system

Abstract

We provide the system. [Solution] Means for obtaining information from the user's electronic device, A means for calculating task priority based on acquired information, A means of notifying users based on priority, A means of recording and analyzing user responses to notifications, A method for improving task prioritization algorithms using user response data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] <00000仃>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, when an individual faces various tasks in their work and daily life, it is not easy to quickly and accurately determine which tasks should be prioritized. Also, due to the flood of information, it has become difficult to overlook important tasks and start actions at the best timing. As a result, efficient time management and task management cannot be achieved, which may damage productivity and personal health. Therefore, a system that realizes appropriate prioritization and rapid information transmission is desired.

Means for Solving the Problems

[0005] This invention provides means for acquiring information from a user's electronic device and means for calculating task priorities according to multiple criteria based on the acquired information. Furthermore, it provides means for notifying the user based on the priorities and means for recording and analyzing the user's response to the notification. By utilizing the user's response data and improving the task prioritization algorithm, the system enables the user to tackle important tasks at the optimal time. This system helps the user efficiently manage tasks through visual and audible notifications.

[0006] A "user" is a person who receives task management and notifications using the system of the present invention.

[0007] "Electronic devices" refer to devices used to acquire, process, and notify information, and primarily include smartphones and tablets.

[0008] "Information" includes data related to a task, and is obtained through calendar events, emails, reminders, etc.

[0009] A "task" refers to an action or task that a user must undertake, and it requires prioritization and execution.

[0010] "Priority" refers to the ranking assigned to each task based on an assessment of its importance and urgency, derived from the information gathered.

[0011] A "notification" is a means of communicating information about tasks that a user needs to perform, and it can be provided visually or audibly.

[0012] A "response" refers to the actions or replies a user takes in response to a notification, and this data is used for system learning and improvement.

[0013] An "algorithm" refers to a set of procedures or calculation methods used to determine the priority of tasks, and is a system that is optimized based on user behavior data. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This system is a digital assistant system that uses the user's electronic devices to support task management and efficient actions. The system primarily consists of interactions between three parties: the terminal, the server, and the user.

[0036] First, the device collects information from the user's smartphone, such as calendar apps, emails, and reminders. This information includes deadlines for scheduled events and tasks, their importance level, and related notes. The device then sends this information to the server.

[0037] Next, the server evaluates the priority of each task based on the information it receives. This evaluation takes into account the deadline, importance, and the user's past behavior history. The evaluation method uses a calculation algorithm, and tasks are ranked based on the resulting score.

[0038] Subsequently, the server generates push notifications for high-priority tasks at the appropriate time. The notification includes a task summary, deadline, recommended start time, and any necessary details. This notification is sent to the user via their device, providing visual and auditory attention.

[0039] When a user receives this notification, they can choose to start the task immediately or be notified later. User actions are recorded, and the server uses this data to optimize notification accuracy and timing. This allows the system to continuously learn and evolve based on the user's work habits and priorities.

[0040] This system can, for example, support efficient task management for professionals during peak seasons. When a user is working on a specific project, the server compares its progress with other tasks and notifies them of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions, directing the user to the most important next action.

[0041] As described above, this system provides an effective means of prioritizing and managing the many tasks that users face, thereby improving productivity and time management.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects task information from sources on the user's electronic device (such as calendars, emails, and reminders). This includes scheduled dates and times, task details, reminder settings, and deadlines.

[0045] Step 2:

[0046] The terminal uses a data communication protocol to establish a connection with the server in order to send the collected information to the server.

[0047] Step 3:

[0048] The server stores the received data and calculates a priority by comprehensively evaluating each task's deadline, importance, and the user's past processing history. The priority score is calculated using an algorithm and recorded in the database.

[0049] Step 4:

[0050] The server selects the highest-priority tasks from the user's current tasks and overall task list. Based on this, it generates an alert if there are any important tasks.

[0051] Step 5:

[0052] The server generates alert information and sends it to the terminal, instructing it to display it as a push notification on the user's device.

[0053] Step 6:

[0054] The device displays alert information received by the user. The notification content is conveyed visually and audibly and includes a task summary and recommended start time.

[0055] Step 7:

[0056] The user responds to the notification by choosing to start the task immediately or by snoozing or dismissing the notification. The selected action is logged.

[0057] Step 8:

[0058] The server analyzes user responses and improves the prioritization algorithm based on behavioral data. This feedback loop continuously adjusts the timing and content of notifications.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] When users face numerous tasks, the challenge lies in providing an efficient method for effectively managing their priorities and completing tasks at the appropriate time. Furthermore, it is necessary to improve the accuracy of task management based on user behavior and provide notifications tailored to individual users.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring data from the user's terminal, means for evaluating and calculating task priority based on the data, and means for sending push notifications to the user. This enables the user to quickly grasp task priority and manage tasks efficiently. Furthermore, improved notification accuracy can enhance user productivity.

[0064] A "terminal" is an electronic device used by a user to send and receive information, and includes devices such as smartphones and tablets.

[0065] "Data" refers to quantified information such as a user's schedule, task information, and past activity history, and is the material necessary for task management.

[0066] A "server" is a computer system that receives and processes data transmitted from terminals via a network.

[0067] "Priority" is an indicator that shows the level of importance and urgency of a task, and is a criterion used to evaluate and optimize the user's action plan.

[0068] A "push notification" is a real-time message sent directly from a server to a user's device, serving as a means of quickly conveying important information to the user.

[0069] "Behavioral data" refers to information that records how users responded to notifications from the system, and serves as foundational data for improving task management methods.

[0070] An "algorithm" refers to a set of calculation procedures or rules, and is a processing method for solving a specific problem.

[0071] This invention provides a specific embodiment of a digital assistant system that utilizes user-owned electronic devices to support task management and efficient behavior. The core of the invention lies in the exchange and processing of data among three parties: a terminal, a server, and the user.

[0072] First, the device is the user's personal device, such as a smartphone or tablet. The device retrieves data from applications such as calendar, email, and reminders via APIs. This data includes the content, deadline, importance level, and supplementary notes of scheduled tasks. The device sends the collected data to the server via a secure protocol.

[0073] A server is a high-performance computer system installed in a data center or cloud environment. Servers analyze incoming data and use specific algorithms to evaluate task priorities. This evaluation is based on factors such as deadlines, importance, and the user's past behavior history, assigning priorities to each task.

[0074] Notifications based on evaluation results are generated by the server. These notifications include a task summary, deadline, recommended start time, and supplementary information. The generated notifications are sent to the device and communicated to the user visually and audibly. The notification feature allows users to understand high-priority tasks at the right time and take necessary actions quickly.

[0075] Next, users can choose to take action on notifications received via their devices, such as immediately starting the task, resending the notification later, or skipping the task. These actions are recorded by the system and sent to the server. The server analyzes this action data to optimize notification accuracy and timing, thereby improving the overall efficiency of the system.

[0076] As a concrete example, consider a situation where a user is managing multiple projects during a busy period. In this system, the server compares the progress of each project with other tasks and notifies the user of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions and issue instructions to the user.

[0077] Examples of prompts include "What are the important tasks for this week?" and "What should I do next?". The system responds immediately to such prompts, supporting optimal task management for the user.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device retrieves information from the user's smartphone or tablet, such as schedule apps, emails, and reminders. Input is done via the APIs of each application, and output is a dataset containing the user's tasks, deadlines, importance levels, and notes. This data retrieval is performed periodically, and synchronization is maintained to keep the information up-to-date.

[0081] Step 2:

[0082] The device sends the collected data to the server using a secure protocol. The input is the user dataset obtained in the previous step, and the output is the completion status of the data transfer to the server. Encryption is applied during transmission to protect privacy.

[0083] Step 3:

[0084] The server analyzes the received data and evaluates the task priority. The input is data sent from the terminal, and the output is a priority score calculated for each task. The server uses a dedicated algorithm to calculate a composite score that takes into account the deadline, importance, and activity history.

[0085] Step 4:

[0086] The server generates a push notification to send to the user based on the calculated priority. The input is the priority score and notification template, and the output is the notification message sent to the user. This message includes information necessary for task management, such as a task summary, deadline, and recommended start time.

[0087] Step 5:

[0088] The device receives notifications from the server and displays them on the user's screen. The input is the notification message from the server, and the output is a visual and audible alert to the user. The device presents the notification appropriately and uses sound and vibration to attract the user's attention.

[0089] Step 6:

[0090] The user checks the notification and chooses whether to start the task immediately or to have the notification revisited later. The input is the notification received via the device, and the output is the user's action choice. The user's choice is recorded as data used to optimize future notifications.

[0091] Step 7:

[0092] The server receives user behavior data and uses it to improve the accuracy and content of notifications. The input is user selection data, and the output is an optimized notification algorithm. Through this, the server can better optimize the timing of future task suggestions and notifications for the user.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In modern manufacturing environments, effectively managing a wide variety of tasks is crucial. However, traditional methods make it difficult to monitor the operating status of machinery and equipment in real time and to take appropriate action in a timely manner. Therefore, optimizing efficient work schedules is essential.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for acquiring information from the user's electronic devices and field devices, means for calculating task priorities based on the acquired information, and means for notifying the user and relevant operators based on the priorities. This enables real-time task management and improved productivity in response to field conditions.

[0098] A "user" is an individual or group that operates electronic devices and receives information from them.

[0099] An "electronic device" is a device that acquires and processes information and notifies the user.

[0100] "Field devices" refer to equipment and sensors used in production sites, and are devices for collecting information about the situation on site.

[0101] "Means of acquiring information" refers to methods or techniques for collecting data from users' electronic devices and field devices.

[0102] A "means for calculating task priorities" is a function that evaluates the importance of tasks based on acquired information and determines their ranking.

[0103] "Means of notification" refer to methods and technologies for informing users or operators of the priority and status of tasks.

[0104] "Means for recording and analyzing user and operator responses" refers to technologies for saving and analyzing the actions and responses of users and operators after notification.

[0105] "Methods for improving task prioritization algorithms" refers to approaches that utilize response data to improve how tasks are prioritized.

[0106] "Visual means of communication" refers to methods of providing notifications in a visually recognizable format.

[0107] "Means of communication via sound" refers to methods of conveying notifications to users through voice.

[0108] "Circumstantial communication methods" refer to techniques or means of providing notification in an appropriate manner according to the site and work environment.

[0109] This invention provides a system that optimizes task management for users and operators, and is primarily implemented through a network configuration including servers, terminals, and field devices. A detailed description of how to implement this system follows.

[0110] The server is programmed in Python or C++ and has built-in algorithms for task prioritization. Each user's electronic devices and field devices use appropriate data collection modules to acquire information. This includes sensors for monitoring field operations, calendar apps for collecting user data, and reminder functions. The data is sent to the server, where it is processed and analyzed. Machine learning frameworks such as TENSORFLOW® are used to calculate task priorities based on the acquired data.

[0111] The server has the capability to generate visual, audible, and even situational notifications. These notifications are sent to users and operators via terminals, facilitating real-time management of work processes. Response data from users and operators after receiving notifications is returned to the server and recorded. This allows the server to continuously optimize its algorithms, evolving to perform task management processes more efficiently and effectively in the future.

[0112] To give a concrete example, suppose a machine on a manufacturing line shows an abnormal temperature. Sensor data from the on-site device is sent to the server and detected as a high-priority task. A notification that maintenance is immediately required is transmitted to the operator's electronic device. At this time, a prompt message like the following is generated: "An abnormality has been detected in machine A on manufacturing line 5. Please perform a priority inspection."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server receives sensor information from field devices, including data such as temperature, vibration, and operating status. The input is raw data from the sensors, which the server filters and preprocesses to remove outliers and noise, resulting in a new output.

[0116] Step 2:

[0117] The terminal collects calendar and reminder information from the user's electronic device. This input provides information related to the user's schedule and tasks. The terminal sends this information to a server via an external API to build a database for processing.

[0118] Step 3:

[0119] The server uses a machine learning model (using TensorFlow) to determine task priorities based on collected device data and user information. This calculation applies a scoring model that takes into account historical data, field conditions, and the user's perceived importance of the task. The output is a prioritized task list.

[0120] Step 4:

[0121] The server generates notifications for users and operators based on a priority list. These notifications include information about tasks requiring immediate attention or those deemed critical. The server sends this information to the terminal as visual and audio notifications.

[0122] Step 5:

[0123] Users and operators receive notifications from the server via their terminals. Based on these notifications, users begin their actual work and record the results as input. The terminal then sends this behavioral data back to the server, where it is used to improve the algorithm for future use.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is a digital personal assistant system that integrates emotion recognition technology and task management functions. This enables flexible and personalized task management that responds to the user's emotional state. The system comprises a terminal, a server, and an emotion engine.

[0126] First, the device retrieves task information from the user's device, such as calendar, email, and reminders. Before sending the aggregated information to the server, the device analyzes the user's emotions using a function that incorporates an emotion engine. The emotion engine identifies the user's current emotional state using the user's facial expressions, voice, or biosensor data.

[0127] Next, the server calculates task priority based on the task information received from the terminal and the emotion data recognized by the emotion engine. This priority is calculated using a dynamic algorithm that takes into account not only traditional deadlines and importance, but also the user's emotional state. For example, if the user is stressed, adjustments are made to prioritize less burdensome tasks.

[0128] Subsequently, the server generates notifications based on the prioritized tasks. These notifications are adapted to the user's emotional state and are delivered to the user in a kind and urgency-conscious text and voice. The notifications are communicated to the user visually and audibly through the device, helping them understand the urgency of the requirements and the recommended actions.

[0129] Users receive notifications and decide whether to proceed with the task. Their choices and actions are recorded and analyzed by the server. The analysis results are used to improve algorithms and enhance the system's accuracy and efficiency.

[0130] For example, if the emotion engine detects that a user is feeling stressed after a morning meeting, the server will postpone important report writing tasks and notify the user of relaxing tasks. In this way, the system supports the completion of the most appropriate tasks while taking the user's emotions into consideration. The aim is to improve the user's productivity and well-being.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device collects task information from calendar, email, and reminder apps installed on the user's electronic device. This includes the date and time of the event, task details, and priority.

[0134] Step 2:

[0135] The device activates an emotion engine and collects data from the camera, microphone, and biosensors to recognize the user's emotional state. Emotional characteristics are extracted from this data and analyzed in real time.

[0136] Step 3:

[0137] The terminal integrates the sentiment analysis results with previously collected task information and generates package data to send to the server.

[0138] Step 4:

[0139] The server receives the composite data sent from the terminal and calculates the task priority. The calculation uses an algorithm that takes into account the task deadline, importance, and the user's emotional state.

[0140] Step 5:

[0141] The server creates a task list optimized for the user based on calculated priorities. Tasks are adjusted to accommodate emotional changes, ensuring a comfortable and orderly workflow for the user.

[0142] Step 6:

[0143] The server sends notifications to the device, generated based on the user's emotions and priorities. These notifications use text and audio templates tailored to the user's current situation.

[0144] Step 7:

[0145] The system displays notifications received by the device to the user visually and audibly. This makes it easier for the user to understand the overview and priority of tasks.

[0146] Step 8:

[0147] The user either works on the task based on the notification or returns a selected action to the system. This includes options such as starting the task immediately or setting it to be notified later.

[0148] Step 9:

[0149] The server analyzes user responses and behavior history to improve task priorities and notifications for the next time. Learning capabilities are used to prepare for providing a more personalized experience.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Traditional task management systems rely solely on static prioritization based on deadlines and importance, which hinders flexible task management that takes into account the user's emotional state. This can lead to burdensome tasks being prioritized for users experiencing stress or fatigue, potentially resulting in decreased productivity and a loss of user well-being.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for acquiring information from the user's terminal device, means for calculating task priorities based on the acquired information and the user's emotional state, and means for collecting and analyzing biometric data to identify the user's emotions. This enables flexible and personalized task management that takes the user's emotional state into consideration.

[0155] A "terminal device" is an information processing device that a user directly operates to input and output data, and includes smartphones and personal computers.

[0156] "Emotional state" refers to the current psychological or emotional state obtained by analyzing data acquired from the user's facial expressions, voice, and biosensors.

[0157] "Task prioritization" refers to criteria used to determine the importance and urgency of tasks that need to be addressed, based on specific factors.

[0158] A "notification" is information presented to the user via a terminal device or voice technology regarding the status of a task or recommended next action.

[0159] "Biometric data" refers to data obtained directly from the user's body, including information that indicates an individual's physiological state, such as heart rate and changes in facial expression.

[0160] An "algorithm" is a set of computational procedures designed to achieve a specific purpose, and in this context, it refers to the processing steps for determining the priority of tasks.

[0161] This invention is a task management system that utilizes emotion recognition and is designed to provide flexible and personalized task management. The system mainly consists of terminal devices, a server, and an emotion recognition engine.

[0162] The terminal device is the user's smartphone or PC, and it retrieves task information from digital calendars and emails using the Google® Calendar API and email protocols (e.g., IMAP). The terminal device also uses the OpenCV library to recognize facial expression data from the camera and analyzes voice data using the Google Speech-to-Text API. Furthermore, it utilizes the user's biometric data by acquiring heart rate and skin electrical responses via Bluetooth from wearable devices such as smartwatches.

[0163] The server receives task information and emotional state data sent from the terminal device and calculates task priorities based on this information. This uses a dynamic algorithm based on the user's past behavior data, employing machine learning libraries such as Scikit-learn. When generating notifications for prioritized tasks, the server uses Natural Language Processing techniques to create flexible and user-friendly text.

[0164] Notifications are transmitted to the user via the device and are provided in the form of push notifications on smartphones or played via voice assistants. For example, if the user is feeling stressed, a suggestion such as, "Let's take a short break before tackling this task," might be made.

[0165] Users receive notifications and decide whether to proceed with the task. During this process, user behavior data is recorded, and the server performs further analysis. Based on this, the system improves prioritization for future tasks.

[0166] As a concrete example, the input to the generation AI model for a prompt would be: "When the user is experiencing stress, please suggest an optimal task schedule that takes into account past task data and emotional state."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The terminal collects task information from the user's digital device. Specifically, it retrieves data from the device's calendar, email, and reminders using APIs. Based on this input, the terminal aggregates task date, time, and priority information. The output is a list of task information.

[0170] Step 2:

[0171] The device activates an emotion recognition engine to determine the user's emotional state. It captures the user's facial expressions using the camera with the OpenCV library and analyzes the audio with the Google Speech-to-Text API. It also acquires heart rate and skin electrochemistry data from the wearable device via Bluetooth. Based on this input, the device obtains data indicating the user's emotional state and outputs the user's emotional state as an analysis result.

[0172] Step 3:

[0173] The terminal sends collected task information and emotional state data to the server. This data is securely transferred using the HTTPS protocol. The input consists of a task information list and user emotional state data, which then serve as the output sent to the server.

[0174] Step 4:

[0175] The server calculates task priorities based on the data it receives. Using the machine learning library Scikit-learn, it applies sentiment data and past user behavior patterns to the algorithm. The input consists of received task information and sentiment state data, and a dynamically prioritized task list is output.

[0176] Step 5:

[0177] The server generates notifications based on prioritized tasks. Using Natural Language Processing (NLP) technology, it creates friendly notification messages tailored to the user's emotional state. The generated notification messages are sent to the terminal as server output.

[0178] Step 6:

[0179] The device transmits notifications to the user. These notifications are delivered to the user via smartphone push notifications or voice assistants. This allows the user to review recommended tasks. The input is the notification text from the server, and the output is the notification delivered to the user.

[0180] Step 7:

[0181] The user receives a notification and decides whether or not to work on the task. The user's choices and actions are recorded, and this data is used to improve future processes. The input is the notification confirmation and the user's choice, and the output is the recording of action data.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In modern industrial settings, worker stress and fatigue pose a problem that impacts productivity and safety. Furthermore, traditional systems determine tasks without considering emotional states, leading to unsuitable work assignments and decreased productivity. Therefore, it is necessary to flexibly adjust task priorities and appropriately manage workloads according to the physiological state of the workers.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for sensing the user's physiological state and analyzing emotional information, means for dynamically adjusting task priorities based on emotional information, and means for appropriately communicating notifications to the user via sight or hearing. This enables flexible task prioritization according to the worker's emotional state, thereby improving efficiency and safety in the production environment.

[0187] An "information processing device" is a device that receives input from a user and processes and analyzes that data, and includes devices such as computers and smartphones.

[0188] "Priority" refers to the order in which tasks that need to be processed or executed are ranked based on their importance and urgency, and serves as a criterion for determining which tasks should be prioritized.

[0189] A "notification" is a means of communicating important information or task-related information to a user, and it is done by informing the user through visual or auditory means.

[0190] "Reaction" refers to the actions or responses a user takes when they receive a notification, and describes the user's behavioral patterns and choices in response to notifications.

[0191] "Physiological state" refers to information that indicates the user's physical condition, including biometric data such as heart rate and skin temperature.

[0192] "Emotional information" refers to data that represents the user's emotional and psychological state, and is identified through facial expressions, tone of voice, and input from biosensors.

[0193] "Dynamic adjustment" means automatically and continuously changing things according to the situation and conditions, and is a process of reviewing priorities and tasks in real time.

[0194] The system for realizing this invention consists of a user information processing unit, a server, and an emotion engine as its main components. The user information processing unit monitors the user's physiological state in real time using smart glasses or a wristband-type biosensor. This data is analyzed by the emotion engine and transmitted to the server as user emotion information.

[0195] The server analyzes the user's physiological state and dynamically incorporates emotional information to determine task priorities. Priorities are then reset based on multiple criteria, including deadlines, importance, and physiological state. This adjustment allows the user to perform tasks best suited to their current emotional state. Notifications are communicated to the user visually or audibly, with a generative AI model selecting the most effective method in real time.

[0196] For example, if the system detects that a worker is experiencing stress at the start of the workday in the morning, the server will reassign tasks to reduce the workload. This allows the worker to focus on relaxing tasks and maintain productivity. An example of a prompt to the generating AI model would be: "Evaluate the current emotional state of the worker and prioritize tasks based on the results. If stress levels are high, adjust the workload to reduce the burden and prioritize relaxing tasks."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The user's device monitors their real-time physiological state using smart glasses or wristband-type biosensors. This acquires data such as heart rate and skin temperature. The acquired physiological data is then sent directly to the emotion engine.

[0200] Step 2:

[0201] The emotion engine receives physiological data sent from the user as input and analyzes it. As part of the data processing, an AI algorithm analyzes patterns in heart rate variability and temperature anomalies to extract the user's emotional information. This extracted emotional information is then passed to the server for use in the next step.

[0202] Step 3:

[0203] The server uses emotional information received from the emotion engine to reprioritize the user's tasks. The server receives multiple criteria data as input, including deadlines, importance, and physiological state, and outputs dynamic task priorities through data calculations based on these.

[0204] Step 4:

[0205] The server generates appropriate notification messages based on task information with reset priorities. Using a generation AI model, notifications are configured to select text and audio that best reflect the user's current emotional state and to be communicated to the user visually or audibly.

[0206] Step 5:

[0207] Users receive notifications from the server via their devices. After reviewing the notification, users select a response and send it to the server. This feedback includes data such as the user's choices and time spent, which is used to adjust tasks in the future.

[0208] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0224] This system is a digital assistant system that uses the user's electronic devices to support task management and efficient actions. The system primarily consists of interactions between three parties: the terminal, the server, and the user.

[0225] First, the device collects information from the user's smartphone, such as calendar apps, emails, and reminders. This information includes deadlines for scheduled events and tasks, their importance level, and related notes. The device then sends this information to the server.

[0226] Next, the server evaluates the priority of each task based on the information it receives. This evaluation takes into account the deadline, importance, and the user's past behavior history. The evaluation method uses a calculation algorithm, and tasks are ranked based on the resulting score.

[0227] Subsequently, the server generates push notifications for high-priority tasks at the appropriate time. The notification includes a task summary, deadline, recommended start time, and any necessary details. This notification is sent to the user via their device, providing visual and auditory attention.

[0228] When a user receives this notification, they can choose to start the task immediately or be notified later. User actions are recorded, and the server uses this data to optimize notification accuracy and timing. This allows the system to continuously learn and evolve based on the user's work habits and priorities.

[0229] This system can, for example, support efficient task management for professionals during peak seasons. When a user is working on a specific project, the server compares its progress with other tasks and notifies them of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions, directing the user to the most important next action.

[0230] As described above, this system provides an effective means of prioritizing and managing the many tasks that users face, thereby improving productivity and time management.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The device collects task information from sources on the user's electronic device (such as calendars, emails, and reminders). This includes scheduled dates and times, task details, reminder settings, and deadlines.

[0234] Step 2:

[0235] The terminal uses a data communication protocol to establish a connection with the server in order to send the collected information to the server.

[0236] Step 3:

[0237] The server stores the received data and calculates a priority by comprehensively evaluating each task's deadline, importance, and the user's past processing history. The priority score is calculated using an algorithm and recorded in the database.

[0238] Step 4:

[0239] The server selects the highest-priority tasks from the user's current tasks and overall task list. Based on this, it generates an alert if there are any important tasks.

[0240] Step 5:

[0241] The server generates alert information and sends it to the terminal, instructing it to display it as a push notification on the user's device.

[0242] Step 6:

[0243] The device displays alert information received by the user. The notification content is conveyed visually and audibly and includes a task summary and recommended start time.

[0244] Step 7:

[0245] The user responds to the notification by choosing to start the task immediately or by snoozing or dismissing the notification. The selected action is logged.

[0246] Step 8:

[0247] The server analyzes user responses and improves the prioritization algorithm based on behavioral data. This feedback loop continuously adjusts the timing and content of notifications.

[0248] (Example 1)

[0249] Next, we will describe Example 1. 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."

[0250] When users face numerous tasks, the challenge lies in providing an efficient method for effectively managing their priorities and completing tasks at the appropriate time. Furthermore, it is necessary to improve the accuracy of task management based on user behavior and provide notifications tailored to individual users.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for acquiring data from the user's terminal, means for evaluating and calculating task priority based on the data, and means for sending push notifications to the user. This enables the user to quickly grasp task priority and manage tasks efficiently. Furthermore, improved notification accuracy can enhance user productivity.

[0253] A "terminal" is an electronic device used by a user to send and receive information, and includes devices such as smartphones and tablets.

[0254] "Data" refers to quantified information such as a user's schedule, task information, and past activity history, and is the material necessary for task management.

[0255] A "server" is a computer system that receives and processes data transmitted from terminals via a network.

[0256] "Priority" is an indicator that shows the level of importance and urgency of a task, and is a criterion used to evaluate and optimize the user's action plan.

[0257] A "push notification" is a real-time message sent directly from a server to a user's device, serving as a means of quickly conveying important information to the user.

[0258] "Behavioral data" refers to information that records how users responded to notifications from the system, and serves as foundational data for improving task management methods.

[0259] An "algorithm" refers to a set of calculation procedures or rules, and is a processing method for solving a specific problem.

[0260] This invention provides a specific embodiment of a digital assistant system that utilizes user-owned electronic devices to support task management and efficient behavior. The core of the invention lies in the exchange and processing of data among three parties: a terminal, a server, and the user.

[0261] First, the device is the user's personal device, such as a smartphone or tablet. The device retrieves data from applications such as calendar, email, and reminders via APIs. This data includes the content, deadline, importance level, and supplementary notes of scheduled tasks. The device sends the collected data to the server via a secure protocol.

[0262] A server is a high-performance computer system installed in a data center or cloud environment. Servers analyze incoming data and use specific algorithms to evaluate task priorities. This evaluation is based on factors such as deadlines, importance, and the user's past behavior history, assigning priorities to each task.

[0263] Notifications based on evaluation results are generated by the server. These notifications include a task summary, deadline, recommended start time, and supplementary information. The generated notifications are sent to the device and communicated to the user visually and audibly. The notification feature allows users to understand high-priority tasks at the right time and take necessary actions quickly.

[0264] Next, users can choose to take action on notifications received via their devices, such as immediately starting the task, resending the notification later, or skipping the task. These actions are recorded by the system and sent to the server. The server analyzes this action data to optimize notification accuracy and timing, thereby improving the overall efficiency of the system.

[0265] As a concrete example, consider a situation where a user is managing multiple projects during a busy period. In this system, the server compares the progress of each project with other tasks and notifies the user of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions and issue instructions to the user.

[0266] Examples of prompts include "What are the important tasks for this week?" and "What should I do next?". The system responds immediately to such prompts, supporting optimal task management for the user.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The device retrieves information from the user's smartphone or tablet, such as schedule apps, emails, and reminders. Input is done via the APIs of each application, and output is a dataset containing the user's tasks, deadlines, importance levels, and notes. This data retrieval is performed periodically, and synchronization is maintained to keep the information up-to-date.

[0270] Step 2:

[0271] The device sends the collected data to the server using a secure protocol. The input is the user dataset obtained in the previous step, and the output is the completion status of the data transfer to the server. Encryption is applied during transmission to protect privacy.

[0272] Step 3:

[0273] The server analyzes the received data and evaluates the task priority. The input is data sent from the terminal, and the output is a priority score calculated for each task. The server uses a dedicated algorithm to calculate a composite score that takes into account the deadline, importance, and activity history.

[0274] Step 4:

[0275] The server generates a push notification to send to the user based on the calculated priority. The input is the priority score and notification template, and the output is the notification message sent to the user. This message includes information necessary for task management, such as a task summary, deadline, and recommended start time.

[0276] Step 5:

[0277] The device receives notifications from the server and displays them on the user's screen. The input is the notification message from the server, and the output is a visual and audible alert to the user. The device presents the notification appropriately and uses sound and vibration to attract the user's attention.

[0278] Step 6:

[0279] The user checks the notification and immediately chooses whether to start the task or to redisplay the notification later. The input is the notification received via the terminal, and the output is the user's action choice. The user's choice is recorded as data to be used for optimizing subsequent notifications.

[0280] Step 7:

[0281] The server receives the user's action data and uses it to improve notification accuracy and content. The input is the user's selection data, and the output is the optimized notification algorithm. Through this, the server can optimize the proposal of subsequent tasks and the timing of notifications for the user.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] In modern production sites, it is important to effectively manage a variety of tasks. However, with conventional methods, it is difficult to grasp the operating status of machines and equipment in real time and take appropriate actions in a timely manner. Therefore, optimization of an efficient work schedule is required.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for acquiring information from the user's electronic device and field devices, means for calculating the priority of tasks based on the acquired information, and means for notifying the user and related operators based on the priority. Thereby, real-time task management according to the on-site situation and improvement of productivity become possible.

[0287] A "user" is an individual or group that operates electronic devices and receives information from them.

[0288] An "electronic device" is a device that acquires and processes information and notifies the user.

[0289] "Field devices" refer to equipment and sensors used in production sites, and are devices for collecting information about the situation on site.

[0290] "Means of acquiring information" refers to methods or techniques for collecting data from users' electronic devices and field devices.

[0291] A "means for calculating task priorities" is a function that evaluates the importance of tasks based on acquired information and determines their ranking.

[0292] "Means of notification" refer to methods and technologies for informing users or operators of the priority and status of tasks.

[0293] "Means for recording and analyzing user and operator responses" refers to technologies for saving and analyzing the actions and responses of users and operators after notification.

[0294] "Methods for improving task prioritization algorithms" refers to approaches that utilize response data to improve how tasks are prioritized.

[0295] "Visual means of communication" refers to methods of providing notifications in a visually recognizable format.

[0296] "Means of communication via sound" refers to methods of conveying notifications to users through voice.

[0297] "Circumstantial communication methods" refer to techniques or means of providing notification in an appropriate manner according to the site and work environment.

[0298] This invention provides a system that optimizes task management for users and operators, and is primarily implemented through a network configuration including servers, terminals, and field devices. A detailed description of how to implement this system follows.

[0299] The server is programmed in Python or C++ and has built-in algorithms for task prioritization. Each user's electronic devices and field devices use appropriate data collection modules to acquire information. This includes sensors for on-site operational monitoring, a calendar app for collecting user data, and reminder functions. The data is sent to the server, where it is processed and analyzed. Machine learning frameworks such as TensorFlow are used to calculate task priorities based on the acquired data.

[0300] The server has the capability to generate visual, audible, and even situational notifications. These notifications are sent to users and operators via terminals, facilitating real-time management of work processes. Response data from users and operators after receiving notifications is returned to the server and recorded. This allows the server to continuously optimize its algorithms, evolving to perform task management processes more efficiently and effectively in the future.

[0301] To give a concrete example, suppose a machine on a manufacturing line shows an abnormal temperature. Sensor data from the on-site device is sent to the server and detected as a high-priority task. A notification that maintenance is immediately required is transmitted to the operator's electronic device. At this time, a prompt message like the following is generated: "An abnormality has been detected in machine A on manufacturing line 5. Please perform a priority inspection."

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The server receives sensor information from on-site devices. This includes data such as temperature, vibration, and operating status. The input is raw data from the sensors, and the server filters this and performs preprocessing to remove outliers and noise to obtain the output.

[0305] Step 2:

[0306] The terminal collects information on calendars and reminders built into the user's electronic devices. From this input, information related to the user's schedule and tasks is obtained. The terminal sends this to the server via an external API to build a database for processing.

[0307] Step 3:

[0308] Based on the collected device data and user information, the server determines the priority of tasks using a machine learning model (using TensorFlow). In this calculation, a scoring model that takes into account past data, on-site conditions, and the importance of the user's tasks is applied. The output is a prioritized task list.

[0309] Step 4:

[0310] Based on the priority list, the server generates notifications for the user and the operator. The content of the notification includes information on tasks that require immediate attention and important tasks. The server sends this to the terminal as visual and audio notifications.

[0311] Step 5:

[0312] The user and the operator receive the notification from the server on the terminal. The user starts the actual work based on the notification and records the result as input. The terminal sends this action data back to the server to be utilized for improving the algorithm in subsequent times.

[0313] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0314] This invention is a digital personal assistant system that integrates emotion recognition technology and task management functions. This enables flexible and personalized task management that responds to the user's emotional state. The system comprises a terminal, a server, and an emotion engine.

[0315] First, the device retrieves task information from the user's device, such as calendar, email, and reminders. Before sending the aggregated information to the server, the device analyzes the user's emotions using a function that incorporates an emotion engine. The emotion engine identifies the user's current emotional state using the user's facial expressions, voice, or biosensor data.

[0316] Next, the server calculates task priority based on the task information received from the terminal and the emotion data recognized by the emotion engine. This priority is calculated using a dynamic algorithm that takes into account not only traditional deadlines and importance, but also the user's emotional state. For example, if the user is stressed, adjustments are made to prioritize less burdensome tasks.

[0317] Subsequently, the server generates notifications based on the prioritized tasks. These notifications are adapted to the user's emotional state and are delivered to the user in a kind and urgency-conscious text and voice. The notifications are communicated to the user visually and audibly through the device, helping them understand the urgency of the requirements and the recommended actions.

[0318] Users receive notifications and decide whether to proceed with the task. Their choices and actions are recorded and analyzed by the server. The analysis results are used to improve algorithms and enhance the system's accuracy and efficiency.

[0319] For example, if the emotion engine detects that a user is feeling stressed after a morning meeting, the server will postpone important report writing tasks and notify the user of relaxing tasks. In this way, the system supports the completion of the most appropriate tasks while taking the user's emotions into consideration. The aim is to improve the user's productivity and well-being.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The device collects task information from calendar, email, and reminder apps installed on the user's electronic device. This includes the date and time of the event, task details, and priority.

[0323] Step 2:

[0324] The device activates an emotion engine and collects data from the camera, microphone, and biosensors to recognize the user's emotional state. Emotional characteristics are extracted from this data and analyzed in real time.

[0325] Step 3:

[0326] The terminal integrates the sentiment analysis results with previously collected task information and generates package data to send to the server.

[0327] Step 4:

[0328] The server receives the composite data sent from the terminal and calculates the task priority. The calculation uses an algorithm that takes into account the task deadline, importance, and the user's emotional state.

[0329] Step 5:

[0330] The server creates a task list optimized for the user based on calculated priorities. Tasks are adjusted to accommodate emotional changes, ensuring a comfortable and orderly workflow for the user.

[0331] Step 6:

[0332] The server sends notifications to the device, generated based on the user's emotions and priorities. These notifications use text and audio templates tailored to the user's current situation.

[0333] Step 7:

[0334] The system displays notifications received by the device to the user visually and audibly. This makes it easier for the user to understand the overview and priority of tasks.

[0335] Step 8:

[0336] The user either works on the task based on the notification or returns a selected action to the system. This includes options such as starting the task immediately or setting it to be notified later.

[0337] Step 9:

[0338] The server analyzes user responses and behavior history to improve task priorities and notifications for the next time. Learning capabilities are used to prepare for providing a more personalized experience.

[0339] (Example 2)

[0340] Next, we will describe Example 2. 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".

[0341] Traditional task management systems rely solely on static prioritization based on deadlines and importance, which hinders flexible task management that takes into account the user's emotional state. This can lead to burdensome tasks being prioritized for users experiencing stress or fatigue, potentially resulting in decreased productivity and a loss of user well-being.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for acquiring information from the user's terminal device, means for calculating task priorities based on the acquired information and the user's emotional state, and means for collecting and analyzing biometric data to identify the user's emotions. This enables flexible and personalized task management that takes the user's emotional state into consideration.

[0344] A "terminal device" is an information processing device that a user directly operates to input and output data, and includes smartphones and personal computers.

[0345] "Emotional state" refers to the current psychological or emotional state obtained by analyzing data acquired from the user's facial expressions, voice, and biosensors.

[0346] "Task prioritization" refers to criteria used to determine the importance and urgency of tasks that need to be addressed, based on specific factors.

[0347] A "notification" is information presented to the user via a terminal device or voice technology regarding the status of a task or recommended next action.

[0348] "Biometric data" refers to data obtained directly from the user's body, including information that indicates an individual's physiological state, such as heart rate and changes in facial expression.

[0349] An "algorithm" is a set of computational procedures designed to achieve a specific purpose, and in this context, it refers to the processing steps for determining the priority of tasks.

[0350] This invention is a task management system that utilizes emotion recognition and is designed to provide flexible and personalized task management. The system mainly consists of terminal devices, a server, and an emotion recognition engine.

[0351] The terminal device is the user's smartphone or PC, and it retrieves task information from digital calendars and emails using the Google Calendar API and email protocols (e.g., IMAP). The terminal device also uses the OpenCV library to recognize facial expressions from the camera and analyzes speech data using the Google Speech-to-Text API. Furthermore, it utilizes the user's biometric data by acquiring heart rate and skin electrical responses via Bluetooth from wearable devices such as smartwatches.

[0352] The server receives task information and emotional state data sent from the terminal device and calculates task priorities based on this information. This uses a dynamic algorithm based on the user's past behavior data, employing machine learning libraries such as Scikit-learn. When generating notifications for prioritized tasks, the server uses Natural Language Processing techniques to create flexible and user-friendly text.

[0353] Notifications are transmitted to the user via the device and are provided in the form of push notifications on smartphones or played via voice assistants. For example, if the user is feeling stressed, a suggestion such as, "Let's take a short break before tackling this task," might be made.

[0354] Users receive notifications and decide whether to proceed with the task. During this process, user behavior data is recorded, and the server performs further analysis. Based on this, the system improves prioritization for future tasks.

[0355] As a concrete example, the input to the generation AI model for a prompt would be: "When the user is experiencing stress, please suggest an optimal task schedule that takes into account past task data and emotional state."

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The terminal collects task information from the user's digital device. Specifically, it retrieves data from the device's calendar, email, and reminders using APIs. Based on this input, the terminal aggregates task date, time, and priority information. The output is a list of task information.

[0359] Step 2:

[0360] The device activates an emotion recognition engine to determine the user's emotional state. It captures the user's facial expressions using the camera with the OpenCV library and analyzes the audio with the Google Speech-to-Text API. It also acquires heart rate and skin electrochemistry data from the wearable device via Bluetooth. Based on this input, the device obtains data indicating the user's emotional state and outputs the user's emotional state as an analysis result.

[0361] Step 3:

[0362] The terminal sends collected task information and emotional state data to the server. This data is securely transferred using the HTTPS protocol. The input consists of a task information list and user emotional state data, which then serve as the output sent to the server.

[0363] Step 4:

[0364] The server calculates task priorities based on the data it receives. Using the machine learning library Scikit-learn, it applies sentiment data and past user behavior patterns to the algorithm. The input consists of received task information and sentiment state data, and a dynamically prioritized task list is output.

[0365] Step 5:

[0366] The server generates notifications based on prioritized tasks. Using Natural Language Processing (NLP) technology, it creates friendly notification messages tailored to the user's emotional state. The generated notification messages are sent to the terminal as server output.

[0367] Step 6:

[0368] The device transmits notifications to the user. These notifications are delivered to the user via smartphone push notifications or voice assistants. This allows the user to review recommended tasks. The input is the notification text from the server, and the output is the notification delivered to the user.

[0369] Step 7:

[0370] The user receives a notification and decides whether or not to work on the task. The user's choices and actions are recorded, and this data is used to improve future processes. The input is the notification confirmation and the user's choice, and the output is the recording of action data.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] In modern industrial settings, worker stress and fatigue pose a problem that impacts productivity and safety. Furthermore, traditional systems determine tasks without considering emotional states, leading to unsuitable work assignments and decreased productivity. Therefore, it is necessary to flexibly adjust task priorities and appropriately manage workloads according to the physiological state of the workers.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for sensing the user's physiological state and analyzing emotional information, means for dynamically adjusting task priorities based on emotional information, and means for appropriately communicating notifications to the user via sight or hearing. This enables flexible task prioritization according to the worker's emotional state, thereby improving efficiency and safety in the production environment.

[0376] An "information processing device" is a device that receives input from a user and processes and analyzes that data, and includes devices such as computers and smartphones.

[0377] "Priority" refers to the order in which tasks that need to be processed or executed are ranked based on their importance and urgency, and serves as a criterion for determining which tasks should be prioritized.

[0378] A "notification" is a means of communicating important information or task-related information to a user, and it is done by informing the user through visual or auditory means.

[0379] "Reaction" refers to the actions or responses a user takes when they receive a notification, and describes the user's behavioral patterns and choices in response to notifications.

[0380] "Physiological state" refers to information that indicates the user's physical condition, including biometric data such as heart rate and skin temperature.

[0381] "Emotional information" refers to data that represents the user's emotional and psychological state, and is identified through facial expressions, tone of voice, and input from biosensors.

[0382] "Dynamic adjustment" means automatically and continuously changing things according to the situation and conditions, and is a process of reviewing priorities and tasks in real time.

[0383] The system for realizing this invention consists of a user information processing unit, a server, and an emotion engine as its main components. The user information processing unit monitors the user's physiological state in real time using smart glasses or a wristband-type biosensor. This data is analyzed by the emotion engine and transmitted to the server as user emotion information.

[0384] The server analyzes the user's physiological state and dynamically incorporates emotional information to determine task priorities. Priorities are then reset based on multiple criteria, including deadlines, importance, and physiological state. This adjustment allows the user to perform tasks best suited to their current emotional state. Notifications are communicated to the user visually or audibly, with a generative AI model selecting the most effective method in real time.

[0385] For example, if the system detects that a worker is experiencing stress at the start of the workday in the morning, the server will reassign tasks to reduce the workload. This allows the worker to focus on relaxing tasks and maintain productivity. An example of a prompt to the generating AI model would be: "Evaluate the current emotional state of the worker and prioritize tasks based on the results. If stress levels are high, adjust the workload to reduce the burden and prioritize relaxing tasks."

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The user's device monitors their real-time physiological state using smart glasses or wristband-type biosensors. This acquires data such as heart rate and skin temperature. The acquired physiological data is then sent directly to the emotion engine.

[0389] Step 2:

[0390] The emotion engine receives physiological data sent from the user as input and analyzes it. As part of the data processing, an AI algorithm analyzes patterns in heart rate variability and temperature anomalies to extract the user's emotional information. This extracted emotional information is then passed to the server for use in the next step.

[0391] Step 3:

[0392] The server uses emotional information received from the emotion engine to reprioritize the user's tasks. The server receives multiple criteria data as input, including deadlines, importance, and physiological state, and outputs dynamic task priorities through data calculations based on these.

[0393] Step 4:

[0394] The server generates appropriate notification messages based on task information with reset priorities. Using a generation AI model, notifications are configured to select text and audio that best reflect the user's current emotional state and to be communicated to the user visually or audibly.

[0395] Step 5:

[0396] Users receive notifications from the server via their devices. After reviewing the notification, users select a response and send it to the server. This feedback includes data such as the user's choices and time spent, which is used to adjust tasks in the future.

[0397] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This system is a digital assistant system that uses the user's electronic devices to support task management and efficient actions. The system primarily consists of interactions between three parties: the terminal, the server, and the user.

[0414] First, the device collects information from the user's smartphone, such as calendar apps, emails, and reminders. This information includes deadlines for scheduled events and tasks, their importance level, and related notes. The device then sends this information to the server.

[0415] Next, the server evaluates the priority of each task based on the information it receives. This evaluation takes into account the deadline, importance, and the user's past behavior history. The evaluation method uses a calculation algorithm, and tasks are ranked based on the resulting score.

[0416] Subsequently, the server generates push notifications for high-priority tasks at the appropriate time. The notification includes a task summary, deadline, recommended start time, and any necessary details. This notification is sent to the user via their device, providing visual and auditory attention.

[0417] When a user receives this notification, they can choose to start the task immediately or be notified later. User actions are recorded, and the server uses this data to optimize notification accuracy and timing. This allows the system to continuously learn and evolve based on the user's work habits and priorities.

[0418] This system can, for example, support efficient task management for professionals during peak seasons. When a user is working on a specific project, the server compares its progress with other tasks and notifies them of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions, directing the user to the most important next action.

[0419] As described above, this system provides an effective means of prioritizing and managing the many tasks that users face, thereby improving productivity and time management.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The device collects task information from sources on the user's electronic device (such as calendars, emails, and reminders). This includes scheduled dates and times, task details, reminder settings, and deadlines.

[0423] Step 2:

[0424] The terminal uses a data communication protocol to establish a connection with the server in order to send the collected information to the server.

[0425] Step 3:

[0426] The server stores the received data and calculates a priority by comprehensively evaluating each task's deadline, importance, and the user's past processing history. The priority score is calculated using an algorithm and recorded in the database.

[0427] Step 4:

[0428] The server selects the highest-priority tasks from the user's current tasks and overall task list. Based on this, it generates an alert if there are any important tasks.

[0429] Step 5:

[0430] The server generates alert information and sends it to the terminal, instructing it to display it as a push notification on the user's device.

[0431] Step 6:

[0432] The device displays alert information received by the user. The notification content is conveyed visually and audibly and includes a task summary and recommended start time.

[0433] Step 7:

[0434] The user responds to the notification by choosing to start the task immediately or by snoozing or dismissing the notification. The selected action is logged.

[0435] Step 8:

[0436] The server analyzes user responses and improves the prioritization algorithm based on behavioral data. This feedback loop continuously adjusts the timing and content of notifications.

[0437] (Example 1)

[0438] Next, we will describe Example 1. 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."

[0439] When users face numerous tasks, the challenge lies in providing an efficient method for effectively managing their priorities and completing tasks at the appropriate time. Furthermore, it is necessary to improve the accuracy of task management based on user behavior and provide notifications tailored to individual users.

[0440] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0441] In this invention, the server includes means for acquiring data from the user's terminal, means for evaluating and calculating task priority based on the data, and means for sending push notifications to the user. This enables the user to quickly grasp task priority and manage tasks efficiently. Furthermore, improved notification accuracy can enhance user productivity.

[0442] A "terminal" is an electronic device used by a user to send and receive information, and includes devices such as smartphones and tablets.

[0443] "Data" refers to quantified information such as a user's schedule, task information, and past activity history, and is the material necessary for task management.

[0444] A "server" is a computer system that receives and processes data transmitted from terminals via a network.

[0445] "Priority" is an indicator that shows the level of importance and urgency of a task, and is a criterion used to evaluate and optimize the user's action plan.

[0446] A "push notification" is a real-time message sent directly from a server to a user's device, serving as a means of quickly conveying important information to the user.

[0447] "Behavioral data" refers to information that records how users responded to notifications from the system, and serves as foundational data for improving task management methods.

[0448] An "algorithm" refers to a set of calculation procedures or rules, and is a processing method for solving a specific problem.

[0449] This invention provides a specific embodiment of a digital assistant system that utilizes user-owned electronic devices to support task management and efficient behavior. The core of the invention lies in the exchange and processing of data among three parties: a terminal, a server, and the user.

[0450] First, the device is the user's personal device, such as a smartphone or tablet. The device retrieves data from applications such as calendar, email, and reminders via APIs. This data includes the content, deadline, importance level, and supplementary notes of scheduled tasks. The device sends the collected data to the server via a secure protocol.

[0451] A server is a high-performance computer system installed in a data center or cloud environment. Servers analyze incoming data and use specific algorithms to evaluate task priorities. This evaluation is based on factors such as deadlines, importance, and the user's past behavior history, assigning priorities to each task.

[0452] Notifications based on evaluation results are generated by the server. These notifications include a task summary, deadline, recommended start time, and supplementary information. The generated notifications are sent to the device and communicated to the user visually and audibly. The notification feature allows users to understand high-priority tasks at the right time and take necessary actions quickly.

[0453] Next, users can choose to take action on notifications received via their devices, such as immediately starting the task, resending the notification later, or skipping the task. These actions are recorded by the system and sent to the server. The server analyzes this action data to optimize notification accuracy and timing, thereby improving the overall efficiency of the system.

[0454] As a concrete example, consider a situation where a user is managing multiple projects during a busy period. In this system, the server compares the progress of each project with other tasks and notifies the user of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions and issue instructions to the user.

[0455] Examples of prompts include "What are the important tasks for this week?" and "What should I do next?". The system responds immediately to such prompts, supporting optimal task management for the user.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The device retrieves information from the user's smartphone or tablet, such as schedule apps, emails, and reminders. Input is done via the APIs of each application, and output is a dataset containing the user's tasks, deadlines, importance levels, and notes. This data retrieval is performed periodically, and synchronization is maintained to keep the information up-to-date.

[0459] Step 2:

[0460] The device sends the collected data to the server using a secure protocol. The input is the user dataset obtained in the previous step, and the output is the completion status of the data transfer to the server. Encryption is applied during transmission to protect privacy.

[0461] Step 3:

[0462] The server analyzes the received data and evaluates the task priority. The input is data sent from the terminal, and the output is a priority score calculated for each task. The server uses a dedicated algorithm to calculate a composite score that takes into account the deadline, importance, and activity history.

[0463] Step 4:

[0464] The server generates a push notification to send to the user based on the calculated priority. The input is the priority score and notification template, and the output is the notification message sent to the user. This message includes information necessary for task management, such as a task summary, deadline, and recommended start time.

[0465] Step 5:

[0466] The device receives notifications from the server and displays them on the user's screen. The input is the notification message from the server, and the output is a visual and audible alert to the user. The device presents the notification appropriately and uses sound and vibration to attract the user's attention.

[0467] Step 6:

[0468] The user checks the notification and chooses whether to start the task immediately or to have the notification revisited later. The input is the notification received via the device, and the output is the user's action choice. The user's choice is recorded as data used to optimize future notifications.

[0469] Step 7:

[0470] The server receives user behavior data and uses it to improve the accuracy and content of notifications. The input is user selection data, and the output is an optimized notification algorithm. Through this, the server can better optimize the timing of future task suggestions and notifications for the user.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, 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."

[0473] In modern manufacturing environments, effectively managing a wide variety of tasks is crucial. However, traditional methods make it difficult to monitor the operating status of machinery and equipment in real time and to take appropriate action in a timely manner. Therefore, optimizing efficient work schedules is essential.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes means for acquiring information from the user's electronic devices and field devices, means for calculating task priorities based on the acquired information, and means for notifying the user and relevant operators based on the priorities. This enables real-time task management and improved productivity in response to field conditions.

[0476] A "user" is an individual or group that operates electronic devices and receives information from them.

[0477] An "electronic device" is a device that acquires and processes information and notifies the user.

[0478] "Field devices" refer to equipment and sensors used in production sites, and are devices for collecting information about the situation on site.

[0479] "Means of acquiring information" refers to methods or techniques for collecting data from users' electronic devices and field devices.

[0480] A "means for calculating task priorities" is a function that evaluates the importance of tasks based on acquired information and determines their ranking.

[0481] "Means of notification" refer to methods and technologies for informing users or operators of the priority and status of tasks.

[0482] "Means for recording and analyzing user and operator responses" refers to technologies for saving and analyzing the actions and responses of users and operators after notification.

[0483] "Methods for improving task prioritization algorithms" refers to approaches that utilize response data to improve how tasks are prioritized.

[0484] "Visual means of communication" refers to methods of providing notifications in a visually recognizable format.

[0485] "Means of communication via sound" refers to methods of conveying notifications to users through voice.

[0486] "Circumstantial communication methods" refer to techniques or means of providing notification in an appropriate manner according to the site and work environment.

[0487] This invention provides a system that optimizes task management for users and operators, and is primarily implemented through a network configuration including servers, terminals, and field devices. A detailed description of how to implement this system follows.

[0488] The server is programmed in Python or C++ and has built-in algorithms for task prioritization. Each user's electronic devices and field devices use appropriate data collection modules to acquire information. This includes sensors for on-site operational monitoring, a calendar app for collecting user data, and reminder functions. The data is sent to the server, where it is processed and analyzed. Machine learning frameworks such as TensorFlow are used to calculate task priorities based on the acquired data.

[0489] The server has the capability to generate visual, audible, and even situational notifications. These notifications are sent to users and operators via terminals, facilitating real-time management of work processes. Response data from users and operators after receiving notifications is returned to the server and recorded. This allows the server to continuously optimize its algorithms, evolving to perform task management processes more efficiently and effectively in the future.

[0490] To give a concrete example, suppose a machine on a manufacturing line shows an abnormal temperature. Sensor data from the on-site device is sent to the server and detected as a high-priority task. A notification that maintenance is immediately required is transmitted to the operator's electronic device. At this time, a prompt message like the following is generated: "An abnormality has been detected in machine A on manufacturing line 5. Please perform a priority inspection."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The server receives sensor information from field devices, including data such as temperature, vibration, and operating status. The input is raw data from the sensors, which the server filters and preprocesses to remove outliers and noise, resulting in a new output.

[0494] Step 2:

[0495] The terminal collects calendar and reminder information from the user's electronic device. This input provides information related to the user's schedule and tasks. The terminal sends this information to a server via an external API to build a database for processing.

[0496] Step 3:

[0497] The server uses a machine learning model (using TensorFlow) to determine task priorities based on collected device data and user information. This calculation applies a scoring model that takes into account historical data, field conditions, and the user's perceived importance of the task. The output is a prioritized task list.

[0498] Step 4:

[0499] The server generates notifications for users and operators based on a priority list. These notifications include information about tasks requiring immediate attention or those deemed critical. The server sends this information to the terminal as visual and audio notifications.

[0500] Step 5:

[0501] Users and operators receive notifications from the server via their terminals. Based on these notifications, users begin their actual work and record the results as input. The terminal then sends this behavioral data back to the server, where it is used to improve the algorithm for future use.

[0502] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0503] This invention is a digital personal assistant system that integrates emotion recognition technology and task management functions. This enables flexible and personalized task management that responds to the user's emotional state. The system comprises a terminal, a server, and an emotion engine.

[0504] First, the device retrieves task information from the user's device, such as calendar, email, and reminders. Before sending the aggregated information to the server, the device analyzes the user's emotions using a function that incorporates an emotion engine. The emotion engine identifies the user's current emotional state using the user's facial expressions, voice, or biosensor data.

[0505] Next, the server calculates task priority based on the task information received from the terminal and the emotion data recognized by the emotion engine. This priority is calculated using a dynamic algorithm that takes into account not only traditional deadlines and importance, but also the user's emotional state. For example, if the user is stressed, adjustments are made to prioritize less burdensome tasks.

[0506] Subsequently, the server generates notifications based on the prioritized tasks. These notifications are adapted to the user's emotional state and are delivered to the user in a kind and urgency-conscious text and voice. The notifications are communicated to the user visually and audibly through the device, helping them understand the urgency of the requirements and the recommended actions.

[0507] Users receive notifications and decide whether to proceed with the task. Their choices and actions are recorded and analyzed by the server. The analysis results are used to improve algorithms and enhance the system's accuracy and efficiency.

[0508] For example, if the emotion engine detects that a user is feeling stressed after a morning meeting, the server will postpone important report writing tasks and notify the user of relaxing tasks. In this way, the system supports the completion of the most appropriate tasks while taking the user's emotions into consideration. The aim is to improve the user's productivity and well-being.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The device collects task information from calendar, email, and reminder apps installed on the user's electronic device. This includes the date and time of the event, task details, and priority.

[0512] Step 2:

[0513] The device activates an emotion engine and collects data from the camera, microphone, and biosensors to recognize the user's emotional state. Emotional characteristics are extracted from this data and analyzed in real time.

[0514] Step 3:

[0515] The terminal integrates the sentiment analysis results with previously collected task information and generates package data to send to the server.

[0516] Step 4:

[0517] The server receives the composite data sent from the terminal and calculates the task priority. The calculation uses an algorithm that takes into account the task deadline, importance, and the user's emotional state.

[0518] Step 5:

[0519] The server creates a task list optimized for the user based on calculated priorities. Tasks are adjusted to accommodate emotional changes, ensuring a comfortable and orderly workflow for the user.

[0520] Step 6:

[0521] The server sends notifications to the device, generated based on the user's emotions and priorities. These notifications use text and audio templates tailored to the user's current situation.

[0522] Step 7:

[0523] The system displays notifications received by the device to the user visually and audibly. This makes it easier for the user to understand the overview and priority of tasks.

[0524] Step 8:

[0525] The user either works on the task based on the notification or returns a selected action to the system. This includes options such as starting the task immediately or setting it to be notified later.

[0526] Step 9:

[0527] The server analyzes user responses and behavior history to improve task priorities and notifications for the next time. Learning capabilities are used to prepare for providing a more personalized experience.

[0528] (Example 2)

[0529] Next, we will describe Example 2. 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."

[0530] Traditional task management systems rely solely on static prioritization based on deadlines and importance, which hinders flexible task management that takes into account the user's emotional state. This can lead to burdensome tasks being prioritized for users experiencing stress or fatigue, potentially resulting in decreased productivity and a loss of user well-being.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0532] In this invention, the server includes means for acquiring information from the user's terminal device, means for calculating task priorities based on the acquired information and the user's emotional state, and means for collecting and analyzing biometric data to identify the user's emotions. This enables flexible and personalized task management that takes the user's emotional state into consideration.

[0533] A "terminal device" is an information processing device that a user directly operates to input and output data, and includes smartphones and personal computers.

[0534] "Emotional state" refers to the current psychological or emotional state obtained by analyzing data acquired from the user's facial expressions, voice, and biosensors.

[0535] "Task prioritization" refers to criteria used to determine the importance and urgency of tasks that need to be addressed, based on specific factors.

[0536] A "notification" is information presented to the user via a terminal device or voice technology regarding the status of a task or recommended next action.

[0537] "Biometric data" refers to data obtained directly from the user's body, including information that indicates an individual's physiological state, such as heart rate and changes in facial expression.

[0538] An "algorithm" is a set of computational procedures designed to achieve a specific purpose, and in this context, it refers to the processing steps for determining the priority of tasks.

[0539] This invention is a task management system that utilizes emotion recognition and is designed to provide flexible and personalized task management. The system mainly consists of terminal devices, a server, and an emotion recognition engine.

[0540] The terminal device is the user's smartphone or PC, and it retrieves task information from digital calendars and emails using the Google Calendar API and email protocols (e.g., IMAP). The terminal device also uses the OpenCV library to recognize facial expressions from the camera and analyzes speech data using the Google Speech-to-Text API. Furthermore, it utilizes the user's biometric data by acquiring heart rate and skin electrical responses via Bluetooth from wearable devices such as smartwatches.

[0541] The server receives task information and emotional state data sent from the terminal device and calculates task priorities based on this information. This uses a dynamic algorithm based on the user's past behavior data, employing machine learning libraries such as Scikit-learn. When generating notifications for prioritized tasks, the server uses Natural Language Processing techniques to create flexible and user-friendly text.

[0542] Notifications are transmitted to the user via the device and are provided in the form of push notifications on smartphones or played via voice assistants. For example, if the user is feeling stressed, a suggestion such as, "Let's take a short break before tackling this task," might be made.

[0543] Users receive notifications and decide whether to proceed with the task. During this process, user behavior data is recorded, and the server performs further analysis. Based on this, the system improves prioritization for future tasks.

[0544] As a concrete example, the input to the generation AI model for a prompt would be: "When the user is experiencing stress, please suggest an optimal task schedule that takes into account past task data and emotional state."

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The terminal collects task information from the user's digital device. Specifically, it retrieves data from the device's calendar, email, and reminders using APIs. Based on this input, the terminal aggregates task date, time, and priority information. The output is a list of task information.

[0548] Step 2:

[0549] The device activates an emotion recognition engine to determine the user's emotional state. It captures the user's facial expressions using the camera with the OpenCV library and analyzes the audio with the Google Speech-to-Text API. It also acquires heart rate and skin electrochemistry data from the wearable device via Bluetooth. Based on this input, the device obtains data indicating the user's emotional state and outputs the user's emotional state as an analysis result.

[0550] Step 3:

[0551] The terminal sends collected task information and emotional state data to the server. This data is securely transferred using the HTTPS protocol. The input consists of a task information list and user emotional state data, which then serve as the output sent to the server.

[0552] Step 4:

[0553] The server calculates task priorities based on the data it receives. Using the machine learning library Scikit-learn, it applies sentiment data and past user behavior patterns to the algorithm. The input consists of received task information and sentiment state data, and a dynamically prioritized task list is output.

[0554] Step 5:

[0555] The server generates notifications based on prioritized tasks. Using Natural Language Processing (NLP) technology, it creates friendly notification messages tailored to the user's emotional state. The generated notification messages are sent to the terminal as server output.

[0556] Step 6:

[0557] The device transmits notifications to the user. These notifications are delivered to the user via smartphone push notifications or voice assistants. This allows the user to review recommended tasks. The input is the notification text from the server, and the output is the notification delivered to the user.

[0558] Step 7:

[0559] The user receives a notification and decides whether or not to work on the task. The user's choices and actions are recorded, and this data is used to improve future processes. The input is the notification confirmation and the user's choice, and the output is the recording of action data.

[0560] (Application Example 2)

[0561] Next, we will explain application example 2. In the following explanation, 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."

[0562] In modern industrial settings, worker stress and fatigue pose a problem that impacts productivity and safety. Furthermore, traditional systems determine tasks without considering emotional states, leading to unsuitable work assignments and decreased productivity. Therefore, it is necessary to flexibly adjust task priorities and appropriately manage workloads according to the physiological state of the workers.

[0563] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0564] In this invention, the server includes means for sensing the user's physiological state and analyzing emotional information, means for dynamically adjusting task priorities based on emotional information, and means for appropriately communicating notifications to the user via sight or hearing. This enables flexible task prioritization according to the worker's emotional state, thereby improving efficiency and safety in the production environment.

[0565] An "information processing device" is a device that receives input from a user and processes and analyzes that data, and includes devices such as computers and smartphones.

[0566] "Priority" refers to the order in which tasks that need to be processed or executed are ranked based on their importance and urgency, and serves as a criterion for determining which tasks should be prioritized.

[0567] A "notification" is a means of communicating important information or task-related information to a user, and it is done by informing the user through visual or auditory means.

[0568] "Reaction" refers to the actions or responses a user takes when they receive a notification, and describes the user's behavioral patterns and choices in response to notifications.

[0569] "Physiological state" refers to information that indicates the user's physical condition, including biometric data such as heart rate and skin temperature.

[0570] "Emotional information" refers to data that represents the user's emotional and psychological state, and is identified through facial expressions, tone of voice, and input from biosensors.

[0571] "Dynamic adjustment" means automatically and continuously changing things according to the situation and conditions, and is a process of reviewing priorities and tasks in real time.

[0572] The system for realizing this invention consists of a user information processing unit, a server, and an emotion engine as its main components. The user information processing unit monitors the user's physiological state in real time using smart glasses or a wristband-type biosensor. This data is analyzed by the emotion engine and transmitted to the server as user emotion information.

[0573] The server analyzes the user's physiological state and dynamically incorporates emotional information to determine task priorities. Priorities are then reset based on multiple criteria, including deadlines, importance, and physiological state. This adjustment allows the user to perform tasks best suited to their current emotional state. Notifications are communicated to the user visually or audibly, with a generative AI model selecting the most effective method in real time.

[0574] For example, if the system detects that a worker is experiencing stress at the start of the workday in the morning, the server will reassign tasks to reduce the workload. This allows the worker to focus on relaxing tasks and maintain productivity. An example of a prompt to the generating AI model would be: "Evaluate the current emotional state of the worker and prioritize tasks based on the results. If stress levels are high, adjust the workload to reduce the burden and prioritize relaxing tasks."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The user's device monitors their real-time physiological state using smart glasses or wristband-type biosensors. This acquires data such as heart rate and skin temperature. The acquired physiological data is then sent directly to the emotion engine.

[0578] Step 2:

[0579] The emotion engine receives physiological data sent from the user as input and analyzes it. As part of the data processing, an AI algorithm analyzes patterns in heart rate variability and temperature anomalies to extract the user's emotional information. This extracted emotional information is then passed to the server for use in the next step.

[0580] Step 3:

[0581] The server uses emotional information received from the emotion engine to reprioritize the user's tasks. The server receives multiple criteria data as input, including deadlines, importance, and physiological state, and outputs dynamic task priorities through data calculations based on these.

[0582] Step 4:

[0583] The server generates appropriate notification messages based on task information with reset priorities. Using a generation AI model, notifications are configured to select text and audio that best reflect the user's current emotional state and to be communicated to the user visually or audibly.

[0584] Step 5:

[0585] Users receive notifications from the server via their devices. After reviewing the notification, users select a response and send it to the server. This feedback includes data such as the user's choices and time spent, which is used to adjust tasks in the future.

[0586] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0587] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0592] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0593] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0594] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0595] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0596] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0597] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0598] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0599] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0600] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0601] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0602] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] This system is a digital assistant system that uses the user's electronic devices to support task management and efficient actions. The system primarily consists of interactions between three parties: the terminal, the server, and the user.

[0604] First, the device collects information from the user's smartphone, such as calendar apps, emails, and reminders. This information includes deadlines for scheduled events and tasks, their importance level, and related notes. The device then sends this information to the server.

[0605] Next, the server evaluates the priority of each task based on the information it receives. This evaluation takes into account the deadline, importance, and the user's past behavior history. The evaluation method uses a calculation algorithm, and tasks are ranked based on the resulting score.

[0606] Subsequently, the server generates push notifications for high-priority tasks at the appropriate time. The notification includes a task summary, deadline, recommended start time, and any necessary details. This notification is sent to the user via their device, providing visual and auditory attention.

[0607] When a user receives this notification, they can choose to start the task immediately or be notified later. User actions are recorded, and the server uses this data to optimize notification accuracy and timing. This allows the system to continuously learn and evolve based on the user's work habits and priorities.

[0608] This system can, for example, support efficient task management for professionals during peak seasons. When a user is working on a specific project, the server compares its progress with other tasks and notifies them of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions, directing the user to the most important next action.

[0609] As described above, this system provides an effective means of prioritizing and managing the many tasks that users face, thereby improving productivity and time management.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The device collects task information from sources on the user's electronic device (such as calendars, emails, and reminders). This includes scheduled dates and times, task details, reminder settings, and deadlines.

[0613] Step 2:

[0614] The terminal uses a data communication protocol to establish a connection with the server in order to send the collected information to the server.

[0615] Step 3:

[0616] The server stores the received data and calculates a priority by comprehensively evaluating each task's deadline, importance, and the user's past processing history. The priority score is calculated using an algorithm and recorded in the database.

[0617] Step 4:

[0618] The server selects the highest-priority tasks from the user's current tasks and overall task list. Based on this, it generates an alert if there are any important tasks.

[0619] Step 5:

[0620] The server generates alert information and sends it to the terminal, instructing it to display it as a push notification on the user's device.

[0621] Step 6:

[0622] The device displays alert information received by the user. The notification content is conveyed visually and audibly and includes a task summary and recommended start time.

[0623] Step 7:

[0624] The user responds to the notification by choosing to start the task immediately or by snoozing or dismissing the notification. The selected action is logged.

[0625] Step 8:

[0626] The server analyzes user responses and improves the prioritization algorithm based on behavioral data. This feedback loop continuously adjusts the timing and content of notifications.

[0627] (Example 1)

[0628] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] When users face numerous tasks, the challenge lies in providing an efficient method for effectively managing their priorities and completing tasks at the appropriate time. Furthermore, it is necessary to improve the accuracy of task management based on user behavior and provide notifications tailored to individual users.

[0630] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0631] In this invention, the server includes means for acquiring data from the user's terminal, means for evaluating and calculating task priority based on the data, and means for sending push notifications to the user. This enables the user to quickly grasp task priority and manage tasks efficiently. Furthermore, improved notification accuracy can enhance user productivity.

[0632] A "terminal" is an electronic device used by a user to send and receive information, and includes devices such as smartphones and tablets.

[0633] "Data" refers to quantified information such as a user's schedule, task information, and past activity history, and is the material necessary for task management.

[0634] A "server" is a computer system that receives and processes data transmitted from terminals via a network.

[0635] "Priority" is an indicator that shows the level of importance and urgency of a task, and is a criterion used to evaluate and optimize the user's action plan.

[0636] A "push notification" is a real-time message sent directly from a server to a user's device, serving as a means of quickly conveying important information to the user.

[0637] "Behavioral data" refers to information that records how users responded to notifications from the system, and serves as foundational data for improving task management methods.

[0638] An "algorithm" refers to a set of calculation procedures or rules, and is a processing method for solving a specific problem.

[0639] This invention provides a specific embodiment of a digital assistant system that utilizes user-owned electronic devices to support task management and efficient behavior. The core of the invention lies in the exchange and processing of data among three parties: a terminal, a server, and the user.

[0640] First, the device is the user's personal device, such as a smartphone or tablet. The device retrieves data from applications such as calendar, email, and reminders via APIs. This data includes the content, deadline, importance level, and supplementary notes of scheduled tasks. The device sends the collected data to the server via a secure protocol.

[0641] A server is a high-performance computer system installed in a data center or cloud environment. Servers analyze incoming data and use specific algorithms to evaluate task priorities. This evaluation is based on factors such as deadlines, importance, and the user's past behavior history, assigning priorities to each task.

[0642] Notifications based on evaluation results are generated by the server. These notifications include a task summary, deadline, recommended start time, and supplementary information. The generated notifications are sent to the device and communicated to the user visually and audibly. The notification feature allows users to understand high-priority tasks at the right time and take necessary actions quickly.

[0643] Next, users can choose to take action on notifications received via their devices, such as immediately starting the task, resending the notification later, or skipping the task. These actions are recorded by the system and sent to the server. The server analyzes this action data to optimize notification accuracy and timing, thereby improving the overall efficiency of the system.

[0644] As a concrete example, consider a situation where a user is managing multiple projects during a busy period. In this system, the server compares the progress of each project with other tasks and notifies the user of which tasks should be started and when, based on priority. It can also flexibly respond to sudden meetings or unexpected task additions and issue instructions to the user.

[0645] Examples of prompts include "What are the important tasks for this week?" and "What should I do next?". The system responds immediately to such prompts, supporting optimal task management for the user.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The device retrieves information from the user's smartphone or tablet, such as schedule apps, emails, and reminders. Input is done via the APIs of each application, and output is a dataset containing the user's tasks, deadlines, importance levels, and notes. This data retrieval is performed periodically, and synchronization is maintained to keep the information up-to-date.

[0649] Step 2:

[0650] The device sends the collected data to the server using a secure protocol. The input is the user dataset obtained in the previous step, and the output is the completion status of the data transfer to the server. Encryption is applied during transmission to protect privacy.

[0651] Step 3:

[0652] The server analyzes the received data and evaluates the task priority. The input is data sent from the terminal, and the output is a priority score calculated for each task. The server uses a dedicated algorithm to calculate a composite score that takes into account the deadline, importance, and activity history.

[0653] Step 4:

[0654] The server generates a push notification to send to the user based on the calculated priority. The input is the priority score and notification template, and the output is the notification message sent to the user. This message includes information necessary for task management, such as a task summary, deadline, and recommended start time.

[0655] Step 5:

[0656] The device receives notifications from the server and displays them on the user's screen. The input is the notification message from the server, and the output is a visual and audible alert to the user. The device presents the notification appropriately and uses sound and vibration to attract the user's attention.

[0657] Step 6:

[0658] The user checks the notification and chooses whether to start the task immediately or to have the notification revisited later. The input is the notification received via the device, and the output is the user's action choice. The user's choice is recorded as data used to optimize future notifications.

[0659] Step 7:

[0660] The server receives user behavior data and uses it to improve the accuracy and content of notifications. The input is user selection data, and the output is an optimized notification algorithm. Through this, the server can better optimize the timing of future task suggestions and notifications for the user.

[0661] (Application Example 1)

[0662] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] In modern manufacturing environments, effectively managing a wide variety of tasks is crucial. However, traditional methods make it difficult to monitor the operating status of machinery and equipment in real time and to take appropriate action in a timely manner. Therefore, optimizing efficient work schedules is essential.

[0664] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0665] In this invention, the server includes means for acquiring information from the user's electronic devices and field devices, means for calculating task priorities based on the acquired information, and means for notifying the user and relevant operators based on the priorities. This enables real-time task management and improved productivity in response to field conditions.

[0666] A "user" is an individual or group that operates electronic devices and receives information from them.

[0667] An "electronic device" is a device that acquires and processes information and notifies the user.

[0668] "Field devices" refer to equipment and sensors used in production sites, and are devices for collecting information about the situation on site.

[0669] "Means of acquiring information" refers to methods or techniques for collecting data from users' electronic devices and field devices.

[0670] A "means for calculating task priorities" is a function that evaluates the importance of tasks based on acquired information and determines their ranking.

[0671] "Means of notification" refer to methods and technologies for informing users or operators of the priority and status of tasks.

[0672] "Means for recording and analyzing user and operator responses" refers to technologies for saving and analyzing the actions and responses of users and operators after notification.

[0673] "Methods for improving task prioritization algorithms" refers to approaches that utilize response data to improve how tasks are prioritized.

[0674] "Visual means of communication" refers to methods of providing notifications in a visually recognizable format.

[0675] "Means of communication via sound" refers to methods of conveying notifications to users through voice.

[0676] "Circumstantial communication methods" refer to techniques or means of providing notification in an appropriate manner according to the site and work environment.

[0677] This invention provides a system that optimizes task management for users and operators, and is primarily implemented through a network configuration including servers, terminals, and field devices. A detailed description of how to implement this system follows.

[0678] The server is programmed in Python or C++ and has built-in algorithms for task prioritization. Each user's electronic devices and field devices use appropriate data collection modules to acquire information. This includes sensors for on-site operational monitoring, a calendar app for collecting user data, and reminder functions. The data is sent to the server, where it is processed and analyzed. Machine learning frameworks such as TensorFlow are used to calculate task priorities based on the acquired data.

[0679] The server has the capability to generate visual, audible, and even situational notifications. These notifications are sent to users and operators via terminals, facilitating real-time management of work processes. Response data from users and operators after receiving notifications is returned to the server and recorded. This allows the server to continuously optimize its algorithms, evolving to perform task management processes more efficiently and effectively in the future.

[0680] To give a concrete example, suppose a machine on a manufacturing line shows an abnormal temperature. Sensor data from the on-site device is sent to the server and detected as a high-priority task. A notification that maintenance is immediately required is transmitted to the operator's electronic device. At this time, a prompt message like the following is generated: "An abnormality has been detected in machine A on manufacturing line 5. Please perform a priority inspection."

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The server receives sensor information from field devices, including data such as temperature, vibration, and operating status. The input is raw data from the sensors, which the server filters and preprocesses to remove outliers and noise, resulting in a new output.

[0684] Step 2:

[0685] The terminal collects calendar and reminder information from the user's electronic device. This input provides information related to the user's schedule and tasks. The terminal sends this information to a server via an external API to build a database for processing.

[0686] Step 3:

[0687] The server uses a machine learning model (using TensorFlow) to determine task priorities based on collected device data and user information. This calculation applies a scoring model that takes into account historical data, field conditions, and the user's perceived importance of the task. The output is a prioritized task list.

[0688] Step 4:

[0689] The server generates notifications for users and operators based on a priority list. These notifications include information about tasks requiring immediate attention or those deemed critical. The server sends this information to the terminal as visual and audio notifications.

[0690] Step 5:

[0691] Users and operators receive notifications from the server via their terminals. Based on these notifications, users begin their actual work and record the results as input. The terminal then sends this behavioral data back to the server, where it is used to improve the algorithm for future use.

[0692] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0693] This invention is a digital personal assistant system that integrates emotion recognition technology and task management functions. This enables flexible and personalized task management that responds to the user's emotional state. The system comprises a terminal, a server, and an emotion engine.

[0694] First, the device retrieves task information from the user's device, such as calendar, email, and reminders. Before sending the aggregated information to the server, the device analyzes the user's emotions using a function that incorporates an emotion engine. The emotion engine identifies the user's current emotional state using the user's facial expressions, voice, or biosensor data.

[0695] Next, the server calculates task priority based on the task information received from the terminal and the emotion data recognized by the emotion engine. This priority is calculated using a dynamic algorithm that takes into account not only traditional deadlines and importance, but also the user's emotional state. For example, if the user is stressed, adjustments are made to prioritize less burdensome tasks.

[0696] Subsequently, the server generates notifications based on the prioritized tasks. These notifications are adapted to the user's emotional state and are delivered to the user in a kind and urgency-conscious text and voice. The notifications are communicated to the user visually and audibly through the device, helping them understand the urgency of the requirements and the recommended actions.

[0697] Users receive notifications and decide whether to proceed with the task. Their choices and actions are recorded and analyzed by the server. The analysis results are used to improve algorithms and enhance the system's accuracy and efficiency.

[0698] For example, if the emotion engine detects that a user is feeling stressed after a morning meeting, the server will postpone important report writing tasks and notify the user of relaxing tasks. In this way, the system supports the completion of the most appropriate tasks while taking the user's emotions into consideration. The aim is to improve the user's productivity and well-being.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The device collects task information from calendar, email, and reminder apps installed on the user's electronic device. This includes the date and time of the event, task details, and priority.

[0702] Step 2:

[0703] The device activates an emotion engine and collects data from the camera, microphone, and biosensors to recognize the user's emotional state. Emotional characteristics are extracted from this data and analyzed in real time.

[0704] Step 3:

[0705] The terminal integrates the sentiment analysis results with previously collected task information and generates package data to send to the server.

[0706] Step 4:

[0707] The server receives the composite data sent from the terminal and calculates the task priority. The calculation uses an algorithm that takes into account the task deadline, importance, and the user's emotional state.

[0708] Step 5:

[0709] The server creates a task list optimized for the user based on calculated priorities. Tasks are adjusted to accommodate emotional changes, ensuring a comfortable and orderly workflow for the user.

[0710] Step 6:

[0711] The server sends notifications to the device, generated based on the user's emotions and priorities. These notifications use text and audio templates tailored to the user's current situation.

[0712] Step 7:

[0713] The system displays notifications received by the device to the user visually and audibly. This makes it easier for the user to understand the overview and priority of tasks.

[0714] Step 8:

[0715] The user either works on the task based on the notification or returns a selected action to the system. This includes options such as starting the task immediately or setting it to be notified later.

[0716] Step 9:

[0717] The server analyzes user responses and behavior history to improve task priorities and notifications for the next time. Learning capabilities are used to prepare for providing a more personalized experience.

[0718] (Example 2)

[0719] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] Traditional task management systems rely solely on static prioritization based on deadlines and importance, which hinders flexible task management that takes into account the user's emotional state. This can lead to burdensome tasks being prioritized for users experiencing stress or fatigue, potentially resulting in decreased productivity and a loss of user well-being.

[0721] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0722] In this invention, the server includes means for acquiring information from the user's terminal device, means for calculating task priorities based on the acquired information and the user's emotional state, and means for collecting and analyzing biometric data to identify the user's emotions. This enables flexible and personalized task management that takes the user's emotional state into consideration.

[0723] A "terminal device" is an information processing device that a user directly operates to input and output data, and includes smartphones and personal computers.

[0724] "Emotional state" refers to the current psychological or emotional state obtained by analyzing data acquired from the user's facial expressions, voice, and biosensors.

[0725] "Task prioritization" refers to criteria used to determine the importance and urgency of tasks that need to be addressed, based on specific factors.

[0726] A "notification" is information presented to the user via a terminal device or voice technology regarding the status of a task or recommended next action.

[0727] "Biometric data" refers to data obtained directly from the user's body, including information that indicates an individual's physiological state, such as heart rate and changes in facial expression.

[0728] An "algorithm" is a set of computational procedures designed to achieve a specific purpose, and in this context, it refers to the processing steps for determining the priority of tasks.

[0729] This invention is a task management system that utilizes emotion recognition and is designed to provide flexible and personalized task management. The system mainly consists of terminal devices, a server, and an emotion recognition engine.

[0730] The terminal device is the user's smartphone or PC, and it retrieves task information from digital calendars and emails using the Google Calendar API and email protocols (e.g., IMAP). The terminal device also uses the OpenCV library to recognize facial expressions from the camera and analyzes speech data using the Google Speech-to-Text API. Furthermore, it utilizes the user's biometric data by acquiring heart rate and skin electrical responses via Bluetooth from wearable devices such as smartwatches.

[0731] The server receives task information and emotional state data sent from the terminal device and calculates task priorities based on this information. This uses a dynamic algorithm based on the user's past behavior data, employing machine learning libraries such as Scikit-learn. When generating notifications for prioritized tasks, the server uses Natural Language Processing techniques to create flexible and user-friendly text.

[0732] Notifications are transmitted to the user via the device and are provided in the form of push notifications on smartphones or played via voice assistants. For example, if the user is feeling stressed, a suggestion such as, "Let's take a short break before tackling this task," might be made.

[0733] Users receive notifications and decide whether to proceed with the task. During this process, user behavior data is recorded, and the server performs further analysis. Based on this, the system improves prioritization for future tasks.

[0734] As a concrete example, the input to the generation AI model for a prompt would be: "When the user is experiencing stress, please suggest an optimal task schedule that takes into account past task data and emotional state."

[0735] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0736] Step 1:

[0737] The terminal collects task information from the user's digital device. Specifically, it retrieves data from the device's calendar, email, and reminders using APIs. Based on this input, the terminal aggregates task date, time, and priority information. The output is a list of task information.

[0738] Step 2:

[0739] The device activates an emotion recognition engine to determine the user's emotional state. It captures the user's facial expressions using the camera with the OpenCV library and analyzes the audio with the Google Speech-to-Text API. It also acquires heart rate and skin electrochemistry data from the wearable device via Bluetooth. Based on this input, the device obtains data indicating the user's emotional state and outputs the user's emotional state as an analysis result.

[0740] Step 3:

[0741] The terminal sends collected task information and emotional state data to the server. This data is securely transferred using the HTTPS protocol. The input consists of a task information list and user emotional state data, which then serve as the output sent to the server.

[0742] Step 4:

[0743] The server calculates task priorities based on the data it receives. Using the machine learning library Scikit-learn, it applies sentiment data and past user behavior patterns to the algorithm. The input consists of received task information and sentiment state data, and a dynamically prioritized task list is output.

[0744] Step 5:

[0745] The server generates notifications based on prioritized tasks. Using Natural Language Processing (NLP) technology, it creates friendly notification messages tailored to the user's emotional state. The generated notification messages are sent to the terminal as server output.

[0746] Step 6:

[0747] The device transmits notifications to the user. These notifications are delivered to the user via smartphone push notifications or voice assistants. This allows the user to review recommended tasks. The input is the notification text from the server, and the output is the notification delivered to the user.

[0748] Step 7:

[0749] The user receives a notification and decides whether or not to work on the task. The user's choices and actions are recorded, and this data is used to improve future processes. The input is the notification confirmation and the user's choice, and the output is the recording of action data.

[0750] (Application Example 2)

[0751] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] In modern industrial settings, worker stress and fatigue pose a problem that impacts productivity and safety. Furthermore, traditional systems determine tasks without considering emotional states, leading to unsuitable work assignments and decreased productivity. Therefore, it is necessary to flexibly adjust task priorities and appropriately manage workloads according to the physiological state of the workers.

[0753] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0754] In this invention, the server includes means for sensing the user's physiological state and analyzing emotional information, means for dynamically adjusting task priorities based on emotional information, and means for appropriately communicating notifications to the user via sight or hearing. This enables flexible task prioritization according to the worker's emotional state, thereby improving efficiency and safety in the production environment.

[0755] An "information processing device" is a device that receives input from a user and processes and analyzes that data, and includes devices such as computers and smartphones.

[0756] "Priority" refers to the order in which tasks that need to be processed or executed are ranked based on their importance and urgency, and serves as a criterion for determining which tasks should be prioritized.

[0757] A "notification" is a means of communicating important information or task-related information to a user, and it is done by informing the user through visual or auditory means.

[0758] "Reaction" refers to the actions or responses a user takes when they receive a notification, and describes the user's behavioral patterns and choices in response to notifications.

[0759] "Physiological state" refers to information that indicates the user's physical condition, including biometric data such as heart rate and skin temperature.

[0760] "Emotional information" refers to data that represents the user's emotional and psychological state, and is identified through facial expressions, tone of voice, and input from biosensors.

[0761] "Dynamic adjustment" means automatically and continuously changing things according to the situation and conditions, and is a process of reviewing priorities and tasks in real time.

[0762] The system for realizing this invention consists of a user information processing unit, a server, and an emotion engine as its main components. The user information processing unit monitors the user's physiological state in real time using smart glasses or a wristband-type biosensor. This data is analyzed by the emotion engine and transmitted to the server as user emotion information.

[0763] The server analyzes the user's physiological state and dynamically incorporates emotional information to determine task priorities. Priorities are then reset based on multiple criteria, including deadlines, importance, and physiological state. This adjustment allows the user to perform tasks best suited to their current emotional state. Notifications are communicated to the user visually or audibly, with a generative AI model selecting the most effective method in real time.

[0764] For example, if the system detects that a worker is experiencing stress at the start of the workday in the morning, the server will reassign tasks to reduce the workload. This allows the worker to focus on relaxing tasks and maintain productivity. An example of a prompt to the generating AI model would be: "Evaluate the current emotional state of the worker and prioritize tasks based on the results. If stress levels are high, adjust the workload to reduce the burden and prioritize relaxing tasks."

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The user's device monitors their real-time physiological state using smart glasses or wristband-type biosensors. This acquires data such as heart rate and skin temperature. The acquired physiological data is then sent directly to the emotion engine.

[0768] Step 2:

[0769] The emotion engine receives physiological data sent from the user as input and analyzes it. As part of the data processing, an AI algorithm analyzes patterns in heart rate variability and temperature anomalies to extract the user's emotional information. This extracted emotional information is then passed to the server for use in the next step.

[0770] Step 3:

[0771] The server uses emotional information received from the emotion engine to reprioritize the user's tasks. The server receives multiple criteria data as input, including deadlines, importance, and physiological state, and outputs dynamic task priorities through data calculations based on these.

[0772] Step 4:

[0773] The server generates appropriate notification messages based on task information with reset priorities. Using a generation AI model, notifications are configured to select text and audio that best reflect the user's current emotional state and to be communicated to the user visually or audibly.

[0774] Step 5:

[0775] Users receive notifications from the server via their devices. After reviewing the notification, users select a response and send it to the server. This feedback includes data such as the user's choices and time spent, which is used to adjust tasks in the future.

[0776] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0777] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0778] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0779] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0780] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0781] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0782] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0783] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0784] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0785] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0786] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0787] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0788] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0789] 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.

[0790] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0791] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0792] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0793] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0794] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0795] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0796] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] Means for obtaining information from the user's electronic device,

[0800] A means for calculating task priority based on acquired information,

[0801] A means of notifying users based on priority,

[0802] A means of recording and analyzing user responses to notifications,

[0803] A method for improving task prioritization algorithms using user response data,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the means for calculating task priority is based on multiple criteria, including deadline, importance, and past performance data.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for communicating notifications to a user visually and audibly.

[0809] "Example 1"

[0810] (Claim 1)

[0811] A means of obtaining data from the user's device,

[0812] A means of sending the acquired data to the server,

[0813] A means by which the server evaluates and calculates task priority based on data,

[0814] A means of sending a push notification to the user via the device based on the evaluation results,

[0815] A means to record the user's actions in response to notifications and for the server to analyze the results,

[0816] A means to optimize the priority evaluation algorithm based on user behavior data,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, in which the evaluation is performed based on multiple criteria that take into account time constraints, importance, and historical data.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the notification includes means of presentation by visual and auditory means.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] Means for acquiring information from the user's electronic devices and field devices,

[0825] A means for calculating task priority based on acquired information,

[0826] A means of notifying users and relevant operators based on priority,

[0827] Means for recording and analyzing user and operator responses to notifications,

[0828] A means for improving the task prioritization algorithm using user and operator response data,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the means for calculating task priority is based on a plurality of criteria, including deadline, importance, past performance data and field condition data.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising means for communicating notifications to users and operators visually, audibly, and situationally.

[0834] "Example 2 of combining an emotion engine"

[0835] (Claim 1)

[0836] A means of obtaining information from the user's terminal device,

[0837] A means for calculating task priorities based on acquired information and the user's emotional state,

[0838] A means of providing notifications tailored to users based on priority,

[0839] A means of recording and analyzing user responses to notifications,

[0840] A means of improving task prioritization algorithms using user response data,

[0841] A means of collecting and analyzing biometric data to identify user emotions,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the means for calculating task priority is based on a number of criteria including deadline, importance, user emotional state, and past performance data.

[0845] (Claim 3)

[0846] The system according to claim 1, comprising means by which notifications to the user are communicated visually and audibly and are adapted to the user's emotional state.

[0847] "Application example 2 when combining with an emotional engine"

[0848] (Claim 1)

[0849] A means of obtaining information from the user's information processing device,

[0850] A means for calculating task priority based on acquired information,

[0851] A means of notifying users based on priority,

[0852] A means of recording and analyzing user responses to notifications,

[0853] A means of sensing the user's physiological state and analyzing emotional information,

[0854] A means of dynamically adjusting task priorities based on emotional information,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the means for calculating task priority is based on a plurality of criteria including deadline, importance, production activity history data, and the user's physiological state.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein notifications to the user are communicated visually or aurally, and the means include using a medium appropriate to the user's emotional state. [Explanation of Symbols]

[0860] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for obtaining information from the user's electronic device, A means for calculating task priority based on acquired information, A means of notifying users based on priority, A means of recording and analyzing user responses to notifications, A method for improving task prioritization algorithms using user response data, A system that includes this.

2. The system according to claim 1, wherein the means for calculating task priority is based on a plurality of criteria, including deadline, importance, and past performance data.

3. The system according to claim 1, comprising means for communicating notifications to a user visually and audibly.

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