system

Abstract

Provide a system. [Solution] means for acquiring task information input by a user; means for collecting environmental data and user health data; means for generating an optimal schedule based on the task information, environmental data, and health data; means for notifying a user terminal of the schedule; The system includes means for collecting feedback from users and incorporating it into the generation of said schedule.

Description

[Technical Field]

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

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

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

[0004] In today's busy lives, people need to efficiently manage their schedules and improve their productivity. Conventional schedule management is based on a fixed timetable and does not take into account individual changes in physical condition or environment, which often results in miscalculating the optimal timing for task execution. This invention aims to solve the problem of realizing efficient and effective time management for users by providing a system that collects and analyzes users' health and environmental data in real time and generates and notifies users of an optimal schedule. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system that includes a means for acquiring task information input by a user, a means for collecting environmental data and user health data, a means for generating an optimal schedule based on the task information, environmental data, and health data, a means for notifying the user of the schedule, and a means for collecting feedback from the user and reflecting it in the generation of the schedule. This allows for the provision of an optimal schedule tailored to the individual situation of the user, enabling efficient time management.

[0006] "User" refers to an individual who uses the system to manage tasks.

[0007] "Task information" refers to information about specific schedules and activities that users input into the system.

[0008] "Environmental data" refers to data relating to the external environment, such as temperature and weather.

[0009] "Health data" refers to data related to the user's physical condition, such as body temperature, heart rate, and sleep data.

[0010] "Means for generating optimal schedules" refers to algorithms and programs that calculate and create efficient schedules using collected task information, environmental data, and health data.

[0011] The "notification means" refers to a function for transmitting the generated schedule to the user's terminal.

[0012] "Means of collecting feedback" refers to the features and processes that allow users to report progress and completion of tasks.

[0013] A "machine learning algorithm" refers to a set of computational techniques that learn patterns from data and make predictions or decisions.

[0014] "Terminal" refers to electronic devices such as smartphones and wearable devices used by users. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] System Overview

[0037] The present invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, environmental data, and the user's health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[0038] Program processing overview

[0039] Collection of User Information

[0040] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0041] Data analysis and schedule generation

[0042] The server integrates the collected task information, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0043] Schedule Notifications

[0044] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[0045] Execution status feedback

[0046] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0047] Specific examples

[0048] Morning Routine

[0049] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. If the user has not had enough sleep, the system will take into consideration such things as adjusting the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00."

[0050] Each time a task is completed, the user reports it as "completed" in the app. For example, after a run, feedback such as "Running completed at 8:30" is sent. This allows the server to reschedule the next running time. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[0051] As described above, the system of the present invention integrates user input data, environmental data, and health data, and generates and notifies optimal schedules, thereby achieving efficient and effective time management.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[0055] Step 2:

[0056] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[0057] Step 3:

[0058] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[0059] Step 4:

[0060] The server integrates the collected task information, environmental data, and health data, and performs data preprocessing, including missing value handling and outlier correction.

[0061] Step 5:

[0062] The server then uses the pre-processed data to generate an optimal schedule using a machine learning algorithm, which learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0063] Step 6:

[0064] The optimal schedule generated by the server is transmitted to the terminal.

[0065] Step 7:

[0066] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0067] Step 8:

[0068] The user performs a task and marks it as "completed" on the smartphone app when finished.

[0069] Step 9:

[0070] The terminal transmits task completion information from the user to the server.

[0071] Step 10:

[0072] The server integrates the feedback information from users and reflects it in the next schedule generation, thereby improving the accuracy of the next schedule.

[0073] By executing each step in this way, the system supports the user in efficient schedule management.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] Conventional schedule management systems create schedules based solely on information entered by individual users, making it difficult to consider changes in the environment or the user's health status. As a result, they are unable to provide a schedule that is suited to the user's lifestyle or health status, which can reduce the effectiveness of the schedule. In addition, they lack a mechanism for incorporating user feedback, making it difficult to continuously improve the accuracy of the schedule.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes means for acquiring task information input by a user, means for collecting weather information and user biological information, means for generating an optimal schedule based on the task information, weather information, and biological information, means for notifying the user of the schedule, and means for collecting feedback from the user and reflecting the feedback in generating the schedule. This makes it possible to provide an optimal schedule that takes into account changes in the environment and the user's health condition, and further makes it possible to successively improve the accuracy of the schedule by reflecting the feedback from the user.

[0079] "Task information" is data indicating the details of plans and activities that a user inputs into the schedule management system.

[0080] "Weather information" refers to data related to the current temperature and weather forecast obtained from an external weather data provider service.

[0081] "Biometric information" is data that indicates the user's health condition, and specifically includes body temperature, heart rate, sleep data, and the like.

[0082] A "terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0083] The "server" is a central processing unit that generates a schedule based on collected data and notifies the user's terminal of the schedule.

[0084] "Feedback" is information about the execution status and evaluation provided by the user after completing a task.

[0085] "Machine learning algorithms" refer to algorithms that learn data patterns and make predictions or decisions based on new data.

[0086] A "schedule" is a plan or timetable that indicates what tasks a user will do and when.

[0087] This invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, weather information, and biometric information input by the user. It also collects feedback from the user and can continuously improve the appropriateness of the schedule.

[0088] System configuration

[0089] Hardware

[0090] The system includes the following hardware configuration:

[0091] Devices such as smartphones and tablets that accept user operations

[0092] Smartwatches and fitness trackers for collecting biometric information

[0093] Server that processes data

[0094] software

[0095] The system includes the following software components:

[0096] A smartphone application for users to enter tasks

[0097] Weather API for obtaining weather information (e.g. OpenWeatherMap API)

[0098] IoT device software for collecting biometric information

[0099] Machine learning algorithms for generating schedules (e.g., Tensorflow®)

[0100] Data processing and calculation methods

[0101] Collection of User Information

[0102] A user opens a smartphone app and inputs today's task information (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and receives biometric information such as body temperature, heart rate, and sleep data collected by a smartwatch or fitness tracker from the device.

[0103] Data analysis and schedule generation

[0104] The server integrates the collected task information, weather information, and biometric information and performs preprocessing. This preprocessing includes filling in missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. The machine learning algorithm learns from past data and patterns and calculates a schedule that adapts to the user's individual situation.

[0105] Schedule Notifications

[0106] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, the notification may be something like "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then perform tasks based on this notification.

[0107] Execution status feedback

[0108] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0109] Specific examples

[0110] Morning Routine

[0111] A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data. For example, if the user has not had enough sleep, the server adjusts the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Completion" in the app. For example, after a run, feedback is sent saying "Running completed at 8:30." The server collects this feedback and reflects it in the next schedule. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[0112] Prompt Sentence Examples

[0113] Today I have plans to run at 8:00, have a remote meeting at 10:00, and have lunch at 12:00. What would be the best schedule?

[0114] You slept for 6 hours last night, and the current temperature is 20°C and your body temperature is 36.5°C. Please adjust your schedule taking these into consideration.

[0115] As described above, this system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] The user opens the smartphone app and enters task information (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.).

[0119] Input: User inputs task information

[0120] Output: Input task information

[0121] Specific operation: The user enters the date, start time, end time, and task details into the app interface, then presses the submit button. This information is saved on the device.

[0122] Step 2:

[0123] The terminal transmits the task information input by the user to the server.

[0124] Input: Task information saved on the device

[0125] Output: Task information sent to the server

[0126] Specific operation: The device periodically communicates with the server and sends the saved task information to the server in JSON format. If the transmission is successful, the task information is saved in the server database.

[0127] Step 3:

[0128] The server calls a weather API (e.g., OpenWeatherMap API) to obtain weather information.

[0129] Input: API call request

[0130] Output: Retrieved weather information

[0131] Specific operation: The server accesses the weather API using the API key to obtain the current temperature and weather forecast for the specified area. This data is stored in a temporary database on the server.

[0132] Step 4:

[0133] The device collects biometric information (such as body temperature, heart rate, and sleep data) from smartwatches and fitness trackers.

[0134] Input: Smartwatch or fitness tracker data

[0135] Output: Collected biometric information

[0136] How it works: The device syncs with the smartwatch via Bluetooth or Wi-Fi to collect body temperature, heart rate, sleep data, etc. The collected data is temporarily stored inside the device.

[0137] Step 5:

[0138] The terminal transmits the collected biometric information to the server.

[0139] Input: Biometric information stored on the device

[0140] Output: Biometric information sent to the server

[0141] Specific operation: The device periodically communicates with the server and sends the stored biometric information to the server in JSON format. If the transmission is successful, the biometric information is stored in the server's database.

[0142] Step 6:

[0143] The server integrates the collected task information, weather information, and biological information and performs pre-processing.

[0144] Input: Task information, weather information, biological information

[0145] Output: Preprocessed data

[0146] How it works: The server cleanses the collected data and performs preprocessing such as filling in missing values ​​and correcting outliers. This preprocessed data is then input into the machine learning algorithm.

[0147] Step 7:

[0148] The server uses machine learning algorithms (e.g., TensorFlow) to generate an optimal schedule.

[0149] Input: Preprocessed data

[0150] Output: The generated optimal schedule

[0151] Specific operation: The server inputs the preprocessed data into the machine learning model to generate an optimal schedule, which is then sent to the device.

[0152] Step 8:

[0153] The terminal displays the generated schedule to the user as a push notification.

[0154] Input: Generated schedule

[0155] Output: Displayed push notification

[0156] Specific operation: The device analyzes the schedule received from the server and displays it on the user's smartphone as a push notification. The user confirms the notification and executes the task.

[0157] Step 9:

[0158] After users complete a task, they mark it as "done" in the smartphone app and provide feedback.

[0159] Input: User feedback on task completion

[0160] Output: Feedback recorded in the app

[0161] What it does: The user presses a button in the app to mark a task as complete and enters the completion status. This information is stored on the device.

[0162] Step 10:

[0163] The terminal sends the recorded user feedback to the server.

[0164] Input: Feedback information stored on the device

[0165] Output: Feedback information sent to the server

[0166] Specific operation: The device periodically communicates with the server and sends the saved feedback information to the server, which uses it when generating the next schedule.

[0167] Through the above processing steps, the system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, and successively improves the accuracy of the schedules based on feedback.

[0168] (Application example 1)

[0169] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0170] Conventional schedule management systems are limited to simple task management based on a user's health and environmental data, and are unable to optimize content viewing schedules to improve quality of life, let alone daily activities. While there are functions for incrementally improving schedules based on feedback, these functions could not be combined with content viewing schedule optimization. This presents additional technical challenges for effectively managing and improving a user's entire lifestyle.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0172] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data and user health data, means for generating an optimal schedule based on the task information, environmental data, and health data, means for notifying the user of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for integrating the task information, environmental data, and health data to generate and notify the user of a content viewing schedule. This makes it possible to optimize a user's tasks and content viewing schedule and improve their quality of life.

[0173] "User" refers to an individual who uses this system.

[0174] "Task information" is information that a user inputs as his or her activity schedule.

[0175] "Environmental data" refers to data that indicates meteorological information and surrounding environmental conditions.

[0176] "Health data" refers to data that indicates the user's health condition, such as body temperature, heart rate, and sleep data.

[0177] A "schedule" is a timetable generated to efficiently manage a user's tasks and activities.

[0178] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0179] "Feedback" refers to the rating or status information a user provides after completing a task or viewing.

[0180] A "content viewing schedule" is a timetable created to allow a user to view media content efficiently and comfortably.

[0181] A "machine learning algorithm" is a computational method that uses past data to learn patterns and make appropriate predictions and suggestions.

[0182] System Overview

[0183] The present invention is a system for optimizing a user's task and content viewing schedule. This system uses a machine learning algorithm to generate and notify an optimal schedule based on task information, environmental data, and user health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[0184] Program processing overview

[0185] Collection of User Information

[0186] A user opens a smartphone app and inputs their tasks and viewing preferences for the day (e.g., "Comedy at 8:00 PM" or "Documentary at 10:00 PM"). The device then sends this task and viewing information to the server. At the same time, the server obtains the current temperature and weather forecast via a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0187] Data analysis and schedule generation

[0188] The server integrates the collected task information, viewing preferences, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then uses a machine learning algorithm to generate an optimal task and viewing schedule. This algorithm learns from past data and patterns to calculate the optimal schedule for the user's individual situation.

[0189] Schedule Notifications

[0190] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Watch this week's popular comedy at 8:00 PM" or "Watch a recommended documentary at 10:00 PM." The user can then watch the content based on this notification.

[0191] Execution status feedback

[0192] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0193] Specific examples

[0194] Content viewing schedule generation

[0195] A user wakes up in the morning, opens the app, and enters their viewing preferences, such as:

[0196] "20:00 Comedy"

[0197] "22:00 Documentary"

[0198] The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. For example, if a user's fatigue level is high based on their viewing and health data, relaxing content will be recommended. The generated schedule is notified to the device as "This week's popular comedy at 8:00 PM" and "Recommended documentaries at 10:00 PM."

[0199] When a user finishes watching, the app asks them to report the completion. For example, "Completed watching comedy at 20:45" or "Completed watching documentary at 22:30." This will help the app better optimize the next viewing schedule.

[0200] Prompt Sentence Examples

[0201] Example prompts to input to a generative AI model:

[0202] User input data:

[0203] Want to watch: "20:00 Comedy" "22:00 Documentary"

[0204] Health data: {Sleep data: "6 hours", Stress level: "Slightly high"}

[0205] Environmental data: {Weather forecast: "Sunny", Temperature: "25℃"}

[0206] Past viewing history: {"20:00 Number of times watched dramas": "10 times", "22:00 Number of times watched documentaries": "5 times"}

[0207] Generate an optimal viewing schedule based on the data above.

[0208] As described above, this system improves the quality of life by optimizing the user's tasks and content viewing schedule.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1: Collect user information

[0211] A user opens a smartphone app and inputs their tasks and viewing preferences for the day. The device then sends this task information and viewing preferences to the server. The device also uses a weather API to obtain the current temperature and weather forecast, and sends health data such as body temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers to the server via the device.

[0212] Input: Task information, viewing preferences, weather data, health data

[0213] Output: User data and environmental datasets

[0214] Step 2: Preprocessing the data

[0215] The server integrates the collected task information, viewing preferences, environmental data, and health data, and performs data preprocessing, which includes filling in missing data and correcting outliers.

[0216] Input: User data and environmental datasets

[0217] Output: Preprocessed dataset

[0218] Step 3: Generate a schedule using machine learning algorithms

[0219] The server then uses machine learning algorithms to generate optimal tasks and viewing schedules based on the pre-processed dataset. The algorithms learn from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0220] Input: Preprocessed dataset

[0221] Output: Optimized task and viewing schedules

[0222] Step 4: Schedule Notification

[0223] The server sends the generated schedule to the device, which then displays it to the user as a push notification. For example, notifications such as "Watch this week's popular comedy at 8 PM" or "Watch recommended documentaries at 10 PM" are sent to the user.

[0224] Input: Optimized task and viewing schedule

[0225] Output: Schedule notification to user device

[0226] Step 5: Performance feedback

[0227] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app, and the device sends this feedback information to the server.

[0228] Input: User task and viewing completion information

[0229] Output: Feedback dataset

[0230] Step 6: Learn and improve

[0231] The server integrates the collected feedback data and uses it to retrain the machine learning model, which is then reflected in the next schedule generation, improving the accuracy of the schedule.

[0232] Input: Feedback dataset

[0233] Output: An updated machine learning model

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] System Overview

[0236] This invention is a system for improving the efficiency of user schedule management. In particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system generates and notifies the user of an optimal schedule based on task information, environmental data, health data, and emotional data input by the user. It can also collect feedback from the user and continuously improve the schedule generation.

[0237] Program processing overview

[0238] Collection of User Information

[0239] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. The server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0240] Additionally, the device collects emotional data using an emotion engine that analyzes the user's facial expressions, tone of voice, language patterns, etc., thereby taking into account the user's emotional state.

[0241] Data analysis and schedule generation

[0242] The server integrates the collected task information, environmental data, health data, and emotional data and performs data preprocessing, including processing missing values ​​and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns and calculates the optimal schedule for the user's individual situation and emotional state.

[0243] Schedule Notifications

[0244] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[0245] Execution status feedback

[0246] When a task is completed, the user marks it as "completed" through a smartphone app. The device also uses an emotion engine to collect emotional data during and after the task and sends it to the server. The server then integrates this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[0247] Specific examples

[0248] Morning Routine

[0249] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state. If the user is feeling stressed, the device will take into consideration such things as adjusting the intensity of the run to a lighter level.

[0250] The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Complete" in the app and sends emotional data during and after the task to the server via the emotion engine. This allows the server to readjust the duration and intensity of the next run.

[0251] As described above, the system of the present invention integrates user input data, environmental data, health data, and emotional data to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[0255] Step 2:

[0256] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[0257] Step 3:

[0258] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[0259] Step 4:

[0260] The device uses a built-in emotion engine to analyze the user's facial expressions, tone of voice, language patterns, etc. to collect emotion data, which is then sent to a server.

[0261] Step 5:

[0262] The server integrates the collected task information, environmental data, health data, and emotion data and performs preprocessing, which includes processing missing data and correcting outliers.

[0263] Step 6:

[0264] The server then generates an optimal schedule based on the pre-processed data using machine learning algorithms that learn from past data and patterns, and that also adapt to the user's individual situation and emotional state.

[0265] Step 7:

[0266] The optimal schedule generated by the server is transmitted to the terminal.

[0267] Step 8:

[0268] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0269] Step 9:

[0270] The user performs a task and marks it as "completed" on the smartphone app when finished.

[0271] Step 10:

[0272] The terminal transmits task completion information from the user and emotion data generated by the emotion engine to the server.

[0273] Step 11:

[0274] The server integrates the feedback information and emotion data from the user and reflects it in the next schedule generation, thereby further improving the accuracy of the next schedule.

[0275] In this way, the present system including the emotion engine provides specific procedures for realizing efficient schedule management for users.

[0276] Example 2

[0277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] Current schedule management systems often focus on simple time management without considering the user's health or emotional state. This makes it difficult to provide an optimal schedule that suits the user's physical and mental state, resulting in problems such as reduced task execution efficiency and user satisfaction. Another issue is the lack of functionality to adaptively adjust the schedule based on feedback.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0280] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data, health data, and emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user of the schedule to the user's information processing terminal, and means for collecting feedback from the user and reflecting it in the generation of the schedule, thereby making it possible to generate and provide an optimal schedule that takes into consideration the user's physical and mental state comprehensively.

[0281] "User information" refers to data related to tasks and schedules that users enter through smartphone apps, etc.

[0282] "Environmental data" refers to data related to the external environment that affects the user's activities, such as temperature, humidity, and weather forecast.

[0283] "Health data" refers to data that indicates the user's health condition, such as the user's body temperature, heart rate, and sleep patterns.

[0284] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, language patterns, and the like.

[0285] A "schedule" is a daily planner for a user that is generated based on task information entered by the user, and collected environmental data, health data, and emotional data.

[0286] A "terminal" is an information processing device used by a user, such as a smartphone or tablet.

[0287] The "server" is a central processing unit that generates a schedule based on collected data and notifies the terminals.

[0288] "Feedback" refers to information that the user sends via the terminal about the progress of the schedule and their emotional state, which is useful for generating the next schedule.

[0289] A "machine learning algorithm" is a computer program that learns from past data and execution results to generate the optimal schedule for the next time.

[0290] This invention is a system for improving the efficiency of user schedule management, and in particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system consists of multiple components, including a server, terminals, and user input means, which work in conjunction with each other.

[0291] First, the user uses the device to input task information for the day into the smartphone app. For example, specific schedules such as "running at 8:00" and "remote meeting at 10:00." This information is then sent to the server via the device.

[0292] The server has multiple ways to obtain environmental, health, and emotion data. For environmental data, it uses weather APIs (e.g., OpenWeatherMap API) to obtain current temperature and weather forecast. For health data, the device collects temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers and sends it to the server.

[0293] Furthermore, to collect emotional data, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, and language patterns. The emotion engine uses services such as the Microsoft® Azure® Emotion API to analyze the user's emotional state, such as stress, happiness, and fatigue, in real time.

[0294] The server integrates this data and performs preprocessing on the data. Missing values ​​are filled in with estimated values, and outliers are corrected using statistical methods (e.g., Z-score or IQR). Once preprocessing is complete, the data is used to generate an optimal schedule using a machine learning algorithm (e.g., Scikit-learn's DecisionTreeClassifier or a library such as TensorFlow).

[0295] The generated schedule is sent from the server to the device. The device displays this schedule to the user as a push notification. Examples of notifications include "Light running recommended at 8:00" or "Remote meeting at 10:00." Based on this notification, the user can carry out tasks.

[0296] When a task is completed, the user marks it as "completed" in the smartphone app. The device also uses an emotion engine to collect emotion data during and after the task and sends it to the server. The server analyzes this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[0297] As a concrete example, consider a morning routine. A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. Furthermore, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, and adjusts the intensity of the run to a lighter level if the stress level is high. This generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00," and the user can spend the day accordingly.

[0298] An example of a prompt is: "Please explain how a system works that generates and notifies users of an optimal schedule based on daily task information entered by the user into a smartphone app and weather, health, and emotional data collected via a server."

[0299] In this way, the system of the present invention comprehensively integrates the user's input data, environmental data, health data, and emotional data, and generates and notifies the user of an optimal schedule, thereby achieving efficient and effective time management.

[0300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0301] Step 1: Enter your user information

[0302] The user starts the smartphone app and inputs the task information for the day (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The input task information is sent to the server via the device.

[0303] Input: Task information (8:00 running, 10:00 remote meeting, etc.)

[0304] Output: Task information sent to the server

[0305] Step 2: Collect data

[0306] The device collects health data such as body temperature, heart rate, and sleep data from the smartwatch or fitness tracker and sends it to the server, which receives it and uses the weather API to retrieve the current temperature and weather forecast.

[0307] Input: Health data from smartwatches and fitness trackers, weather API keys

[0308] Output: Health data sent to the server, weather data obtained

[0309] Step 3: Collecting emotion data

[0310] The device uses the smartphone's camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns in real time to collect emotional data. The emotion engine uses existing AI services (such as Emotion API).

[0311] Input: Camera video, audio data

[0312] Output: Parsed emotion data

[0313] Step 4: Preprocessing the data

[0314] The server integrates the collected task information, environmental data, health data, and emotion data and performs data preprocessing. Specifically, it fills in missing values ​​and corrects outliers. For example, if there are missing values, it fills them in with the most recent past data. Outliers are corrected using statistical methods such as Z-score and IQR.

[0315] Input: Integrated task information, environmental data, health data, and emotional data

[0316] Output: Preprocessed data

[0317] Step 5: Generate a schedule

[0318] The server uses machine learning algorithms based on the preprocessed data to generate an optimal schedule. This uses models that have also learned from past data and patterns. Specifically, Scikit-learn's DecisionTreeClassifier and TensorFlow are used.

[0319] Input: Preprocessed data, machine learning model

[0320] Output: The generated optimal schedule

[0321] Step 6: Schedule Notification

[0322] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, it may say, "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0323] Input: Generated schedule

[0324] Output: Schedule notified to the user's device

[0325] Step 7: Performance feedback

[0326] When the task is completed, the user marks it as "completed" in the smartphone app. The device then uses the emotion engine again to collect emotion data during and after the task and sends it to the server. The server then analyzes this feedback information and reflects it in the next schedule generation.

[0327] Input: Completed task information, emotion data

[0328] Output: Feedback information reflected in the next schedule generation

[0329] The above is the specific processing flow of the program of this system and the operations performed at each step.

[0330] (Application example 2)

[0331] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0332] Although self-driving vehicles are becoming more common in modern society, there are no systems that propose optimal routes that take into account passengers' emotions and health conditions. As a result, operation plans are created that lack consideration for passenger stress and fatigue, making it difficult to provide a comfortable travel experience. In addition, generating flexible routes that take passengers' physical condition and emotions into account is complex, and a system that can do this efficiently is needed.

[0333] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring task information input by the user, means for collecting environmental data and user health data, means for collecting and analyzing emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user's terminal of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for proposing a route for the autonomous vehicle. This makes it possible to generate optimal routes and schedules based on passengers' emotions and health conditions, providing a comfortable and safe travel experience.

[0334] A "user" is a person who uses the system and is the subject of inputting emotional data, health data, task information, and the like.

[0335] "Task information" refers to information related to plans and schedules entered by the user.

[0336] "Environmental data" refers to data relating to the external conditions surrounding the user, such as weather information and traffic information.

[0337] "Health data" refers to data related to the user's physical condition, such as body temperature, heart rate, and sleep status.

[0338] "Emotion data" is data that indicates the user's emotional state, and is obtained by analyzing facial expressions, tone of voice, language patterns, and the like.

[0339] A "schedule" is a user's activity plan that is generated based on task information, environmental data, health data, and emotion data.

[0340] "Terminal" refers to any device operated by a user, including smartphones, tablets, and vehicle infotainment systems.

[0341] "Feedback" is input or evaluation provided by a user that is used to improve the system's schedule generation process.

[0342] An "autonomous vehicle" is a vehicle that operates autonomously and follows a route based on suggestions from the system.

[0343] "Route suggestion" is a system function that takes into account emotional and health data to present a route suitable for the user.

[0344] System Overview

[0345] This invention is a system that generates optimal schedules and routes for autonomous vehicles by taking into account the user's emotional and health states. Specifically, it collects task information, environmental data, health data, and emotional data from the user, and optimizes the schedule and route based on this data. This makes it possible to provide the user with a comfortable and safe travel experience.

[0346] Collection of User Information

[0347] First, a user inputs upcoming tasks into the system using a device such as a smartphone or tablet. For example, they might enter task information such as "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server simultaneously obtains environmental data such as the current temperature, weather forecast, and traffic information through a weather API, and collects health data such as heart rate, body temperature, and sleep data from a smartwatch or other device. The device then collects and analyzes emotional data, using an emotion engine to understand the user's emotional state based on facial expressions, tone of voice, and language patterns.

[0348] Data analysis and schedule generation

[0349] The server integrates the collected task information, environmental data, health data, and emotional data, and performs data preprocessing, such as filling in missing values ​​and correcting outliers. The server then uses machine learning algorithms to generate optimal schedules and routes. These algorithms learn from past data and patterns to calculate the optimal plan for the user's individual situation and emotional state.

[0350] Schedule and route notifications

[0351] The schedule and route generated by the server are sent to the device. For example, specific items such as "Meeting at 8:00," "Relax at a cafe at 10:00," and "There is a traffic jam on the central road, so we will suggest a detour" are notified. The device displays these to the user as push notifications.

[0352] Execution status feedback

[0353] After completing a task or route, the user provides feedback to the system. The device uses an emotion engine to collect emotion data during and after the task or route, and sends it to the server. The server then integrates this feedback information and reflects it in the generation of the next schedule and route. This allows the system to continuously improve its accuracy and comfort.

[0354] Hardware and software used

[0355] Hardware: Smartphones, smartwatches, in-cabin cameras and microphones in autonomous vehicles, infotainment systems

[0356] Software: Python, RESTful API, machine learning algorithms, emotion engine

[0357] Prompt Sentence Examples

[0358] The following is an example of a prompt that the system can use to obtain user emotion data:

[0359] "Tell me how you're feeling right now."

[0360] "Have you been feeling stressed lately?"

[0361] "How are you feeling today?"

[0362] As described above, this system provides a mechanism for integrating diverse user data to generate optimal schedules and routes for autonomous vehicles that take into account emotions and health conditions.

[0363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0364] Step 1:

[0365] A user inputs task information using a device such as a smartphone or tablet. The input task information may include "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server then receives the task information as input data and uses it as the basis for generating future schedules.

[0366] Step 2:

[0367] The server uses a weather API to obtain environmental data such as current temperature, weather forecast, and traffic information. At the same time, it collects health data such as heart rate, body temperature, and sleep data obtained from devices such as smartwatches. This allows the environmental data and health data to be aggregated on the server as input data.

[0368] Step 3:

[0369] The user also inputs emotional data using the device. The device uses a camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns, and generates emotional data using an emotion engine. For example, the user is asked, "Have you been feeling stressed recently?" and emotional data is collected by answering the question. This collected emotional data is sent to the server.

[0370] Step 4:

[0371] The server integrates the collected task information, environmental data, health data, and emotion data. This integrated dataset undergoes preprocessing, such as filling in missing values ​​and correcting outliers. For example, missing weather information is filled in, and abnormally high heart rate data is corrected. This preprocessing makes the dataset suitable for schedule generation.

[0372] Step 5:

[0373] Based on the pre-processed dataset, the server uses machine learning algorithms to generate optimal schedules and routes. For example, it learns from past data and newly collected data to calculate the schedule and route that best suits the user's emotional and health conditions. This calculation generates output data for the schedule and route.

[0374] Step 6:

[0375] The server sends the generated optimal schedule and driving route to the device. The device receives this information and displays it to the user as a push notification. For example, the device may notify the user with a message such as "Meeting at 8:00" and "There is traffic congestion on the central road, so we will suggest a detour." This allows the user to check the recommended schedule and driving route.

[0376] Step 7:

[0377] The user actually performs the task and inputs feedback into the device. After completing the task, the user provides feedback such as their thoughts and evaluation of the task, and emotion data is collected again through the emotion engine. This allows emotion data to be collected about the task, during operation, and after execution.

[0378] Step 8:

[0379] The collected feedback information and emotion data are sent to the server and reflected in the generation of the next schedule and route, allowing the system to continuously improve its accuracy and comfort, and provide schedules and routes that better suit the user's needs.

[0380] The above is the flow of specific processing steps of the program of the system that realizes the application example.

[0381] 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.

[0382] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0383] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0384] [Second embodiment]

[0385] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0386] 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.

[0387] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0388] 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.

[0389] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0390] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0391] 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.

[0392] 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.

[0393] 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 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.

[0394] 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.

[0395] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0396] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0397] System Overview

[0398] The present invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, environmental data, and the user's health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[0399] Program processing overview

[0400] Collection of User Information

[0401] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0402] Data analysis and schedule generation

[0403] The server integrates the collected task information, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0404] Schedule Notifications

[0405] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[0406] Execution status feedback

[0407] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0408] Specific examples

[0409] Morning Routine

[0410] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. If the user has not had enough sleep, the system will take into consideration such things as adjusting the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00."

[0411] Each time a task is completed, the user reports it as "completed" in the app. For example, after a run, feedback such as "Running completed at 8:30" is sent. This allows the server to reschedule the next running time. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[0412] As described above, the system of the present invention integrates user input data, environmental data, and health data, and generates and notifies optimal schedules, thereby achieving efficient and effective time management.

[0413] The processing flow will be explained below.

[0414] Step 1:

[0415] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[0416] Step 2:

[0417] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[0418] Step 3:

[0419] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[0420] Step 4:

[0421] The server integrates the collected task information, environmental data, and health data, and performs data preprocessing, including missing value handling and outlier correction.

[0422] Step 5:

[0423] The server then uses the pre-processed data to generate an optimal schedule using a machine learning algorithm, which learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0424] Step 6:

[0425] The optimal schedule generated by the server is transmitted to the terminal.

[0426] Step 7:

[0427] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0428] Step 8:

[0429] The user performs a task and marks it as "completed" on the smartphone app when finished.

[0430] Step 9:

[0431] The terminal transmits task completion information from the user to the server.

[0432] Step 10:

[0433] The server integrates the feedback information from users and reflects it in the next schedule generation, thereby improving the accuracy of the next schedule.

[0434] By executing each step in this way, the system supports the user in efficient schedule management.

[0435] Example 1

[0436] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0437] Conventional schedule management systems create schedules based solely on information entered by individual users, making it difficult to consider changes in the environment or the user's health status. As a result, they are unable to provide a schedule that is suited to the user's lifestyle or health status, which can reduce the effectiveness of the schedule. In addition, they lack a mechanism for incorporating user feedback, making it difficult to continuously improve the accuracy of the schedule.

[0438] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0439] In this invention, the server includes means for acquiring task information input by a user, means for collecting weather information and user biological information, means for generating an optimal schedule based on the task information, weather information, and biological information, means for notifying the user of the schedule, and means for collecting feedback from the user and reflecting the feedback in generating the schedule. This makes it possible to provide an optimal schedule that takes into account changes in the environment and the user's health condition, and further makes it possible to successively improve the accuracy of the schedule by reflecting the feedback from the user.

[0440] "Task information" is data indicating the details of plans and activities that a user inputs into the schedule management system.

[0441] "Weather information" refers to data related to the current temperature and weather forecast obtained from an external weather data provider service.

[0442] "Biometric information" is data that indicates the user's health condition, and specifically includes body temperature, heart rate, sleep data, and the like.

[0443] A "terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0444] The "server" is a central processing unit that generates a schedule based on collected data and notifies the user's terminal of the schedule.

[0445] "Feedback" is information about the execution status and evaluation provided by the user after completing a task.

[0446] "Machine learning algorithms" refer to algorithms that learn data patterns and make predictions or decisions based on new data.

[0447] A "schedule" is a plan or timetable that indicates what tasks a user will do and when.

[0448] This invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, weather information, and biometric information input by the user. It also collects feedback from the user and can continuously improve the appropriateness of the schedule.

[0449] System configuration

[0450] Hardware

[0451] The system includes the following hardware configuration:

[0452] Devices such as smartphones and tablets that accept user operations

[0453] Smartwatches and fitness trackers for collecting biometric information

[0454] Server that processes data

[0455] software

[0456] The system includes the following software components:

[0457] A smartphone application for users to enter tasks

[0458] Weather API for obtaining weather information (e.g. OpenWeatherMap API)

[0459] IoT device software for collecting biometric information

[0460] Machine learning algorithms (e.g., TensorFlow) to generate schedules

[0461] Data processing and calculation methods

[0462] Collection of User Information

[0463] A user opens a smartphone app and inputs today's task information (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and receives biometric information such as body temperature, heart rate, and sleep data collected by a smartwatch or fitness tracker from the device.

[0464] Data analysis and schedule generation

[0465] The server integrates the collected task information, weather information, and biometric information and performs preprocessing. This preprocessing includes filling in missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. The machine learning algorithm learns from past data and patterns and calculates a schedule that adapts to the user's individual situation.

[0466] Schedule Notifications

[0467] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, the notification may be something like "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then perform tasks based on this notification.

[0468] Execution status feedback

[0469] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0470] Specific examples

[0471] Morning Routine

[0472] A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data. For example, if the user has not had enough sleep, the server adjusts the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Completion" in the app. For example, after a run, feedback is sent saying "Running completed at 8:30." The server collects this feedback and reflects it in the next schedule. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[0473] Prompt Sentence Examples

[0474] Today I have plans to run at 8:00, have a remote meeting at 10:00, and have lunch at 12:00. What would be the best schedule?

[0475] You slept for 6 hours last night, and the current temperature is 20°C and your body temperature is 36.5°C. Please adjust your schedule taking these into consideration.

[0476] As described above, this system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[0477] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0478] Step 1:

[0479] The user opens the smartphone app and enters task information (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.).

[0480] Input: User inputs task information

[0481] Output: Input task information

[0482] Specific operation: The user enters the date, start time, end time, and task details into the app interface, then presses the submit button. This information is saved on the device.

[0483] Step 2:

[0484] The terminal transmits the task information input by the user to the server.

[0485] Input: Task information saved on the device

[0486] Output: Task information sent to the server

[0487] Specific operation: The device periodically communicates with the server and sends the saved task information to the server in JSON format. If the transmission is successful, the task information is saved in the server database.

[0488] Step 3:

[0489] The server calls a weather API (e.g., OpenWeatherMap API) to obtain weather information.

[0490] Input: API call request

[0491] Output: Retrieved weather information

[0492] Specific operation: The server accesses the weather API using the API key to obtain the current temperature and weather forecast for the specified area. This data is stored in a temporary database on the server.

[0493] Step 4:

[0494] The device collects biometric information (such as body temperature, heart rate, and sleep data) from smartwatches and fitness trackers.

[0495] Input: Smartwatch or fitness tracker data

[0496] Output: Collected biometric information

[0497] How it works: The device syncs with the smartwatch via Bluetooth or Wi-Fi to collect body temperature, heart rate, sleep data, etc. The collected data is temporarily stored inside the device.

[0498] Step 5:

[0499] The terminal transmits the collected biometric information to the server.

[0500] Input: Biometric information stored on the device

[0501] Output: Biometric information sent to the server

[0502] Specific operation: The device periodically communicates with the server and sends the stored biometric information to the server in JSON format. If the transmission is successful, the biometric information is stored in the server's database.

[0503] Step 6:

[0504] The server integrates the collected task information, weather information, and biological information and performs pre-processing.

[0505] Input: Task information, weather information, biological information

[0506] Output: Preprocessed data

[0507] How it works: The server cleanses the collected data and performs preprocessing such as filling in missing values ​​and correcting outliers. This preprocessed data is then input into the machine learning algorithm.

[0508] Step 7:

[0509] The server uses machine learning algorithms (e.g., TensorFlow) to generate an optimal schedule.

[0510] Input: Preprocessed data

[0511] Output: The generated optimal schedule

[0512] Specific operation: The server inputs the preprocessed data into the machine learning model to generate an optimal schedule, which is then sent to the device.

[0513] Step 8:

[0514] The terminal displays the generated schedule to the user as a push notification.

[0515] Input: Generated schedule

[0516] Output: Displayed push notification

[0517] Specific operation: The device analyzes the schedule received from the server and displays it on the user's smartphone as a push notification. The user confirms the notification and executes the task.

[0518] Step 9:

[0519] After users complete a task, they mark it as "done" in the smartphone app and provide feedback.

[0520] Input: User feedback on task completion

[0521] Output: Feedback recorded in the app

[0522] What it does: The user presses a button in the app to mark a task as complete and enters the completion status. This information is stored on the device.

[0523] Step 10:

[0524] The terminal sends the recorded user feedback to the server.

[0525] Input: Feedback information stored on the device

[0526] Output: Feedback information sent to the server

[0527] Specific operation: The device periodically communicates with the server and sends the saved feedback information to the server, which uses it when generating the next schedule.

[0528] Through the above processing steps, the system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, and successively improves the accuracy of the schedules based on feedback.

[0529] (Application example 1)

[0530] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0531] Conventional schedule management systems are limited to simple task management based on a user's health and environmental data, and are unable to optimize content viewing schedules to improve quality of life, let alone daily activities. While there are functions for incrementally improving schedules based on feedback, these functions could not be combined with content viewing schedule optimization. This presents additional technical challenges for effectively managing and improving a user's entire lifestyle.

[0532] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0533] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data and user health data, means for generating an optimal schedule based on the task information, environmental data, and health data, means for notifying the user of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for integrating the task information, environmental data, and health data to generate and notify the user of a content viewing schedule. This makes it possible to optimize a user's tasks and content viewing schedule and improve their quality of life.

[0534] "User" refers to an individual who uses this system.

[0535] "Task information" is information that a user inputs as his or her activity schedule.

[0536] "Environmental data" refers to data that indicates meteorological information and surrounding environmental conditions.

[0537] "Health data" refers to data that indicates the user's health condition, such as body temperature, heart rate, and sleep data.

[0538] A "schedule" is a timetable generated to efficiently manage a user's tasks and activities.

[0539] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0540] "Feedback" refers to the rating or status information a user provides after completing a task or viewing.

[0541] A "content viewing schedule" is a timetable created to allow a user to view media content efficiently and comfortably.

[0542] A "machine learning algorithm" is a computational method that uses past data to learn patterns and make appropriate predictions and suggestions.

[0543] System Overview

[0544] The present invention is a system for optimizing a user's task and content viewing schedule. This system uses a machine learning algorithm to generate and notify an optimal schedule based on task information, environmental data, and user health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[0545] Program processing overview

[0546] Collection of User Information

[0547] A user opens a smartphone app and inputs their tasks and viewing preferences for the day (e.g., "Comedy at 8:00 PM" or "Documentary at 10:00 PM"). The device then sends this task and viewing information to the server. At the same time, the server obtains the current temperature and weather forecast via a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0548] Data analysis and schedule generation

[0549] The server integrates the collected task information, viewing preferences, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then uses a machine learning algorithm to generate an optimal task and viewing schedule. This algorithm learns from past data and patterns to calculate the optimal schedule for the user's individual situation.

[0550] Schedule Notifications

[0551] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Watch this week's popular comedy at 8:00 PM" or "Watch a recommended documentary at 10:00 PM." The user can then watch the content based on this notification.

[0552] Execution status feedback

[0553] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0554] Specific examples

[0555] Content viewing schedule generation

[0556] A user wakes up in the morning, opens the app, and enters their viewing preferences, such as:

[0557] "20:00 Comedy"

[0558] "22:00 Documentary"

[0559] The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. For example, if a user's fatigue level is high based on their viewing and health data, relaxing content will be recommended. The generated schedule is notified to the device as "This week's popular comedy at 8:00 PM" and "Recommended documentaries at 10:00 PM."

[0560] When a user finishes watching, the app asks them to report the completion. For example, "Completed watching comedy at 20:45" or "Completed watching documentary at 22:30." This will help the app better optimize the next viewing schedule.

[0561] Prompt Sentence Examples

[0562] Example prompts to input to a generative AI model:

[0563] User input data:

[0564] Want to watch: "20:00 Comedy" "22:00 Documentary"

[0565] Health data: {Sleep data: "6 hours", Stress level: "Slightly high"}

[0566] Environmental data: {Weather forecast: "Sunny", Temperature: "25℃"}

[0567] Past viewing history: {"20:00 Number of times watched dramas": "10 times", "22:00 Number of times watched documentaries": "5 times"}

[0568] Generate an optimal viewing schedule based on the data above.

[0569] As described above, this system improves the quality of life by optimizing the user's tasks and content viewing schedule.

[0570] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0571] Step 1: Collect user information

[0572] A user opens a smartphone app and inputs their tasks and viewing preferences for the day. The device then sends this task information and viewing preferences to the server. The device also uses a weather API to obtain the current temperature and weather forecast, and sends health data such as body temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers to the server via the device.

[0573] Input: Task information, viewing preferences, weather data, health data

[0574] Output: User data and environmental datasets

[0575] Step 2: Preprocessing the data

[0576] The server integrates the collected task information, viewing preferences, environmental data, and health data, and performs data preprocessing, which includes filling in missing data and correcting outliers.

[0577] Input: User data and environmental datasets

[0578] Output: Preprocessed dataset

[0579] Step 3: Generate a schedule using machine learning algorithms

[0580] The server then uses machine learning algorithms to generate optimal tasks and viewing schedules based on the pre-processed dataset. The algorithms learn from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0581] Input: Preprocessed dataset

[0582] Output: Optimized task and viewing schedules

[0583] Step 4: Schedule Notification

[0584] The server sends the generated schedule to the device, which then displays it to the user as a push notification. For example, notifications such as "Watch this week's popular comedy at 8 PM" or "Watch recommended documentaries at 10 PM" are sent to the user.

[0585] Input: Optimized task and viewing schedule

[0586] Output: Schedule notification to user device

[0587] Step 5: Performance feedback

[0588] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app, and the device sends this feedback information to the server.

[0589] Input: User task and viewing completion information

[0590] Output: Feedback dataset

[0591] Step 6: Learn and improve

[0592] The server integrates the collected feedback data and uses it to retrain the machine learning model, which is then reflected in the next schedule generation, improving the accuracy of the schedule.

[0593] Input: Feedback dataset

[0594] Output: An updated machine learning model

[0595] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0596] System Overview

[0597] This invention is a system for improving the efficiency of user schedule management. In particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system generates and notifies the user of an optimal schedule based on task information, environmental data, health data, and emotional data input by the user. It can also collect feedback from the user and continuously improve the schedule generation.

[0598] Program processing overview

[0599] Collection of User Information

[0600] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. The server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0601] Additionally, the device collects emotional data using an emotion engine that analyzes the user's facial expressions, tone of voice, language patterns, etc., thereby taking into account the user's emotional state.

[0602] Data analysis and schedule generation

[0603] The server integrates the collected task information, environmental data, health data, and emotional data and performs data preprocessing, including processing missing values ​​and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns and calculates the optimal schedule for the user's individual situation and emotional state.

[0604] Schedule Notifications

[0605] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[0606] Execution status feedback

[0607] When a task is completed, the user marks it as "completed" through a smartphone app. The device also uses an emotion engine to collect emotional data during and after the task and sends it to the server. The server then integrates this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[0608] Specific examples

[0609] Morning Routine

[0610] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state. If the user is feeling stressed, the device will take into consideration such things as adjusting the intensity of the run to a lighter level.

[0611] The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Complete" in the app and sends emotional data during and after the task to the server via the emotion engine. This allows the server to readjust the duration and intensity of the next run.

[0612] As described above, the system of the present invention integrates user input data, environmental data, health data, and emotional data to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[0613] The processing flow will be explained below.

[0614] Step 1:

[0615] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[0616] Step 2:

[0617] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[0618] Step 3:

[0619] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[0620] Step 4:

[0621] The device uses a built-in emotion engine to analyze the user's facial expressions, tone of voice, language patterns, etc. to collect emotion data, which is then sent to a server.

[0622] Step 5:

[0623] The server integrates the collected task information, environmental data, health data, and emotion data and performs preprocessing, which includes processing missing data and correcting outliers.

[0624] Step 6:

[0625] The server then generates an optimal schedule based on the pre-processed data using machine learning algorithms that learn from past data and patterns, and that also adapt to the user's individual situation and emotional state.

[0626] Step 7:

[0627] The optimal schedule generated by the server is transmitted to the terminal.

[0628] Step 8:

[0629] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0630] Step 9:

[0631] The user performs a task and marks it as "completed" on the smartphone app when finished.

[0632] Step 10:

[0633] The terminal transmits task completion information from the user and emotion data generated by the emotion engine to the server.

[0634] Step 11:

[0635] The server integrates the feedback information and emotion data from the user and reflects it in the next schedule generation, thereby further improving the accuracy of the next schedule.

[0636] In this way, the present system including the emotion engine provides specific procedures for realizing efficient schedule management for users.

[0637] Example 2

[0638] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] Current schedule management systems often focus on simple time management without considering the user's health or emotional state. This makes it difficult to provide an optimal schedule that suits the user's physical and mental state, resulting in problems such as reduced task execution efficiency and user satisfaction. Another issue is the lack of functionality to adaptively adjust the schedule based on feedback.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0641] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data, health data, and emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user of the schedule to the user's information processing terminal, and means for collecting feedback from the user and reflecting it in the generation of the schedule, thereby making it possible to generate and provide an optimal schedule that takes into consideration the user's physical and mental state comprehensively.

[0642] "User information" refers to data related to tasks and schedules that users enter through smartphone apps, etc.

[0643] "Environmental data" refers to data related to the external environment that affects the user's activities, such as temperature, humidity, and weather forecast.

[0644] "Health data" refers to data that indicates the user's health condition, such as the user's body temperature, heart rate, and sleep patterns.

[0645] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, language patterns, and the like.

[0646] A "schedule" is a daily planner for a user that is generated based on task information entered by the user, and collected environmental data, health data, and emotional data.

[0647] A "terminal" is an information processing device used by a user, such as a smartphone or tablet.

[0648] The "server" is a central processing unit that generates a schedule based on collected data and notifies the terminals.

[0649] "Feedback" refers to information that the user sends via the terminal about the progress of the schedule and their emotional state, which is useful for generating the next schedule.

[0650] A "machine learning algorithm" is a computer program that learns from past data and execution results to generate the optimal schedule for the next time.

[0651] This invention is a system for improving the efficiency of user schedule management, and in particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system consists of multiple components, including a server, terminals, and user input means, which work in conjunction with each other.

[0652] First, the user uses the device to input task information for the day into the smartphone app. For example, specific schedules such as "running at 8:00" and "remote meeting at 10:00." This information is then sent to the server via the device.

[0653] The server has multiple ways to obtain environmental, health, and emotion data. For environmental data, it uses weather APIs (e.g., OpenWeatherMap API) to obtain current temperature and weather forecast. For health data, the device collects temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers and sends it to the server.

[0654] Furthermore, to collect emotional data, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, and language patterns. The emotion engine uses services such as Microsoft Azure's Emotion API to analyze the user's emotional state, such as stress, happiness, and fatigue, in real time.

[0655] The server integrates this data and performs preprocessing on the data. Missing values ​​are filled in with estimated values, and outliers are corrected using statistical methods (e.g., Z-score or IQR). Once preprocessing is complete, the data is used to generate an optimal schedule using a machine learning algorithm (e.g., Scikit-learn's DecisionTreeClassifier or a library such as TensorFlow).

[0656] The generated schedule is sent from the server to the device. The device displays this schedule to the user as a push notification. Examples of notifications include "Light running recommended at 8:00" or "Remote meeting at 10:00." Based on this notification, the user can carry out tasks.

[0657] When a task is completed, the user marks it as "completed" in the smartphone app. The device also uses an emotion engine to collect emotion data during and after the task and sends it to the server. The server analyzes this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[0658] As a concrete example, consider a morning routine. A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. Furthermore, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, and adjusts the intensity of the run to a lighter level if the stress level is high. This generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00," and the user can spend the day accordingly.

[0659] An example of a prompt is: "Please explain how a system works that generates and notifies users of an optimal schedule based on daily task information entered by the user into a smartphone app and weather, health, and emotional data collected via a server."

[0660] In this way, the system of the present invention comprehensively integrates the user's input data, environmental data, health data, and emotional data, and generates and notifies the user of an optimal schedule, thereby achieving efficient and effective time management.

[0661] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0662] Step 1: Enter your user information

[0663] The user starts the smartphone app and inputs the task information for the day (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The input task information is sent to the server via the device.

[0664] Input: Task information (8:00 running, 10:00 remote meeting, etc.)

[0665] Output: Task information sent to the server

[0666] Step 2: Collect data

[0667] The device collects health data such as body temperature, heart rate, and sleep data from the smartwatch or fitness tracker and sends it to the server, which receives it and uses the weather API to retrieve the current temperature and weather forecast.

[0668] Input: Health data from smartwatches and fitness trackers, weather API keys

[0669] Output: Health data sent to the server, weather data obtained

[0670] Step 3: Collecting emotion data

[0671] The device uses the smartphone's camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns in real time to collect emotional data. The emotion engine uses existing AI services (such as Emotion API).

[0672] Input: Camera video, audio data

[0673] Output: Parsed emotion data

[0674] Step 4: Preprocessing the data

[0675] The server integrates the collected task information, environmental data, health data, and emotion data and performs data preprocessing. Specifically, it fills in missing values ​​and corrects outliers. For example, if there are missing values, it fills them in with the most recent past data. Outliers are corrected using statistical methods such as Z-score and IQR.

[0676] Input: Integrated task information, environmental data, health data, and emotional data

[0677] Output: Preprocessed data

[0678] Step 5: Generate a schedule

[0679] The server uses machine learning algorithms based on the preprocessed data to generate an optimal schedule. This uses models that have also learned from past data and patterns. Specifically, Scikit-learn's DecisionTreeClassifier and TensorFlow are used.

[0680] Input: Preprocessed data, machine learning model

[0681] Output: The generated optimal schedule

[0682] Step 6: Schedule Notification

[0683] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, it may say, "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0684] Input: Generated schedule

[0685] Output: Schedule notified to the user's device

[0686] Step 7: Performance feedback

[0687] When the task is completed, the user marks it as "completed" in the smartphone app. The device then uses the emotion engine again to collect emotion data during and after the task and sends it to the server. The server then analyzes this feedback information and reflects it in the next schedule generation.

[0688] Input: Completed task information, emotion data

[0689] Output: Feedback information reflected in the next schedule generation

[0690] The above is the specific processing flow of the program of this system and the operations performed at each step.

[0691] (Application example 2)

[0692] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0693] Although self-driving vehicles are becoming more common in modern society, there are no systems that propose optimal routes that take into account passengers' emotions and health conditions. As a result, operation plans are created that lack consideration for passenger stress and fatigue, making it difficult to provide a comfortable travel experience. In addition, generating flexible routes that take passengers' physical condition and emotions into account is complex, and a system that can do this efficiently is needed.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring task information input by the user, means for collecting environmental data and user health data, means for collecting and analyzing emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user's terminal of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for proposing a route for the autonomous vehicle. This makes it possible to generate optimal routes and schedules based on passengers' emotions and health conditions, providing a comfortable and safe travel experience.

[0695] A "user" is a person who uses the system and is the subject of inputting emotional data, health data, task information, and the like.

[0696] "Task information" refers to information related to plans and schedules entered by the user.

[0697] "Environmental data" refers to data relating to the external conditions surrounding the user, such as weather information and traffic information.

[0698] "Health data" refers to data related to the user's physical condition, such as body temperature, heart rate, and sleep status.

[0699] "Emotion data" is data that indicates the user's emotional state, and is obtained by analyzing facial expressions, tone of voice, language patterns, and the like.

[0700] A "schedule" is a user's activity plan that is generated based on task information, environmental data, health data, and emotion data.

[0701] "Terminal" refers to any device operated by a user, including smartphones, tablets, and vehicle infotainment systems.

[0702] "Feedback" is input or evaluation provided by a user that is used to improve the system's schedule generation process.

[0703] An "autonomous vehicle" is a vehicle that operates autonomously and follows a route based on suggestions from the system.

[0704] "Route suggestion" is a system function that takes into account emotional and health data to present a route suitable for the user.

[0705] System Overview

[0706] This invention is a system that generates optimal schedules and routes for autonomous vehicles by taking into account the user's emotional and health states. Specifically, it collects task information, environmental data, health data, and emotional data from the user, and optimizes the schedule and route based on this data. This makes it possible to provide the user with a comfortable and safe travel experience.

[0707] Collection of User Information

[0708] First, a user inputs upcoming tasks into the system using a device such as a smartphone or tablet. For example, they might enter task information such as "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server simultaneously obtains environmental data such as the current temperature, weather forecast, and traffic information through a weather API, and collects health data such as heart rate, body temperature, and sleep data from a smartwatch or other device. The device then collects and analyzes emotional data, using an emotion engine to understand the user's emotional state based on facial expressions, tone of voice, and language patterns.

[0709] Data analysis and schedule generation

[0710] The server integrates the collected task information, environmental data, health data, and emotional data, and performs data preprocessing, such as filling in missing values ​​and correcting outliers. The server then uses machine learning algorithms to generate optimal schedules and routes. These algorithms learn from past data and patterns to calculate the optimal plan for the user's individual situation and emotional state.

[0711] Schedule and route notifications

[0712] The schedule and route generated by the server are sent to the device. For example, specific items such as "Meeting at 8:00," "Relax at a cafe at 10:00," and "There is a traffic jam on the central road, so we will suggest a detour" are notified. The device displays these to the user as push notifications.

[0713] Execution status feedback

[0714] After completing a task or route, the user provides feedback to the system. The device uses an emotion engine to collect emotion data during and after the task or route, and sends it to the server. The server then integrates this feedback information and reflects it in the generation of the next schedule and route. This allows the system to continuously improve its accuracy and comfort.

[0715] Hardware and software used

[0716] Hardware: Smartphones, smartwatches, in-cabin cameras and microphones in autonomous vehicles, infotainment systems

[0717] Software: Python, RESTful API, machine learning algorithms, emotion engine

[0718] Prompt Sentence Examples

[0719] The following is an example of a prompt that the system can use to obtain user emotion data:

[0720] "Tell me how you're feeling right now."

[0721] "Have you been feeling stressed lately?"

[0722] "How are you feeling today?"

[0723] As described above, this system provides a mechanism for integrating diverse user data to generate optimal schedules and routes for autonomous vehicles that take into account emotions and health conditions.

[0724] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0725] Step 1:

[0726] A user inputs task information using a device such as a smartphone or tablet. The input task information may include "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server then receives the task information as input data and uses it as the basis for generating future schedules.

[0727] Step 2:

[0728] The server uses a weather API to obtain environmental data such as current temperature, weather forecast, and traffic information. At the same time, it collects health data such as heart rate, body temperature, and sleep data obtained from devices such as smartwatches. This allows the environmental data and health data to be aggregated on the server as input data.

[0729] Step 3:

[0730] The user also inputs emotional data using the device. The device uses a camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns, and generates emotional data using an emotion engine. For example, the user is asked, "Have you been feeling stressed recently?" and emotional data is collected by answering the question. This collected emotional data is sent to the server.

[0731] Step 4:

[0732] The server integrates the collected task information, environmental data, health data, and emotion data. This integrated dataset undergoes preprocessing, such as filling in missing values ​​and correcting outliers. For example, missing weather information is filled in, and abnormally high heart rate data is corrected. This preprocessing makes the dataset suitable for schedule generation.

[0733] Step 5:

[0734] Based on the pre-processed dataset, the server uses machine learning algorithms to generate optimal schedules and routes. For example, it learns from past data and newly collected data to calculate the schedule and route that best suits the user's emotional and health conditions. This calculation generates output data for the schedule and route.

[0735] Step 6:

[0736] The server sends the generated optimal schedule and driving route to the device. The device receives this information and displays it to the user as a push notification. For example, the device may notify the user with a message such as "Meeting at 8:00" and "There is traffic congestion on the central road, so we will suggest a detour." This allows the user to check the recommended schedule and driving route.

[0737] Step 7:

[0738] The user actually performs the task and inputs feedback into the device. After completing the task, the user provides feedback such as their thoughts and evaluation of the task, and emotion data is collected again through the emotion engine. This allows emotion data to be collected about the task, during operation, and after execution.

[0739] Step 8:

[0740] The collected feedback information and emotion data are sent to the server and reflected in the generation of the next schedule and route, allowing the system to continuously improve its accuracy and comfort, and provide schedules and routes that better suit the user's needs.

[0741] The above is the flow of specific processing steps of the program of the system that realizes the application example.

[0742] 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.

[0743] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0744] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0745] [Third embodiment]

[0746] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0747] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0748] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0749] 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.

[0750] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0751] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0752] 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.

[0753] 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.

[0754] 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 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.

[0755] 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.

[0756] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0757] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0758] System Overview

[0759] The present invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, environmental data, and the user's health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[0760] Program processing overview

[0761] Collection of User Information

[0762] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0763] Data analysis and schedule generation

[0764] The server integrates the collected task information, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0765] Schedule Notifications

[0766] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[0767] Execution status feedback

[0768] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0769] Specific examples

[0770] Morning Routine

[0771] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. If the user has not had enough sleep, the system will take into consideration such things as adjusting the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00."

[0772] Each time a task is completed, the user reports it as "completed" in the app. For example, after a run, feedback such as "Running completed at 8:30" is sent. This allows the server to reschedule the next running time. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[0773] As described above, the system of the present invention integrates user input data, environmental data, and health data, and generates and notifies optimal schedules, thereby achieving efficient and effective time management.

[0774] The processing flow will be explained below.

[0775] Step 1:

[0776] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[0777] Step 2:

[0778] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[0779] Step 3:

[0780] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[0781] Step 4:

[0782] The server integrates the collected task information, environmental data, and health data, and performs data preprocessing, including missing value handling and outlier correction.

[0783] Step 5:

[0784] The server then uses the pre-processed data to generate an optimal schedule using a machine learning algorithm, which learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0785] Step 6:

[0786] The optimal schedule generated by the server is transmitted to the terminal.

[0787] Step 7:

[0788] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0789] Step 8:

[0790] The user performs a task and marks it as "completed" on the smartphone app when finished.

[0791] Step 9:

[0792] The terminal transmits task completion information from the user to the server.

[0793] Step 10:

[0794] The server integrates the feedback information from users and reflects it in the next schedule generation, thereby improving the accuracy of the next schedule.

[0795] By executing each step in this way, the system supports the user in efficient schedule management.

[0796] Example 1

[0797] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0798] Conventional schedule management systems create schedules based solely on information entered by individual users, making it difficult to consider changes in the environment or the user's health status. As a result, they are unable to provide a schedule that is suited to the user's lifestyle or health status, which can reduce the effectiveness of the schedule. In addition, they lack a mechanism for incorporating user feedback, making it difficult to continuously improve the accuracy of the schedule.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0800] In this invention, the server includes means for acquiring task information input by a user, means for collecting weather information and user biological information, means for generating an optimal schedule based on the task information, weather information, and biological information, means for notifying the user of the schedule, and means for collecting feedback from the user and reflecting the feedback in generating the schedule. This makes it possible to provide an optimal schedule that takes into account changes in the environment and the user's health condition, and further makes it possible to successively improve the accuracy of the schedule by reflecting the feedback from the user.

[0801] "Task information" is data indicating the details of plans and activities that a user inputs into the schedule management system.

[0802] "Weather information" refers to data related to the current temperature and weather forecast obtained from an external weather data provider service.

[0803] "Biometric information" is data that indicates the user's health condition, and specifically includes body temperature, heart rate, sleep data, and the like.

[0804] A "terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0805] The "server" is a central processing unit that generates a schedule based on collected data and notifies the user's terminal of the schedule.

[0806] "Feedback" is information about the execution status and evaluation provided by the user after completing a task.

[0807] "Machine learning algorithms" refer to algorithms that learn data patterns and make predictions or decisions based on new data.

[0808] A "schedule" is a plan or timetable that indicates what tasks a user will do and when.

[0809] This invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, weather information, and biometric information input by the user. It also collects feedback from the user and can continuously improve the appropriateness of the schedule.

[0810] System configuration

[0811] Hardware

[0812] The system includes the following hardware configuration:

[0813] Devices such as smartphones and tablets that accept user operations

[0814] Smartwatches and fitness trackers for collecting biometric information

[0815] Server that processes data

[0816] software

[0817] The system includes the following software components:

[0818] A smartphone application for users to enter tasks

[0819] Weather API for obtaining weather information (e.g. OpenWeatherMap API)

[0820] IoT device software for collecting biometric information

[0821] Machine learning algorithms (e.g., TensorFlow) to generate schedules

[0822] Data processing and calculation methods

[0823] Collection of User Information

[0824] A user opens a smartphone app and inputs today's task information (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and receives biometric information such as body temperature, heart rate, and sleep data collected by a smartwatch or fitness tracker from the device.

[0825] Data analysis and schedule generation

[0826] The server integrates the collected task information, weather information, and biometric information and performs preprocessing. This preprocessing includes filling in missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. The machine learning algorithm learns from past data and patterns and calculates a schedule that adapts to the user's individual situation.

[0827] Schedule Notifications

[0828] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, the notification may be something like "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then perform tasks based on this notification.

[0829] Execution status feedback

[0830] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0831] Specific examples

[0832] Morning Routine

[0833] A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data. For example, if the user has not had enough sleep, the server adjusts the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Completion" in the app. For example, after a run, feedback is sent saying "Running completed at 8:30." The server collects this feedback and reflects it in the next schedule. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[0834] Prompt Sentence Examples

[0835] Today I have plans to run at 8:00, have a remote meeting at 10:00, and have lunch at 12:00. What would be the best schedule?

[0836] You slept for 6 hours last night, and the current temperature is 20°C and your body temperature is 36.5°C. Please adjust your schedule taking these into consideration.

[0837] As described above, this system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[0838] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0839] Step 1:

[0840] The user opens the smartphone app and enters task information (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.).

[0841] Input: User inputs task information

[0842] Output: Input task information

[0843] Specific operation: The user enters the date, start time, end time, and task details into the app interface, then presses the submit button. This information is saved on the device.

[0844] Step 2:

[0845] The terminal transmits the task information input by the user to the server.

[0846] Input: Task information saved on the device

[0847] Output: Task information sent to the server

[0848] Specific operation: The device periodically communicates with the server and sends the saved task information to the server in JSON format. If the transmission is successful, the task information is saved in the server database.

[0849] Step 3:

[0850] The server calls a weather API (e.g., OpenWeatherMap API) to obtain weather information.

[0851] Input: API call request

[0852] Output: Retrieved weather information

[0853] Specific operation: The server accesses the weather API using the API key to obtain the current temperature and weather forecast for the specified area. This data is stored in a temporary database on the server.

[0854] Step 4:

[0855] The device collects biometric information (such as body temperature, heart rate, and sleep data) from smartwatches and fitness trackers.

[0856] Input: Smartwatch or fitness tracker data

[0857] Output: Collected biometric information

[0858] How it works: The device syncs with the smartwatch via Bluetooth or Wi-Fi to collect body temperature, heart rate, sleep data, etc. The collected data is temporarily stored inside the device.

[0859] Step 5:

[0860] The terminal transmits the collected biometric information to the server.

[0861] Input: Biometric information stored on the device

[0862] Output: Biometric information sent to the server

[0863] Specific operation: The device periodically communicates with the server and sends the stored biometric information to the server in JSON format. If the transmission is successful, the biometric information is stored in the server's database.

[0864] Step 6:

[0865] The server integrates the collected task information, weather information, and biological information and performs pre-processing.

[0866] Input: Task information, weather information, biological information

[0867] Output: Preprocessed data

[0868] How it works: The server cleanses the collected data and performs preprocessing such as filling in missing values ​​and correcting outliers. This preprocessed data is then input into the machine learning algorithm.

[0869] Step 7:

[0870] The server uses machine learning algorithms (e.g., TensorFlow) to generate an optimal schedule.

[0871] Input: Preprocessed data

[0872] Output: The generated optimal schedule

[0873] Specific operation: The server inputs the preprocessed data into the machine learning model to generate an optimal schedule, which is then sent to the device.

[0874] Step 8:

[0875] The terminal displays the generated schedule to the user as a push notification.

[0876] Input: Generated schedule

[0877] Output: Displayed push notification

[0878] Specific operation: The device analyzes the schedule received from the server and displays it on the user's smartphone as a push notification. The user confirms the notification and executes the task.

[0879] Step 9:

[0880] After users complete a task, they mark it as "done" in the smartphone app and provide feedback.

[0881] Input: User feedback on task completion

[0882] Output: Feedback recorded in the app

[0883] What it does: The user presses a button in the app to mark a task as complete and enters the completion status. This information is stored on the device.

[0884] Step 10:

[0885] The terminal sends the recorded user feedback to the server.

[0886] Input: Feedback information stored on the device

[0887] Output: Feedback information sent to the server

[0888] Specific operation: The device periodically communicates with the server and sends the saved feedback information to the server, which uses it when generating the next schedule.

[0889] Through the above processing steps, the system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, and successively improves the accuracy of the schedules based on feedback.

[0890] (Application example 1)

[0891] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0892] Conventional schedule management systems are limited to simple task management based on a user's health and environmental data, and are unable to optimize content viewing schedules to improve quality of life, let alone daily activities. While there are functions for incrementally improving schedules based on feedback, these functions could not be combined with content viewing schedule optimization. This presents additional technical challenges for effectively managing and improving a user's entire lifestyle.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0894] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data and user health data, means for generating an optimal schedule based on the task information, environmental data, and health data, means for notifying the user of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for integrating the task information, environmental data, and health data to generate and notify the user of a content viewing schedule. This makes it possible to optimize a user's tasks and content viewing schedule and improve their quality of life.

[0895] "User" refers to an individual who uses this system.

[0896] "Task information" is information that a user inputs as his or her activity schedule.

[0897] "Environmental data" refers to data that indicates meteorological information and surrounding environmental conditions.

[0898] "Health data" refers to data that indicates the user's health condition, such as body temperature, heart rate, and sleep data.

[0899] A "schedule" is a timetable generated to efficiently manage a user's tasks and activities.

[0900] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0901] "Feedback" refers to the rating or status information a user provides after completing a task or viewing.

[0902] A "content viewing schedule" is a timetable created to allow a user to view media content efficiently and comfortably.

[0903] A "machine learning algorithm" is a computational method that uses past data to learn patterns and make appropriate predictions and suggestions.

[0904] System Overview

[0905] The present invention is a system for optimizing a user's task and content viewing schedule. This system uses a machine learning algorithm to generate and notify an optimal schedule based on task information, environmental data, and user health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[0906] Program processing overview

[0907] Collection of User Information

[0908] A user opens a smartphone app and inputs their tasks and viewing preferences for the day (e.g., "Comedy at 8:00 PM" or "Documentary at 10:00 PM"). The device then sends this task and viewing information to the server. At the same time, the server obtains the current temperature and weather forecast via a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0909] Data analysis and schedule generation

[0910] The server integrates the collected task information, viewing preferences, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then uses a machine learning algorithm to generate an optimal task and viewing schedule. This algorithm learns from past data and patterns to calculate the optimal schedule for the user's individual situation.

[0911] Schedule Notifications

[0912] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Watch this week's popular comedy at 8:00 PM" or "Watch a recommended documentary at 10:00 PM." The user can then watch the content based on this notification.

[0913] Execution status feedback

[0914] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[0915] Specific examples

[0916] Content viewing schedule generation

[0917] A user wakes up in the morning, opens the app, and enters their viewing preferences, such as:

[0918] "20:00 Comedy"

[0919] "22:00 Documentary"

[0920] The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. For example, if a user's fatigue level is high based on their viewing and health data, relaxing content will be recommended. The generated schedule is notified to the device as "This week's popular comedy at 8:00 PM" and "Recommended documentaries at 10:00 PM."

[0921] When a user finishes watching, the app asks them to report the completion. For example, "Completed watching comedy at 20:45" or "Completed watching documentary at 22:30." This will help the app better optimize the next viewing schedule.

[0922] Prompt Sentence Examples

[0923] Example prompts to input to a generative AI model:

[0924] User input data:

[0925] Want to watch: "20:00 Comedy" "22:00 Documentary"

[0926] Health data: {Sleep data: "6 hours", Stress level: "Slightly high"}

[0927] Environmental data: {Weather forecast: "Sunny", Temperature: "25℃"}

[0928] Past viewing history: {"20:00 Number of times watched dramas": "10 times", "22:00 Number of times watched documentaries": "5 times"}

[0929] Generate an optimal viewing schedule based on the data above.

[0930] As described above, this system improves the quality of life by optimizing the user's tasks and content viewing schedule.

[0931] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0932] Step 1: Collect user information

[0933] A user opens a smartphone app and inputs their tasks and viewing preferences for the day. The device then sends this task information and viewing preferences to the server. The device also uses a weather API to obtain the current temperature and weather forecast, and sends health data such as body temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers to the server via the device.

[0934] Input: Task information, viewing preferences, weather data, health data

[0935] Output: User data and environmental datasets

[0936] Step 2: Preprocessing the data

[0937] The server integrates the collected task information, viewing preferences, environmental data, and health data, and performs data preprocessing, which includes filling in missing data and correcting outliers.

[0938] Input: User data and environmental datasets

[0939] Output: Preprocessed dataset

[0940] Step 3: Generate a schedule using machine learning algorithms

[0941] The server then uses machine learning algorithms to generate optimal tasks and viewing schedules based on the pre-processed dataset. The algorithms learn from past data and patterns to calculate the optimal schedule for each user's individual situation.

[0942] Input: Preprocessed dataset

[0943] Output: Optimized task and viewing schedules

[0944] Step 4: Schedule Notification

[0945] The server sends the generated schedule to the device, which then displays it to the user as a push notification. For example, notifications such as "Watch this week's popular comedy at 8 PM" or "Watch recommended documentaries at 10 PM" are sent to the user.

[0946] Input: Optimized task and viewing schedule

[0947] Output: Schedule notification to user device

[0948] Step 5: Performance feedback

[0949] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app, and the device sends this feedback information to the server.

[0950] Input: User task and viewing completion information

[0951] Output: Feedback dataset

[0952] Step 6: Learn and improve

[0953] The server integrates the collected feedback data and uses it to retrain the machine learning model, which is then reflected in the next schedule generation, improving the accuracy of the schedule.

[0954] Input: Feedback dataset

[0955] Output: An updated machine learning model

[0956] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0957] System Overview

[0958] This invention is a system for improving the efficiency of user schedule management. In particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system generates and notifies the user of an optimal schedule based on task information, environmental data, health data, and emotional data input by the user. It can also collect feedback from the user and continuously improve the schedule generation.

[0959] Program processing overview

[0960] Collection of User Information

[0961] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. The server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[0962] Additionally, the device collects emotional data using an emotion engine that analyzes the user's facial expressions, tone of voice, language patterns, etc., thereby taking into account the user's emotional state.

[0963] Data analysis and schedule generation

[0964] The server integrates the collected task information, environmental data, health data, and emotional data and performs data preprocessing, including processing missing values ​​and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns and calculates the optimal schedule for the user's individual situation and emotional state.

[0965] Schedule Notifications

[0966] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[0967] Execution status feedback

[0968] When a task is completed, the user marks it as "completed" through a smartphone app. The device also uses an emotion engine to collect emotional data during and after the task and sends it to the server. The server then integrates this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[0969] Specific examples

[0970] Morning Routine

[0971] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state. If the user is feeling stressed, the device will take into consideration such things as adjusting the intensity of the run to a lighter level.

[0972] The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Complete" in the app and sends emotional data during and after the task to the server via the emotion engine. This allows the server to readjust the duration and intensity of the next run.

[0973] As described above, the system of the present invention integrates user input data, environmental data, health data, and emotional data to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[0974] The processing flow will be explained below.

[0975] Step 1:

[0976] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[0977] Step 2:

[0978] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[0979] Step 3:

[0980] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[0981] Step 4:

[0982] The device uses a built-in emotion engine to analyze the user's facial expressions, tone of voice, language patterns, etc. to collect emotion data, which is then sent to a server.

[0983] Step 5:

[0984] The server integrates the collected task information, environmental data, health data, and emotion data and performs preprocessing, which includes processing missing data and correcting outliers.

[0985] Step 6:

[0986] The server then generates an optimal schedule based on the pre-processed data using machine learning algorithms that learn from past data and patterns, and that also adapt to the user's individual situation and emotional state.

[0987] Step 7:

[0988] The optimal schedule generated by the server is transmitted to the terminal.

[0989] Step 8:

[0990] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[0991] Step 9:

[0992] The user performs a task and marks it as "completed" on the smartphone app when finished.

[0993] Step 10:

[0994] The terminal transmits task completion information from the user and emotion data generated by the emotion engine to the server.

[0995] Step 11:

[0996] The server integrates the feedback information and emotion data from the user and reflects it in the next schedule generation, thereby further improving the accuracy of the next schedule.

[0997] In this way, the present system including the emotion engine provides specific procedures for realizing efficient schedule management for users.

[0998] Example 2

[0999] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] Current schedule management systems often focus on simple time management without considering the user's health or emotional state. This makes it difficult to provide an optimal schedule that suits the user's physical and mental state, resulting in problems such as reduced task execution efficiency and user satisfaction. Another issue is the lack of functionality to adaptively adjust the schedule based on feedback.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1002] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data, health data, and emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user of the schedule to the user's information processing terminal, and means for collecting feedback from the user and reflecting it in the generation of the schedule, thereby making it possible to generate and provide an optimal schedule that takes into consideration the user's physical and mental state comprehensively.

[1003] "User information" refers to data related to tasks and schedules that users enter through smartphone apps, etc.

[1004] "Environmental data" refers to data related to the external environment that affects the user's activities, such as temperature, humidity, and weather forecast.

[1005] "Health data" refers to data that indicates the user's health condition, such as the user's body temperature, heart rate, and sleep patterns.

[1006] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, language patterns, and the like.

[1007] A "schedule" is a daily planner for a user that is generated based on task information entered by the user, and collected environmental data, health data, and emotional data.

[1008] A "terminal" is an information processing device used by a user, such as a smartphone or tablet.

[1009] The "server" is a central processing unit that generates a schedule based on collected data and notifies the terminals.

[1010] "Feedback" refers to information that the user sends via the terminal about the progress of the schedule and their emotional state, which is useful for generating the next schedule.

[1011] A "machine learning algorithm" is a computer program that learns from past data and execution results to generate the optimal schedule for the next time.

[1012] This invention is a system for improving the efficiency of user schedule management, and in particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system consists of multiple components, including a server, terminals, and user input means, which work in conjunction with each other.

[1013] First, the user uses the device to input task information for the day into the smartphone app. For example, specific schedules such as "running at 8:00" and "remote meeting at 10:00." This information is then sent to the server via the device.

[1014] The server has multiple ways to obtain environmental, health, and emotion data. For environmental data, it uses weather APIs (e.g., OpenWeatherMap API) to obtain current temperature and weather forecast. For health data, the device collects temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers and sends it to the server.

[1015] Furthermore, to collect emotional data, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, and language patterns. The emotion engine uses services such as Microsoft Azure's Emotion API to analyze the user's emotional state, such as stress, happiness, and fatigue, in real time.

[1016] The server integrates this data and performs preprocessing on the data. Missing values ​​are filled in with estimated values, and outliers are corrected using statistical methods (e.g., Z-score or IQR). Once preprocessing is complete, the data is used to generate an optimal schedule using a machine learning algorithm (e.g., Scikit-learn's DecisionTreeClassifier or a library such as TensorFlow).

[1017] The generated schedule is sent from the server to the device. The device displays this schedule to the user as a push notification. Examples of notifications include "Light running recommended at 8:00" or "Remote meeting at 10:00." Based on this notification, the user can carry out tasks.

[1018] When a task is completed, the user marks it as "completed" in the smartphone app. The device also uses an emotion engine to collect emotion data during and after the task and sends it to the server. The server analyzes this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[1019] As a concrete example, consider a morning routine. A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. Furthermore, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, and adjusts the intensity of the run to a lighter level if the stress level is high. This generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00," and the user can spend the day accordingly.

[1020] An example of a prompt is: "Please explain how a system works that generates and notifies users of an optimal schedule based on daily task information entered by the user into a smartphone app and weather, health, and emotional data collected via a server."

[1021] In this way, the system of the present invention comprehensively integrates the user's input data, environmental data, health data, and emotional data, and generates and notifies the user of an optimal schedule, thereby achieving efficient and effective time management.

[1022] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1023] Step 1: Enter your user information

[1024] The user starts the smartphone app and inputs the task information for the day (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The input task information is sent to the server via the device.

[1025] Input: Task information (8:00 running, 10:00 remote meeting, etc.)

[1026] Output: Task information sent to the server

[1027] Step 2: Collect data

[1028] The device collects health data such as body temperature, heart rate, and sleep data from the smartwatch or fitness tracker and sends it to the server, which receives it and uses the weather API to retrieve the current temperature and weather forecast.

[1029] Input: Health data from smartwatches and fitness trackers, weather API keys

[1030] Output: Health data sent to the server, weather data obtained

[1031] Step 3: Collecting emotion data

[1032] The device uses the smartphone's camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns in real time to collect emotional data. The emotion engine uses existing AI services (such as Emotion API).

[1033] Input: Camera video, audio data

[1034] Output: Parsed emotion data

[1035] Step 4: Preprocessing the data

[1036] The server integrates the collected task information, environmental data, health data, and emotion data and performs data preprocessing. Specifically, it fills in missing values ​​and corrects outliers. For example, if there are missing values, it fills them in with the most recent past data. Outliers are corrected using statistical methods such as Z-score and IQR.

[1037] Input: Integrated task information, environmental data, health data, and emotional data

[1038] Output: Preprocessed data

[1039] Step 5: Generate a schedule

[1040] The server uses machine learning algorithms based on the preprocessed data to generate an optimal schedule. This uses models that have also learned from past data and patterns. Specifically, Scikit-learn's DecisionTreeClassifier and TensorFlow are used.

[1041] Input: Preprocessed data, machine learning model

[1042] Output: The generated optimal schedule

[1043] Step 6: Schedule Notification

[1044] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, it may say, "Light running recommended at 8:00" or "Remote meeting at 10:00."

[1045] Input: Generated schedule

[1046] Output: Schedule notified to the user's device

[1047] Step 7: Performance feedback

[1048] When the task is completed, the user marks it as "completed" in the smartphone app. The device then uses the emotion engine again to collect emotion data during and after the task and sends it to the server. The server then analyzes this feedback information and reflects it in the next schedule generation.

[1049] Input: Completed task information, emotion data

[1050] Output: Feedback information reflected in the next schedule generation

[1051] The above is the specific processing flow of the program of this system and the operations performed at each step.

[1052] (Application example 2)

[1053] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1054] Although self-driving vehicles are becoming more common in modern society, there are no systems that propose optimal routes that take into account passengers' emotions and health conditions. As a result, operation plans are created that lack consideration for passenger stress and fatigue, making it difficult to provide a comfortable travel experience. In addition, generating flexible routes that take passengers' physical condition and emotions into account is complex, and a system that can do this efficiently is needed.

[1055] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring task information input by the user, means for collecting environmental data and user health data, means for collecting and analyzing emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user's terminal of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for proposing a route for the autonomous vehicle. This makes it possible to generate optimal routes and schedules based on passengers' emotions and health conditions, providing a comfortable and safe travel experience.

[1056] A "user" is a person who uses the system and is the subject of inputting emotional data, health data, task information, and the like.

[1057] "Task information" refers to information related to plans and schedules entered by the user.

[1058] "Environmental data" refers to data relating to the external conditions surrounding the user, such as weather information and traffic information.

[1059] "Health data" refers to data related to the user's physical condition, such as body temperature, heart rate, and sleep status.

[1060] "Emotion data" is data that indicates the user's emotional state, and is obtained by analyzing facial expressions, tone of voice, language patterns, and the like.

[1061] A "schedule" is a user's activity plan that is generated based on task information, environmental data, health data, and emotion data.

[1062] "Terminal" refers to any device operated by a user, including smartphones, tablets, and vehicle infotainment systems.

[1063] "Feedback" is input or evaluation provided by a user that is used to improve the system's schedule generation process.

[1064] An "autonomous vehicle" is a vehicle that operates autonomously and follows a route based on suggestions from the system.

[1065] "Route suggestion" is a system function that takes into account emotional and health data to present a route suitable for the user.

[1066] System Overview

[1067] This invention is a system that generates optimal schedules and routes for autonomous vehicles by taking into account the user's emotional and health states. Specifically, it collects task information, environmental data, health data, and emotional data from the user, and optimizes the schedule and route based on this data. This makes it possible to provide the user with a comfortable and safe travel experience.

[1068] Collection of User Information

[1069] First, a user inputs upcoming tasks into the system using a device such as a smartphone or tablet. For example, they might enter task information such as "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server simultaneously obtains environmental data such as the current temperature, weather forecast, and traffic information through a weather API, and collects health data such as heart rate, body temperature, and sleep data from a smartwatch or other device. The device then collects and analyzes emotional data, using an emotion engine to understand the user's emotional state based on facial expressions, tone of voice, and language patterns.

[1070] Data analysis and schedule generation

[1071] The server integrates the collected task information, environmental data, health data, and emotional data, and performs data preprocessing, such as filling in missing values ​​and correcting outliers. The server then uses machine learning algorithms to generate optimal schedules and routes. These algorithms learn from past data and patterns to calculate the optimal plan for the user's individual situation and emotional state.

[1072] Schedule and route notifications

[1073] The schedule and route generated by the server are sent to the device. For example, specific items such as "Meeting at 8:00," "Relax at a cafe at 10:00," and "There is a traffic jam on the central road, so we will suggest a detour" are notified. The device displays these to the user as push notifications.

[1074] Execution status feedback

[1075] After completing a task or route, the user provides feedback to the system. The device uses an emotion engine to collect emotion data during and after the task or route, and sends it to the server. The server then integrates this feedback information and reflects it in the generation of the next schedule and route. This allows the system to continuously improve its accuracy and comfort.

[1076] Hardware and software used

[1077] Hardware: Smartphones, smartwatches, in-cabin cameras and microphones in autonomous vehicles, infotainment systems

[1078] Software: Python, RESTful API, machine learning algorithms, emotion engine

[1079] Prompt Sentence Examples

[1080] The following is an example of a prompt that the system can use to obtain user emotion data:

[1081] "Tell me how you're feeling right now."

[1082] "Have you been feeling stressed lately?"

[1083] "How are you feeling today?"

[1084] As described above, this system provides a mechanism for integrating diverse user data to generate optimal schedules and routes for autonomous vehicles that take into account emotions and health conditions.

[1085] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1086] Step 1:

[1087] A user inputs task information using a device such as a smartphone or tablet. The input task information may include "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server then receives the task information as input data and uses it as the basis for generating future schedules.

[1088] Step 2:

[1089] The server uses a weather API to obtain environmental data such as current temperature, weather forecast, and traffic information. At the same time, it collects health data such as heart rate, body temperature, and sleep data obtained from devices such as smartwatches. This allows the environmental data and health data to be aggregated on the server as input data.

[1090] Step 3:

[1091] The user also inputs emotional data using the device. The device uses a camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns, and generates emotional data using an emotion engine. For example, the user is asked, "Have you been feeling stressed recently?" and emotional data is collected by answering the question. This collected emotional data is sent to the server.

[1092] Step 4:

[1093] The server integrates the collected task information, environmental data, health data, and emotion data. This integrated dataset undergoes preprocessing, such as filling in missing values ​​and correcting outliers. For example, missing weather information is filled in, and abnormally high heart rate data is corrected. This preprocessing makes the dataset suitable for schedule generation.

[1094] Step 5:

[1095] Based on the pre-processed dataset, the server uses machine learning algorithms to generate optimal schedules and routes. For example, it learns from past data and newly collected data to calculate the schedule and route that best suits the user's emotional and health conditions. This calculation generates output data for the schedule and route.

[1096] Step 6:

[1097] The server sends the generated optimal schedule and driving route to the device. The device receives this information and displays it to the user as a push notification. For example, the device may notify the user with a message such as "Meeting at 8:00" and "There is traffic congestion on the central road, so we will suggest a detour." This allows the user to check the recommended schedule and driving route.

[1098] Step 7:

[1099] The user actually performs the task and inputs feedback into the device. After completing the task, the user provides feedback such as their thoughts and evaluation of the task, and emotion data is collected again through the emotion engine. This allows emotion data to be collected about the task, during operation, and after execution.

[1100] Step 8:

[1101] The collected feedback information and emotion data are sent to the server and reflected in the generation of the next schedule and route, allowing the system to continuously improve its accuracy and comfort, and provide schedules and routes that better suit the user's needs.

[1102] The above is the flow of specific processing steps of the program of the system that realizes the application example.

[1103] 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.

[1104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1105] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1106] [Fourth embodiment]

[1107] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1108] 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.

[1109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1110] 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.

[1111] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1112] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1113] 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.

[1114] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1115] 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.

[1116] 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 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.

[1117] 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.

[1118] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1119] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1120] System Overview

[1121] The present invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, environmental data, and the user's health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[1122] Program processing overview

[1123] Collection of User Information

[1124] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[1125] Data analysis and schedule generation

[1126] The server integrates the collected task information, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[1127] Schedule Notifications

[1128] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[1129] Execution status feedback

[1130] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[1131] Specific examples

[1132] Morning Routine

[1133] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. If the user has not had enough sleep, the system will take into consideration such things as adjusting the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00."

[1134] Each time a task is completed, the user reports it as "completed" in the app. For example, after a run, feedback such as "Running completed at 8:30" is sent. This allows the server to reschedule the next running time. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[1135] As described above, the system of the present invention integrates user input data, environmental data, and health data, and generates and notifies optimal schedules, thereby achieving efficient and effective time management.

[1136] The processing flow will be explained below.

[1137] Step 1:

[1138] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[1139] Step 2:

[1140] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[1141] Step 3:

[1142] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[1143] Step 4:

[1144] The server integrates the collected task information, environmental data, and health data, and performs data preprocessing, including missing value handling and outlier correction.

[1145] Step 5:

[1146] The server then uses the pre-processed data to generate an optimal schedule using a machine learning algorithm, which learns from past data and patterns to calculate the optimal schedule for each user's individual situation.

[1147] Step 6:

[1148] The optimal schedule generated by the server is transmitted to the terminal.

[1149] Step 7:

[1150] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[1151] Step 8:

[1152] The user performs a task and marks it as "completed" on the smartphone app when finished.

[1153] Step 9:

[1154] The terminal transmits task completion information from the user to the server.

[1155] Step 10:

[1156] The server integrates the feedback information from users and reflects it in the next schedule generation, thereby improving the accuracy of the next schedule.

[1157] By executing each step in this way, the system supports the user in efficient schedule management.

[1158] Example 1

[1159] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1160] Conventional schedule management systems create schedules based solely on information entered by individual users, making it difficult to consider changes in the environment or the user's health status. As a result, they are unable to provide a schedule that is suited to the user's lifestyle or health status, which can reduce the effectiveness of the schedule. In addition, they lack a mechanism for incorporating user feedback, making it difficult to continuously improve the accuracy of the schedule.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1162] In this invention, the server includes means for acquiring task information input by a user, means for collecting weather information and user biological information, means for generating an optimal schedule based on the task information, weather information, and biological information, means for notifying the user of the schedule, and means for collecting feedback from the user and reflecting the feedback in generating the schedule. This makes it possible to provide an optimal schedule that takes into account changes in the environment and the user's health condition, and further makes it possible to successively improve the accuracy of the schedule by reflecting the feedback from the user.

[1163] "Task information" is data indicating the details of plans and activities that a user inputs into the schedule management system.

[1164] "Weather information" refers to data related to the current temperature and weather forecast obtained from an external weather data provider service.

[1165] "Biometric information" is data that indicates the user's health condition, and specifically includes body temperature, heart rate, sleep data, and the like.

[1166] A "terminal" is an electronic device used by a user, such as a smartphone or tablet.

[1167] The "server" is a central processing unit that generates a schedule based on collected data and notifies the user's terminal of the schedule.

[1168] "Feedback" is information about the execution status and evaluation provided by the user after completing a task.

[1169] "Machine learning algorithms" refer to algorithms that learn data patterns and make predictions or decisions based on new data.

[1170] A "schedule" is a plan or timetable that indicates what tasks a user will do and when.

[1171] This invention is a system for improving the efficiency of a user's schedule management. This system generates and notifies an optimal schedule based on task information, weather information, and biometric information input by the user. It also collects feedback from the user and can continuously improve the appropriateness of the schedule.

[1172] System configuration

[1173] Hardware

[1174] The system includes the following hardware configuration:

[1175] Devices such as smartphones and tablets that accept user operations

[1176] Smartwatches and fitness trackers for collecting biometric information

[1177] Server that processes data

[1178] software

[1179] The system includes the following software components:

[1180] A smartphone application for users to enter tasks

[1181] Weather API for obtaining weather information (e.g. OpenWeatherMap API)

[1182] IoT device software for collecting biometric information

[1183] Machine learning algorithms (e.g., TensorFlow) to generate schedules

[1184] Data processing and calculation methods

[1185] Collection of User Information

[1186] A user opens a smartphone app and inputs today's task information (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. At the same time, the server obtains the current temperature and weather forecast through a weather API, and receives biometric information such as body temperature, heart rate, and sleep data collected by a smartwatch or fitness tracker from the device.

[1187] Data analysis and schedule generation

[1188] The server integrates the collected task information, weather information, and biometric information and performs preprocessing. This preprocessing includes filling in missing data and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. The machine learning algorithm learns from past data and patterns and calculates a schedule that adapts to the user's individual situation.

[1189] Schedule Notifications

[1190] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, the notification may be something like "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then perform tasks based on this notification.

[1191] Execution status feedback

[1192] When a task is completed, the user provides feedback by marking it as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[1193] Specific examples

[1194] Morning Routine

[1195] A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data. For example, if the user has not had enough sleep, the server adjusts the intensity of the run to a lighter level. The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Completion" in the app. For example, after a run, feedback is sent saying "Running completed at 8:30." The server collects this feedback and reflects it in the next schedule. This process is repeated daily, allowing the user's schedule to be managed more efficiently.

[1196] Prompt Sentence Examples

[1197] Today I have plans to run at 8:00, have a remote meeting at 10:00, and have lunch at 12:00. What would be the best schedule?

[1198] You slept for 6 hours last night, and the current temperature is 20°C and your body temperature is 36.5°C. Please adjust your schedule taking these into consideration.

[1199] As described above, this system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[1200] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1201] Step 1:

[1202] The user opens the smartphone app and enters task information (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.).

[1203] Input: User inputs task information

[1204] Output: Input task information

[1205] Specific operation: The user enters the date, start time, end time, and task details into the app interface, then presses the submit button. This information is saved on the device.

[1206] Step 2:

[1207] The terminal transmits the task information input by the user to the server.

[1208] Input: Task information saved on the device

[1209] Output: Task information sent to the server

[1210] Specific operation: The device periodically communicates with the server and sends the saved task information to the server in JSON format. If the transmission is successful, the task information is saved in the server database.

[1211] Step 3:

[1212] The server calls a weather API (e.g., OpenWeatherMap API) to obtain weather information.

[1213] Input: API call request

[1214] Output: Retrieved weather information

[1215] Specific operation: The server accesses the weather API using the API key to obtain the current temperature and weather forecast for the specified area. This data is stored in a temporary database on the server.

[1216] Step 4:

[1217] The device collects biometric information (such as body temperature, heart rate, and sleep data) from smartwatches and fitness trackers.

[1218] Input: Smartwatch or fitness tracker data

[1219] Output: Collected biometric information

[1220] How it works: The device syncs with the smartwatch via Bluetooth or Wi-Fi to collect body temperature, heart rate, sleep data, etc. The collected data is temporarily stored inside the device.

[1221] Step 5:

[1222] The terminal transmits the collected biometric information to the server.

[1223] Input: Biometric information stored on the device

[1224] Output: Biometric information sent to the server

[1225] Specific operation: The device periodically communicates with the server and sends the stored biometric information to the server in JSON format. If the transmission is successful, the biometric information is stored in the server's database.

[1226] Step 6:

[1227] The server integrates the collected task information, weather information, and biological information and performs pre-processing.

[1228] Input: Task information, weather information, biological information

[1229] Output: Preprocessed data

[1230] How it works: The server cleanses the collected data and performs preprocessing such as filling in missing values ​​and correcting outliers. This preprocessed data is then input into the machine learning algorithm.

[1231] Step 7:

[1232] The server uses machine learning algorithms (e.g., TensorFlow) to generate an optimal schedule.

[1233] Input: Preprocessed data

[1234] Output: The generated optimal schedule

[1235] Specific operation: The server inputs the preprocessed data into the machine learning model to generate an optimal schedule, which is then sent to the device.

[1236] Step 8:

[1237] The terminal displays the generated schedule to the user as a push notification.

[1238] Input: Generated schedule

[1239] Output: Displayed push notification

[1240] Specific operation: The device analyzes the schedule received from the server and displays it on the user's smartphone as a push notification. The user confirms the notification and executes the task.

[1241] Step 9:

[1242] After users complete a task, they mark it as "done" in the smartphone app and provide feedback.

[1243] Input: User feedback on task completion

[1244] Output: Feedback recorded in the app

[1245] What it does: The user presses a button in the app to mark a task as complete and enters the completion status. This information is stored on the device.

[1246] Step 10:

[1247] The terminal sends the recorded user feedback to the server.

[1248] Input: Feedback information stored on the device

[1249] Output: Feedback information sent to the server

[1250] Specific operation: The device periodically communicates with the server and sends the saved feedback information to the server, which uses it when generating the next schedule.

[1251] Through the above processing steps, the system integrates user input data, weather information, and biometric information to generate and notify optimal schedules, and successively improves the accuracy of the schedules based on feedback.

[1252] (Application example 1)

[1253] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1254] Conventional schedule management systems are limited to simple task management based on a user's health and environmental data, and are unable to optimize content viewing schedules to improve quality of life, let alone daily activities. While there are functions for incrementally improving schedules based on feedback, these functions could not be combined with content viewing schedule optimization. This presents additional technical challenges for effectively managing and improving a user's entire lifestyle.

[1255] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1256] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data and user health data, means for generating an optimal schedule based on the task information, environmental data, and health data, means for notifying the user of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for integrating the task information, environmental data, and health data to generate and notify the user of a content viewing schedule. This makes it possible to optimize a user's tasks and content viewing schedule and improve their quality of life.

[1257] "User" refers to an individual who uses this system.

[1258] "Task information" is information that a user inputs as his or her activity schedule.

[1259] "Environmental data" refers to data that indicates meteorological information and surrounding environmental conditions.

[1260] "Health data" refers to data that indicates the user's health condition, such as body temperature, heart rate, and sleep data.

[1261] A "schedule" is a timetable generated to efficiently manage a user's tasks and activities.

[1262] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[1263] "Feedback" refers to the rating or status information a user provides after completing a task or viewing.

[1264] A "content viewing schedule" is a timetable created to allow a user to view media content efficiently and comfortably.

[1265] A "machine learning algorithm" is a computational method that uses past data to learn patterns and make appropriate predictions and suggestions.

[1266] System Overview

[1267] The present invention is a system for optimizing a user's task and content viewing schedule. This system uses a machine learning algorithm to generate and notify an optimal schedule based on task information, environmental data, and user health data input by the user. It can also collect feedback from the user and continuously improve the appropriateness of the schedule.

[1268] Program processing overview

[1269] Collection of User Information

[1270] A user opens a smartphone app and inputs their tasks and viewing preferences for the day (e.g., "Comedy at 8:00 PM" or "Documentary at 10:00 PM"). The device then sends this task and viewing information to the server. At the same time, the server obtains the current temperature and weather forecast via a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[1271] Data analysis and schedule generation

[1272] The server integrates the collected task information, viewing preferences, environmental data, and health data and performs preprocessing. This preprocessing includes processing missing data and correcting outliers. The server then uses a machine learning algorithm to generate an optimal task and viewing schedule. This algorithm learns from past data and patterns to calculate the optimal schedule for the user's individual situation.

[1273] Schedule Notifications

[1274] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Watch this week's popular comedy at 8:00 PM" or "Watch a recommended documentary at 10:00 PM." The user can then watch the content based on this notification.

[1275] Execution status feedback

[1276] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app. The device then sends this feedback information to the server. The server then integrates the collected feedback and reflects it in the next schedule generation. This allows the schedule to be continuously improved in accuracy.

[1277] Specific examples

[1278] Content viewing schedule generation

[1279] A user wakes up in the morning, opens the app, and enters their viewing preferences, such as:

[1280] "20:00 Comedy"

[1281] "22:00 Documentary"

[1282] The server retrieves the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. For example, if a user's fatigue level is high based on their viewing and health data, relaxing content will be recommended. The generated schedule is notified to the device as "This week's popular comedy at 8:00 PM" and "Recommended documentaries at 10:00 PM."

[1283] When a user finishes watching, the app asks them to report the completion. For example, "Completed watching comedy at 20:45" or "Completed watching documentary at 22:30." This will help the app better optimize the next viewing schedule.

[1284] Prompt Sentence Examples

[1285] Example prompts to input to a generative AI model:

[1286] User input data:

[1287] Want to watch: "20:00 Comedy" "22:00 Documentary"

[1288] Health data: {Sleep data: "6 hours", Stress level: "Slightly high"}

[1289] Environmental data: {Weather forecast: "Sunny", Temperature: "25℃"}

[1290] Past viewing history: {"20:00 Number of times watched dramas": "10 times", "22:00 Number of times watched documentaries": "5 times"}

[1291] Generate an optimal viewing schedule based on the data above.

[1292] As described above, this system improves the quality of life by optimizing the user's tasks and content viewing schedule.

[1293] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1294] Step 1: Collect user information

[1295] A user opens a smartphone app and inputs their tasks and viewing preferences for the day. The device then sends this task information and viewing preferences to the server. The device also uses a weather API to obtain the current temperature and weather forecast, and sends health data such as body temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers to the server via the device.

[1296] Input: Task information, viewing preferences, weather data, health data

[1297] Output: User data and environmental datasets

[1298] Step 2: Preprocessing the data

[1299] The server integrates the collected task information, viewing preferences, environmental data, and health data, and performs data preprocessing, which includes filling in missing data and correcting outliers.

[1300] Input: User data and environmental datasets

[1301] Output: Preprocessed dataset

[1302] Step 3: Generate a schedule using machine learning algorithms

[1303] The server then uses machine learning algorithms to generate optimal tasks and viewing schedules based on the pre-processed dataset. The algorithms learn from past data and patterns to calculate the optimal schedule for each user's individual situation.

[1304] Input: Preprocessed dataset

[1305] Output: Optimized task and viewing schedules

[1306] Step 4: Schedule Notification

[1307] The server sends the generated schedule to the device, which then displays it to the user as a push notification. For example, notifications such as "Watch this week's popular comedy at 8 PM" or "Watch recommended documentaries at 10 PM" are sent to the user.

[1308] Input: Optimized task and viewing schedule

[1309] Output: Schedule notification to user device

[1310] Step 5: Performance feedback

[1311] Once the task and viewing are completed, the user provides feedback by marking the task as "completed" through the smartphone app, and the device sends this feedback information to the server.

[1312] Input: User task and viewing completion information

[1313] Output: Feedback dataset

[1314] Step 6: Learn and improve

[1315] The server integrates the collected feedback data and uses it to retrain the machine learning model, which is then reflected in the next schedule generation, improving the accuracy of the schedule.

[1316] Input: Feedback dataset

[1317] Output: An updated machine learning model

[1318] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1319] System Overview

[1320] This invention is a system for improving the efficiency of user schedule management. In particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system generates and notifies the user of an optimal schedule based on task information, environmental data, health data, and emotional data input by the user. It can also collect feedback from the user and continuously improve the schedule generation.

[1321] Program processing overview

[1322] Collection of User Information

[1323] A user opens a smartphone app and inputs their tasks for the day (e.g., "Run at 8:00," "Remote meeting at 10:00," etc.). The device sends this task information to the server. The server obtains the current temperature and weather forecast through a weather API, and collects health data such as body temperature, heart rate, and sleep data obtained from devices such as smartwatches and fitness trackers.

[1324] Additionally, the device collects emotional data using an emotion engine that analyzes the user's facial expressions, tone of voice, language patterns, etc., thereby taking into account the user's emotional state.

[1325] Data analysis and schedule generation

[1326] The server integrates the collected task information, environmental data, health data, and emotional data and performs data preprocessing, including processing missing values ​​and correcting outliers. The server then generates an optimal schedule using a machine learning algorithm. This algorithm learns from past data and patterns and calculates the optimal schedule for the user's individual situation and emotional state.

[1327] Schedule Notifications

[1328] The optimal schedule generated by the server is sent to the device, which then displays this schedule to the user as a push notification. For example, the device may notify the user of a schedule such as "Light running recommended at 8:00" or "Remote meeting at 10:00." The user can then carry out tasks based on this notification.

[1329] Execution status feedback

[1330] When a task is completed, the user marks it as "completed" through a smartphone app. The device also uses an emotion engine to collect emotional data during and after the task and sends it to the server. The server then integrates this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[1331] Specific examples

[1332] Morning Routine

[1333] The user enters the following into the smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state. If the user is feeling stressed, the device will take into consideration such things as adjusting the intensity of the run to a lighter level.

[1334] The generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." Each time a task is completed, the user reports "Complete" in the app and sends emotional data during and after the task to the server via the emotion engine. This allows the server to readjust the duration and intensity of the next run.

[1335] As described above, the system of the present invention integrates user input data, environmental data, health data, and emotional data to generate and notify optimal schedules, thereby achieving efficient and effective time management.

[1336] The processing flow will be explained below.

[1337] Step 1:

[1338] The user opens the smartphone app and enters today's tasks (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The device then sends this task information to the server.

[1339] Step 2:

[1340] The server obtains the current temperature and weather forecast through the weather API, which collects external environmental data.

[1341] Step 3:

[1342] The device collects health data such as body temperature, heart rate, and sleep data from smartwatches and fitness trackers and sends it to a server.

[1343] Step 4:

[1344] The device uses a built-in emotion engine to analyze the user's facial expressions, tone of voice, language patterns, etc. to collect emotion data, which is then sent to a server.

[1345] Step 5:

[1346] The server integrates the collected task information, environmental data, health data, and emotion data and performs preprocessing, which includes processing missing data and correcting outliers.

[1347] Step 6:

[1348] The server then generates an optimal schedule based on the pre-processed data using machine learning algorithms that learn from past data and patterns, and that also adapt to the user's individual situation and emotional state.

[1349] Step 7:

[1350] The optimal schedule generated by the server is transmitted to the terminal.

[1351] Step 8:

[1352] The device will display the schedule to the user as a push notification, such as "Light running recommended at 8:00" or "Remote meeting at 10:00."

[1353] Step 9:

[1354] The user performs a task and marks it as "completed" on the smartphone app when finished.

[1355] Step 10:

[1356] The terminal transmits task completion information from the user and emotion data generated by the emotion engine to the server.

[1357] Step 11:

[1358] The server integrates the feedback information and emotion data from the user and reflects it in the next schedule generation, thereby further improving the accuracy of the next schedule.

[1359] In this way, the present system including the emotion engine provides specific procedures for realizing efficient schedule management for users.

[1360] Example 2

[1361] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1362] Current schedule management systems often focus on simple time management without considering the user's health or emotional state. This makes it difficult to provide an optimal schedule that suits the user's physical and mental state, resulting in problems such as reduced task execution efficiency and user satisfaction. Another issue is the lack of functionality to adaptively adjust the schedule based on feedback.

[1363] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1364] In this invention, the server includes means for acquiring task information input by a user, means for collecting environmental data, health data, and emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user of the schedule to the user's information processing terminal, and means for collecting feedback from the user and reflecting it in the generation of the schedule, thereby making it possible to generate and provide an optimal schedule that takes into consideration the user's physical and mental state comprehensively.

[1365] "User information" refers to data related to tasks and schedules that users enter through smartphone apps, etc.

[1366] "Environmental data" refers to data related to the external environment that affects the user's activities, such as temperature, humidity, and weather forecast.

[1367] "Health data" refers to data that indicates the user's health condition, such as the user's body temperature, heart rate, and sleep patterns.

[1368] "Emotion data" is data that indicates the user's emotional state, analyzed from the user's facial expression, tone of voice, language patterns, and the like.

[1369] A "schedule" is a daily planner for a user that is generated based on task information entered by the user, and collected environmental data, health data, and emotional data.

[1370] A "terminal" is an information processing device used by a user, such as a smartphone or tablet.

[1371] The "server" is a central processing unit that generates a schedule based on collected data and notifies the terminals.

[1372] "Feedback" refers to information that the user sends via the terminal about the progress of the schedule and their emotional state, which is useful for generating the next schedule.

[1373] A "machine learning algorithm" is a computer program that learns from past data and execution results to generate the optimal schedule for the next time.

[1374] This invention is a system for improving the efficiency of user schedule management, and in particular, by incorporating an emotion engine, it realizes the generation of an optimal schedule based on the user's emotional state. This system consists of multiple components, including a server, terminals, and user input means, which work in conjunction with each other.

[1375] First, the user uses the device to input task information for the day into the smartphone app. For example, specific schedules such as "running at 8:00" and "remote meeting at 10:00." This information is then sent to the server via the device.

[1376] The server has multiple ways to obtain environmental, health, and emotion data. For environmental data, it uses weather APIs (e.g., OpenWeatherMap API) to obtain current temperature and weather forecast. For health data, the device collects temperature, heart rate, and sleep data from devices such as smartwatches and fitness trackers and sends it to the server.

[1377] Furthermore, to collect emotional data, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, and language patterns. The emotion engine uses services such as Microsoft Azure's Emotion API to analyze the user's emotional state, such as stress, happiness, and fatigue, in real time.

[1378] The server integrates this data and performs preprocessing on the data. Missing values ​​are filled in with estimated values, and outliers are corrected using statistical methods (e.g., Z-score or IQR). Once preprocessing is complete, the data is used to generate an optimal schedule using a machine learning algorithm (e.g., Scikit-learn's DecisionTreeClassifier or a library such as TensorFlow).

[1379] The generated schedule is sent from the server to the device. The device displays this schedule to the user as a push notification. Examples of notifications include "Light running recommended at 8:00" or "Remote meeting at 10:00." Based on this notification, the user can carry out tasks.

[1380] When a task is completed, the user marks it as "completed" in the smartphone app. The device also uses an emotion engine to collect emotion data during and after the task and sends it to the server. The server analyzes this feedback information and reflects it in the next schedule generation. This allows the accuracy of the schedule to be improved over time.

[1381] As a concrete example, consider a morning routine. A user inputs the following into a smartphone app: "Wake up at 7:00," "Run at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00." The server obtains the previous day's sleep data, current body temperature, and predicted temperature, and integrates this data via the device. Furthermore, the device uses an emotion engine to analyze the user's facial expressions and tone of voice, and adjusts the intensity of the run to a lighter level if the stress level is high. This generated schedule is notified to the device as "Wake up at 7:00," "Light run recommended at 8:00," "Breakfast at 9:00," and "Remote meeting at 10:00," and the user can spend the day accordingly.

[1382] An example of a prompt is: "Please explain how a system works that generates and notifies users of an optimal schedule based on daily task information entered by the user into a smartphone app and weather, health, and emotional data collected via a server."

[1383] In this way, the system of the present invention comprehensively integrates the user's input data, environmental data, health data, and emotional data, and generates and notifies the user of an optimal schedule, thereby achieving efficient and effective time management.

[1384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1385] Step 1: Enter your user information

[1386] The user starts the smartphone app and inputs the task information for the day (e.g., "Running at 8:00," "Remote meeting at 10:00," etc.). The input task information is sent to the server via the device.

[1387] Input: Task information (8:00 running, 10:00 remote meeting, etc.)

[1388] Output: Task information sent to the server

[1389] Step 2: Collect data

[1390] The device collects health data such as body temperature, heart rate, and sleep data from the smartwatch or fitness tracker and sends it to the server, which receives it and uses the weather API to retrieve the current temperature and weather forecast.

[1391] Input: Health data from smartwatches and fitness trackers, weather API keys

[1392] Output: Health data sent to the server, weather data obtained

[1393] Step 3: Collecting emotion data

[1394] The device uses the smartphone's camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns in real time to collect emotional data. The emotion engine uses existing AI services (such as Emotion API).

[1395] Input: Camera video, audio data

[1396] Output: Parsed emotion data

[1397] Step 4: Preprocessing the data

[1398] The server integrates the collected task information, environmental data, health data, and emotion data and performs data preprocessing. Specifically, it fills in missing values ​​and corrects outliers. For example, if there are missing values, it fills them in with the most recent past data. Outliers are corrected using statistical methods such as Z-score and IQR.

[1399] Input: Integrated task information, environmental data, health data, and emotional data

[1400] Output: Preprocessed data

[1401] Step 5: Generate a schedule

[1402] The server uses machine learning algorithms based on the preprocessed data to generate an optimal schedule. This uses models that have also learned from past data and patterns. Specifically, Scikit-learn's DecisionTreeClassifier and TensorFlow are used.

[1403] Input: Preprocessed data, machine learning model

[1404] Output: The generated optimal schedule

[1405] Step 6: Schedule Notification

[1406] The generated optimal schedule is sent from the server to the device, which then displays it to the user as a push notification. For example, it may say, "Light running recommended at 8:00" or "Remote meeting at 10:00."

[1407] Input: Generated schedule

[1408] Output: Schedule notified to the user's device

[1409] Step 7: Performance feedback

[1410] When the task is completed, the user marks it as "completed" in the smartphone app. The device then uses the emotion engine again to collect emotion data during and after the task and sends it to the server. The server then analyzes this feedback information and reflects it in the next schedule generation.

[1411] Input: Completed task information, emotion data

[1412] Output: Feedback information reflected in the next schedule generation

[1413] The above is the specific processing flow of the program of this system and the operations performed at each step.

[1414] (Application example 2)

[1415] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1416] Although self-driving vehicles are becoming more common in modern society, there are no systems that propose optimal routes that take into account passengers' emotions and health conditions. As a result, operation plans are created that lack consideration for passenger stress and fatigue, making it difficult to provide a comfortable travel experience. In addition, generating flexible routes that take passengers' physical condition and emotions into account is complex, and a system that can do this efficiently is needed.

[1417] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring task information input by the user, means for collecting environmental data and user health data, means for collecting and analyzing emotional data, means for generating an optimal schedule based on the task information, environmental data, health data, and emotional data, means for notifying the user's terminal of the schedule, means for collecting feedback from the user and reflecting it in the generation of the schedule, and means for proposing a route for the autonomous vehicle. This makes it possible to generate optimal routes and schedules based on passengers' emotions and health conditions, providing a comfortable and safe travel experience.

[1418] A "user" is a person who uses the system and is the subject of inputting emotional data, health data, task information, and the like.

[1419] "Task information" refers to information related to plans and schedules entered by the user.

[1420] "Environmental data" refers to data relating to the external conditions surrounding the user, such as weather information and traffic information.

[1421] "Health data" refers to data related to the user's physical condition, such as body temperature, heart rate, and sleep status.

[1422] "Emotion data" is data that indicates the user's emotional state, and is obtained by analyzing facial expressions, tone of voice, language patterns, and the like.

[1423] A "schedule" is a user's activity plan that is generated based on task information, environmental data, health data, and emotion data.

[1424] "Terminal" refers to any device operated by a user, including smartphones, tablets, and vehicle infotainment systems.

[1425] "Feedback" is input or evaluation provided by a user that is used to improve the system's schedule generation process.

[1426] An "autonomous vehicle" is a vehicle that operates autonomously and follows a route based on suggestions from the system.

[1427] "Route suggestion" is a system function that takes into account emotional and health data to present a route suitable for the user.

[1428] System Overview

[1429] This invention is a system that generates optimal schedules and routes for autonomous vehicles by taking into account the user's emotional and health states. Specifically, it collects task information, environmental data, health data, and emotional data from the user, and optimizes the schedule and route based on this data. This makes it possible to provide the user with a comfortable and safe travel experience.

[1430] Collection of User Information

[1431] First, a user inputs upcoming tasks into the system using a device such as a smartphone or tablet. For example, they might enter task information such as "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server simultaneously obtains environmental data such as the current temperature, weather forecast, and traffic information through a weather API, and collects health data such as heart rate, body temperature, and sleep data from a smartwatch or other device. The device then collects and analyzes emotional data, using an emotion engine to understand the user's emotional state based on facial expressions, tone of voice, and language patterns.

[1432] Data analysis and schedule generation

[1433] The server integrates the collected task information, environmental data, health data, and emotional data, and performs data preprocessing, such as filling in missing values ​​and correcting outliers. The server then uses machine learning algorithms to generate optimal schedules and routes. These algorithms learn from past data and patterns to calculate the optimal plan for the user's individual situation and emotional state.

[1434] Schedule and route notifications

[1435] The schedule and route generated by the server are sent to the device. For example, specific items such as "Meeting at 8:00," "Relax at a cafe at 10:00," and "There is a traffic jam on the central road, so we will suggest a detour" are notified. The device displays these to the user as push notifications.

[1436] Execution status feedback

[1437] After completing a task or route, the user provides feedback to the system. The device uses an emotion engine to collect emotion data during and after the task or route, and sends it to the server. The server then integrates this feedback information and reflects it in the generation of the next schedule and route. This allows the system to continuously improve its accuracy and comfort.

[1438] Hardware and software used

[1439] Hardware: Smartphones, smartwatches, in-cabin cameras and microphones in autonomous vehicles, infotainment systems

[1440] Software: Python, RESTful API, machine learning algorithms, emotion engine

[1441] Prompt Sentence Examples

[1442] The following is an example of a prompt that the system can use to obtain user emotion data:

[1443] "Tell me how you're feeling right now."

[1444] "Have you been feeling stressed lately?"

[1445] "How are you feeling today?"

[1446] As described above, this system provides a mechanism for integrating diverse user data to generate optimal schedules and routes for autonomous vehicles that take into account emotions and health conditions.

[1447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1448] Step 1:

[1449] A user inputs task information using a device such as a smartphone or tablet. The input task information may include "Meeting at 8:00" or "Relax at a cafe at 10:00." The device then sends this task information to the server. The server then receives the task information as input data and uses it as the basis for generating future schedules.

[1450] Step 2:

[1451] The server uses a weather API to obtain environmental data such as current temperature, weather forecast, and traffic information. At the same time, it collects health data such as heart rate, body temperature, and sleep data obtained from devices such as smartwatches. This allows the environmental data and health data to be aggregated on the server as input data.

[1452] Step 3:

[1453] The user also inputs emotional data using the device. The device uses a camera and microphone to analyze the user's facial expressions, tone of voice, and language patterns, and generates emotional data using an emotion engine. For example, the user is asked, "Have you been feeling stressed recently?" and emotional data is collected by answering the question. This collected emotional data is sent to the server.

[1454] Step 4:

[1455] The server integrates the collected task information, environmental data, health data, and emotion data. This integrated dataset undergoes preprocessing, such as filling in missing values ​​and correcting outliers. For example, missing weather information is filled in, and abnormally high heart rate data is corrected. This preprocessing makes the dataset suitable for schedule generation.

[1456] Step 5:

[1457] Based on the pre-processed dataset, the server uses machine learning algorithms to generate optimal schedules and routes. For example, it learns from past data and newly collected data to calculate the schedule and route that best suits the user's emotional and health conditions. This calculation generates output data for the schedule and route.

[1458] Step 6:

[1459] The server sends the generated optimal schedule and driving route to the device. The device receives this information and displays it to the user as a push notification. For example, the device may notify the user with a message such as "Meeting at 8:00" and "There is traffic congestion on the central road, so we will suggest a detour." This allows the user to check the recommended schedule and driving route.

[1460] Step 7:

[1461] The user actually performs the task and inputs feedback into the device. After completing the task, the user provides feedback such as their thoughts and evaluation of the task, and emotion data is collected again through the emotion engine. This allows emotion data to be collected about the task, during operation, and after execution.

[1462] Step 8:

[1463] The collected feedback information and emotion data are sent to the server and reflected in the generation of the next schedule and route, allowing the system to continuously improve its accuracy and comfort, and provide schedules and routes that better suit the user's needs.

[1464] The above is the flow of specific processing steps of the program of the system that realizes the application example.

[1465] 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.

[1466] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1467] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1468] 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.

[1469] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1470] 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.

[1471] 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).

[1472] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, 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.

[1473] 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."

[1474] 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.

[1475] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1476] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1481] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] The following is further disclosed regarding the above embodiment.

[1487] (Claim 1)

[1488] means for acquiring task information input by a user;

[1489] means for collecting environmental data and user health data;

[1490] means for generating an optimal schedule based on the task information, environmental data, and health data;

[1491] means for notifying a user terminal of the schedule;

[1492] The system includes means for collecting feedback from users and incorporating it into the generation of said schedule.

[1493] (Claim 2)

[1494] 10. The system of claim 1, wherein the health data includes the user's temperature, heart rate, and sleep data.

[1495] (Claim 3)

[1496] 2. The system of claim 1, wherein the schedule is generated using a machine learning algorithm.

[1497] "Example 1"

[1498] (Claim 1)

[1499] means for acquiring task information input by a user;

[1500] means for collecting weather information and biometric information of a user;

[1501] means for generating an optimal schedule based on the task information, weather information, and biological information;

[1502] means for notifying a user terminal of the schedule;

[1503] The system includes means for collecting feedback from users and incorporating it into the generation of said schedule.

[1504] (Claim 2)

[1505] 2. The system of claim 1, wherein the biometric information includes the user's temperature, heart rate, and sleep data.

[1506] (Claim 3)

[1507] 2. The system of claim 1, wherein the schedule is generated using a machine learning algorithm.

[1508] "Application Example 1"

[1509] (Claim 1)

[1510] means for acquiring task information input by a user;

[1511] means for collecting environmental data and user health data;

[1512] means for generating an optimal schedule based on the task information, environmental data, and health data;

[1513] means for notifying a user terminal of the schedule;

[1514] a means for collecting feedback from users and reflecting it in generating the schedule;

[1515] The system includes a means for integrating the task information, environmental data, and health data to generate and notify the user of a content viewing schedule.

[1516] (Claim 2)

[1517] 10. The system of claim 1, wherein the health data includes the user's temperature, heart rate, and sleep data.

[1518] (Claim 3)

[1519] 2. The system of claim 1, wherein the schedule is generated using a machine learning algorithm.

[1520] "Example 2: Combining Emotion Engines"

[1521] (Claim 1)

[1522] means for acquiring task information input by a user;

[1523] means for collecting environmental, health and emotional data;

[1524] means for generating an optimal schedule based on the task information, environmental data, health data, and emotion data;

[1525] means for notifying said schedule to a user's information processing terminal;

[1526] The system includes means for collecting feedback from users and incorporating it into the generation of said schedule.

[1527] (Claim 2)

[1528] 2. The system of claim 1, wherein the health data includes the user's temperature, heart rate, and sleep data, and the emotional data includes the user's facial expressions, tone of voice, and language patterns.

[1529] (Claim 3)

[1530] 2. The system according to claim 1, wherein the schedule is generated using a machine learning algorithm, and the feedback information is reflected in the next schedule generation.

[1531] "Application example 2 when combining emotion engines"

[1532] (Claim 1)

[1533] means for acquiring task information input by a user;

[1534] means for collecting environmental data and user health data;

[1535] a means for collecting and analyzing emotion data;

[1536] means for generating an optimal schedule based on the task information, environmental data, health data, and emotion data;

[1537] means for notifying a user terminal of the schedule;

[1538] a means for collecting feedback from users and reflecting it in generating the schedule;

[1539] A means for proposing a route for an autonomous vehicle;

[1540] A system including:

[1541] (Claim 2)

[1542] 10. The system of claim 1, wherein the health data includes the user's temperature, heart rate, and sleep data.

[1543] (Claim 3)

[1544] 2. The system of claim 1, wherein the schedule is generated using a machine learning algorithm. [Explanation of symbols]

[1545] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring task information input by a user; means for collecting environmental data and user health data; means for generating an optimal schedule based on the task information, environmental data, and health data; means for notifying a user terminal of the schedule; The system includes means for collecting feedback from users and incorporating it into the generation of said schedule.

2. 10. The system of claim 1, wherein the health data includes the user's temperature, heart rate, and sleep data.

3. The system of claim 1 , wherein the schedule is generated using a machine learning algorithm.

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