system

Abstract

Provide a system. 【Solution means】 An information analysis device, A device that creates operation items based on the analyzed information, A device that records the created operation items, A device that detects that an operation item has been completed, A device that removes the completed operation item from the record, A device that collects and analyzes opinions from citizens, A device that determines priorities based on the collected opinions, A device that notifies relevant parties of information on events and emergencies in the city, A system including the above.

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]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern business environments and personal lives, as the volume of electronic communication means and meeting records increases, the risk of overlooking important action items and deadlines is rising. Therefore, it is difficult to manually manage all action items, resulting in problems such as information leakage and task incompletion. To solve this, a system that rationalizes information extraction, action item generation, management, and completion processing and manages them efficiently is required.

Means for Solving the Problems

[0005] This invention automates management by automatically generating action items from electronic communication content and meeting records using information analysis means, and recording these action items. Furthermore, it includes a function to detect when an action item is completed and automatically delete it from the record, preventing omissions of action items. In addition, it provides a means for manually inputting the completion of action items through a user interface, enabling flexible management and improving efficiency and accuracy.

[0006] "Information analysis tools" refer to technologies and methods for extracting meaningful information from data such as electronic communication content and meeting records, and processing it as actionable items.

[0007] "Means for generating action items" refer to technologies and methods that construct specific tasks and action plans based on extracted information and convert them into a format that can be recorded and managed.

[0008] "Means for recording action items" refer to technologies and methods that allow generated action items to be stored in a database or recording medium for later reference and updating.

[0009] "Means for detecting completion" refer to processes or mechanisms for determining whether an action item has actually been completed, and these can be confirmed through automated analysis or user input.

[0010] "Means for removing completed action items from records" refers to techniques or methods for removing action items that have been confirmed as completed from databases or recording media, thereby maintaining the list in an up-to-date state.

[0011] "Means of acquiring electronic communication content from external data infrastructure" refers to technologies and methods for collecting data from external information sources such as email systems and conferencing platforms via the internet or networks, and incorporating it into a system.

[0012] "Means of input via user interface" refers to the design and use of interfaces and devices that enable users to directly input data or manage action items through a terminal. [Brief explanation of the drawing]

[0013] [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 the data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled 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 disk (e.g., hard disk), or magnetic tape, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is an AI agent system designed to enable business environments and individual users to efficiently manage task lists. The system consists of three main elements: a server, a terminal, and a user interface.

[0035] System Overview

[0036] The server's role is to acquire and analyze the user's electronic communications. The server is equipped with a natural language processing (NLP) engine, which designs action plans from emails and meeting records. This generates action items, which are then recorded in a database.

[0037] The terminal is a device that provides a user interface (UI) for users to view and manage their activity items. The terminal receives the latest activity item information transmitted from the server and has the functionality to allow users to view, edit, and delete it. It also allows users to directly input the completion status of activity items.

[0038] Users can access their task list using their device to add new actions or complete existing tasks. Users are expected to continue processing tasks in a timely manner through deadline reminders.

[0039] Program processing explanation in natural language

[0040] 1. The server acquires electronic communication content: The server acquires the necessary data through interfaces with the user's email system and conferencing system. This data is then used for subsequent analysis.

[0041] 2. Information Analysis and Action Item Generation: The server's NLP engine analyzes the electronic communication content, identifies task names, related deadlines, assigned personnel, etc., and automatically generates action items. These generated action items are compiled into a task list.

[0042] 3. Management of Action Items: The server stores the generated action items in a relational database and monitors their completion status. When completion of an action item is detected, it is automatically deleted from the database and the task list is updated.

[0043] 4. Providing a user interface on the device: The device displays a task list in a user-friendly format, allowing users to add new tasks or update existing tasks to a completed state. The device also has a notification function to remind users of important events.

[0044] Specific examples

[0045] Suppose a user receives instructions in a meeting to "create a report by the next meeting." When this information is entered into the system as meeting minutes, the server's NLP engine generates an action item called "Create a report" and sets an appropriate deadline. This action item is displayed on the terminal, and the user receives notifications from the terminal as the deadline approaches, allowing them to manage the task's completion status.

[0046] In this way, this system allows users to efficiently and reliably manage their tasks and avoid missing important deadlines.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server retrieves electronic communications. The server periodically retrieves data through the user's email server or conferencing system API, checking for new messages and meeting minutes. The verified data is stored for further processing.

[0050] Step 2:

[0051] The server analyzes the information and generates action items. Upon receiving the electronic communication content, the server uses a natural language processing engine to analyze the data, extracting information such as task name, deadline, and assigned person, and generating action items. This results in the formation of a concrete task list.

[0052] Step 3:

[0053] The server records the action items in a database. The generated action items are stored in a relational database for later access and management. This database stores the overall structure of the task list.

[0054] Step 4:

[0055] The terminal displays the task list in its user interface. The terminal reflects the updated task list received from the server in the user interface and displays it in a way that makes it easy for the user to check the details of the tasks.

[0056] Step 5:

[0057] Users manage tasks using their devices. They view their task list on their devices, complete or edit tasks as needed, and add new tasks. This allows them to manage the progress of their personal projects.

[0058] Step 6:

[0059] The server detects the completion of an action item and deletes the item. Based on completion input from the user or new information received, the server confirms the completion of a task, deletes the corresponding action item from the database, and updates the task list to its latest state.

[0060] This series of steps automates the task management process and helps users avoid overlooking anything.

[0061] (Example 1)

[0062] 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."

[0063] In today's information-saturated environment, it is becoming increasingly difficult for individuals and organizations to efficiently manage their daily tasks. In particular, there is a lack of systems to effectively extract and manage important action items from vast amounts of information, such as emails and meeting records. Traditional methods require significant time for manual data entry and management, making it highly likely that important tasks will be overlooked. Therefore, there is a growing need for systems that efficiently handle everything from automated information analysis to task generation and completion management.

[0064] 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.

[0065] In this invention, the server includes a device for analyzing information, a device for creating action items based on the analyzed information, a device for storing the created action items, a device for detecting when an action item has been achieved, and a device for generating notifications regarding completed action items. This enables efficient extraction of action items from a vast amount of electronic communication content, allowing users to manage tasks appropriately.

[0066] A "device for analyzing information" is a device that uses technologies such as natural language processing to analyze electronic communication content and extract necessary information and action items.

[0067] A "device for creating action items" is a device that automatically generates specific tasks and actions based on analyzed information and manages them as items.

[0068] A "device for storing action items" is a device that stores generated action items in a storage device such as a database, and has the function of allowing them to be referenced and edited as needed.

[0069] A "device for detecting when an action item has been completed" is a device that monitors the status of saved action items and confirms that the task has been completed by the user.

[0070] A "device that generates notifications regarding completed action items" is a device that has the function of automatically creating and sending notifications to inform users of information related to the deadline and completion status of action items.

[0071] A "device for acquiring electronic communication content from an external information infrastructure" is a device that accesses an electronic communication platform via APIs or network connections to acquire data such as emails and meeting minutes.

[0072] A "device for inputting the completion of action items via a user interface" is a device that provides an intuitive interface for users to operate and has a mechanism for reporting that an action item has been completed.

[0073] This invention is a system that automates the entire process from information analysis to task management. The system consists of three main elements: a server, a terminal, and a user. The operation of each component is described below.

[0074] The server acquires data through API connections with electronic communication platforms. It uses APIs such as the Google Workspace® API and the Microsoft® Graph API to retrieve emails and meeting records and analyze the information. For this analysis, it utilizes software libraries that provide natural language processing (NLP) technology, such as spaCy and NLTK. Using these technologies, the server extracts keywords related to the task and automatically generates specific action items. The generated action items are structured in JSON format and stored in a relational database such as MySQL®.

[0075] The device provides a user interface that allows users to efficiently manage their tasks. Users can view and edit their latest tasks via a web browser or mobile app. Specifically, users indicate task completion by clicking a checkbox on the device. They can also add new tasks or edit existing ones using the plus button. The device provides important notifications to users, notifying them of tasks with approaching deadlines via push notifications.

[0076] Users can access task lists generated through their devices and efficiently manage their daily work. For example, if a user receives a task via email such as "Prepare a report before the next meeting," the system automatically reflects that information in the task list. An example of a prompt message could be, "Find the important tasks in my emails and meeting minutes and list them with deadlines and assignees." This allows users to efficiently carry out their work without overlooking important tasks even amidst information overload.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server establishes an API connection with the electronic communication platform and retrieves data from the user's emails and meeting records. Inputs include the user's authentication information and the API used (e.g., Google® Workspace API, Microsoft Graph API). The server uses this information to securely aggregate the latest messages and meeting details from the platform. The output is raw electronic communication data.

[0080] Step 2:

[0081] The server uses the acquired electronic communication data to perform analysis using natural language processing (NLP) techniques. The input is the raw data from Step 1. Specifically, the server uses libraries such as spaCy and NLTK to tokenize the text and extract keywords. It then identifies action items and extracts related information. The output is structured information such as action items, responsible persons, and deadlines.

[0082] Step 3:

[0083] The server stores structured action items in JSON format in a database. The input is the structured information generated in step 2. The server stores this information in a relational database such as MySQL and manages attributes such as task ID and status. The output is the saved action item information.

[0084] Step 4:

[0085] The terminal provides a user interface for users to view and manage their task list. Input consists of action item data obtained from the server. The terminal retrieves this data via an API and displays it in an intuitive, user-friendly format. Specifically, users can indicate task completion by checking a box on the terminal screen. The output is that information on completed tasks is updated on the server.

[0086] Step 5:

[0087] Users can add new actions or edit and complete existing tasks through their terminal. Input is task information directly entered by the user into the interface. The terminal sends this information to the server, where it is reflected in the list. Output is stored in the database, containing new and updated task information.

[0088] Step 6:

[0089] The server automatically detects completed tasks and deletes or archives the information. Inputs are task completion notifications from terminals and task status information in the database. Output is a cleaned database necessary to maintain an up-to-date task list.

[0090] (Application Example 1)

[0091] 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."

[0092] In smart cities, a wide variety of information, including events, citizen opinions, and emergencies, frequently arises, requiring efficient management and rapid response. However, traditional methods have presented challenges in appropriately collecting and analyzing this information and prioritizing responses.

[0093] 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.

[0094] In this invention, the server includes an information analysis device, a device for creating action items based on the analyzed information, a device for recording the created action items, a device for detecting when an action item has been completed, a device for removing completed action items from the record, a device for collecting and analyzing opinions from citizens, a device for determining priorities based on the collected opinions, and a device for notifying relevant parties of information regarding events and emergencies in the city. This enables efficient management of information within the smart city and prompt responses that take into account the opinions of citizens.

[0095] An "information analysis device" is a device that analyzes data such as electronic communication content and opinions from citizens to extract useful information.

[0096] A "device for creating action items" is a device that generates specific action plans and tasks based on analysis results.

[0097] A "device for recording operation items" is a device that saves the created operation items to a storage medium.

[0098] A "device that detects when an operation item has been completed" is a device that monitors the completion status of a set operation item and recognizes when it has been completed.

[0099] A "device for removing completed operation items from records" is a device that erases operation items that have been confirmed to be completed from a database or recording medium.

[0100] A "device for collecting and analyzing opinions from citizens" is a device that collects feedback and opinions from citizens, analyzes their content, and converts it into useful information.

[0101] A "prioritization device" is a device that, based on collected data, determines which tasks or events should be prioritized for processing.

[0102] A "device for notifying relevant parties of information regarding events and emergencies" is a device designed to quickly inform relevant parties of important events and emergencies within a smart city.

[0103] To implement this invention, it is necessary to construct an information analysis system and link multiple devices and software. The core of the system is a server, which performs the following processes.

[0104] First, the server uses information analysis equipment to analyze the content of electronic communications and opinions collected from citizens and stakeholders. Specifically, it uses natural language processing technologies such as the Google NLP API to classify opinions and evaluate their importance. The collected data is analyzed with high accuracy and converted into useful information.

[0105] Next, a device that creates action items based on the analysis results is activated. This device automatically generates priority tasks and countermeasures based on the analyzed data. The generated action items are notified to relevant parties in real time using cloud services such as AWS® Lambda.

[0106] After the operation items are generated, the device that records the operation items records them in a database. This is done using a relational database such as Amazon RDS.

[0107] Users can use smartphones or smart glasses as terminals to view the created actions in real time. The applications installed on the terminals are built with cross-platform development frameworks such as React Native, providing an intuitive interface.

[0108] Furthermore, the server monitors whether each task has been completed and automatically removes it from the record once completion is detected. This ensures that the task list is always kept up-to-date.

[0109] For example, if a citizen submits feedback stating that "noise-making activities are taking place in the city library," this information will be recognized as important by the information analysis system. Based on the analysis results, the library administrator will be immediately notified and required to take action.

[0110] An example of a prompt message for a related generative AI model is: "Based on feedback from citizens, please list the tasks that should be prioritized. Please notify us promptly of any matters that are particularly urgent."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server acquires electronic communication content and feedback from citizens from external information infrastructure. It receives data from mail servers and online feedback forms as input. The received data is processed into a format suitable for analysis using natural language processing techniques.

[0114] Step 2:

[0115] The server analyzes the acquired data using an information analysis device. Here, the Google NLP API is used to extract keywords and urgency levels related to the operational items. The input is the data processed in step 1, and the output is the keywords and related information resulting from the analysis.

[0116] Step 3:

[0117] The server creates action items based on the analysis results. The creation device uses the output from step 2 as input to generate an action item list. The output is a list that includes the corresponding tasks and their priorities.

[0118] Step 4:

[0119] The server saves information to a database to record the created action items. Using Amazon RDS, it receives the output from step 3 as input and writes it to the database. This ensures that the action items are permanently stored.

[0120] Step 5:

[0121] The terminal displays action items retrieved from the database in the user interface. Input is recorded data, and output is a visually organized task list. Users can view this list and submit completion reports as needed.

[0122] Step 6:

[0123] The user notifies the server from their terminal that they have completed an action item. The server then receives the completion report as input and checks it against the database. As output, the completion status of the corresponding action item is updated.

[0124] Step 7:

[0125] The server removes completed tasks from its records. This is the process of deleting unnecessary data from the database based on the completion report in step 6. The final output is an updated task list, which is reflected in the user interface.

[0126] 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.

[0127] This invention is an AI agent system designed to personalize and streamline user task management, incorporating an emotion engine. The system consists of a server, terminals, and users, and features new functions utilizing the emotion engine.

[0128] System Overview

[0129] The server not only acquires and analyzes the user's electronic communication content to generate action items, but also has the function of analyzing the user's emotional state using an emotion engine. This makes it possible to adjust the priority and timing of action items based on the emotional state.

[0130] In addition to displaying a task list to the user, the device generates notifications and reminders that take into account the user's emotional state. The user interface customizes notifications to suit the user's current mood, helping to reduce stress and improve motivation.

[0131] Users manage tasks through their devices, prioritizing tasks based on their emotional state and utilizing customized reminders. The user interface incorporates emotional data-based feedback features, allowing users to understand their own emotional tendencies and improve their self-management skills.

[0132] Program processing explanation in natural language

[0133] 1. The server acquires electronic communication content and the user's emotional state: The server collects user communication information from an external data infrastructure and analyzes the user's emotional data using an emotion engine. The emotion engine quantifies and records the emotional state using text and biosensor data.

[0134] 2. Generation and Adjustment of Behavioral Items: The server analyzes emails and meeting records to generate behavioral items, while simultaneously using emotion engine data to adjust task priorities. When the user is relaxed, it recommends more difficult tasks, and when the user is stressed, it adjusts to prioritize easier tasks.

[0135] 3. Task List and Notification Management: The server records the coordinated action items in a database and sends a real-time updated task list to the device. In addition, emotion-sensitive reminders are designed to be sent to the user from the device at the appropriate time.

[0136] 4. User Interaction: Users review and manage tasks through the user interface on their device. Feedback based on sentiment data is provided, allowing users to understand their own emotional tendencies and work on tasks efficiently.

[0137] Specific examples

[0138] For example, if the emotion engine detects that a user is experiencing stress, the server will prioritize recommending less demanding tasks from the available activity list. The device will then notify the user and display this as a list of feasible tasks. Simultaneously, personalized notifications offering tips on how to relax will be provided to create an environment where the user can perform tasks more comfortably.

[0139] This system aims to improve work efficiency and enhance the user experience by highly integrating task management and user emotion management.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The server acquires electronic communication content and biometric data. The server downloads communication data from the user's email and conferencing systems, and simultaneously receives emotional data from the user's biosensors. This data is temporarily stored for analysis.

[0143] Step 2:

[0144] The server analyzes the information and generates action items. The acquired communication content is analyzed using a natural language processing engine to extract task information and generate action items. This includes task name, details, deadline, etc.

[0145] Step 3:

[0146] The server uses an emotion engine to analyze the user's emotional state. Based on biometric data, the emotion engine quantifies the user's emotional state (e.g., stress level, satisfaction level) and uses this as a criterion for adjusting the priority of behavioral items.

[0147] Step 4:

[0148] The server adjusts the priority of the action items. Based on the analysis results of the emotion engine, the server re-evaluates the priority of the action items and rearranges the tasks in an order that is appropriate to the user's emotional state. For example, if the user is stressed, less burdensome tasks will be placed higher in the priority list.

[0149] Step 5:

[0150] The server records the adjusted action items in the database. It saves a list of the prioritized action items in the database and prepares for the changes to be reflected on the terminals.

[0151] Step 6:

[0152] The device provides the user with a task list and notifications. The device receives the latest task list from the server and displays it for the user to view. It also provides flexible reminders tailored to the user's emotional state at appropriate times.

[0153] Step 7:

[0154] Users manage tasks on their devices. Through their devices, users can check their tasks, report their completion status, or add new tasks. This allows them to progress through tasks while receiving self-management and emotional feedback.

[0155] Through this process, the system adjusts task management according to the user's emotions, providing a more personalized experience.

[0156] (Example 2)

[0157] 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".

[0158] Traditional task management systems often provide uniform task notifications and prioritization without considering the user's emotional state, which hinders stress reduction and motivation improvement. Incorporating sentiment analysis is necessary to enable more personalized and efficient task management.

[0159] 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.

[0160] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the priority of action items based on the analyzed emotional state, means for displaying action items according to the adjusted priority, and means for generating notifications that take the user's emotional state into consideration. This enables task prioritization and notifications that are appropriate to the user's emotional state.

[0161] "Means for analyzing a user's emotional state" refers to processes that use analytical techniques to quantify or classify a user's emotions, and this includes text analysis and data collection using biosensors.

[0162] "Means of adjusting the priority of action items based on analyzed emotional states" refers to algorithms or logic that dynamically change task priorities using user emotional data.

[0163] "Means for displaying action items according to adjusted priorities" refers to an interface or method for presenting users with tasks whose priorities have been re-evaluated.

[0164] "A means of generating notifications that take into account the user's emotional state" refers to a mechanism that generates and sends notifications with content and timing that match the user's current emotional condition.

[0165] "Means of acquiring electronic communication information from an external data infrastructure" refers to methods of collecting communication data such as emails and chat records via the internet or cloud services.

[0166] "Means of inputting the completion of an action item via the user interface" refers to the operation method or input device that allows the user to notify the system of task completion.

[0167] This invention is an AI agent system that analyzes the user's emotional state and adjusts the priority of the task list based on that analysis. The system mainly consists of a server, a terminal, and user interaction, with each element working together.

[0168] The server connects to the internet and cloud services to acquire electronic communication information from external data infrastructure. During this process, it utilizes APIs to retrieve user emails and chat logs, which are then analyzed using an emotion engine. The emotion engine employs tools utilizing natural language processing technology, such as IBM Watson® Tone Analyzer. This allows for the quantification of emotional states from user text messages.

[0169] Based on the analysis results, the server generates action items and records them in a large-scale database. Here, a generative AI model is used to algorithmically adjust the priority of tasks according to the user's emotional state. Once this process is complete, the optimized task list is sent to the terminal using real-time communication technology.

[0170] The device has a user interface implemented through which the user can check tasks and manage their progress. Specifically, the device generates and notifies the user of reminders appropriate to their emotional state, thereby improving the user's work efficiency.

[0171] For example, by incorporating instructions such as, "If the user's emotional state is stress level 0.7 or higher, include a guide to relieve tension in the reminder," the user can receive tasks and related information at the appropriate time.

[0172] In this way, the system aims to enable users to perform their tasks efficiently while reducing stress by allowing for emotion-based task management.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The server connects to an external data infrastructure via an API to retrieve user emails and chat logs. The input requires login information and access permission settings for the external service. After retrieving this data, the server prepares it for analysis. The output is communication data in text format. This process is performed periodically to ensure that new data is always available and up-to-date.

[0176] Step 2:

[0177] The server processes text data acquired using an emotion analysis engine. Specifically, it passes the input text data to the emotion engine, which then quantifies the emotional state using natural language processing. The generated output is an emotion score associated with each message. This score quantitatively indicates the user's emotional state and serves as a basis for decisions in subsequent processing steps.

[0178] Step 3:

[0179] The server extracts action items from communication data such as emails and meeting minutes. It analyzes the content using text mining techniques to identify keywords related to the tasks. The input is the text data obtained in the previous step. The output is a list of user action items, which will be used later for prioritizing.

[0180] Step 4:

[0181] The server adjusts the priority of action items based on the emotion score. A generative AI model is used to determine the optimal task order for each emotional state. The input is the emotion score and a list of action items, and the output is a task list with adjusted priorities. This process ensures the user receives the most optimal task order.

[0182] Step 5:

[0183] The server records the adjusted task list in the database and prepares it for transmission to the terminal. The output is a user-specific task list. This list is transmitted to the terminal using real-time communication technology, allowing the user to access it immediately.

[0184] Step 6:

[0185] The terminal receives a task list sent from the server and displays it in the user interface. Simultaneously with the display, it generates a customized reminder that takes the user's emotional state into account and schedules a notification. The input is the task list and emotional score, and the output is the customized notification. This notification is crucial for improving the user's work efficiency.

[0186] Step 7:

[0187] Users view task lists and reminders provided on their devices, and then execute and manage tasks. Completed tasks can be easily recorded through the user interface. Input is direct user interaction, and output is updated task progress. This interaction allows users to optimize their tasks while leveraging their emotional data.

[0188] (Application Example 2)

[0189] 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".

[0190] In today's information society, effectively managing and efficiently executing the diverse tasks individuals face is becoming an increasingly important challenge. In particular, there is a need to adjust tasks according to the user's emotional state and select appropriate notification methods. Conventional technologies have lacked sufficient task management systems that take user emotions into account, leading to increased user stress and decreased work efficiency.

[0191] 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.

[0192] In this invention, the server includes information analysis means, means for generating action items based on the analyzed information, means for recording the generated action items, means for analyzing the user's emotional state and adjusting the priority of the action items, and means for customizing and sending notifications based on the emotional state. This enables the adjustment of task priorities and personalized notifications according to the user's emotional state.

[0193] "Information analysis means" refers to devices or programs that have the function of analyzing data acquired from users and extracting useful information or patterns.

[0194] An "action item" refers to a specific task or action that the user should perform, and is an execution plan generated by the system.

[0195] "Means of recording" refer to devices or programs for saving generated data such as behavioral items and emotional states.

[0196] "Emotional state" is a representation of a user's current psychological condition using numbers or categories, and is information analyzed by the emotion engine.

[0197] A "means for adjusting priorities" refers to a function that changes the order in which actions are performed based on the user's emotional state and the importance of the tasks.

[0198] "Means for customizing and sending notifications" refers to a function that creates notifications in a format and content appropriate to the user's emotional state and sends them to the user's device.

[0199] This invention is a system for efficiently managing user tasks, providing task adjustment and notification functions that take into account the user's emotional state. The system consists of a server, terminals, and users.

[0200] The server analyzes data collected from users using information analysis tools. This data includes electronic communication content and emotional states obtained from biosensors. Based on the analyzed information, the server generates behavioral items and quantifies emotional states using an emotion engine. Software such as IBM Watson Tone Analyzer can be used as the emotion engine. This adjusts the priority of behavioral items based on the user's emotional state and records it in a database.

[0201] The device presents recorded activity items and notifications to the user through a user interface. Notifications are customized considering the user's emotional state and sent at the appropriate time using a push notification service. Specific notification services that can be used include Twilio and Firebase Cloud Messaging.

[0202] Users can review and manage their tasks through the provided user interface. Customized reminders and feedback features generated by the device allow users to understand their emotional tendencies and improve their self-management skills.

[0203] As a concrete example, this system can support citizens in efficiently managing and performing tasks related to public services with minimal stress. Furthermore, by utilizing a generative AI model, it can provide optimal suggestions to users based on an example prompt such as, "Create a notification that suggests challenging exercises available in the morning based on the gym's reservation status, thereby increasing the user's motivation."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server acquires user electronic communication content and biosensor data from an external data infrastructure. Inputs include emails, chat logs, and sensor information, while output is a dataset for analysis by an emotion engine. This input data is collected securely in accordance with security protocols.

[0207] Step 2:

[0208] The server uses an emotion engine to analyze the acquired data and quantify the user's emotional state. The input is the dataset obtained in step 1, and the output is the quantified emotion result. In this process, text data analysis and statistical processing of sensor data are performed to identify the emotion category corresponding to each data point.

[0209] Step 3:

[0210] The server generates action items based on the analysis results and adjusts task priorities while taking emotional states into account. The input is the numerical results from step 2 and past task data, and the output is a list of prioritized action items. Specifically, it uses an algorithm to execute a logic that recommends difficult tasks in a low-stress state.

[0211] Step 4:

[0212] The server sends the adjusted action items to the terminal and records them in the database. The input is the action item list from step 3, and the output is the task list displayed in the task management application. The data is updated in real time and provided in a format accessible to the user.

[0213] Step 5:

[0214] The device displays a task list to the user via a user interface and sends customized notifications based on the user's emotional state. The input is the task list received in step 4, and the output is visual and auditory feedback to the user. The device sends push notifications and alerts at appropriate times.

[0215] Step 6:

[0216] Users check and manage the completion status of tasks using an interface on their device. Inputs include notifications and task list information, while outputs include completed tasks and user feedback. When a user completes a task, they are provided with feedback to help them move on to the next step.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is an AI agent system designed to enable business environments and individual users to efficiently manage task lists. The system consists of three main elements: a server, a terminal, and a user interface.

[0234] System Overview

[0235] The server's role is to acquire and analyze the user's electronic communications. The server is equipped with a natural language processing (NLP) engine, which designs action plans from emails and meeting records. This generates action items, which are then recorded in a database.

[0236] The terminal is a device that provides a user interface (UI) for users to view and manage their activity items. The terminal receives the latest activity item information transmitted from the server and has the functionality to allow users to view, edit, and delete it. It also allows users to directly input the completion status of activity items.

[0237] Users can access their task list using their device to add new actions or complete existing tasks. Users are expected to continue processing tasks in a timely manner through deadline reminders.

[0238] Program processing explanation in natural language

[0239] 1. The server acquires electronic communication content: The server acquires the necessary data through interfaces with the user's email system and conferencing system. This data is then used for subsequent analysis.

[0240] 2. Information Analysis and Action Item Generation: The server's NLP engine analyzes the electronic communication content, identifies task names, related deadlines, assigned personnel, etc., and automatically generates action items. These generated action items are compiled into a task list.

[0241] 3. Management of Action Items: The server stores the generated action items in a relational database and monitors their completion status. When completion of an action item is detected, it is automatically deleted from the database and the task list is updated.

[0242] 4. Providing a user interface on the device: The device displays a task list in a user-friendly format, allowing users to add new tasks or update existing tasks to a completed state. The device also has a notification function to remind users of important events.

[0243] Specific examples

[0244] Suppose a user receives instructions in a meeting to "create a report by the next meeting." When this information is entered into the system as meeting minutes, the server's NLP engine generates an action item called "Create a report" and sets an appropriate deadline. This action item is displayed on the terminal, and the user receives notifications from the terminal as the deadline approaches, allowing them to manage the task's completion status.

[0245] In this way, this system allows users to efficiently and reliably manage their tasks and avoid missing important deadlines.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The server retrieves electronic communications. The server periodically retrieves data through the user's email server or conferencing system API, checking for new messages and meeting minutes. The verified data is stored for further processing.

[0249] Step 2:

[0250] The server analyzes the information and generates action items. Upon receiving the electronic communication content, the server uses a natural language processing engine to analyze the data, extracting information such as task name, deadline, and assigned person, and generating action items. This results in the formation of a concrete task list.

[0251] Step 3:

[0252] The server records the action items in a database. The generated action items are stored in a relational database for later access and management. This database stores the overall structure of the task list.

[0253] Step 4:

[0254] The terminal displays the task list in its user interface. The terminal reflects the updated task list received from the server in the user interface and displays it in a way that makes it easy for the user to check the details of the tasks.

[0255] Step 5:

[0256] Users manage tasks using their devices. They view their task list on their devices, complete or edit tasks as needed, and add new tasks. This allows them to manage the progress of their personal projects.

[0257] Step 6:

[0258] The server detects the completion of an action item and deletes the item. Based on completion input from the user or new information received, the server confirms the completion of a task, deletes the corresponding action item from the database, and updates the task list to its latest state.

[0259] This series of steps automates the task management process and helps users avoid overlooking anything.

[0260] (Example 1)

[0261] 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."

[0262] In today's information-saturated environment, it is becoming increasingly difficult for individuals and organizations to efficiently manage their daily tasks. In particular, there is a lack of systems to effectively extract and manage important action items from vast amounts of information, such as emails and meeting records. Traditional methods require significant time for manual data entry and management, making it highly likely that important tasks will be overlooked. Therefore, there is a growing need for systems that efficiently handle everything from automated information analysis to task generation and completion management.

[0263] 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.

[0264] In this invention, the server includes a device for analyzing information, a device for creating action items based on the analyzed information, a device for storing the created action items, a device for detecting when an action item has been achieved, and a device for generating notifications regarding completed action items. This enables efficient extraction of action items from a vast amount of electronic communication content, allowing users to manage tasks appropriately.

[0265] A "device for analyzing information" is a device that uses technologies such as natural language processing to analyze electronic communication content and extract necessary information and action items.

[0266] A "device for creating action items" is a device that automatically generates specific tasks and actions based on analyzed information and manages them as items.

[0267] A "device for storing action items" is a device that stores generated action items in a storage device such as a database, and has the function of allowing them to be referenced and edited as needed.

[0268] A "device for detecting when an action item has been completed" is a device that monitors the status of saved action items and confirms that the task has been completed by the user.

[0269] A "device that generates notifications regarding completed action items" is a device that has the function of automatically creating and sending notifications to inform users of information related to the deadline and completion status of action items.

[0270] A "device for acquiring electronic communication content from an external information infrastructure" is a device that accesses an electronic communication platform via APIs or network connections to acquire data such as emails and meeting minutes.

[0271] A "device for inputting the completion of action items via a user interface" is a device that provides an intuitive interface for users to operate and has a mechanism for reporting that an action item has been completed.

[0272] This invention is a system that automates the entire process from information analysis to task management. The system consists of three main elements: a server, a terminal, and a user. The operation of each component is described below.

[0273] The server acquires data through API connections with electronic communication platforms. It uses APIs such as the Google Workspace API and Microsoft Graph API to retrieve emails and meeting records, and then analyzes the information. For this analysis, it utilizes software libraries that provide natural language processing (NLP) technology, such as spaCy and NLTK. Using these technologies, the server extracts keywords related to the task and automatically generates specific action items. These generated action items are structured in JSON format and stored in a relational database such as MySQL.

[0274] The device provides a user interface that allows users to efficiently manage their tasks. Users can view and edit their latest tasks via a web browser or mobile app. Specifically, users indicate task completion by clicking a checkbox on the device. They can also add new tasks or edit existing ones using the plus button. The device provides important notifications to users, notifying them of tasks with approaching deadlines via push notifications.

[0275] Users can access task lists generated through their devices and efficiently manage their daily work. For example, if a user receives a task via email such as "Prepare a report before the next meeting," the system automatically reflects that information in the task list. An example of a prompt message could be, "Find the important tasks in my emails and meeting minutes and list them with deadlines and assignees." This allows users to efficiently carry out their work without overlooking important tasks even amidst information overload.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The server establishes an API connection with the electronic communication platform and retrieves data from the user's emails and meeting records. Inputs include the user's authentication information and the API being used (e.g., Google Workspace API, Microsoft Graph API). The server uses this information to securely aggregate the latest messages and meeting details from the platform. The output is raw electronic communication data.

[0279] Step 2:

[0280] The server uses the acquired electronic communication data to perform analysis using natural language processing (NLP) techniques. The input is the raw data from Step 1. Specifically, the server uses libraries such as spaCy and NLTK to tokenize the text and extract keywords. It then identifies action items and extracts related information. The output is structured information such as action items, responsible persons, and deadlines.

[0281] Step 3:

[0282] The server stores structured action items in a database in JSON format. The input is the structured information generated in Step 2. The server stores this information in a relational database such as MySQL and manages attributes such as the task ID and status. The output is the saved action item information.

[0283] Step 4:

[0284] The terminal provides a user interface for the user to view and manage the task list. The input is the action item data obtained from the server. The terminal obtains this data through an API and displays it in a format that is intuitive and easy for the user to operate. As a specific operation, the user can indicate the completion of a task by checking the task on the terminal screen. The output is that the information on the tasks completed by the user is updated on the server.

[0285] Step 5:

[0286] The user adds new action items or edits and completes existing tasks through the terminal. The input is the task information directly entered by the user into the interface. The terminal sends this information to the server and reflects it in the list. As output, the new or updated task information is stored in the database.

[0287] Step 6:

[0288] The server automatically detects action items in a completed state and deletes or archives that information. The input is the task completion notification from the terminal and the task status information in the database. The output is a cleaned-up database necessary to maintain the latest task list.

[0289] (Application Example 1)

[0290] 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".

[0291] In smart cities, a wide variety of information, including events, citizen opinions, and emergencies, frequently arises, requiring efficient management and rapid response. However, traditional methods have presented challenges in appropriately collecting and analyzing this information and prioritizing responses.

[0292] 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.

[0293] In this invention, the server includes an information analysis device, a device for creating action items based on the analyzed information, a device for recording the created action items, a device for detecting when an action item has been completed, a device for removing completed action items from the record, a device for collecting and analyzing opinions from citizens, a device for determining priorities based on the collected opinions, and a device for notifying relevant parties of information regarding events and emergencies in the city. This enables efficient management of information within the smart city and prompt responses that take into account the opinions of citizens.

[0294] An "information analysis device" is a device that analyzes data such as electronic communication content and opinions from citizens to extract useful information.

[0295] A "device for creating action items" is a device that generates specific action plans and tasks based on analysis results.

[0296] A "device for recording operation items" is a device that saves the created operation items to a storage medium.

[0297] A "device that detects when an operation item has been completed" is a device that monitors the completion status of a set operation item and recognizes when it has been completed.

[0298] A "device for removing completed operation items from records" is a device that erases operation items that have been confirmed to be completed from a database or recording medium.

[0299] A "device for collecting and analyzing opinions from citizens" is a device that collects feedback and opinions from citizens, analyzes their content, and converts it into useful information.

[0300] A "prioritization device" is a device that, based on collected data, determines which tasks or events should be prioritized for processing.

[0301] A "device for notifying relevant parties of information regarding events and emergencies" is a device designed to quickly inform relevant parties of important events and emergencies within a smart city.

[0302] To implement this invention, it is necessary to construct an information analysis system and link multiple devices and software. The core of the system is a server, which performs the following processes.

[0303] First, the server uses information analysis equipment to analyze the content of electronic communications and opinions collected from citizens and stakeholders. Specifically, it uses natural language processing technologies such as the Google NLP API to classify opinions and evaluate their importance. The collected data is analyzed with high accuracy and converted into useful information.

[0304] Next, a device that creates action items based on the analysis results is activated. This device automatically generates priority tasks and countermeasures based on the analyzed data. The generated action items are notified to relevant parties in real time using cloud services such as AWS Lambda.

[0305] After the operation items are generated, the device that records the operation items records them in a database. This is done using a relational database such as Amazon RDS.

[0306] The user can use a smartphone or smart glasses as a terminal and view the created operation items in real time. The application installed on the terminal is built with a cross-platform development framework such as React Native and provides an intuitive interface.

[0307] Furthermore, the server monitors whether the operation items are completed, and automatically removes them from the record when completion is detected. This ensures that the task list is always managed in an up-to-date state.

[0308] As a specific example, when a citizen sends feedback saying that "quiet work is being carried out in the city library", this information is recognized as an important matter by the information analysis device. According to the analysis result, the library administrator is immediately notified of this, and a response is required.

[0309] An example of a prompt sentence for the generative AI model related to this is "Based on the opinions of citizens, please list the tasks that should be prioritized for handling. Please notify promptly regarding matters of particular urgency."

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The server obtains electronic communication content and feedback from citizens from the external information infrastructure. As input, it receives data from the mail server or online feedback forms. The received data is processed into a form suitable for analysis using natural language processing technology.

[0313] Step 2:

[0314] The server analyzes the acquired data using an information analysis device. Here, the Google NLP API is used to extract keywords and urgency levels related to the operational items. The input is the data processed in step 1, and the output is the keywords and related information resulting from the analysis.

[0315] Step 3:

[0316] The server creates action items based on the analysis results. The creation device uses the output from step 2 as input to generate an action item list. The output is a list that includes the corresponding tasks and their priorities.

[0317] Step 4:

[0318] The server saves information to a database to record the created action items. Using Amazon RDS, it receives the output from step 3 as input and writes it to the database. This ensures that the action items are permanently stored.

[0319] Step 5:

[0320] The terminal displays action items retrieved from the database in the user interface. Input is recorded data, and output is a visually organized task list. Users can view this list and submit completion reports as needed.

[0321] Step 6:

[0322] The user notifies the server from their terminal that they have completed an action item. The server then receives the completion report as input and checks it against the database. As output, the completion status of the corresponding action item is updated.

[0323] Step 7:

[0324] The server removes completed tasks from its records. This is the process of deleting unnecessary data from the database based on the completion report in step 6. The final output is an updated task list, which is reflected in the user interface.

[0325] 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.

[0326] This invention is an AI agent system designed to personalize and streamline user task management, incorporating an emotion engine. The system consists of a server, terminals, and users, and features new functions utilizing the emotion engine.

[0327] System Overview

[0328] The server not only acquires and analyzes the user's electronic communication content to generate action items, but also has the function of analyzing the user's emotional state using an emotion engine. This makes it possible to adjust the priority and timing of action items based on the emotional state.

[0329] In addition to displaying a task list to the user, the device generates notifications and reminders that take into account the user's emotional state. The user interface customizes notifications to suit the user's current mood, helping to reduce stress and improve motivation.

[0330] Users manage tasks through their devices, prioritizing tasks based on their emotional state and utilizing customized reminders. The user interface incorporates emotional data-based feedback features, allowing users to understand their own emotional tendencies and improve their self-management skills.

[0331] Program processing explanation in natural language

[0332] 1. The server acquires electronic communication content and the user's emotional state: The server collects user communication information from an external data infrastructure and analyzes the user's emotional data using an emotion engine. The emotion engine quantifies and records the emotional state using text and biosensor data.

[0333] 2. Generation and Adjustment of Behavioral Items: The server analyzes emails and meeting records to generate behavioral items, while simultaneously using emotion engine data to adjust task priorities. When the user is relaxed, it recommends more difficult tasks, and when the user is stressed, it adjusts to prioritize easier tasks.

[0334] 3. Task List and Notification Management: The server records the coordinated action items in a database and sends a real-time updated task list to the device. In addition, emotion-sensitive reminders are designed to be sent to the user from the device at the appropriate time.

[0335] 4. User Interaction: Users review and manage tasks through the user interface on their device. Feedback based on sentiment data is provided, allowing users to understand their own emotional tendencies and work on tasks efficiently.

[0336] Specific examples

[0337] For example, if the emotion engine detects that a user is experiencing stress, the server will prioritize recommending less demanding tasks from the available activity list. The device will then notify the user and display this as a list of feasible tasks. Simultaneously, personalized notifications offering tips on how to relax will be provided to create an environment where the user can perform tasks more comfortably.

[0338] This system aims to improve work efficiency and enhance the user experience by highly integrating task management and user emotion management.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The server acquires electronic communication content and biometric data. The server downloads communication data from the user's email and conferencing systems, and simultaneously receives emotional data from the user's biosensors. This data is temporarily stored for analysis.

[0342] Step 2:

[0343] The server analyzes the information and generates action items. The acquired communication content is analyzed using a natural language processing engine to extract task information and generate action items. This includes task name, details, deadline, etc.

[0344] Step 3:

[0345] The server uses an emotion engine to analyze the user's emotional state. Based on biometric data, the emotion engine quantifies the user's emotional state (e.g., stress level, satisfaction level) and uses this as a criterion for adjusting the priority of behavioral items.

[0346] Step 4:

[0347] The server adjusts the priority of the action items. Based on the analysis results of the emotion engine, the server re-evaluates the priority of the action items and rearranges the tasks in an order that is appropriate to the user's emotional state. For example, if the user is stressed, less burdensome tasks will be placed higher in the priority list.

[0348] Step 5:

[0349] The server records the adjusted action items in the database. It saves a list of the prioritized action items in the database and prepares for the changes to be reflected on the terminals.

[0350] Step 6:

[0351] The device provides the user with a task list and notifications. The device receives the latest task list from the server and displays it for the user to view. It also provides flexible reminders tailored to the user's emotional state at appropriate times.

[0352] Step 7:

[0353] Users manage tasks on their devices. Through their devices, users can check their tasks, report their completion status, or add new tasks. This allows them to progress through tasks while receiving self-management and emotional feedback.

[0354] Through this process, the system adjusts task management according to the user's emotions, providing a more personalized experience.

[0355] (Example 2)

[0356] 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".

[0357] Traditional task management systems often provide uniform task notifications and prioritization without considering the user's emotional state, which hinders stress reduction and motivation improvement. Incorporating sentiment analysis is necessary to enable more personalized and efficient task management.

[0358] 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.

[0359] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the priority of action items based on the analyzed emotional state, means for displaying action items according to the adjusted priority, and means for generating notifications that take the user's emotional state into consideration. This enables task prioritization and notifications that are appropriate to the user's emotional state.

[0360] "Means for analyzing a user's emotional state" refers to processes that use analytical techniques to quantify or classify a user's emotions, and this includes text analysis and data collection using biosensors.

[0361] "Means of adjusting the priority of action items based on analyzed emotional states" refers to algorithms or logic that dynamically change task priorities using user emotional data.

[0362] "Means for displaying action items according to adjusted priorities" refers to an interface or method for presenting users with tasks whose priorities have been re-evaluated.

[0363] "A means of generating notifications that take into account the user's emotional state" refers to a mechanism that generates and sends notifications with content and timing that match the user's current emotional condition.

[0364] "Means of acquiring electronic communication information from an external data infrastructure" refers to methods of collecting communication data such as emails and chat records via the internet or cloud services.

[0365] "Means of inputting the completion of an action item via the user interface" refers to the operation method or input device that allows the user to notify the system of task completion.

[0366] This invention is an AI agent system that analyzes the user's emotional state and adjusts the priority of the task list based on that analysis. The system mainly consists of a server, a terminal, and user interaction, with each element working together.

[0367] The server connects to the internet and cloud services to acquire electronic communication information from external data infrastructure. During this process, it utilizes APIs to retrieve user emails and chat logs, which are then analyzed using an emotion engine. The emotion engine employs tools utilizing natural language processing technology, such as IBM Watson Tone Analyzer. This allows for the quantification of emotional states from user text messages.

[0368] Based on the analysis results, the server generates action items and records them in a large-scale database. Here, a generative AI model is used to algorithmically adjust the priority of tasks according to the user's emotional state. Once this process is complete, the optimized task list is sent to the terminal using real-time communication technology.

[0369] The device has a user interface implemented through which the user can check tasks and manage their progress. Specifically, the device generates and notifies the user of reminders appropriate to their emotional state, thereby improving the user's work efficiency.

[0370] For example, by incorporating instructions such as, "If the user's emotional state is stress level 0.7 or higher, include a guide to relieve tension in the reminder," the user can receive tasks and related information at the appropriate time.

[0371] In this way, the system aims to enable users to perform their tasks efficiently while reducing stress by allowing for emotion-based task management.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The server connects to an external data infrastructure via an API to retrieve user emails and chat logs. The input requires login information and access permission settings for the external service. After retrieving this data, the server prepares it for analysis. The output is communication data in text format. This process is performed periodically to ensure that new data is always available and up-to-date.

[0375] Step 2:

[0376] The server processes text data acquired using an emotion analysis engine. Specifically, it passes the input text data to the emotion engine, which then quantifies the emotional state using natural language processing. The generated output is an emotion score associated with each message. This score quantitatively indicates the user's emotional state and serves as a basis for decisions in subsequent processing steps.

[0377] Step 3:

[0378] The server extracts action items from communication data such as emails and meeting minutes. It analyzes the content using text mining techniques to identify keywords related to the tasks. The input is the text data obtained in the previous step. The output is a list of user action items, which will be used later for prioritizing.

[0379] Step 4:

[0380] The server adjusts the priority of action items based on the emotion score. A generative AI model is used to determine the optimal task order for each emotional state. The input is the emotion score and a list of action items, and the output is a task list with adjusted priorities. This process ensures the user receives the most optimal task order.

[0381] Step 5:

[0382] The server records the adjusted task list in the database and prepares it for transmission to the terminal. The output is a user-specific task list. This list is transmitted to the terminal using real-time communication technology, allowing the user to access it immediately.

[0383] Step 6:

[0384] The terminal receives a task list sent from the server and displays it in the user interface. Simultaneously with the display, it generates a customized reminder that takes the user's emotional state into account and schedules a notification. The input is the task list and emotional score, and the output is the customized notification. This notification is crucial for improving the user's work efficiency.

[0385] Step 7:

[0386] Users view task lists and reminders provided on their devices, and then execute and manage tasks. Completed tasks can be easily recorded through the user interface. Input is direct user interaction, and output is updated task progress. This interaction allows users to optimize their tasks while leveraging their emotional data.

[0387] (Application Example 2)

[0388] 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."

[0389] In today's information society, effectively managing and efficiently executing the diverse tasks individuals face is becoming an increasingly important challenge. In particular, there is a need to adjust tasks according to the user's emotional state and select appropriate notification methods. Conventional technologies have lacked sufficient task management systems that take user emotions into account, leading to increased user stress and decreased work efficiency.

[0390] 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.

[0391] In this invention, the server includes information analysis means, means for generating action items based on the analyzed information, means for recording the generated action items, means for analyzing the user's emotional state and adjusting the priority of the action items, and means for customizing and sending notifications based on the emotional state. This enables the adjustment of task priorities and personalized notifications according to the user's emotional state.

[0392] "Information analysis means" refers to devices or programs that have the function of analyzing data acquired from users and extracting useful information or patterns.

[0393] An "action item" refers to a specific task or action that the user should perform, and is an execution plan generated by the system.

[0394] "Means of recording" refer to devices or programs for saving generated data such as behavioral items and emotional states.

[0395] "Emotional state" is a representation of a user's current psychological condition using numbers or categories, and is information analyzed by the emotion engine.

[0396] A "means for adjusting priorities" refers to a function that changes the order in which actions are performed based on the user's emotional state and the importance of the tasks.

[0397] "Means for customizing and sending notifications" refers to a function that creates notifications in a format and content appropriate to the user's emotional state and sends them to the user's device.

[0398] This invention is a system for efficiently managing user tasks, providing task adjustment and notification functions that take into account the user's emotional state. The system consists of a server, terminals, and users.

[0399] The server analyzes data collected from users using information analysis tools. This data includes electronic communication content and emotional states obtained from biosensors. Based on the analyzed information, the server generates behavioral items and quantifies emotional states using an emotion engine. Software such as IBM Watson Tone Analyzer can be used as the emotion engine. This adjusts the priority of behavioral items based on the user's emotional state and records it in a database.

[0400] The device presents recorded activity items and notifications to the user through a user interface. Notifications are customized considering the user's emotional state and sent at the appropriate time using a push notification service. Specific notification services that can be used include Twilio and Firebase Cloud Messaging.

[0401] Users can review and manage their tasks through the provided user interface. Customized reminders and feedback features generated by the device allow users to understand their emotional tendencies and improve their self-management skills.

[0402] As a concrete example, this system can support citizens in efficiently managing and performing tasks related to public services with minimal stress. Furthermore, by utilizing a generative AI model, it can provide optimal suggestions to users based on an example prompt such as, "Create a notification that suggests challenging exercises available in the morning based on the gym's reservation status, thereby increasing the user's motivation."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The server acquires user electronic communication content and biosensor data from an external data infrastructure. Inputs include emails, chat logs, and sensor information, while output is a dataset for analysis by an emotion engine. This input data is collected securely in accordance with security protocols.

[0406] Step 2:

[0407] The server uses an emotion engine to analyze the acquired data and quantify the user's emotional state. The input is the dataset obtained in step 1, and the output is the quantified emotion result. In this process, text data analysis and statistical processing of sensor data are performed to identify the emotion category corresponding to each data point.

[0408] Step 3:

[0409] The server generates action items based on the analysis results and adjusts task priorities while taking emotional states into account. The input is the numerical results from step 2 and past task data, and the output is a list of prioritized action items. Specifically, it uses an algorithm to execute a logic that recommends difficult tasks in a low-stress state.

[0410] Step 4:

[0411] The server sends the adjusted action items to the terminal and records them in the database. The input is the action item list from step 3, and the output is the task list displayed in the task management application. The data is updated in real time and provided in a format accessible to the user.

[0412] Step 5:

[0413] The device displays a task list to the user via a user interface and sends customized notifications based on the user's emotional state. The input is the task list received in step 4, and the output is visual and auditory feedback to the user. The device sends push notifications and alerts at appropriate times.

[0414] Step 6:

[0415] Users check and manage the completion status of tasks using an interface on their device. Inputs include notifications and task list information, while outputs include completed tasks and user feedback. When a user completes a task, they are provided with feedback to help them move on to the next step.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention is an AI agent system designed to enable business environments and individual users to efficiently manage task lists. The system consists of three main elements: a server, a terminal, and a user interface.

[0433] System Overview

[0434] The server's role is to acquire and analyze the user's electronic communications. The server is equipped with a natural language processing (NLP) engine, which designs action plans from emails and meeting records. This generates action items, which are then recorded in a database.

[0435] The terminal is a device that provides a user interface (UI) for users to view and manage their activity items. The terminal receives the latest activity item information transmitted from the server and has the functionality to allow users to view, edit, and delete it. It also allows users to directly input the completion status of activity items.

[0436] Users can access their task list using their device to add new actions or complete existing tasks. Users are expected to continue processing tasks in a timely manner through deadline reminders.

[0437] Program processing explanation in natural language

[0438] 1. The server acquires electronic communication content: The server acquires the necessary data through interfaces with the user's email system and conferencing system. This data is then used for subsequent analysis.

[0439] 2. Information Analysis and Action Item Generation: The server's NLP engine analyzes the electronic communication content, identifies task names, related deadlines, assigned personnel, etc., and automatically generates action items. These generated action items are compiled into a task list.

[0440] 3. Management of Action Items: The server stores the generated action items in a relational database and monitors their completion status. When completion of an action item is detected, it is automatically deleted from the database and the task list is updated.

[0441] 4. Providing a user interface on the device: The device displays a task list in a user-friendly format, allowing users to add new tasks or update existing tasks to a completed state. The device also has a notification function to remind users of important events.

[0442] Specific examples

[0443] Suppose a user receives instructions in a meeting to "create a report by the next meeting." When this information is entered into the system as meeting minutes, the server's NLP engine generates an action item called "Create a report" and sets an appropriate deadline. This action item is displayed on the terminal, and the user receives notifications from the terminal as the deadline approaches, allowing them to manage the task's completion status.

[0444] In this way, this system allows users to efficiently and reliably manage their tasks and avoid missing important deadlines.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The server retrieves electronic communications. The server periodically retrieves data through the user's email server or conferencing system API, checking for new messages and meeting minutes. The verified data is stored for further processing.

[0448] Step 2:

[0449] The server analyzes the information and generates action items. Upon receiving the electronic communication content, the server uses a natural language processing engine to analyze the data, extracting information such as task name, deadline, and assigned person, and generating action items. This results in the formation of a concrete task list.

[0450] Step 3:

[0451] The server records the action items in a database. The generated action items are stored in a relational database for later access and management. This database stores the overall structure of the task list.

[0452] Step 4:

[0453] The terminal displays the task list in its user interface. The terminal reflects the updated task list received from the server in the user interface and displays it in a way that makes it easy for the user to check the details of the tasks.

[0454] Step 5:

[0455] Users manage tasks using their devices. They view their task list on their devices, complete or edit tasks as needed, and add new tasks. This allows them to manage the progress of their personal projects.

[0456] Step 6:

[0457] The server detects the completion of an action item and deletes the item. Based on completion input from the user or new information received, the server confirms the completion of a task, deletes the corresponding action item from the database, and updates the task list to its latest state.

[0458] This series of steps automates the task management process and helps users avoid overlooking anything.

[0459] (Example 1)

[0460] 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."

[0461] In today's information-saturated environment, it is becoming increasingly difficult for individuals and organizations to efficiently manage their daily tasks. In particular, there is a lack of systems to effectively extract and manage important action items from vast amounts of information, such as emails and meeting records. Traditional methods require significant time for manual data entry and management, making it highly likely that important tasks will be overlooked. Therefore, there is a growing need for systems that efficiently handle everything from automated information analysis to task generation and completion management.

[0462] 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.

[0463] In this invention, the server includes a device for analyzing information, a device for creating action items based on the analyzed information, a device for storing the created action items, a device for detecting when an action item has been achieved, and a device for generating notifications regarding completed action items. This enables efficient extraction of action items from a vast amount of electronic communication content, allowing users to manage tasks appropriately.

[0464] A "device for analyzing information" is a device that uses technologies such as natural language processing to analyze electronic communication content and extract necessary information and action items.

[0465] A "device for creating action items" is a device that automatically generates specific tasks and actions based on analyzed information and manages them as items.

[0466] A "device for storing action items" is a device that stores generated action items in a storage device such as a database, and has the function of allowing them to be referenced and edited as needed.

[0467] A "device for detecting when an action item has been completed" is a device that monitors the status of saved action items and confirms that the task has been completed by the user.

[0468] A "device that generates notifications regarding completed action items" is a device that has the function of automatically creating and sending notifications to inform users of information related to the deadline and completion status of action items.

[0469] A "device for acquiring electronic communication content from an external information infrastructure" is a device that accesses an electronic communication platform via APIs or network connections to acquire data such as emails and meeting minutes.

[0470] A "device for inputting the completion of action items via a user interface" is a device that provides an intuitive interface for users to operate and has a mechanism for reporting that an action item has been completed.

[0471] This invention is a system that automates the entire process from information analysis to task management. The system consists of three main elements: a server, a terminal, and a user. The operation of each component is described below.

[0472] The server acquires data through API connections with electronic communication platforms. It uses APIs such as the Google Workspace API and Microsoft Graph API to retrieve emails and meeting records, and then analyzes the information. For this analysis, it utilizes software libraries that provide natural language processing (NLP) technology, such as spaCy and NLTK. Using these technologies, the server extracts keywords related to the task and automatically generates specific action items. These generated action items are structured in JSON format and stored in a relational database such as MySQL.

[0473] The device provides a user interface that allows users to efficiently manage their tasks. Users can view and edit their latest tasks via a web browser or mobile app. Specifically, users indicate task completion by clicking a checkbox on the device. They can also add new tasks or edit existing ones using the plus button. The device provides important notifications to users, notifying them of tasks with approaching deadlines via push notifications.

[0474] Users can access task lists generated through their devices and efficiently manage their daily work. For example, if a user receives a task via email such as "Prepare a report before the next meeting," the system automatically reflects that information in the task list. An example of a prompt message could be, "Find the important tasks in my emails and meeting minutes and list them with deadlines and assignees." This allows users to efficiently carry out their work without overlooking important tasks even amidst information overload.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The server establishes an API connection with the electronic communication platform and retrieves data from the user's emails and meeting records. Inputs include the user's authentication information and the API being used (e.g., Google Workspace API, Microsoft Graph API). The server uses this information to securely aggregate the latest messages and meeting details from the platform. The output is raw electronic communication data.

[0478] Step 2:

[0479] The server uses the acquired electronic communication data to perform analysis using natural language processing (NLP) techniques. The input is the raw data from Step 1. Specifically, the server uses libraries such as spaCy and NLTK to tokenize the text and extract keywords. It then identifies action items and extracts related information. The output is structured information such as action items, responsible persons, and deadlines.

[0480] Step 3:

[0481] The server stores structured action items in JSON format in a database. The input is the structured information generated in step 2. The server stores this information in a relational database such as MySQL and manages attributes such as task ID and status. The output is the saved action item information.

[0482] Step 4:

[0483] The terminal provides a user interface for users to view and manage their task list. Input consists of action item data obtained from the server. The terminal retrieves this data via an API and displays it in an intuitive, user-friendly format. Specifically, users can indicate task completion by checking a box on the terminal screen. The output is that information on completed tasks is updated on the server.

[0484] Step 5:

[0485] Users can add new actions or edit and complete existing tasks through their terminal. Input is task information directly entered by the user into the interface. The terminal sends this information to the server, where it is reflected in the list. Output is stored in the database, containing new and updated task information.

[0486] Step 6:

[0487] The server automatically detects completed tasks and deletes or archives the information. Inputs are task completion notifications from terminals and task status information in the database. Output is a cleaned database necessary to maintain an up-to-date task list.

[0488] (Application Example 1)

[0489] 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."

[0490] In smart cities, a wide variety of information, including events, citizen opinions, and emergencies, frequently arises, requiring efficient management and rapid response. However, traditional methods have presented challenges in appropriately collecting and analyzing this information and prioritizing responses.

[0491] 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.

[0492] In this invention, the server includes an information analysis device, a device for creating action items based on the analyzed information, a device for recording the created action items, a device for detecting when an action item has been completed, a device for removing completed action items from the record, a device for collecting and analyzing opinions from citizens, a device for determining priorities based on the collected opinions, and a device for notifying relevant parties of information regarding events and emergencies in the city. This enables efficient management of information within the smart city and prompt responses that take into account the opinions of citizens.

[0493] An "information analysis device" is a device that analyzes data such as electronic communication content and opinions from citizens to extract useful information.

[0494] A "device for creating action items" is a device that generates specific action plans and tasks based on analysis results.

[0495] A "device for recording operation items" is a device that saves the created operation items to a storage medium.

[0496] A "device that detects when an operation item has been completed" is a device that monitors the completion status of a set operation item and recognizes when it has been completed.

[0497] A "device for removing completed operation items from records" is a device that erases operation items that have been confirmed to be completed from a database or recording medium.

[0498] A "device for collecting and analyzing opinions from citizens" is a device that collects feedback and opinions from citizens, analyzes their content, and converts it into useful information.

[0499] A "prioritization device" is a device that, based on collected data, determines which tasks or events should be prioritized for processing.

[0500] A "device for notifying relevant parties of information regarding events and emergencies" is a device designed to quickly inform relevant parties of important events and emergencies within a smart city.

[0501] To implement this invention, it is necessary to construct an information analysis system and link multiple devices and software. The core of the system is a server, which performs the following processes.

[0502] First, the server uses information analysis equipment to analyze the content of electronic communications and opinions collected from citizens and stakeholders. Specifically, it uses natural language processing technologies such as the Google NLP API to classify opinions and evaluate their importance. The collected data is analyzed with high accuracy and converted into useful information.

[0503] Next, a device that creates action items based on the analysis results is activated. This device automatically generates priority tasks and countermeasures based on the analyzed data. The generated action items are notified to relevant parties in real time using cloud services such as AWS Lambda.

[0504] After the operation items are generated, the device that records the operation items records them in a database. This is done using a relational database such as Amazon RDS.

[0505] Users can use smartphones or smart glasses as terminals to view the created actions in real time. The applications installed on the terminals are built with cross-platform development frameworks such as React Native, providing an intuitive interface.

[0506] Furthermore, the server monitors whether each task has been completed and automatically removes it from the record once completion is detected. This ensures that the task list is always kept up-to-date.

[0507] For example, if a citizen submits feedback stating that "noise-making activities are taking place in the city library," this information will be recognized as important by the information analysis system. Based on the analysis results, the library administrator will be immediately notified and required to take action.

[0508] An example of a prompt message for a related generative AI model is: "Based on feedback from citizens, please list the tasks that should be prioritized. Please notify us promptly of any matters that are particularly urgent."

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The server acquires electronic communication content and feedback from citizens from external information infrastructure. It receives data from mail servers and online feedback forms as input. The received data is processed into a format suitable for analysis using natural language processing techniques.

[0512] Step 2:

[0513] The server analyzes the acquired data using an information analysis device. Here, the Google NLP API is used to extract keywords and urgency levels related to the operational items. The input is the data processed in step 1, and the output is the keywords and related information resulting from the analysis.

[0514] Step 3:

[0515] The server creates action items based on the analysis results. The creation device uses the output from step 2 as input to generate an action item list. The output is a list that includes the corresponding tasks and their priorities.

[0516] Step 4:

[0517] The server saves information to a database to record the created action items. Using Amazon RDS, it receives the output from step 3 as input and writes it to the database. This ensures that the action items are permanently stored.

[0518] Step 5:

[0519] The terminal displays action items retrieved from the database in the user interface. Input is recorded data, and output is a visually organized task list. Users can view this list and submit completion reports as needed.

[0520] Step 6:

[0521] The user notifies the server from their terminal that they have completed an action item. The server then receives the completion report as input and checks it against the database. As output, the completion status of the corresponding action item is updated.

[0522] Step 7:

[0523] The server removes completed tasks from its records. This is the process of deleting unnecessary data from the database based on the completion report in step 6. The final output is an updated task list, which is reflected in the user interface.

[0524] 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.

[0525] This invention is an AI agent system designed to personalize and streamline user task management, incorporating an emotion engine. The system consists of a server, terminals, and users, and features new functions utilizing the emotion engine.

[0526] System Overview

[0527] The server not only acquires and analyzes the user's electronic communication content to generate action items, but also has the function of analyzing the user's emotional state using an emotion engine. This makes it possible to adjust the priority and timing of action items based on the emotional state.

[0528] In addition to displaying a task list to the user, the device generates notifications and reminders that take into account the user's emotional state. The user interface customizes notifications to suit the user's current mood, helping to reduce stress and improve motivation.

[0529] Users manage tasks through their devices, prioritizing tasks based on their emotional state and utilizing customized reminders. The user interface incorporates emotional data-based feedback features, allowing users to understand their own emotional tendencies and improve their self-management skills.

[0530] Program processing explanation in natural language

[0531] 1. The server acquires electronic communication content and the user's emotional state: The server collects user communication information from an external data infrastructure and analyzes the user's emotional data using an emotion engine. The emotion engine quantifies and records the emotional state using text and biosensor data.

[0532] 2. Generation and Adjustment of Behavioral Items: The server analyzes emails and meeting records to generate behavioral items, while simultaneously using emotion engine data to adjust task priorities. When the user is relaxed, it recommends more difficult tasks, and when the user is stressed, it adjusts to prioritize easier tasks.

[0533] 3. Task List and Notification Management: The server records the coordinated action items in a database and sends a real-time updated task list to the device. In addition, emotion-sensitive reminders are designed to be sent to the user from the device at the appropriate time.

[0534] 4. User Interaction: Users review and manage tasks through the user interface on their device. Feedback based on sentiment data is provided, allowing users to understand their own emotional tendencies and work on tasks efficiently.

[0535] Specific examples

[0536] For example, if the emotion engine detects that a user is experiencing stress, the server will prioritize recommending less demanding tasks from the available activity list. The device will then notify the user and display this as a list of feasible tasks. Simultaneously, personalized notifications offering tips on how to relax will be provided to create an environment where the user can perform tasks more comfortably.

[0537] This system aims to improve work efficiency and enhance the user experience by highly integrating task management and user emotion management.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The server acquires electronic communication content and biometric data. The server downloads communication data from the user's email and conferencing systems, and simultaneously receives emotional data from the user's biosensors. This data is temporarily stored for analysis.

[0541] Step 2:

[0542] The server analyzes the information and generates action items. The acquired communication content is analyzed using a natural language processing engine to extract task information and generate action items. This includes task name, details, deadline, etc.

[0543] Step 3:

[0544] The server uses an emotion engine to analyze the user's emotional state. Based on biometric data, the emotion engine quantifies the user's emotional state (e.g., stress level, satisfaction level) and uses this as a criterion for adjusting the priority of behavioral items.

[0545] Step 4:

[0546] The server adjusts the priority of the action items. Based on the analysis results of the emotion engine, the server re-evaluates the priority of the action items and rearranges the tasks in an order that is appropriate to the user's emotional state. For example, if the user is stressed, less burdensome tasks will be placed higher in the priority list.

[0547] Step 5:

[0548] The server records the adjusted action items in the database. It saves a list of the prioritized action items in the database and prepares for the changes to be reflected on the terminals.

[0549] Step 6:

[0550] The device provides the user with a task list and notifications. The device receives the latest task list from the server and displays it for the user to view. It also provides flexible reminders tailored to the user's emotional state at appropriate times.

[0551] Step 7:

[0552] Users manage tasks on their devices. Through their devices, users can check their tasks, report their completion status, or add new tasks. This allows them to progress through tasks while receiving self-management and emotional feedback.

[0553] Through this process, the system adjusts task management according to the user's emotions, providing a more personalized experience.

[0554] (Example 2)

[0555] 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."

[0556] Traditional task management systems often provide uniform task notifications and prioritization without considering the user's emotional state, which hinders stress reduction and motivation improvement. Incorporating sentiment analysis is necessary to enable more personalized and efficient task management.

[0557] 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.

[0558] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the priority of action items based on the analyzed emotional state, means for displaying action items according to the adjusted priority, and means for generating notifications that take the user's emotional state into consideration. This enables task prioritization and notifications that are appropriate to the user's emotional state.

[0559] "Means for analyzing a user's emotional state" refers to processes that use analytical techniques to quantify or classify a user's emotions, and this includes text analysis and data collection using biosensors.

[0560] "Means of adjusting the priority of action items based on analyzed emotional states" refers to algorithms or logic that dynamically change task priorities using user emotional data.

[0561] "Means for displaying action items according to adjusted priorities" refers to an interface or method for presenting users with tasks whose priorities have been re-evaluated.

[0562] "A means of generating notifications that take into account the user's emotional state" refers to a mechanism that generates and sends notifications with content and timing that match the user's current emotional condition.

[0563] "Means of acquiring electronic communication information from an external data infrastructure" refers to methods of collecting communication data such as emails and chat records via the internet or cloud services.

[0564] "Means of inputting the completion of an action item via the user interface" refers to the operation method or input device that allows the user to notify the system of task completion.

[0565] This invention is an AI agent system that analyzes the user's emotional state and adjusts the priority of the task list based on that analysis. The system mainly consists of a server, a terminal, and user interaction, with each element working together.

[0566] The server connects to the internet and cloud services to acquire electronic communication information from external data infrastructure. During this process, it utilizes APIs to retrieve user emails and chat logs, which are then analyzed using an emotion engine. The emotion engine employs tools utilizing natural language processing technology, such as IBM Watson Tone Analyzer. This allows for the quantification of emotional states from user text messages.

[0567] Based on the analysis results, the server generates action items and records them in a large-scale database. Here, a generative AI model is used to algorithmically adjust the priority of tasks according to the user's emotional state. Once this process is complete, the optimized task list is sent to the terminal using real-time communication technology.

[0568] The device has a user interface implemented through which the user can check tasks and manage their progress. Specifically, the device generates and notifies the user of reminders appropriate to their emotional state, thereby improving the user's work efficiency.

[0569] For example, by incorporating instructions such as, "If the user's emotional state is stress level 0.7 or higher, include a guide to relieve tension in the reminder," the user can receive tasks and related information at the appropriate time.

[0570] In this way, the system aims to enable users to perform their tasks efficiently while reducing stress by allowing for emotion-based task management.

[0571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0572] Step 1:

[0573] The server connects to an external data infrastructure via an API to retrieve user emails and chat logs. The input requires login information and access permission settings for the external service. After retrieving this data, the server prepares it for analysis. The output is communication data in text format. This process is performed periodically to ensure that new data is always available and up-to-date.

[0574] Step 2:

[0575] The server processes text data acquired using an emotion analysis engine. Specifically, it passes the input text data to the emotion engine, which then quantifies the emotional state using natural language processing. The generated output is an emotion score associated with each message. This score quantitatively indicates the user's emotional state and serves as a basis for decisions in subsequent processing steps.

[0576] Step 3:

[0577] The server extracts action items from communication data such as emails and meeting minutes. It analyzes the content using text mining techniques to identify keywords related to the tasks. The input is the text data obtained in the previous step. The output is a list of user action items, which will be used later for prioritizing.

[0578] Step 4:

[0579] The server adjusts the priority of action items based on the emotion score. A generative AI model is used to determine the optimal task order for each emotional state. The input is the emotion score and a list of action items, and the output is a task list with adjusted priorities. This process ensures the user receives the most optimal task order.

[0580] Step 5:

[0581] The server records the adjusted task list in the database and prepares it for transmission to the terminal. The output is a user-specific task list. This list is transmitted to the terminal using real-time communication technology, allowing the user to access it immediately.

[0582] Step 6:

[0583] The terminal receives a task list sent from the server and displays it in the user interface. Simultaneously with the display, it generates a customized reminder that takes the user's emotional state into account and schedules a notification. The input is the task list and emotional score, and the output is the customized notification. This notification is crucial for improving the user's work efficiency.

[0584] Step 7:

[0585] Users view task lists and reminders provided on their devices, and then execute and manage tasks. Completed tasks can be easily recorded through the user interface. Input is direct user interaction, and output is updated task progress. This interaction allows users to optimize their tasks while leveraging their emotional data.

[0586] (Application Example 2)

[0587] 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."

[0588] In today's information society, effectively managing and efficiently executing the diverse tasks individuals face is becoming an increasingly important challenge. In particular, there is a need to adjust tasks according to the user's emotional state and select appropriate notification methods. Conventional technologies have lacked sufficient task management systems that take user emotions into account, leading to increased user stress and decreased work efficiency.

[0589] 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.

[0590] In this invention, the server includes information analysis means, means for generating action items based on the analyzed information, means for recording the generated action items, means for analyzing the user's emotional state and adjusting the priority of the action items, and means for customizing and sending notifications based on the emotional state. This enables the adjustment of task priorities and personalized notifications according to the user's emotional state.

[0591] "Information analysis means" refers to devices or programs that have the function of analyzing data acquired from users and extracting useful information or patterns.

[0592] An "action item" refers to a specific task or action that the user should perform, and is an execution plan generated by the system.

[0593] "Means of recording" refer to devices or programs for saving generated data such as behavioral items and emotional states.

[0594] "Emotional state" is a representation of a user's current psychological condition using numbers or categories, and is information analyzed by the emotion engine.

[0595] A "means for adjusting priorities" refers to a function that changes the order in which actions are performed based on the user's emotional state and the importance of the tasks.

[0596] "Means for customizing and sending notifications" refers to a function that creates notifications in a format and content appropriate to the user's emotional state and sends them to the user's device.

[0597] This invention is a system for efficiently managing user tasks, providing task adjustment and notification functions that take into account the user's emotional state. The system consists of a server, terminals, and users.

[0598] The server analyzes data collected from users using information analysis tools. This data includes electronic communication content and emotional states obtained from biosensors. Based on the analyzed information, the server generates behavioral items and quantifies emotional states using an emotion engine. Software such as IBM Watson Tone Analyzer can be used as the emotion engine. This adjusts the priority of behavioral items based on the user's emotional state and records it in a database.

[0599] The device presents recorded activity items and notifications to the user through a user interface. Notifications are customized considering the user's emotional state and sent at the appropriate time using a push notification service. Specific notification services that can be used include Twilio and Firebase Cloud Messaging.

[0600] Users can review and manage their tasks through the provided user interface. Customized reminders and feedback features generated by the device allow users to understand their emotional tendencies and improve their self-management skills.

[0601] As a concrete example, this system can support citizens in efficiently managing and performing tasks related to public services with minimal stress. Furthermore, by utilizing a generative AI model, it can provide optimal suggestions to users based on an example prompt such as, "Create a notification that suggests challenging exercises available in the morning based on the gym's reservation status, thereby increasing the user's motivation."

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The server acquires user electronic communication content and biosensor data from an external data infrastructure. Inputs include emails, chat logs, and sensor information, while output is a dataset for analysis by an emotion engine. This input data is collected securely in accordance with security protocols.

[0605] Step 2:

[0606] The server uses an emotion engine to analyze the acquired data and quantify the user's emotional state. The input is the dataset obtained in step 1, and the output is the quantified emotion result. In this process, text data analysis and statistical processing of sensor data are performed to identify the emotion category corresponding to each data point.

[0607] Step 3:

[0608] The server generates action items based on the analysis results and adjusts task priorities while taking emotional states into account. The input is the numerical results from step 2 and past task data, and the output is a list of prioritized action items. Specifically, it uses an algorithm to execute a logic that recommends difficult tasks in a low-stress state.

[0609] Step 4:

[0610] The server sends the adjusted action items to the terminal and records them in the database. The input is the action item list from step 3, and the output is the task list displayed in the task management application. The data is updated in real time and provided in a format accessible to the user.

[0611] Step 5:

[0612] The device displays a task list to the user via a user interface and sends customized notifications based on the user's emotional state. The input is the task list received in step 4, and the output is visual and auditory feedback to the user. The device sends push notifications and alerts at appropriate times.

[0613] Step 6:

[0614] Users check and manage the completion status of tasks using an interface on their device. Inputs include notifications and task list information, while outputs include completed tasks and user feedback. When a user completes a task, they are provided with feedback to help them move on to the next step.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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 of 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention is an AI agent system designed to enable business environments and individual users to efficiently manage task lists. The system consists of three main elements: a server, a terminal, and a user interface.

[0633] System Overview

[0634] The server's role is to acquire and analyze the user's electronic communications. The server is equipped with a natural language processing (NLP) engine, which designs action plans from emails and meeting records. This generates action items, which are then recorded in a database.

[0635] The terminal is a device that provides a user interface (UI) for users to view and manage their activity items. The terminal receives the latest activity item information transmitted from the server and has the functionality to allow users to view, edit, and delete it. It also allows users to directly input the completion status of activity items.

[0636] Users can access their task list using their device to add new actions or complete existing tasks. Users are expected to continue processing tasks in a timely manner through deadline reminders.

[0637] Program processing explanation in natural language

[0638] 1. The server acquires electronic communication content: The server acquires the necessary data through interfaces with the user's email system and conferencing system. This data is then used for subsequent analysis.

[0639] 2. Information Analysis and Action Item Generation: The server's NLP engine analyzes the electronic communication content, identifies task names, related deadlines, assigned personnel, etc., and automatically generates action items. These generated action items are compiled into a task list.

[0640] 3. Management of Action Items: The server stores the generated action items in a relational database and monitors their completion status. When completion of an action item is detected, it is automatically deleted from the database and the task list is updated.

[0641] 4. Providing a user interface on the device: The device displays a task list in a user-friendly format, allowing users to add new tasks or update existing tasks to a completed state. The device also has a notification function to remind users of important events.

[0642] Specific examples

[0643] Suppose a user receives instructions in a meeting to "create a report by the next meeting." When this information is entered into the system as meeting minutes, the server's NLP engine generates an action item called "Create a report" and sets an appropriate deadline. This action item is displayed on the terminal, and the user receives notifications from the terminal as the deadline approaches, allowing them to manage the task's completion status.

[0644] In this way, this system allows users to efficiently and reliably manage their tasks and avoid missing important deadlines.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The server retrieves electronic communications. The server periodically retrieves data through the user's email server or conferencing system API, checking for new messages and meeting minutes. The verified data is stored for further processing.

[0648] Step 2:

[0649] The server analyzes the information and generates action items. Upon receiving the electronic communication content, the server uses a natural language processing engine to analyze the data, extracting information such as task name, deadline, and assigned person, and generating action items. This results in the formation of a concrete task list.

[0650] Step 3:

[0651] The server records the action items in a database. The generated action items are stored in a relational database for later access and management. This database stores the overall structure of the task list.

[0652] Step 4:

[0653] The terminal displays the task list in its user interface. The terminal reflects the updated task list received from the server in the user interface and displays it in a way that makes it easy for the user to check the details of the tasks.

[0654] Step 5:

[0655] Users manage tasks using their devices. They view their task list on their devices, complete or edit tasks as needed, and add new tasks. This allows them to manage the progress of their personal projects.

[0656] Step 6:

[0657] The server detects the completion of an action item and deletes the item. Based on completion input from the user or new information received, the server confirms the completion of a task, deletes the corresponding action item from the database, and updates the task list to its latest state.

[0658] This series of steps automates the task management process and helps users avoid overlooking anything.

[0659] (Example 1)

[0660] 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".

[0661] In today's information-saturated environment, it is becoming increasingly difficult for individuals and organizations to efficiently manage their daily tasks. In particular, there is a lack of systems to effectively extract and manage important action items from vast amounts of information, such as emails and meeting records. Traditional methods require significant time for manual data entry and management, making it highly likely that important tasks will be overlooked. Therefore, there is a growing need for systems that efficiently handle everything from automated information analysis to task generation and completion management.

[0662] 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.

[0663] In this invention, the server includes a device for analyzing information, a device for creating action items based on the analyzed information, a device for storing the created action items, a device for detecting when an action item has been achieved, and a device for generating notifications regarding completed action items. This enables efficient extraction of action items from a vast amount of electronic communication content, allowing users to manage tasks appropriately.

[0664] A "device for analyzing information" is a device that uses technologies such as natural language processing to analyze electronic communication content and extract necessary information and action items.

[0665] A "device for creating action items" is a device that automatically generates specific tasks and actions based on analyzed information and manages them as items.

[0666] A "device for storing action items" is a device that stores generated action items in a storage device such as a database, and has the function of allowing them to be referenced and edited as needed.

[0667] A "device for detecting when an action item has been completed" is a device that monitors the status of saved action items and confirms that the task has been completed by the user.

[0668] A "device that generates notifications regarding completed action items" is a device that has the function of automatically creating and sending notifications to inform users of information related to the deadline and completion status of action items.

[0669] A "device for acquiring electronic communication content from an external information infrastructure" is a device that accesses an electronic communication platform via APIs or network connections to acquire data such as emails and meeting minutes.

[0670] A "device for inputting the completion of action items via a user interface" is a device that provides an intuitive interface for users to operate and has a mechanism for reporting that an action item has been completed.

[0671] This invention is a system that automates the entire process from information analysis to task management. The system consists of three main elements: a server, a terminal, and a user. The operation of each component is described below.

[0672] The server acquires data through API connections with electronic communication platforms. It uses APIs such as the Google Workspace API and Microsoft Graph API to retrieve emails and meeting records, and then analyzes the information. For this analysis, it utilizes software libraries that provide natural language processing (NLP) technology, such as spaCy and NLTK. Using these technologies, the server extracts keywords related to the task and automatically generates specific action items. These generated action items are structured in JSON format and stored in a relational database such as MySQL.

[0673] The device provides a user interface that allows users to efficiently manage their tasks. Users can view and edit their latest tasks via a web browser or mobile app. Specifically, users indicate task completion by clicking a checkbox on the device. They can also add new tasks or edit existing ones using the plus button. The device provides important notifications to users, notifying them of tasks with approaching deadlines via push notifications.

[0674] Users can access task lists generated through their devices and efficiently manage their daily work. For example, if a user receives a task via email such as "Prepare a report before the next meeting," the system automatically reflects that information in the task list. An example of a prompt message could be, "Find the important tasks in my emails and meeting minutes and list them with deadlines and assignees." This allows users to efficiently carry out their work without overlooking important tasks even amidst information overload.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The server establishes an API connection with the electronic communication platform and retrieves data from the user's emails and meeting records. Inputs include the user's authentication information and the API being used (e.g., Google Workspace API, Microsoft Graph API). The server uses this information to securely aggregate the latest messages and meeting details from the platform. The output is raw electronic communication data.

[0678] Step 2:

[0679] The server uses the acquired electronic communication data to perform analysis using natural language processing (NLP) techniques. The input is the raw data from Step 1. Specifically, the server uses libraries such as spaCy and NLTK to tokenize the text and extract keywords. It then identifies action items and extracts related information. The output is structured information such as action items, responsible persons, and deadlines.

[0680] Step 3:

[0681] The server stores structured action items in JSON format in a database. The input is the structured information generated in step 2. The server stores this information in a relational database such as MySQL and manages attributes such as task ID and status. The output is the saved action item information.

[0682] Step 4:

[0683] The terminal provides a user interface for users to view and manage their task list. Input consists of action item data obtained from the server. The terminal retrieves this data via an API and displays it in an intuitive, user-friendly format. Specifically, users can indicate task completion by checking a box on the terminal screen. The output is that information on completed tasks is updated on the server.

[0684] Step 5:

[0685] Users can add new actions or edit and complete existing tasks through their terminal. Input is task information directly entered by the user into the interface. The terminal sends this information to the server, where it is reflected in the list. Output is stored in the database, containing new and updated task information.

[0686] Step 6:

[0687] The server automatically detects completed tasks and deletes or archives the information. Inputs are task completion notifications from terminals and task status information in the database. Output is a cleaned database necessary to maintain an up-to-date task list.

[0688] (Application Example 1)

[0689] 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".

[0690] In smart cities, a wide variety of information, including events, citizen opinions, and emergencies, frequently arises, requiring efficient management and rapid response. However, traditional methods have presented challenges in appropriately collecting and analyzing this information and prioritizing responses.

[0691] 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.

[0692] In this invention, the server includes an information analysis device, a device for creating action items based on the analyzed information, a device for recording the created action items, a device for detecting when an action item has been completed, a device for removing completed action items from the record, a device for collecting and analyzing opinions from citizens, a device for determining priorities based on the collected opinions, and a device for notifying relevant parties of information regarding events and emergencies in the city. This enables efficient management of information within the smart city and prompt responses that take into account the opinions of citizens.

[0693] An "information analysis device" is a device that analyzes data such as electronic communication content and opinions from citizens to extract useful information.

[0694] A "device for creating action items" is a device that generates specific action plans and tasks based on analysis results.

[0695] A "device for recording operation items" is a device that saves the created operation items to a storage medium.

[0696] A "device that detects when an operation item has been completed" is a device that monitors the completion status of a set operation item and recognizes when it has been completed.

[0697] A "device for removing completed operation items from records" is a device that erases operation items that have been confirmed to be completed from a database or recording medium.

[0698] A "device for collecting and analyzing opinions from citizens" is a device that collects feedback and opinions from citizens, analyzes their content, and converts it into useful information.

[0699] A "prioritization device" is a device that, based on collected data, determines which tasks or events should be prioritized for processing.

[0700] A "device for notifying relevant parties of information regarding events and emergencies" is a device designed to quickly inform relevant parties of important events and emergencies within a smart city.

[0701] To implement this invention, it is necessary to construct an information analysis system and link multiple devices and software. The core of the system is a server, which performs the following processes.

[0702] First, the server uses information analysis equipment to analyze the content of electronic communications and opinions collected from citizens and stakeholders. Specifically, it uses natural language processing technologies such as the Google NLP API to classify opinions and evaluate their importance. The collected data is analyzed with high accuracy and converted into useful information.

[0703] Next, a device that creates action items based on the analysis results is activated. This device automatically generates priority tasks and countermeasures based on the analyzed data. The generated action items are notified to relevant parties in real time using cloud services such as AWS Lambda.

[0704] After the operation items are generated, the device that records the operation items records them in a database. This is done using a relational database such as Amazon RDS.

[0705] Users can use smartphones or smart glasses as terminals to view the created actions in real time. The applications installed on the terminals are built with cross-platform development frameworks such as React Native, providing an intuitive interface.

[0706] Furthermore, the server monitors whether each task has been completed and automatically removes it from the record once completion is detected. This ensures that the task list is always kept up-to-date.

[0707] For example, if a citizen submits feedback stating that "noise-making activities are taking place in the city library," this information will be recognized as important by the information analysis system. Based on the analysis results, the library administrator will be immediately notified and required to take action.

[0708] An example of a prompt message for a related generative AI model is: "Based on feedback from citizens, please list the tasks that should be prioritized. Please notify us promptly of any matters that are particularly urgent."

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The server acquires electronic communication content and feedback from citizens from external information infrastructure. It receives data from mail servers and online feedback forms as input. The received data is processed into a format suitable for analysis using natural language processing techniques.

[0712] Step 2:

[0713] The server analyzes the acquired data using an information analysis device. Here, the Google NLP API is used to extract keywords and urgency levels related to the operational items. The input is the data processed in step 1, and the output is the keywords and related information resulting from the analysis.

[0714] Step 3:

[0715] The server creates action items based on the analysis results. The creation device uses the output from step 2 as input to generate an action item list. The output is a list that includes the corresponding tasks and their priorities.

[0716] Step 4:

[0717] The server saves information to a database to record the created action items. Using Amazon RDS, it receives the output from step 3 as input and writes it to the database. This ensures that the action items are permanently stored.

[0718] Step 5:

[0719] The terminal displays action items retrieved from the database in the user interface. Input is recorded data, and output is a visually organized task list. Users can view this list and submit completion reports as needed.

[0720] Step 6:

[0721] The user notifies the server from their terminal that they have completed an action item. The server then receives the completion report as input and checks it against the database. As output, the completion status of the corresponding action item is updated.

[0722] Step 7:

[0723] The server removes completed tasks from its records. This is the process of deleting unnecessary data from the database based on the completion report in step 6. The final output is an updated task list, which is reflected in the user interface.

[0724] 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.

[0725] This invention is an AI agent system designed to personalize and streamline user task management, incorporating an emotion engine. The system consists of a server, terminals, and users, and features new functions utilizing the emotion engine.

[0726] System Overview

[0727] The server not only acquires and analyzes the user's electronic communication content to generate action items, but also has the function of analyzing the user's emotional state using an emotion engine. This makes it possible to adjust the priority and timing of action items based on the emotional state.

[0728] In addition to displaying a task list to the user, the device generates notifications and reminders that take into account the user's emotional state. The user interface customizes notifications to suit the user's current mood, helping to reduce stress and improve motivation.

[0729] Users manage tasks through their devices, prioritizing tasks based on their emotional state and utilizing customized reminders. The user interface incorporates emotional data-based feedback features, allowing users to understand their own emotional tendencies and improve their self-management skills.

[0730] Program processing explanation in natural language

[0731] 1. The server acquires electronic communication content and the user's emotional state: The server collects user communication information from an external data infrastructure and analyzes the user's emotional data using an emotion engine. The emotion engine quantifies and records the emotional state using text and biosensor data.

[0732] 2. Generation and Adjustment of Behavioral Items: The server analyzes emails and meeting records to generate behavioral items, while simultaneously using emotion engine data to adjust task priorities. When the user is relaxed, it recommends more difficult tasks, and when the user is stressed, it adjusts to prioritize easier tasks.

[0733] 3. Task List and Notification Management: The server records the coordinated action items in a database and sends a real-time updated task list to the device. In addition, emotion-sensitive reminders are designed to be sent to the user from the device at the appropriate time.

[0734] 4. User Interaction: Users review and manage tasks through the user interface on their device. Feedback based on sentiment data is provided, allowing users to understand their own emotional tendencies and work on tasks efficiently.

[0735] Specific examples

[0736] For example, if the emotion engine detects that a user is experiencing stress, the server will prioritize recommending less demanding tasks from the available activity list. The device will then notify the user and display this as a list of feasible tasks. Simultaneously, personalized notifications offering tips on how to relax will be provided to create an environment where the user can perform tasks more comfortably.

[0737] This system aims to improve work efficiency and enhance the user experience by highly integrating task management and user emotion management.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The server acquires electronic communication content and biometric data. The server downloads communication data from the user's email and conferencing systems, and simultaneously receives emotional data from the user's biosensors. This data is temporarily stored for analysis.

[0741] Step 2:

[0742] The server analyzes the information and generates action items. The acquired communication content is analyzed using a natural language processing engine to extract task information and generate action items. This includes task name, details, deadline, etc.

[0743] Step 3:

[0744] The server uses an emotion engine to analyze the user's emotional state. Based on biometric data, the emotion engine quantifies the user's emotional state (e.g., stress level, satisfaction level) and uses this as a criterion for adjusting the priority of behavioral items.

[0745] Step 4:

[0746] The server adjusts the priority of the action items. Based on the analysis results of the emotion engine, the server re-evaluates the priority of the action items and rearranges the tasks in an order that is appropriate to the user's emotional state. For example, if the user is stressed, less burdensome tasks will be placed higher in the priority list.

[0747] Step 5:

[0748] The server records the adjusted action items in the database. It saves a list of the prioritized action items in the database and prepares for the changes to be reflected on the terminals.

[0749] Step 6:

[0750] The device provides the user with a task list and notifications. The device receives the latest task list from the server and displays it for the user to view. It also provides flexible reminders tailored to the user's emotional state at appropriate times.

[0751] Step 7:

[0752] Users manage tasks on their devices. Through their devices, users can check their tasks, report their completion status, or add new tasks. This allows them to progress through tasks while receiving self-management and emotional feedback.

[0753] Through this process, the system adjusts task management according to the user's emotions, providing a more personalized experience.

[0754] (Example 2)

[0755] 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".

[0756] Traditional task management systems often provide uniform task notifications and prioritization without considering the user's emotional state, which hinders stress reduction and motivation improvement. Incorporating sentiment analysis is necessary to enable more personalized and efficient task management.

[0757] 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.

[0758] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the priority of action items based on the analyzed emotional state, means for displaying action items according to the adjusted priority, and means for generating notifications that take the user's emotional state into consideration. This enables task prioritization and notifications that are appropriate to the user's emotional state.

[0759] "Means for analyzing a user's emotional state" refers to processes that use analytical techniques to quantify or classify a user's emotions, and this includes text analysis and data collection using biosensors.

[0760] "Means of adjusting the priority of action items based on analyzed emotional states" refers to algorithms or logic that dynamically change task priorities using user emotional data.

[0761] "Means for displaying action items according to adjusted priorities" refers to an interface or method for presenting users with tasks whose priorities have been re-evaluated.

[0762] "A means of generating notifications that take into account the user's emotional state" refers to a mechanism that generates and sends notifications with content and timing that match the user's current emotional condition.

[0763] "Means of acquiring electronic communication information from an external data infrastructure" refers to methods of collecting communication data such as emails and chat records via the internet or cloud services.

[0764] "Means of inputting the completion of an action item via the user interface" refers to the operation method or input device that allows the user to notify the system of task completion.

[0765] This invention is an AI agent system that analyzes the user's emotional state and adjusts the priority of the task list based on that analysis. The system mainly consists of a server, a terminal, and user interaction, with each element working together.

[0766] The server connects to the internet and cloud services to acquire electronic communication information from external data infrastructure. During this process, it utilizes APIs to retrieve user emails and chat logs, which are then analyzed using an emotion engine. The emotion engine employs tools utilizing natural language processing technology, such as IBM Watson Tone Analyzer. This allows for the quantification of emotional states from user text messages.

[0767] Based on the analysis results, the server generates action items and records them in a large-scale database. Here, a generative AI model is used to algorithmically adjust the priority of tasks according to the user's emotional state. Once this process is complete, the optimized task list is sent to the terminal using real-time communication technology.

[0768] The device has a user interface implemented through which the user can check tasks and manage their progress. Specifically, the device generates and notifies the user of reminders appropriate to their emotional state, thereby improving the user's work efficiency.

[0769] For example, by incorporating instructions such as, "If the user's emotional state is stress level 0.7 or higher, include a guide to relieve tension in the reminder," the user can receive tasks and related information at the appropriate time.

[0770] In this way, the system aims to enable users to perform their tasks efficiently while reducing stress by allowing for emotion-based task management.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The server connects to an external data infrastructure via an API to retrieve user emails and chat logs. The input requires login information and access permission settings for the external service. After retrieving this data, the server prepares it for analysis. The output is communication data in text format. This process is performed periodically to ensure that new data is always available and up-to-date.

[0774] Step 2:

[0775] The server processes text data acquired using an emotion analysis engine. Specifically, it passes the input text data to the emotion engine, which then quantifies the emotional state using natural language processing. The generated output is an emotion score associated with each message. This score quantitatively indicates the user's emotional state and serves as a basis for decisions in subsequent processing steps.

[0776] Step 3:

[0777] The server extracts action items from communication data such as emails and meeting minutes. It analyzes the content using text mining techniques to identify keywords related to the tasks. The input is the text data obtained in the previous step. The output is a list of user action items, which will be used later for prioritizing.

[0778] Step 4:

[0779] The server adjusts the priority of action items based on the emotion score. A generative AI model is used to determine the optimal task order for each emotional state. The input is the emotion score and a list of action items, and the output is a task list with adjusted priorities. This process ensures the user receives the most optimal task order.

[0780] Step 5:

[0781] The server records the adjusted task list in the database and prepares it for transmission to the terminal. The output is a user-specific task list. This list is transmitted to the terminal using real-time communication technology, allowing the user to access it immediately.

[0782] Step 6:

[0783] The terminal receives a task list sent from the server and displays it in the user interface. Simultaneously with the display, it generates a customized reminder that takes the user's emotional state into account and schedules a notification. The input is the task list and emotional score, and the output is the customized notification. This notification is crucial for improving the user's work efficiency.

[0784] Step 7:

[0785] Users view task lists and reminders provided on their devices, and then execute and manage tasks. Completed tasks can be easily recorded through the user interface. Input is direct user interaction, and output is updated task progress. This interaction allows users to optimize their tasks while leveraging their emotional data.

[0786] (Application Example 2)

[0787] 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".

[0788] In today's information society, effectively managing and efficiently executing the diverse tasks individuals face is becoming an increasingly important challenge. In particular, there is a need to adjust tasks according to the user's emotional state and select appropriate notification methods. Conventional technologies have lacked sufficient task management systems that take user emotions into account, leading to increased user stress and decreased work efficiency.

[0789] 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.

[0790] In this invention, the server includes information analysis means, means for generating action items based on the analyzed information, means for recording the generated action items, means for analyzing the user's emotional state and adjusting the priority of the action items, and means for customizing and sending notifications based on the emotional state. This enables the adjustment of task priorities and personalized notifications according to the user's emotional state.

[0791] "Information analysis means" refers to devices or programs that have the function of analyzing data acquired from users and extracting useful information or patterns.

[0792] An "action item" refers to a specific task or action that the user should perform, and is an execution plan generated by the system.

[0793] "Means of recording" refer to devices or programs for saving generated data such as behavioral items and emotional states.

[0794] "Emotional state" is a representation of a user's current psychological condition using numbers or categories, and is information analyzed by the emotion engine.

[0795] A "means for adjusting priorities" refers to a function that changes the order in which actions are performed based on the user's emotional state and the importance of the tasks.

[0796] "Means for customizing and sending notifications" refers to a function that creates notifications in a format and content appropriate to the user's emotional state and sends them to the user's device.

[0797] This invention is a system for efficiently managing user tasks, providing task adjustment and notification functions that take into account the user's emotional state. The system consists of a server, terminals, and users.

[0798] The server analyzes data collected from users using information analysis tools. This data includes electronic communication content and emotional states obtained from biosensors. Based on the analyzed information, the server generates behavioral items and quantifies emotional states using an emotion engine. Software such as IBM Watson Tone Analyzer can be used as the emotion engine. This adjusts the priority of behavioral items based on the user's emotional state and records it in a database.

[0799] The device presents recorded activity items and notifications to the user through a user interface. Notifications are customized considering the user's emotional state and sent at the appropriate time using a push notification service. Specific notification services that can be used include Twilio and Firebase Cloud Messaging.

[0800] Users can review and manage their tasks through the provided user interface. Customized reminders and feedback features generated by the device allow users to understand their emotional tendencies and improve their self-management skills.

[0801] As a concrete example, this system can support citizens in efficiently managing and performing tasks related to public services with minimal stress. Furthermore, by utilizing a generative AI model, it can provide optimal suggestions to users based on an example prompt such as, "Create a notification that suggests challenging exercises available in the morning based on the gym's reservation status, thereby increasing the user's motivation."

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The server acquires user electronic communication content and biosensor data from an external data infrastructure. Inputs include emails, chat logs, and sensor information, while output is a dataset for analysis by an emotion engine. This input data is collected securely in accordance with security protocols.

[0805] Step 2:

[0806] The server uses an emotion engine to analyze the acquired data and quantify the user's emotional state. The input is the dataset obtained in step 1, and the output is the quantified emotion result. In this process, text data analysis and statistical processing of sensor data are performed to identify the emotion category corresponding to each data point.

[0807] Step 3:

[0808] The server generates action items based on the analysis results and adjusts task priorities while taking emotional states into account. The input is the numerical results from step 2 and past task data, and the output is a list of prioritized action items. Specifically, it uses an algorithm to execute a logic that recommends difficult tasks in a low-stress state.

[0809] Step 4:

[0810] The server sends the adjusted action items to the terminal and records them in the database. The input is the action item list from step 3, and the output is the task list displayed in the task management application. The data is updated in real time and provided in a format accessible to the user.

[0811] Step 5:

[0812] The device displays a task list to the user via a user interface and sends customized notifications based on the user's emotional state. The input is the task list received in step 4, and the output is visual and auditory feedback to the user. The device sends push notifications and alerts at appropriate times.

[0813] Step 6:

[0814] Users check and manage the completion status of tasks using an interface on their device. Inputs include notifications and task list information, while outputs include completed tasks and user feedback. When a user completes a task, they are provided with feedback to help them move on to the next step.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] Information analysis means,

[0839] A means of generating action items based on the analyzed information,

[0840] A means for recording the generated action items,

[0841] A means for detecting when an action item has been completed,

[0842] A means of deleting completed action items from the record,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for acquiring electronic communication content from an external data infrastructure.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for inputting the completion of an action item from a user interface.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A device for analyzing information,

[0851] A device that creates action items based on analyzed information,

[0852] A device for saving the created action items,

[0853] A device that detects when an action item has been achieved,

[0854] A device for deleting completed action items from storage,

[0855] A method for compiling and saving action items based on the analyzed information,

[0856] A device that generates notifications regarding completed action items,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising a device for acquiring electronic communication content from an external information infrastructure.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising a device for inputting the completion of an action item via a user interface.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] Information analysis device,

[0865] A device that creates operational items based on the analyzed information,

[0866] A device for recording the created operation items,

[0867] A device that detects when an operation item has been completed,

[0868] A device for removing completed operation items from the record,

[0869] A device for collecting and analyzing opinions from citizens,

[0870] A device that determines priorities based on collected opinions,

[0871] A device that notifies relevant parties of information regarding events and emergencies in the city,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising a device for acquiring electronic communication content from an external information infrastructure.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising a device for inputting the completion of an operation item from a user operation unit.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means of analyzing the user's emotional state,

[0880] A means of adjusting the priority of behavioral items based on the analyzed emotional state,

[0881] A means of displaying action items according to adjusted priorities,

[0882] A means of generating notifications that take into account the user's emotional state,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, further comprising means for acquiring electronic communication information from an external data infrastructure.

[0886] (Claim 3)

[0887] The system according to claim 1, further comprising means for inputting the completion of an action item from a user interface.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] Information analysis means,

[0891] A means of generating action items based on the analyzed information,

[0892] A means for recording the generated action items,

[0893] A means for detecting when an action item has been completed,

[0894] A means of deleting completed action items from the record,

[0895] A means of analyzing the user's emotional state and adjusting the priority of behavioral items,

[0896] A means of customizing and sending notifications based on emotional state,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, further comprising means for acquiring electronic communication content from an external data infrastructure, and coordinating notifications of public services in a smart city.

[0900] (Claim 3)

[0901] The system according to claim 1, further comprising means for inputting the completion of an action item from a user interface, and providing feedback based on the user's emotional state. [Explanation of symbols]

[0902] 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. Information analysis device, A device that creates operational items based on the analyzed information, A device for recording the created operation items, A device that detects when an operation item has been completed, A device for removing completed operation items from the record, A device for collecting and analyzing opinions from citizens, A device that determines priorities based on collected opinions, A device that notifies relevant parties of information regarding events and emergencies in the city, A system that includes this.

2. The system according to claim 1, further comprising a device for acquiring electronic communication content from an external information infrastructure.

3. The system according to claim 1, further comprising a device for inputting the completion of an operation item from a user operation unit.

Images