasks. This allows it to suggest optimal time periods when similar tasks arise. The generation AI can also learn a user's tendency to complete tasks on specific days of the week or at specific times based on the user's past task completion data, and suggest new tasks based on these patterns. For example, it could set important tasks for every Monday morning. The generation AI can also analyze a user's past task completion data and make adjustments to allow for leeway before and after specific tasks. For example, it could reserve breaks before and after an important presentation. This allows it to suggest optimal task management patterns based on the user's past task completion data.

[0035] The task management unit can suggest an optimal task schedule based on the user's energy level. For example, the generation AI analyzes activity meter data obtained from the user's wearable device and suggests setting important tasks for times when energy levels are high. The generation AI also monitors the user's energy level and prioritizes lighter tasks when energy levels are low. For example, lighter tasks are prioritized during weeks when energy levels are low. The generation AI also suggests setting important tasks for times when the user has the most energy, based on the activity meter data. For example, setting a presentation in the afternoon when energy levels are high. This makes it possible to suggest an optimal task schedule based on the user's energy level.

[0036] The task management unit also works with the tasks of the user's colleagues and team members at work, optimizing overall task management. For example, the task management unit's generation AI works with the task management tools of the user's colleagues and team members to integrate all tasks and propose an optimal schedule. For example, it suggests a meeting time that all team members can attend. The generation AI also takes into account the tasks of the user's colleagues and team members and sets tasks during common free time. For example, it suggests a brainstorming session that all team members can participate in. The generation AI also works with the tasks of colleagues and team members to keep track of the progress of important projects in real time and adjust tasks as needed. For example, it rearranges tasks as the project progresses. This allows the generation AI to work with the tasks of the user's colleagues and team members at work to optimize overall task management.

[0037] The task management unit can automatically add tasks based on the user's long-term goals or career plan. For example, the generation AI learns the user's long-term goals and career plan and automatically adds tasks based on them. For example, if the user is aiming to advance their career, it will suggest tasks for acquiring related skills. The generation AI also sets tasks to check progress periodically based on the user's career plan. For example, it adds a task to review the career plan every six months. The generation AI also sets steps toward long-term goals as specific tasks and supports the user in moving toward their goals. For example, it sets project milestones as tasks. This makes it possible to automatically add tasks based on the user's long-term goals and career plan.

[0038] The weather forecast providing unit can analyze past weather data and user behavior patterns to provide the optimal weather forecast. In the weather forecast providing unit, for example, the generation AI analyzes weather data and user behavior patterns from the past year to learn behavior under specific weather conditions. This allows it to suggest optimal behavior for days when similar weather is expected. The generation AI also suggests optimal behavior under specific weather conditions based on the user's past behavior patterns. For example, it suggests indoor activities on rainy days. The generation AI also analyzes past weather data and user behavior patterns to suggest optimal behavior under specific weather conditions. For example, it suggests outdoor activities on sunny days. This allows it to provide the optimal weather forecast based on past weather data and user behavior patterns.

[0039] The weather forecast providing unit can provide a health-conscious weather forecast based on the user's health data. For example, the generation AI of the weather forecast providing unit may suggest refraining from going outside on days with high pollen counts based on the user's allergy information. For example, it may suggest indoor activities on days with high pollen counts. The generation AI may also take the user's health data into consideration and make suggestions to avoid health risks under specific weather conditions. For example, it may suggest cold weather protection measures on days when the temperature drops suddenly. The generation AI may also provide a weather forecast based on the health data that helps the user stay healthy. For example, it may suggest using sunscreen on days with strong UV rays. In this way, it is possible to provide a health-conscious weather forecast based on the user's health data.

[0040] The weather forecast providing unit can provide a weather forecast for a specific location based on the user's travel plans and event schedule. In the weather forecast providing unit, for example, the generation AI provides a weather forecast for a travel destination based on the user's travel plans. For example, if the weather at the travel destination is bad, an alternative plan is suggested. The generation AI also provides a weather forecast for a specific location based on the user's event schedule. For example, on the day of an outdoor event, it suggests necessary measures based on the weather forecast. The generation AI also provides a weather forecast for a specific location based on the travel plans and event schedule. For example, if the weather at the travel destination is good, it suggests tourist spots. In this way, it is possible to provide a weather forecast for a specific location based on the user's travel plans and event schedule.

[0041] The weather forecast providing unit can provide a weather forecast suitable for the user's pet based on the health condition of the pet. For example, the generation AI of the weather forecast providing unit provides a weather forecast suitable for the pet based on the health condition of the user's pet. For example, if the pet is sensitive to heat, it suggests taking the pet for a walk during cooler hours. The generation AI also takes the health condition of the user's pet into consideration and makes suggestions to avoid health risks under specific weather conditions. For example, it suggests indoor activities on rainy days. The generation AI also provides a weather forecast that will keep the pet comfortable based on the pet's health condition. For example, if the pet is sensitive to cold, it suggests taking the pet for a walk during warmer hours. In this way, a weather forecast suitable for the pet can be provided based on the health condition of the user's pet.

[0042] The traffic information providing unit can analyze past traffic data and the user's movement patterns to propose the optimal route. In the traffic information providing unit, for example, the generation AI analyzes traffic data from the past year and the user's movement patterns to propose the optimal route for a specific time period or day of the week. For example, it proposes a route that avoids traffic congestion during rush hour. The generation AI also proposes the optimal route for a specific time period or day of the week based on the user's past movement patterns. For example, it proposes the optimal route for weekend travel. The generation AI also analyzes past traffic data and the user's movement patterns to propose the optimal route for a specific time period or day of the week. For example, it proposes a route that avoids traffic congestion during rush hour. This makes it possible to propose the optimal route based on past traffic data and the user's movement patterns.

[0043] The traffic information providing unit can propose the most economical route based on the user's vehicle data. In the traffic information providing unit, for example, the generation AI proposes a route with good fuel efficiency based on the user's vehicle data. For example, it proposes a route that can be driven at a speed with good fuel efficiency. The generation AI also considers the user's vehicle data and proposes the most economical route. For example, it proposes a route with good fuel efficiency. The generation AI also proposes a route that allows the user to travel most economically based on the vehicle data. For example, it proposes a route with good fuel efficiency. In this way, the most economical route can be proposed based on the user's vehicle data.

[0044] The traffic information providing unit can suggest the optimal means of transportation based on the user's public transportation usage. In the traffic information providing unit, for example, the generation AI suggests the optimal means of transportation based on the user's public transportation usage. For example, it suggests a route that is convenient to use a train or bus. The generation AI also considers the user's public transportation usage and suggests the optimal means of transportation. For example, it suggests a route that is convenient to use a train or bus. The generation AI also considers the user's public transportation usage and suggests the most convenient means of transportation for the user based on the public transportation usage. For example, it suggests a route that is convenient to use a train or bus. In this way, it is possible to suggest the optimal means of transportation based on the user's public transportation usage.

[0045] The traffic information providing unit can suggest a health-conscious route based on the user's cycling or walking movements. In the traffic information providing unit, for example, the generation AI takes into consideration the user's cycling or walking movements and suggests a health-conscious route. For example, it suggests a route that is convenient for cycling. The generation AI also takes into consideration the user's cycling or walking movements and suggests a health-conscious route. For example, it suggests a route that is convenient for walking. The generation AI also suggests a route that is most health-conscious for the user based on cycling or walking movements. For example, it suggests a route that is convenient for cycling. In this way, it is possible to suggest a health-conscious route based on the user's cycling or walking movements.

[0046] The real-time information acquisition unit acquires the user's real-time biometric data and can propose an optimal schedule. For example, the real-time information acquisition unit proposes an optimal schedule based on heart rate data acquired by the generation AI from the user's wearable device. For example, it proposes relaxing activities during times when the heart rate is high. The generation AI also proposes an optimal schedule taking the user's real-time biometric data into consideration. For example, it sets important tasks for times when the heart rate is low. The generation AI also proposes a schedule that will allow the user to stay the healthiest based on the biometric data. For example, it proposes relaxing activities during times when the heart rate is high. In this way, it is possible to propose an optimal schedule based on the user's real-time biometric data.

[0047] The real-time information acquisition unit can acquire the user's real-time location information and propose the optimal travel route. For example, the real-time information acquisition unit proposes the optimal travel route based on real-time location information acquired by the generation AI from the user's smartphone. For example, it proposes the shortest route from the current location to the destination. The generation AI also considers the user's real-time location information and proposes the optimal travel route based on traffic conditions and weather information. For example, it proposes a route that avoids traffic jams or rain. The generation AI also acquires the user's location information in real time and proposes the optimal travel route taking into account the usage status of public transportation. For example, it provides train and bus transfer information. This makes it possible to propose the optimal travel route based on the user's real-time location information.

[0048] The real-time information acquisition unit can analyze the user's real-time social media activity and suggest an optimal schedule. In the real-time information acquisition unit, for example, the generation AI analyzes the user's social media activity in real time and suggests an optimal schedule. For example, it suggests a time to check social media based on the user's most frequent social media activity. The generation AI also suggests an optimal schedule for a specific time period based on the user's social media activity. For example, it sets important tasks for the time period when the user is most active. The generation AI also analyzes the user's social media activity in real time and suggests an optimal schedule. For example, it suggests a time to check social media based on the time period when the user is most relaxed. This makes it possible to suggest an optimal schedule based on the user's real-time social media activity.

[0049] The real-time information acquisition unit can analyze the user's real-time purchasing history and propose an optimal shopping schedule. In the real-time information acquisition unit, for example, the generation AI analyzes the user's purchasing history in real time and proposes an optimal shopping schedule. For example, it proposes a shopping schedule based on the time periods when the user frequently shops. The generation AI also proposes an optimal shopping schedule for a specific time period based on the user's purchasing history. For example, it sets shopping times when the user is least busy. The generation AI also analyzes the user's purchasing history in real time and proposes an optimal shopping schedule. For example, it proposes a shopping schedule based on the time periods when the user is most relaxed. In this way, it is possible to propose an optimal shopping schedule based on the user's real-time purchasing history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The schedule adjustment unit can automatically insert a schedule for refreshing based on the user's hobbies and interests. For example, the generation AI learns the user's hobbies and interests and automatically inserts a schedule for refreshing. If the user likes movies, it will suggest a weekend movie viewing schedule. The generation AI will also suggest regular refreshing activities based on the user's interests. If the user likes the outdoors, it will insert a hiking or camping schedule. Furthermore, the generation AI will take into account the hobbies and interests and set a schedule for refreshing during a time when the user can relax. If the user likes music, it will suggest a music festival schedule. In this way, it is possible to automatically insert a schedule for refreshing based on the user's hobbies and interests.

[0052] The task management unit can automatically add tasks based on the user's long-term goals or career plan. For example, the generation AI learns the user's long-term goals and career plan and automatically adds tasks based on them. If the user is aiming to advance their career, it will suggest tasks for acquiring related skills. The generation AI also sets tasks to regularly check progress based on the user's career plan. It adds a task to review the career plan every six months. Furthermore, the generation AI sets steps toward long-term goals as specific tasks and supports the user in moving toward their goals. It sets project milestones as tasks. This allows tasks to be automatically added based on the user's long-term goals and career plan.

[0053] The weather forecast providing unit can provide a weather forecast for a specific location based on the user's travel plans and event schedule. For example, the generation AI provides a weather forecast for a travel destination based on the user's travel plans. If the weather at the travel destination is bad, an alternative plan is suggested. The generation AI also provides a weather forecast for a specific location based on the user's event schedule. On the day of an outdoor event, it suggests necessary measures based on the weather forecast. Furthermore, the generation AI provides a weather forecast for a specific location based on the travel plans and event schedule. If the weather at the travel destination is good, it suggests tourist spots. This makes it possible to provide a weather forecast for a specific location based on the user's travel plans and event schedule.

[0054] The traffic information provision unit can suggest the optimal means of transportation based on the user's public transportation usage. For example, the generation AI suggests the optimal means of transportation based on the user's public transportation usage. It suggests routes that are convenient for using trains or buses. The generation AI also takes into account the user's public transportation usage and suggests the optimal means of transportation. It suggests routes that are convenient for using trains or buses. The generation AI also suggests the most convenient means of transportation for the user based on the user's public transportation usage. It suggests routes that are convenient for using trains or buses. This makes it possible to suggest the optimal means of transportation based on the user's public transportation usage.

[0055] The real-time information acquisition unit can analyze a user's real-time social media activity and suggest an optimal schedule. For example, the generation AI analyzes a user's social media activity in real time and suggests an optimal schedule. It suggests times to check social media based on the user's most frequent social media activity. The generation AI also suggests an optimal schedule for specific time periods based on the user's social media activity. It sets important tasks for times when the user is most active. The generation AI also analyzes a user's social media activity in real time and suggests an optimal schedule. It suggests times to check social media when the user is most relaxed. This makes it possible to suggest an optimal schedule based on the user's real-time social media activity.

[0056] The real-time information acquisition unit can analyze the user's real-time purchasing history and suggest an optimal shopping schedule. For example, the generation AI analyzes the user's purchasing history in real time and suggests an optimal shopping schedule. It suggests a shopping plan based on the time periods when the user frequently shops. The generation AI also suggests an optimal shopping schedule for a specific time period based on the user's purchasing history. It sets shopping for the time periods when the user is least busy. Furthermore, the generation AI analyzes the user's purchasing history in real time and suggests an optimal shopping schedule. It suggests a shopping plan for the time periods when the user is most relaxed. This makes it possible to suggest an optimal shopping schedule based on the user's real-time purchasing history.

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

[0058] Step 1: The schedule adjustment unit adjusts the schedule. For example, the generation AI can work with the user's calendar app to automatically suggest a new time if a meeting is rescheduled and notify all parties involved. The generation AI can also analyze the user's past schedule history to learn and suggest optimal schedule adjustment patterns. Furthermore, the generation AI can also take into account the user's health data to suggest optimal schedules. Step 2: The task management unit manages tasks. For example, the generation AI determines the priority of tasks added by the user and sets reminders at appropriate times. The generation AI can also analyze the user's past task completion data to learn and suggest optimal task management patterns. Furthermore, the generation AI can also take into account the user's energy level and suggest optimal task schedules. Step 3: The weather forecast provider provides a weather forecast. For example, the generation AI provides a weather forecast based on the user's schedule and suggests necessary measures if an outdoor event is planned. The generation AI can also analyze past weather data and the user's behavioral patterns to provide an optimal weather forecast. Furthermore, the generation AI can take into account the user's health data and provide a health-conscious weather forecast. Step 4: The traffic information provider provides traffic information. For example, the generation AI provides traffic information about the user's travel and suggests the optimal route if traffic congestion is expected when commuting to work. The generation AI can also analyze past traffic data and the user's travel patterns to suggest the optimal route. Furthermore, the generation AI can also take into account the user's vehicle data to suggest the most economical route. Step 5: The real-time information acquisition unit acquires information in real time and optimizes the schedule. For example, the generation AI can quickly respond to unexpected issues, such as a sudden meeting cancellation or a delay due to a traffic accident, and propose a new schedule. The generation AI can also acquire the user's real-time biometric data and propose an optimal schedule. Furthermore, the generation AI can acquire the user's real-time location information and propose the optimal travel route.

[0059] (Example 2) An AI platform according to an embodiment of the present invention is a system that optimizes a user's schedule in cooperation with a calendar app and IoT devices. This system adjusts appointments, manages tasks, provides weather forecasts, provides traffic information, obtains real-time information, and optimizes the schedule. This allows the AI ​​platform to optimize the user's schedule and quickly respond to unexpected problems and troubles.

[0060] The AI ​​platform according to the embodiment includes a schedule adjustment unit, a task management unit, a weather forecast providing unit, a traffic information providing unit, and a real-time information acquisition unit. The schedule adjustment unit adjusts schedules. For example, the generation AI works with a user's calendar app to automatically propose a new time when a meeting is rescheduled and notify all relevant parties. The generation AI can also analyze the user's past schedule history to learn and propose optimal schedule adjustment patterns. The generation AI can also consider the user's health data to propose optimal schedules. The task management unit manages tasks. For example, the generation AI can determine the priority of tasks added by the user and set reminders at appropriate times. The generation AI can also analyze the user's past task completion data to learn and propose optimal task management patterns. The generation AI can also consider the user's energy level to propose optimal task schedules. The weather forecast providing unit provides weather forecasts. For example, the generation AI can provide a weather forecast based on the user's schedule and suggest necessary measures if an outdoor event is planned. The generation AI can also analyze past weather data and the user's behavioral patterns to provide optimal weather forecasts. Furthermore, the generation AI can also provide health-conscious weather forecasts by taking into account the user's health data. The traffic information provision unit provides traffic information. For example, the generation AI can provide traffic information about the user's travel and suggest the optimal route if traffic congestion is expected when commuting to work. The generation AI can also analyze past traffic data and the user's travel patterns to suggest the optimal route. Furthermore, the generation AI can suggest the most economical route by taking into account the user's vehicle data. The real-time information acquisition unit acquires information in real time and optimizes the schedule. For example, the generation AI can quickly respond to unexpected problems, such as a sudden meeting cancellation or a delay due to a traffic accident, and propose a new schedule. The generation AI can also acquire the user's real-time biometric data to suggest the optimal schedule.Furthermore, the generation AI can obtain the user's real-time location information and propose optimal travel routes. This allows the AI ​​platform according to the embodiment to optimize the user's schedule and quickly respond to unexpected problems or issues. For example, the output unit notifies the user of schedule changes or proposals. The user can check the schedule optimization in real time through a web application or mobile application. Notifications can also be sent by email, allowing the user to respond quickly.

[0061] The schedule adjustment unit can analyze the user's past schedule history, learn, and suggest optimal schedule adjustment patterns. For example, the generation AI in the schedule adjustment unit analyzes the user's schedule history over the past year and learns patterns of frequent meetings and events. This allows it to suggest the optimal time slot when a similar appointment occurs. The generation AI can also learn the user's tendency to schedule appointments on specific days of the week or at specific times based on the user's past schedule history, and suggest new appointments based on that pattern. For example, it could schedule a meeting every Monday morning. The generation AI can also analyze the user's past schedule history and make adjustments to allow for leeway before and after specific events or meetings. For example, it could reserve breaks before and after important presentations. This allows it to suggest optimal schedules based on the user's past schedule history.

[0062] The schedule adjustment unit can suggest optimal schedules based on the user's health data. For example, the generation AI analyzes sleep data obtained from the user's wearable device and suggests scheduling an important meeting in the morning on a day when the user has had enough sleep. The generation AI also monitors the user's stress level and prioritizes suggestions for relaxing schedules during times of high stress. For example, it suggests taking a vacation to refresh yourself during a week of high stress. The generation AI also suggests scheduling important tasks or meetings based on the user's health data during times when the user has the most energy. For example, it schedules a presentation in the afternoon when energy levels are high. This makes it possible to suggest optimal schedules based on the user's health condition.

[0063] The schedule adjustment unit can use the emotion estimation function to analyze the user's emotional state and adjust the schedule to reduce stress. For example, the schedule adjustment unit uses the emotion estimation function to prioritize relaxing schedules when the user is feeling stressed. For example, it can schedule massages or yoga during weeks when stress is high. The emotion estimation function can also be used to monitor the user's emotional state in real time and suggest postponing an important meeting if the user's negative emotions are strong. For example, it can prioritize light tasks on days with a low emotion score. The emotion estimation function can also be used to adjust the schedule to a time when the user is most relaxed. For example, it can suggest a refreshing walk in the evening when the emotion score is high. In this way, the schedule can be adjusted to reduce stress based on the user's emotional state.

[0064] The schedule adjustment unit also works with the schedules of the user's family and friends to optimize everyone's schedules. For example, the generation AI works with the calendar apps of the user's family and friends to integrate everyone's schedules and propose optimal plans. For example, it suggests weekend events that the whole family can attend. The generation AI also takes into account the schedules of the user's friends and family and sets plans for common free time. For example, it adjusts plans for dinner with friends to fit everyone's free time. The generation AI also works with the schedules of family and friends to set reminders to prevent important events and anniversaries from being forgotten. For example, family birthdays and wedding anniversaries are automatically added to the calendar. This makes it possible to optimize everyone's schedules by working with the schedules of the user's family and friends.

[0065] The schedule adjustment unit can automatically insert a schedule for refreshing based on the user's hobbies or interests. In the schedule adjustment unit, for example, the generation AI learns the user's hobbies and interests and automatically inserts a schedule for refreshing. For example, if the user likes movies, it suggests a weekend movie viewing schedule. The generation AI also suggests activities that can refresh the user regularly based on the user's interests. For example, if the user likes the outdoors, it inserts a hiking or camping schedule. The generation AI also takes into account the hobbies and interests and sets a schedule for refreshing during a time when the user can relax. For example, if the user likes music, it suggests a music festival schedule. In this way, it is possible to automatically insert a schedule for refreshing based on the user's hobbies and interests.

[0066] The task management unit can analyze a user's past task completion data, learn, and suggest optimal task management patterns. For example, the task management unit's generation AI analyzes a user's task completion data from the past year and learns patterns of frequently performed tasks. This allows it to suggest optimal time periods when similar tasks arise. The generation AI can also learn a user's tendency to complete tasks on specific days of the week or at specific times based on the user's past task completion data, and suggest new tasks based on these patterns. For example, it could set important tasks for every Monday morning. The generation AI can also analyze a user's past task completion data and make adjustments to allow for leeway before and after specific tasks. For example, it could reserve breaks before and after an important presentation. This allows it to suggest optimal task management patterns based on the user's past task completion data.

[0067] The task management unit can suggest an optimal task schedule based on the user's energy level. For example, the generation AI analyzes activity meter data obtained from the user's wearable device and suggests setting important tasks for times when energy levels are high. The generation AI also monitors the user's energy level and prioritizes lighter tasks when energy levels are low. For example, lighter tasks are prioritized during weeks when energy levels are low. The generation AI also suggests setting important tasks for times when the user has the most energy, based on the activity meter data. For example, setting a presentation in the afternoon when energy levels are high. This makes it possible to suggest an optimal task schedule based on the user's energy level.

[0068] The task management unit can use the emotion estimation function to analyze the user's emotional state and manage tasks to increase motivation. For example, the task management unit uses the emotion estimation function to prioritize important tasks when the user is feeling motivated. For example, an important project is advanced in a week when the emotion score is high. The emotion estimation function is also used to monitor the user's emotional state in real time and suggests prioritizing lighter tasks when negative emotions are strong. For example, lighter tasks are prioritized on days when the emotion score is low. The emotion estimation function is also used to set important tasks for times when the user feels most motivated. For example, important tasks are set in the morning when the emotion score is high. This allows task management to increase motivation based on the user's emotional state.

[0069] The task management unit also works with the tasks of the user's colleagues and team members at work, optimizing overall task management. For example, the task management unit's generation AI works with the task management tools of the user's colleagues and team members to integrate all tasks and propose an optimal schedule. For example, it suggests a meeting time that all team members can attend. The generation AI also takes into account the tasks of the user's colleagues and team members and sets tasks during common free time. For example, it suggests a brainstorming session that all team members can participate in. The generation AI also works with the tasks of colleagues and team members to keep track of the progress of important projects in real time and adjust tasks as needed. For example, it rearranges tasks as the project progresses. This allows the generation AI to work with the tasks of the user's colleagues and team members at work to optimize overall task management.

[0070] The task management unit can automatically add tasks based on the user's long-term goals or career plan. For example, the generation AI learns the user's long-term goals and career plan and automatically adds tasks based on them. For example, if the user is aiming to advance their career, it will suggest tasks for acquiring related skills. The generation AI also sets tasks to check progress periodically based on the user's career plan. For example, it adds a task to review the career plan every six months. The generation AI also sets steps toward long-term goals as specific tasks and supports the user in moving toward their goals. For example, it sets project milestones as tasks. This makes it possible to automatically add tasks based on the user's long-term goals and career plan.

[0071] The task management unit can use the emotion estimation function to assign tasks to time periods when the user can concentrate best. For example, the task management unit uses the emotion estimation function to set important tasks to time periods when the user can concentrate best. For example, important tasks are set in the morning when the emotion score is high. The emotion estimation function is also used to monitor the user's emotional state in real time and assign tasks to time periods when concentration is high. For example, preparing for a presentation is done during time periods when the emotion score is high. The emotion estimation function is also used to assign tasks to time periods when the user can concentrate best. For example, important tasks are set in the afternoon when the emotion score is high. This makes it possible to assign tasks to time periods when the user can concentrate best.

[0072] The weather forecast providing unit can analyze past weather data and user behavior patterns to provide the optimal weather forecast. In the weather forecast providing unit, for example, the generation AI analyzes weather data and user behavior patterns from the past year to learn behavior under specific weather conditions. This allows it to suggest optimal behavior for days when similar weather is expected. The generation AI also suggests optimal behavior under specific weather conditions based on the user's past behavior patterns. For example, it suggests indoor activities on rainy days. The generation AI also analyzes past weather data and user behavior patterns to suggest optimal behavior under specific weather conditions. For example, it suggests outdoor activities on sunny days. This allows it to provide the optimal weather forecast based on past weather data and user behavior patterns.

[0073] The weather forecast providing unit can provide a health-conscious weather forecast based on the user's health data. For example, the generation AI of the weather forecast providing unit may suggest refraining from going outside on days with high pollen counts based on the user's allergy information. For example, it may suggest indoor activities on days with high pollen counts. The generation AI may also take the user's health data into consideration and make suggestions to avoid health risks under specific weather conditions. For example, it may suggest cold weather protection measures on days when the temperature drops suddenly. The generation AI may also provide a weather forecast based on the health data that helps the user stay healthy. For example, it may suggest using sunscreen on days with strong UV rays. In this way, it is possible to provide a health-conscious weather forecast based on the user's health data.

[0074] The weather forecast providing unit can use the emotion estimation function to analyze the emotional state of the user and provide a weather forecast to improve the user's mood. The weather forecast providing unit, for example, uses the emotion estimation function to provide a weather forecast to improve the user's mood. For example, on days when the emotion score is low, activities for sunny days are suggested. The emotion estimation function is also used to monitor the user's emotional state in real time and provide a weather forecast to improve the user's mood. For example, on days when the emotion score is low, relaxing activities are suggested. The emotion estimation function is also used to provide a weather forecast to improve the user's mood. For example, outdoor activities are suggested on days when the emotion score is high. In this way, a weather forecast to improve the user's mood can be provided based on the user's emotional state.

[0075] The weather forecast providing unit can provide a weather forecast for a specific location based on the user's travel plans and event schedule. In the weather forecast providing unit, for example, the generation AI provides a weather forecast for a travel destination based on the user's travel plans. For example, if the weather at the travel destination is bad, an alternative plan is suggested. The generation AI also provides a weather forecast for a specific location based on the user's event schedule. For example, on the day of an outdoor event, it suggests necessary measures based on the weather forecast. The generation AI also provides a weather forecast for a specific location based on the travel plans and event schedule. For example, if the weather at the travel destination is good, it suggests tourist spots. In this way, it is possible to provide a weather forecast for a specific location based on the user's travel plans and event schedule.

[0076] The weather forecast providing unit can provide a weather forecast suitable for the user's pet based on the health condition of the pet. For example, the generation AI of the weather forecast providing unit provides a weather forecast suitable for the pet based on the health condition of the user's pet. For example, if the pet is sensitive to heat, it suggests taking the pet for a walk during cooler hours. The generation AI also takes the health condition of the user's pet into consideration and makes suggestions to avoid health risks under specific weather conditions. For example, it suggests indoor activities on rainy days. The generation AI also provides a weather forecast that will keep the pet comfortable based on the pet's health condition. For example, if the pet is sensitive to cold, it suggests taking the pet for a walk during warmer hours. In this way, a weather forecast suitable for the pet can be provided based on the health condition of the user's pet.

[0077] The weather forecast providing unit can use the emotion estimation function to suggest weather conditions that will be most comfortable for the user. The weather forecast providing unit, for example, uses the emotion estimation function to suggest weather conditions that will be most comfortable for the user. For example, on days with a high emotion score, activities for sunny days are suggested. The emotion estimation function is also used to monitor the user's emotional state in real time and suggest weather conditions that will be comfortable for the user. For example, on days with a low emotion score, relaxing activities are suggested. The emotion estimation function is also used to suggest weather conditions that will be most comfortable for the user. For example, outdoor activities are suggested on days with a high emotion score. In this way, it is possible to suggest weather conditions that will be most comfortable for the user based on the user's emotional state.

[0078] The traffic information providing unit can analyze past traffic data and the user's movement patterns to propose the optimal route. In the traffic information providing unit, for example, the generation AI analyzes traffic data from the past year and the user's movement patterns to propose the optimal route for a specific time period or day of the week. For example, it proposes a route that avoids traffic congestion during rush hour. The generation AI also proposes the optimal route for a specific time period or day of the week based on the user's past movement patterns. For example, it proposes the optimal route for weekend travel. The generation AI also analyzes past traffic data and the user's movement patterns to propose the optimal route for a specific time period or day of the week. For example, it proposes a route that avoids traffic congestion during rush hour. This makes it possible to propose the optimal route based on past traffic data and the user's movement patterns.

[0079] The traffic information providing unit can propose the most economical route based on the user's vehicle data. In the traffic information providing unit, for example, the generation AI proposes a route with good fuel efficiency based on the user's vehicle data. For example, it proposes a route that can be driven at a speed with good fuel efficiency. The generation AI also considers the user's vehicle data and proposes the most economical route. For example, it proposes a route with good fuel efficiency. The generation AI also proposes a route that allows the user to travel most economically based on the vehicle data. For example, it proposes a route with good fuel efficiency. In this way, the most economical route can be proposed based on the user's vehicle data.

[0080] The traffic information providing unit can use the emotion estimation function to analyze the emotional state of the user and suggest a route to reduce stress. The traffic information providing unit, for example, uses the emotion estimation function to suggest a relaxing route when the user is feeling stressed. For example, on days when the emotion score is low, a route that avoids traffic jams is suggested. The emotion estimation function can also be used to monitor the user's emotional state in real time and suggest a route to reduce stress. For example, on days when the emotion score is low, a route with a beautiful view is suggested. The emotion estimation function can also be used to suggest a route that allows the user to reduce stress. For example, on days when the emotion score is high, a relaxing route is suggested. In this way, a route to reduce stress can be suggested based on the user's emotional state.

[0081] The traffic information providing unit can suggest the optimal means of transportation based on the user's public transportation usage. In the traffic information providing unit, for example, the generation AI suggests the optimal means of transportation based on the user's public transportation usage. For example, it suggests a route that is convenient to use a train or bus. The generation AI also considers the user's public transportation usage and suggests the optimal means of transportation. For example, it suggests a route that is convenient to use a train or bus. The generation AI also considers the user's public transportation usage and suggests the most convenient means of transportation for the user based on the public transportation usage. For example, it suggests a route that is convenient to use a train or bus. In this way, it is possible to suggest the optimal means of transportation based on the user's public transportation usage.

[0082] The traffic information providing unit can suggest a health-conscious route based on the user's cycling or walking movements. In the traffic information providing unit, for example, the generation AI takes into consideration the user's cycling or walking movements and suggests a health-conscious route. For example, it suggests a route that is convenient for cycling. The generation AI also takes into consideration the user's cycling or walking movements and suggests a health-conscious route. For example, it suggests a route that is convenient for walking. The generation AI also suggests a route that is most health-conscious for the user based on cycling or walking movements. For example, it suggests a route that is convenient for cycling. In this way, it is possible to suggest a health-conscious route based on the user's cycling or walking movements.

[0083] The traffic information providing unit can use the emotion estimation function to suggest the most relaxing means of transportation for the user. The traffic information providing unit, for example, uses the emotion estimation function to suggest the most relaxing means of transportation for the user. For example, on days when the emotion score is high, it suggests using a train or bus. In addition, it uses the emotion estimation function to monitor the user's emotional state in real time and suggest a relaxing means of transportation. For example, on days when the emotion score is low, it suggests traveling by bicycle or walking. In addition, it uses the emotion estimation function to suggest the most relaxing means of transportation for the user. For example, on days when the emotion score is high, it suggests using a train or bus. In this way, it is possible to suggest the most relaxing means of transportation based on the user's emotional state.

[0084] The real-time information acquisition unit acquires the user's real-time biometric data and can propose an optimal schedule. For example, the real-time information acquisition unit proposes an optimal schedule based on heart rate data acquired by the generation AI from the user's wearable device. For example, it proposes relaxing activities during times when the heart rate is high. The generation AI also proposes an optimal schedule taking the user's real-time biometric data into consideration. For example, it sets important tasks for times when the heart rate is low. The generation AI also proposes a schedule that will allow the user to stay the healthiest based on the biometric data. For example, it proposes relaxing activities during times when the heart rate is high. In this way, it is possible to propose an optimal schedule based on the user's real-time biometric data.

[0085] The real-time information acquisition unit can acquire the user's real-time location information and propose the optimal travel route. For example, the real-time information acquisition unit proposes the optimal travel route based on real-time location information acquired by the generation AI from the user's smartphone. For example, it proposes the shortest route from the current location to the destination. The generation AI also considers the user's real-time location information and proposes the optimal travel route based on traffic conditions and weather information. For example, it proposes a route that avoids traffic jams or rain. The generation AI also acquires the user's location information in real time and proposes the optimal travel route taking into account the usage status of public transportation. For example, it provides train and bus transfer information. This makes it possible to propose the optimal travel route based on the user's real-time location information.

[0086] The real-time information acquisition unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest a schedule for reducing stress. The real-time information acquisition unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest a schedule for reducing stress. For example, on days when the emotion score is low, it suggests a relaxing activity. The emotion estimation function can also be used to monitor the user's emotional state in real time and suggest a schedule for reducing stress. For example, on days when the emotion score is low, it prioritizes light tasks. The emotion estimation function can also be used to adjust the schedule to a time period when the user is most relaxed. For example, it can suggest a refreshing walk in the evening when the emotion score is high. In this way, a schedule for reducing stress can be suggested based on the user's emotional state.

[0087] The real-time information acquisition unit can analyze the user's real-time social media activity and suggest an optimal schedule. In the real-time information acquisition unit, for example, the generation AI analyzes the user's social media activity in real time and suggests an optimal schedule. For example, it suggests a time to check social media based on the user's most frequent social media activity. The generation AI also suggests an optimal schedule for a specific time period based on the user's social media activity. For example, it sets important tasks for the time period when the user is most active. The generation AI also analyzes the user's social media activity in real time and suggests an optimal schedule. For example, it suggests a time to check social media based on the time period when the user is most relaxed. This makes it possible to suggest an optimal schedule based on the user's real-time social media activity.

[0088] The real-time information acquisition unit can analyze the user's real-time purchasing history and propose an optimal shopping schedule. In the real-time information acquisition unit, for example, the generation AI analyzes the user's purchasing history in real time and proposes an optimal shopping schedule. For example, it proposes a shopping schedule based on the time periods when the user frequently shops. The generation AI also proposes an optimal shopping schedule for a specific time period based on the user's purchasing history. For example, it sets shopping times when the user is least busy. The generation AI also analyzes the user's purchasing history in real time and proposes an optimal shopping schedule. For example, it proposes a shopping schedule based on the time periods when the user is most relaxed. In this way, it is possible to propose an optimal shopping schedule based on the user's real-time purchasing history.

[0089] The real-time information acquisition unit can use the emotion estimation function to adjust the schedule to a time period when the user can be most relaxed. The real-time information acquisition unit, for example, uses the emotion estimation function to adjust the schedule to a time period when the user can be most relaxed. For example, a refreshing walk is suggested in the evening when the emotion score is high. The emotion estimation function is also used to monitor the user's emotional state in real time and adjust the schedule to a time period when the user can be most relaxed. For example, light tasks are prioritized on days when the emotion score is low. The emotion estimation function is also used to adjust the schedule to a time period when the user can be most relaxed. For example, a refreshing walk is suggested in the evening when the emotion score is high. In this way, the schedule can be adjusted to a time period when the user can be most relaxed based on the user's emotional state.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The schedule adjustment unit can automatically insert a schedule for refreshing based on the user's hobbies and interests. For example, the generation AI learns the user's hobbies and interests and automatically inserts a schedule for refreshing. If the user likes movies, it will suggest a weekend movie viewing schedule. The generation AI will also suggest regular refreshing activities based on the user's interests. If the user likes the outdoors, it will insert a hiking or camping schedule. Furthermore, the generation AI will take into account the hobbies and interests and set a schedule for refreshing during a time when the user can relax. If the user likes music, it will suggest a music festival schedule. In this way, it is possible to automatically insert a schedule for refreshing based on the user's hobbies and interests.

[0092] The task management unit can automatically add tasks based on the user's long-term goals or career plan. For example, the generation AI learns the user's long-term goals and career plan and automatically adds tasks based on them. If the user is aiming to advance their career, it will suggest tasks for acquiring related skills. The generation AI also sets tasks to regularly check progress based on the user's career plan. It adds a task to review the career plan every six months. Furthermore, the generation AI sets steps toward long-term goals as specific tasks and supports the user in moving toward their goals. It sets project milestones as tasks. This allows tasks to be automatically added based on the user's long-term goals and career plan.

[0093] The task management unit can use the emotion estimation function to analyze the user's emotional state and manage tasks to increase motivation. For example, the emotion estimation function can be used to prioritize important tasks when the user is feeling motivated. Important projects can be progressed in weeks when the emotion score is high. The emotion estimation function can also be used to monitor the user's emotional state in real time and suggest prioritizing lighter tasks when negative emotions are strong. Lighter tasks are prioritized on days when the emotion score is low. Furthermore, the emotion estimation function can be used to set important tasks for times when the user feels most motivated. Important tasks can be set in the morning when the emotion score is high. This allows task management to increase motivation based on the user's emotional state.

[0094] The weather forecast providing unit can provide a weather forecast for a specific location based on the user's travel plans and event schedule. For example, the generation AI provides a weather forecast for a travel destination based on the user's travel plans. If the weather at the travel destination is bad, an alternative plan is suggested. The generation AI also provides a weather forecast for a specific location based on the user's event schedule. On the day of an outdoor event, it suggests necessary measures based on the weather forecast. Furthermore, the generation AI provides a weather forecast for a specific location based on the travel plans and event schedule. If the weather at the travel destination is good, it suggests tourist spots. This makes it possible to provide a weather forecast for a specific location based on the user's travel plans and event schedule.

[0095] The weather forecast providing unit can use the emotion estimation function to analyze the emotional state of the user and provide a weather forecast to improve the user's mood. For example, the emotion estimation function is used to provide a weather forecast to improve the user's mood. On days with a low emotion score, sunny day activities are suggested. Also, the emotion estimation function is used to monitor the user's emotional state in real time and provide a weather forecast to improve the user's mood. On days with a low emotion score, relaxing activities are suggested. Furthermore, the emotion estimation function is used to provide a weather forecast to improve the user's mood. On days with a high emotion score, outdoor activities are suggested. In this way, a weather forecast to improve the user's mood can be provided based on the user's emotional state.

[0096] The traffic information provision unit can suggest the optimal means of transportation based on the user's public transportation usage. For example, the generation AI suggests the optimal means of transportation based on the user's public transportation usage. It suggests routes that are convenient for using trains or buses. The generation AI also takes into account the user's public transportation usage and suggests the optimal means of transportation. It suggests routes that are convenient for using trains or buses. The generation AI also suggests the most convenient means of transportation for the user based on the user's public transportation usage. It suggests routes that are convenient for using trains or buses. This makes it possible to suggest the optimal means of transportation based on the user's public transportation usage.

[0097] The traffic information providing unit can use the emotion estimation function to analyze the user's emotional state and suggest routes to reduce stress. For example, the emotion estimation function can be used to suggest a relaxing route when the user is feeling stressed. For example, the emotion estimation function can be used to suggest a route that avoids traffic jams on days when the emotion score is low. The emotion estimation function can also be used to monitor the user's emotional state in real time and suggest routes to reduce stress. For example, the emotion estimation function can be used to suggest a scenic route on days when the emotion score is low. The emotion estimation function can also be used to suggest routes that allow the user to reduce stress. For example, the emotion estimation function can be used to suggest a relaxing route on days when the emotion score is high. In this way, routes to reduce stress can be suggested based on the user's emotional state.

[0098] The real-time information acquisition unit can analyze a user's real-time social media activity and suggest an optimal schedule. For example, the generation AI analyzes a user's social media activity in real time and suggests an optimal schedule. It suggests times to check social media based on the user's most frequent social media activity. The generation AI also suggests an optimal schedule for specific time periods based on the user's social media activity. It sets important tasks for times when the user is most active. The generation AI also analyzes a user's social media activity in real time and suggests an optimal schedule. It suggests times to check social media when the user is most relaxed. This makes it possible to suggest an optimal schedule based on the user's real-time social media activity.

[0099] The real-time information acquisition unit can use the emotion estimation function to adjust the schedule to a time period when the user can be most relaxed. For example, the emotion estimation function is used to adjust the schedule to a time period when the user can be most relaxed. A refreshing walk is suggested in the evening when the emotion score is high. The emotion estimation function is also used to monitor the user's emotional state in real time and adjust the schedule to a time period when the user can be most relaxed. On days when the emotion score is low, light tasks are prioritized. The emotion estimation function is also used to adjust the schedule to a time period when the user can be most relaxed. A refreshing walk is suggested in the evening when the emotion score is high. In this way, the schedule can be adjusted to a time period when the user can be most relaxed based on the user's emotional state.

[0100] The real-time information acquisition unit can analyze the user's real-time purchasing history and suggest an optimal shopping schedule. For example, the generation AI analyzes the user's purchasing history in real time and suggests an optimal shopping schedule. It suggests a shopping plan based on the time periods when the user frequently shops. The generation AI also suggests an optimal shopping schedule for a specific time period based on the user's purchasing history. It sets shopping for the time periods when the user is least busy. Furthermore, the generation AI analyzes the user's purchasing history in real time and suggests an optimal shopping schedule. It suggests a shopping plan for the time periods when the user is most relaxed. This makes it possible to suggest an optimal shopping schedule based on the user's real-time purchasing history.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The schedule adjustment unit adjusts the schedule. For example, the generation AI can work with the user's calendar app to automatically suggest a new time if a meeting is rescheduled and notify all parties involved. The generation AI can also analyze the user's past schedule history to learn and suggest optimal schedule adjustment patterns. Furthermore, the generation AI can also take into account the user's health data to suggest optimal schedules. Step 2: The task management unit manages tasks. For example, the generation AI determines the priority of tasks added by the user and sets reminders at appropriate times. The generation AI can also analyze the user's past task completion data to learn and suggest optimal task management patterns. Furthermore, the generation AI can also take into account the user's energy level and suggest optimal task schedules. Step 3: The weather forecast provider provides a weather forecast. For example, the generation AI provides a weather forecast based on the user's schedule and suggests necessary measures if an outdoor event is planned. The generation AI can also analyze past weather data and the user's behavioral patterns to provide an optimal weather forecast. Furthermore, the generation AI can take into account the user's health data and provide a health-conscious weather forecast. Step 4: The traffic information provider provides traffic information. For example, the generation AI provides traffic information about the user's travel and suggests the optimal route if traffic congestion is expected when commuting to work. The generation AI can also analyze past traffic data and the user's travel patterns to suggest the optimal route. Furthermore, the generation AI can also take into account the user's vehicle data to suggest the most economical route. Step 5: The real-time information acquisition unit acquires information in real time and optimizes the schedule. For example, the generation AI can quickly respond to unexpected issues, such as a sudden meeting cancellation or a delay due to a traffic accident, and propose a new schedule. The generation AI can also acquire the user's real-time biometric data and propose an optimal schedule. Furthermore, the generation AI can acquire the user's real-time location information and propose the optimal travel route.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 AI 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 AI 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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 AI 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 platform that works with calendar apps and IoT devices to optimize users' schedules, a schedule adjustment unit that adjusts schedules; a task management unit that manages tasks; a weather forecast providing unit that provides a weather forecast; a traffic information providing unit that provides traffic information; A real-time information acquisition unit that acquires information in real time and optimizes the schedule. A system characterized by:

2. The schedule adjustment unit Analyze the user's past schedule history, learn and propose optimal schedule adjustment patterns 2. The system of claim 1.

3. The schedule adjustment unit Propose optimal schedules based on the user's health data 2. The system of claim 1.

4. The schedule adjustment unit Analyzing the user's emotional state and adjusting schedules to reduce stress 2. The system of claim 1.

5. The schedule adjustment unit It also connects with the schedules of the user's family and friends to optimize everyone's schedules.

2. The system of claim 1.

6. The schedule adjustment unit Automatically inserting refreshment appointments based on the user's hobbies or interests 2. The system of claim 1.

7. The task management unit Analyze the user's past task completion data, learn and suggest optimal task management patterns 2. The system of claim 1.

8. The task management unit Suggesting an optimal task schedule based on the user's energy level 2. The system of claim 1.

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