system

Abstract

We provide the system. [Solution] A means of analyzing electronic information and extracting important elements, A means of identifying tasks and setting priorities based on extracted elements, A means of converting audio information into text and extracting the key points of a meeting, A means of visually presenting the generated information to the user, A means of analyzing alarms and monitoring sounds to notify of high-priority events, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 that responds 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 the modern knowledge work environment, individuals and organizations are facing problems such as information overload, task management complexity, and the increase in remote work. As a result, it has become difficult to manage tasks efficiently, and there is a demand for quick decision-making. The present invention aims to solve such problems and improve the productivity of knowledge workers through the analysis of electronic communications, task prioritization, summarization of voice data, and visual presentation of this information.

Means for Solving the Problems

[0005] This invention proposes a system comprising means for analyzing electronic communications to extract important information, means for identifying tasks and setting priorities based on the extracted information, means for converting audio data into text and extracting key points from meetings, and means for visually presenting the generated information to the user. Furthermore, by including means for adjusting priorities through user input and means for analyzing the user's schedule information and proposing the optimal task execution order, the system aims to improve the efficiency of task management and decision-making processes.

[0006] "Electronic communications" refers to information sent and received via email or messaging applications.

[0007] "Analysis" refers to the process of thoroughly examining information and data to understand their meaning and patterns.

[0008] "Important information" refers to information whose value and usefulness are clearly defined based on specific conditions or criteria.

[0009] A "task" refers to a specific task or activity that needs to be accomplished.

[0010] "Priority" refers to the criteria used to determine the order and importance of tasks in order to accomplish them.

[0011] "Audio data" refers to data in which sound is recorded in digital format.

[0012] "Text" refers to data or information that is visually represented as characters or sentences.

[0013] "Key points" refer to the most important parts or key points of information.

[0014] "Visual presentation" refers to providing information to users in a visible form through screens or written documents.

[0015] "Knowledge workers" refer to workers who create added value by processing, analyzing, and utilizing information.

[0016] "Schedule information" refers to information indicating schedules or plans based on specific times or dates.

[0017] "Execution order" refers to the specific order when performing tasks or operations.

Brief Description of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a task management and strategic decision support system designed to improve the operational efficiency of users. This system is implemented through the interaction of servers, terminals, and users.

[0040] System configuration:

[0041] This system primarily offers the following functions: First, the server analyzes electronic communications and extracts important elements from the information. This makes it easy to find necessary actions from a large volume of daily emails. Second, the server converts audio data into text and summarizes the meeting content. This allows users to efficiently review the key points.

[0042] Specific example 1:

[0043] When a user receives a new email, the server analyzes its contents and extracts important events such as contract deadlines and meeting schedules. The device instantly notifies the user of this information and visually lists them as high-priority tasks. This list is dynamically updated according to the urgency and importance of the tasks.

[0044] Specific example 2:

[0045] During remote meetings, the server uses a feature that converts audio conversations to text in real time. It automatically summarizes key decisions and next steps from the meeting and presents them to the user via their device. This eliminates the need for users to take extensive notes after the meeting, allowing them to quickly plan their next actions.

[0046] User roles:

[0047] Users can determine the order in which tasks are executed based on the information presented by their device. Furthermore, they can accept or adjust schedule optimization suggestions to effectively carry out their work.

[0048] Thus, the present invention supports improved work efficiency and faster decision-making by extracting necessary tasks from a large amount of information and presenting them visually in an easy-to-understand manner for the user.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The server accesses a specific mailbox and retrieves new electronic communications.

[0052] Step 2:

[0053] The server analyzes the acquired electronic communications using natural language processing algorithms and extracts important information. This extraction is performed based on pre-configured keywords and phrases.

[0054] Step 3:

[0055] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is set based on the importance and urgency of each task.

[0056] Step 4:

[0057] The terminal visually displays a prioritized task list received from the server to the user and provides notifications.

[0058] Step 5:

[0059] Users can review the displayed task list and adjust the task priority according to its content.

[0060] Step 6:

[0061] The device transmits audio from the remote meeting to the server in real time.

[0062] Step 7:

[0063] The server converts the received audio data into text using speech recognition technology.

[0064] Step 8:

[0065] The server analyzes the converted text data and automatically extracts the key points of the meeting.

[0066] Step 9:

[0067] The device presents the extracted key points to the user to help them decide on their next action.

[0068] Step 10:

[0069] Based on the information provided, users review their schedule, incorporate newly assigned tasks, and proceed with their work.

[0070] (Example 1)

[0071] 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."

[0072] In today's information-saturated society, it is difficult for users to quickly and efficiently extract important tasks and decisions from the information they receive daily through electronic communications and meetings. Furthermore, manual prioritization and task management in schedule management is time-consuming and labor-intensive, hindering work efficiency. Solving these issues is essential to improving user work efficiency and providing a comfortable work environment.

[0073] 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.

[0074] In this invention, the server includes means for processing electronic data and extracting important items, means for converting audio data into text information and extracting key points from meetings, etc., and means for analyzing the user's personal schedule information and suggesting the optimal order for executing tasks. This enables the user to make informed, efficient prioritizations and quick decisions.

[0075] "Electronic data" refers to information such as text and emails that are sent and received over computers and networks.

[0076] "Means" refers to the apparatus, method, or function used to achieve a particular purpose.

[0077] "Extracting items" refers to the process of identifying and extracting important or relevant information from the original data.

[0078] "Audio data" refers to information in the form of recorded conversations or voices.

[0079] "Converting to text information" refers to the process of analyzing audio data and converting it into a corresponding string of characters.

[0080] "Meeting summaries" refer to the important topics and decisions discussed during the meeting.

[0081] "Personal schedule information" refers to information related to a user's personal schedule and time management.

[0082] "Suggesting a work order" refers to proposing a recommended sequence of tasks for efficient work execution.

[0083] To implement this invention, a system is constructed in which users, servers, and terminals work together.

[0084] When the server receives electronic communications accessible to the user, it analyzes them. Specifically, it uses software available as a natural language processing engine, such as Python's NLTK library or Google's Natural Language API, to extract important items from the electronic data. In this process, the server examines the content of emails and identifies important information such as contract deadlines and meeting schedules.

[0085] Furthermore, the server acquires audio data and converts it into text using speech recognition technologies such as Google Cloud Speech-to-Text and IBM Watson® Speech to Text. This allows the server to extract key points from meetings and discussions and organize them into a summary.

[0086] The terminal visually presents tasks to the user based on information provided by the server. Suitable task management software for this purpose includes Asana and Trello. Through this software, the terminal displays a list of tasks to the user that reflects their priority.

[0087] Users can efficiently manage their work tasks based on recommendations from their devices. Furthermore, users can receive suggestions for optimizing their schedules via their devices and use them as a basis for decision-making.

[0088] As a concrete example, a user might enter the prompt, "Please review the important documents in preparation for tomorrow's meeting." The system then immediately searches the server for meeting-related materials and sends them to the user's terminal to support their preparation.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The server accesses the user's mailbox and retrieves new electronic communications. Based on this input data, it analyzes the text using natural language processing techniques. The analysis extracts important event information from the email (e.g., "meeting schedule," "document deadline") and stores this information in a database. The output is metadata for the extracted important items.

[0092] Step 2:

[0093] The server receives audio data acquired during the meeting as input. Using speech recognition software such as Google Cloud Speech-to-Text, this audio data is converted into text. Next, the converted information is summarized, and the key points of the meeting are extracted. The output of this process is text information containing a summary of the meeting.

[0094] Step 3:

[0095] The terminal receives important information and meeting summaries sent from the server. Based on this data, task management software (e.g., Asana, Trello) is used to visually display a list of tasks to the user based on their priority. The input is the analysis results sent from the server, and the output is a task list that the user can visually review.

[0096] Step 4:

[0097] The user reviews the task list displayed on the device and decides on an action. Based on the user's input, the device adds new tasks or updates the completion status of existing tasks. It also generates an updated task list and schedule suggestions as output, based on whether the user accepts or adjusts schedule optimization suggestions.

[0098] (Application Example 1)

[0099] 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."

[0100] In today's business environment, users receive a massive influx of information daily in the form of emails and audio data, increasing the risk of overlooking important information or delaying the execution of urgent tasks. Furthermore, monitoring activities require the rapid identification of events that warrant immediate attention. This invention aims to solve these problems and provide an environment that enables users to perform their tasks efficiently and effectively and make quick decisions.

[0101] 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.

[0102] In this invention, the server includes means for analyzing electronic information and extracting key elements, means for identifying tasks and setting priorities based on the extracted elements, means for converting audio information into text and extracting key points of meetings, and means for analyzing alarms and monitoring audio and notifying users of high-priority events. This enables users to receive important information with dynamically optimized priorities and take immediate action as needed.

[0103] "Electronic information" refers to messages and data expressed in digital format, including information such as emails and text messages.

[0104] "Key elements" are pieces of information extracted from electronic or audio data that directly and immediately influence user behavior and decision-making.

[0105] "Work" refers to work activities that consist of tasks and responsibilities that a user must accomplish.

[0106] "Priority" is a measure that indicates the urgency or importance of an action in processing a particular task or piece of information.

[0107] "Audio information" refers to information conveyed orally, such as in meetings or conversations, and is recorded as audio data.

[0108] A "meeting" is a gathering where discussions and decisions are made among multiple participants.

[0109] An "alarm" is a notification or warning information that alerts the system to an anomaly or danger detected by the system.

[0110] "Surveillance audio" refers to audio recordings detected through surveillance equipment, and is information used for security and safety management.

[0111] A "notification" is a communication or message intended to convey specific information or warnings to a user.

[0112] The system based on this invention can analyze electronic information to extract key elements and set priorities based on them, in order to provide users with an environment in which they can perform their tasks efficiently. The system is realized through interaction between a server, a terminal, and the user.

[0113] The server uses Amazon Web Services (AWS®) for data management and, after receiving electronic information, applies OpenAI® generative AI models as a natural language processing tool to extract important elements. Audio information is converted to text using the Google Cloud Speech-to-Text API to summarize the key points of meetings. Furthermore, the server analyzes alarm and monitoring audio and notifies users of high-priority events in real time.

[0114] The application runs on iOS or Android® mobile devices, allowing users to receive information visually. This enables users to always understand their work priorities in an optimized state and supports quick decision-making.

[0115] As a concrete example, if a user receives urgent electronic information while at work, the system automatically analyzes it, extracts the necessary action items, and notifies the user's terminal. This is achieved through prompts such as "Analyze the surveillance camera audio and provide a text summary of the key points of suspicious activity" or "Analyze the received security report and summarize the most urgent incidents." This allows users to take immediate action or prioritize tasks.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server receives electronic information. It receives digital information such as emails and text messages as input and places it in a state ready for analysis. As output, a set of analyzable electronic information is constructed.

[0119] Step 2:

[0120] The server performs natural language processing on the received electronic information and extracts important elements. Specifically, it uses a generative AI model to extract important elements from the data based on prompt phrases such as "contract deadline" and "urgent matters." The output is a list of the extracted important elements.

[0121] Step 3:

[0122] The terminal receives a list of important elements from the server and presents the information visually through the user interface. The input is a list of important elements, and based on this, a task list is output with priorities assigned according to urgency and importance.

[0123] Step 4:

[0124] The server receives audio information and converts it to text using the Google Cloud Speech-to-Text API. It takes audio recordings of meetings or voice conversations as input and produces transcribed data as output.

[0125] Step 5:

[0126] The server analyzes the transcribed audio data and summarizes the key points. Using a generative AI model, it extracts essential information such as "meeting conclusions" and "next steps" from a series of conversational data. The output is a summarized text.

[0127] Step 6:

[0128] The device notifies the user of the summarized meeting points. Based on the received summary text, the user interface displays the content for review.

[0129] Step 7:

[0130] The server receives alarms and monitoring audio, analyzes them, and identifies high-priority events. It organizes the identified events, such as "detection of abnormal sounds" or "suspicious person's movements," as data. The output is a list of high-priority events.

[0131] Step 8:

[0132] The device will push notifications to the user regarding high-priority issues. Based on the list of issues received as input, it will display a notification prompting the user to take immediate action.

[0133] 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.

[0134] This invention provides a system for task management and strategic decision-making support that takes user emotions into account. This system is realized through a combination of server, terminal, and user interaction, along with an emotion engine.

[0135] System configuration:

[0136] This system analyzes electronic communications to extract important information, and based on that, it has the means to identify and prioritize tasks. It can also convert audio data into text to extract key points from meetings and present this information visually to the user. Furthermore, it evaluates the user's emotional state in real time through an emotion engine and reflects this in task management.

[0137] Specific example 1:

[0138] When an email is received, the server analyzes its contents to extract important tasks and set priorities. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if the user is stressed, the server will design the task list to postpone high-priority and complex tasks and start with easier ones.

[0139] Specific example 2:

[0140] Audio data generated during meetings is converted into text in real time by the server, and the key points are extracted. The emotion engine records emotional fluctuations during the meeting based on the user's reactions. When following up on the meeting later, the user's emotional history is referred to, and task allocation that minimizes stress for the user is suggested.

[0141] Operating instructions:

[0142] Users can visually review tasks and meeting information through their devices, and accept or modify emotionally-based suggestions. Task management that reflects user emotions is expected to improve work efficiency and reduce stress.

[0143] By combining these emotional engines, we can achieve flexible task management that takes user emotions into account, thereby supporting knowledge work more comfortably and effectively.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The server accesses the designated mailbox and retrieves new electronic communications.

[0147] Step 2:

[0148] The server analyzes the acquired electronic communications using natural language processing algorithms to extract important information. The extraction criteria depend on whether specific keywords or phrases are included.

[0149] Step 3:

[0150] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is calculated based on the importance and urgency of each task.

[0151] Step 4:

[0152] The terminal displays a prioritized task list received from the server to the user, presenting it in a visual and interactive format.

[0153] Step 5:

[0154] Users provide their facial expressions and voice through their devices as input to the emotion engine, allowing the system to recognize their emotional state.

[0155] Step 6:

[0156] The server uses an emotion engine to analyze the user's emotional state. Based on this analysis, tasks are adjusted according to the user's emotions.

[0157] Step 7:

[0158] The server dynamically adjusts the priority of the task list to reflect the user's emotional state. For example, if the user is stressed, complex and important tasks will be postponed, and simpler tasks will be placed first.

[0159] Step 8:

[0160] The device then presents the user with a revised task list, displaying tasks in an order that reflects their emotions.

[0161] Step 9:

[0162] Users review the task list displayed on their device and make final adjustments to suit their work style and emotional state.

[0163] Step 10:

[0164] Daily work activities are carried out based on the information confirmed by the user. Emotion-based task management allows for work to be performed while reducing stress.

[0165] (Example 2)

[0166] 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".

[0167] In today's information and communication environment, it is difficult for users to efficiently manage large amounts of information and make optimal decisions. In particular, there is a need for a means to accurately analyze data from electronic communications and meetings, and to enable flexible work management that responds to users' emotional states. Providing a system that enables rapid information analysis and emotion-based management is a key challenge.

[0168] 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.

[0169] In this invention, the server includes means for analyzing electronic communications to extract important information, means for identifying tasks and setting priorities based on the extracted information, means for converting audio information into text information and extracting key points of meetings, and means for evaluating the user's emotional state and reflecting this in task management. As a result, users can efficiently process large amounts of information and make optimal decisions according to their emotional state.

[0170] "Electronic communications" refers to all activities involving the sending and receiving of information in digital format, including email and messaging applications.

[0171] "Important information" refers to data elements extracted from acquired data that have value and directly contribute to business operations and decision-making.

[0172] "Tasks" refer to specific tasks or projects that users are required to accomplish, and they play a role as part of organizational activities.

[0173] Prioritization is the process of determining the order in which tasks and work should be carried out, based on their importance and urgency.

[0174] "Speech information" refers to sound wave data obtained from speech, which is recorded as an acoustic signal.

[0175] "Textual information" refers to data obtained by converting audio or video data into character codes, and is treated as text.

[0176] "Meeting summaries" are a summary of the main points discussed and decisions made during a meeting, and their purpose is to facilitate efficient information dissemination.

[0177] "User" refers to an individual or organization that uses the system for information management or business operations.

[0178] "Emotional state" refers to the psychological and emotional attitudes that a user exhibits in specific situations, and is evaluated based on facial expressions, tone of voice, word choice, and other factors.

[0179] One embodiment of this invention is realized through a system in which a server, a terminal, and a user collaborate to perform electronic communication analysis, extraction of important information, transcription and summarization of voice data, and evaluation of emotional states.

[0180] Program Description

[0181] The server acquires electronic communications (e.g., emails and messages) and analyzes their content. This analysis uses natural language processing techniques to extract important information and keywords. The server leverages generative AI models to identify important tasks from the user's electronic communications and set task priorities. For audio information, the server converts audio data into text in real time and extracts the key points. For example, AI speech recognition technology is implemented as an API to support the text conversion process.

[0182] A server equipped with an emotion engine evaluates the user's emotional state in real time based on input data from cameras and microphones. By using machine learning libraries in this process, it is possible to estimate the user's psychological state from their facial expressions and tone of voice, and reflect this in business management.

[0183] The device presents this information to the user through a visual interface. By utilizing a GUI and displaying the schedule in an easy-to-understand format, the user can confirm the order of the suggested tasks and provide further feedback.

[0184] Specific example

[0185] For example, suppose the server analyzes the content of an email and extracts tasks that the user should prioritize. If the emotion engine detects a high stress level based on the user's facial expressions, the server can suggest postponing more complex tasks and starting with simpler ones.

[0186] Examples of prompts for the generative AI model include, "Generate optimal task priorities based on the user's emotional state," and "Analyze emotional fluctuations during a meeting and suggest appropriate follow-up strategies." In this way, the system can provide flexible task management support through the suggested language.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The server acquires electronic communications. In this process, it receives new emails from the mail server and imports them as text data. The input is the raw email data, which is then parsed to extract its content. Data processing includes analyzing the email metadata and converting the content to text. The output is the parsed email content.

[0190] Step 2:

[0191] The server analyzes the content of emails and extracts important information. The input is the analyzed email content obtained in the previous step. Using a generative AI model, keywords and tasks are identified using natural language processing techniques. Specifically, this involves applying an algorithm to identify high-priority information. The output is the extracted important tasks and related information.

[0192] Step 3:

[0193] The server uses an emotion engine to evaluate the user's emotional state. Input consists of real-time data from the camera and microphone, including facial expressions and voice tone data. The server performs emotion analysis using a machine learning model and outputs the results. Specific operations include emotion estimation through facial recognition and voice analysis. The output is an indicator of the user's emotional state.

[0194] Step 4:

[0195] The server prioritizes tasks based on extracted key information and emotional states. Inputs include key task information and emotional state indicators. Data processing utilizes a priority determination algorithm to determine the order of tasks according to the user's stress level. The specific actions involve restructuring the task list and adjusting priorities. The output is a prioritized task list presented to the user.

[0196] Step 5:

[0197] The terminal visually presents information to the user. The input is a prioritized task list sent from the server. The information is formatted into an easily viewable format and displayed on the screen. Specific operations include GUI design and application. The output is the visual interface provided to the user.

[0198] Step 6:

[0199] The user provides feedback based on visualized task information, either correcting the task order or adding new tasks. The input for this step is the task list presented from the terminal and the user's judgment. The output is the corrected task list and feedback data, which the system uses for subsequent processing. Specific actions include operating and changing settings in task management software.

[0200] (Application Example 2)

[0201] 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 device 14 will be referred to as the "terminal."

[0202] Traditional task management systems simply set priorities without considering the user's emotional state. This made it difficult to manage tasks efficiently while reducing user stress. Furthermore, there was a need for a flexible task management approach that accommodated individual emotions in daily life.

[0203] 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.

[0204] In this invention, the server includes means for analyzing electronic data and extracting important information, means for identifying and prioritizing tasks based on the extracted information, and means for evaluating the user's emotional state and adjusting tasks based on those emotions. This enables flexible task management that takes the user's emotions into consideration.

[0205] "Means for analyzing electronic data" refers to methods or devices for analyzing electronically recorded information and extracting necessary information.

[0206] "Means for extracting important information" refers to methods or devices for extracting valuable data from a large amount of data that is relevant to a particular situation or purpose.

[0207] "Means for identifying tasks" refers to methods or devices for clarifying specific action items using extracted information.

[0208] "Means of prioritizing" refers to a method or apparatus for ordering identified tasks based on their importance or urgency.

[0209] "Means for converting audio data into documents" refers to a method or apparatus for analyzing audio information and converting it into text information.

[0210] "Means for extracting the essentials of information" refers to a method or apparatus for extracting core information from documented data.

[0211] "Means of visual presentation" refers to methods or devices for displaying generated information in a way that is easily understood visually by the user.

[0212] "Means for evaluating a user's emotional state" refers to a method or device for detecting and evaluating a user's emotions from their facial expressions, voice, etc.

[0213] "Means of adjusting tasks based on emotions" refers to methods or devices for changing the priority or order of tasks, taking into account the user's emotional state.

[0214] This invention realizes a task management system that takes user emotions into consideration. The system uses a server, terminals, and an emotion engine to effectively support the user's task management.

[0215] The server receives electronic data sent by users, analyzes it, and extracts important information. This process utilizes natural language processing techniques and includes algorithms to efficiently extract targeted information from large amounts of data. The server also uses a speech recognition API to convert audio data into text, recording key points from meetings and communications as text. This allows users to easily review past information.

[0216] The terminal visually presents information generated by the server to the user. This uses display devices such as screens and smartphones, and the information can be intuitively manipulated through the interface. Furthermore, by receiving input from the terminal, the user can manually adjust priorities.

[0217] The emotion engine evaluates the user's emotional state in real time using sensors and voice analysis technology. Specifically, it uses facial recognition cameras and voice tone analysis software to determine the user's emotional state and transmits this information to the server. Based on this information, the server rearranges the order of tasks to suit the user's emotions, thereby reducing the burden on the user.

[0218] For example, if a user is relaxed after lunch, they can prioritize and complete important but complex tasks. On the other hand, if the user is stressed, the system will adjust to prioritize relatively easy tasks.

[0219] Examples of prompt messages include, "What are your tasks for today? How are you feeling?" Using prompts tailored to the user's situation enables effective task management. This allows users to smoothly carry out their daily activities through the system.

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The server receives electronic data sent by users. This includes emails and document data. The server analyzes the input data and extracts important information using natural language processing techniques. As a result, the identified important information is output.

[0223] Step 2:

[0224] The server identifies and prioritizes tasks based on the extracted information. The input is the extracted key information, and its numerical priority is output. Machine learning algorithms are used in this process to ensure efficient prioritization.

[0225] Step 3:

[0226] The terminal visually presents information generated by the server to the user. To display the information on the user's screen, it utilizes a graphical user interface (GUI) to convert the information into a format that is easily understandable to the user.

[0227] Step 4:

[0228] The user's emotional state is evaluated through an emotion engine. The system takes the user's emotional data as input and analyzes the emotions in real time based on data from sensors and voice analysis technology, then sends the resulting state to the server. The detected emotional state is then output.

[0229] Step 5:

[0230] The server adjusts task priorities based on emotional data received from the emotion engine. Inputs include emotional state and a prioritized list of tasks, and based on this, it outputs a task order appropriate to the user's emotions. This adjustment is performed by an algorithm based on psychological data.

[0231] Step 6:

[0232] Users review the visualized information on their device and manually adjust priorities as needed. The user's input is output as the new priority, and the system uses this information in the next task management cycle.

[0233] Through the above series of steps, effective task management that takes into account the user's emotional state is achieved.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] This invention is a task management and strategic decision support system designed to improve the operational efficiency of users. This system is implemented through the interaction of servers, terminals, and users.

[0251] System configuration:

[0252] This system primarily offers the following functions: First, the server analyzes electronic communications and extracts important elements from the information. This makes it easy to find necessary actions from a large volume of daily emails. Second, the server converts audio data into text and summarizes the meeting content. This allows users to efficiently review the key points.

[0253] Specific example 1:

[0254] When a user receives a new email, the server analyzes its contents and extracts important events such as contract deadlines and meeting schedules. The device instantly notifies the user of this information and visually lists them as high-priority tasks. This list is dynamically updated according to the urgency and importance of the tasks.

[0255] Specific example 2:

[0256] During remote meetings, the server uses a feature that converts audio conversations to text in real time. It automatically summarizes key decisions and next steps from the meeting and presents them to the user via their device. This eliminates the need for users to take extensive notes after the meeting, allowing them to quickly plan their next actions.

[0257] User roles:

[0258] Users can determine the order in which tasks are executed based on the information presented by their device. Furthermore, they can accept or adjust schedule optimization suggestions to effectively carry out their work.

[0259] Thus, the present invention supports improved work efficiency and faster decision-making by extracting necessary tasks from a large amount of information and presenting them visually in an easy-to-understand manner for the user.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The server accesses a specific mailbox and retrieves new electronic communications.

[0263] Step 2:

[0264] The server analyzes the acquired electronic communications using natural language processing algorithms and extracts important information. This extraction is performed based on pre-configured keywords and phrases.

[0265] Step 3:

[0266] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is set based on the importance and urgency of each task.

[0267] Step 4:

[0268] The terminal visually displays a prioritized task list received from the server to the user and provides notifications.

[0269] Step 5:

[0270] Users can review the displayed task list and adjust the task priority according to its content.

[0271] Step 6:

[0272] The device transmits audio from the remote meeting to the server in real time.

[0273] Step 7:

[0274] The server converts the received audio data into text using speech recognition technology.

[0275] Step 8:

[0276] The server analyzes the converted text data and automatically extracts the key points of the meeting.

[0277] Step 9:

[0278] The terminal presents the extracted key points to the user and helps in making decisions on the next actions.

[0279] Step 10:

[0280] Based on the presented information, the user checks the schedule, incorporates newly set tasks, and proceeds with the work.

[0281] (Example 1)

[0282] Next, 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".

[0283] In modern society with an overload of information, it is difficult for users to quickly and efficiently extract important tasks and decision-making matters from the electronic communications and meeting information they receive daily. Also, in schedule management, manual prioritization and task management are time-consuming and labor-intensive, which is one of the factors hindering work efficiency. By solving this, it is required to improve the work efficiency of users and provide a comfortable working environment.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0285] In this invention, the server includes means for processing electronic data and extracting important items, means for converting voice data into character information and extracting key points such as in a meeting, and means for analyzing the user's personal schedule information and presenting the optimal execution order of tasks. Thereby, the user can perform efficient prioritization based on information and make quick decisions.

[0286] "Electronic data" refers to information such as text and emails that are transmitted and received on computers and networks.

[0287] "Means" refers to a device, method, or function used to achieve a specific purpose.

[0288] "Extracting items" refers to the process of identifying and retrieving important or relevant information from the original data.

[0289] "Voice data" refers to information in the form of recorded conversations or voices.

[0290] "Converting to character information" refers to the process of analyzing voice data to obtain the corresponding character string.

[0291] "Highlights of the meeting" refer to important issues and decisions handled during the meeting.

[0292] "Personal schedule information" refers to information related to the user's personal schedule and time management.

[0293] "Presenting the execution order of tasks" refers to proposing the order of tasks recommended for efficient task performance.

[0294] To implement this invention, a system is constructed in which the user, server, and terminal operate in cooperation.

[0295] When the server receives electronic communications accessible to the user, it analyzes them. Specifically, using software available as a natural language processing engine, such as Python's NLTK library or Google's Natural Language API, important items are extracted from the electronic data. In this process, the server examines the content of the emails and identifies important information such as contract deadlines and meeting schedules.

[0296] Furthermore, the server acquires the audio data and converts it into text using speech recognition technologies such as Google Cloud Speech-to-Text and IBM Watson Speech to Text. This allows it to extract the key points of meetings and discussions and organize them into a summary.

[0297] The terminal visually presents tasks to the user based on information provided by the server. Suitable task management software for this purpose includes Asana and Trello. Through this software, the terminal displays a list of tasks to the user that reflects their priority.

[0298] Users can efficiently manage their work tasks based on recommendations from their devices. Furthermore, users can receive suggestions for optimizing their schedules via their devices and use them as a basis for decision-making.

[0299] As a concrete example, a user might enter the prompt, "Please review the important documents in preparation for tomorrow's meeting." The system then immediately searches the server for meeting-related materials and sends them to the user's terminal to support their preparation.

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] The server accesses the user's mailbox and retrieves new electronic communications. Based on this input data, it analyzes the text using natural language processing techniques. The analysis extracts important event information from the email (e.g., "meeting schedule," "document deadline") and stores this information in a database. The output is metadata for the extracted important items.

[0303] Step 2:

[0304] The server receives the voice data acquired during the meeting as input. Using speech recognition software such as Google Cloud Speech-to-Text, this voice data is converted into character information. Next, the converted information is summarized to extract the key points of the meeting. The output of this process is text information including the summary of the meeting.

[0305] Step 3:

[0306] The terminal receives the important items and the summary information of the meeting sent from the server. Based on this data, using task management software (e.g., Asana, Trello), a list based on the priority of tasks is visually displayed to the user. The input is the analysis result sent from the server, and the output is a task list that the user can visually confirm.

[0307] Step 4:

[0308] The user checks the task list displayed on the terminal and decides on an action. In accordance with the instruction input from the user, the terminal adds new tasks or updates the completion status of existing tasks. Also, based on the operation of accepting or adjusting the proposed schedule optimization, an updated task list and schedule proposal are generated as output.

[0309] (Application Example 1)

[0310] 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".

[0311] In the modern business environment, a large amount of information flows to users as emails and voice data every day, and there is a risk of overlooking important information or delaying the execution of urgent tasks. Also, in monitoring activities, it is required to quickly grasp events that should prompt immediate attention. The purpose of the present invention is to solve these problems and provide an environment in which users can efficiently and effectively perform their work and make quick decisions.

[0312] 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.

[0313] In this invention, the server includes means for analyzing electronic information and extracting key elements, means for identifying tasks and setting priorities based on the extracted elements, means for converting audio information into text and extracting key points of meetings, and means for analyzing alarms and monitoring audio and notifying users of high-priority events. This enables users to receive important information with dynamically optimized priorities and take immediate action as needed.

[0314] "Electronic information" refers to messages and data expressed in digital format, including information such as emails and text messages.

[0315] "Key elements" are pieces of information extracted from electronic or audio data that directly and immediately influence user behavior and decision-making.

[0316] "Work" refers to work activities that consist of tasks and responsibilities that a user must accomplish.

[0317] "Priority" is a measure that indicates the urgency or importance of an action in processing a particular task or piece of information.

[0318] "Audio information" refers to information conveyed orally, such as in meetings or conversations, and is recorded as audio data.

[0319] A "meeting" is a gathering where discussions and decisions are made among multiple participants.

[0320] An "alarm" is a notification or warning information that alerts the system to an anomaly or danger detected by the system.

[0321] "Surveillance audio" refers to audio recordings detected through surveillance equipment, and is information used for security and safety management.

[0322] A "notification" is a communication or message intended to convey specific information or warnings to a user.

[0323] The system based on this invention can analyze electronic information to extract key elements and set priorities based on them, in order to provide users with an environment in which they can perform their tasks efficiently. The system is realized through interaction between a server, a terminal, and the user.

[0324] The server uses Amazon Web Services (AWS) for data management and, after receiving electronic information, applies OpenAI's generative AI model as a natural language processing tool to extract important elements. Audio information is converted to text using the Google Cloud Speech-to-Text API to summarize the key points of meetings. Furthermore, the server analyzes alarm and monitoring audio and notifies users of high-priority events in real time.

[0325] The device runs on iOS or Android mobile devices, and users can receive information visually through the application. This allows users to always understand their work priorities in an optimized state and supports quick decision-making.

[0326] As a concrete example, if a user receives urgent electronic information while at work, the system automatically analyzes it, extracts the necessary action items, and notifies the user's terminal. This is achieved through prompts such as "Analyze the surveillance camera audio and provide a text summary of the key points of suspicious activity" or "Analyze the received security report and summarize the most urgent incidents." This allows users to take immediate action or prioritize tasks.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The server receives electronic information. It receives digital information such as emails and text messages as input and places it in a state ready for analysis. As output, a set of analyzable electronic information is constructed.

[0330] Step 2:

[0331] The server performs natural language processing on the received electronic information and extracts important elements. Specifically, it uses a generative AI model to extract important elements from the data based on prompt phrases such as "contract deadline" and "urgent matters." The output is a list of the extracted important elements.

[0332] Step 3:

[0333] The terminal receives a list of important elements from the server and presents the information visually through the user interface. The input is a list of important elements, and based on this, a task list is output with priorities assigned according to urgency and importance.

[0334] Step 4:

[0335] The server receives audio information and converts it to text using the Google Cloud Speech-to-Text API. It takes audio recordings of meetings or voice conversations as input and produces transcribed data as output.

[0336] Step 5:

[0337] The server analyzes the transcribed audio data and summarizes the key points. Using a generative AI model, it extracts essential information such as "meeting conclusions" and "next steps" from a series of conversational data. The output is a summarized text.

[0338] Step 6:

[0339] The device notifies the user of the summarized meeting points. Based on the received summary text, the user interface displays the content for review.

[0340] Step 7:

[0341] The server receives alarms and monitoring audio, analyzes them, and identifies high-priority events. It organizes the identified events, such as "detection of abnormal sounds" or "suspicious person's movements," as data. The output is a list of high-priority events.

[0342] Step 8:

[0343] The device will push notifications to the user regarding high-priority issues. Based on the list of issues received as input, it will display a notification prompting the user to take immediate action.

[0344] 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.

[0345] This invention provides a system for task management and strategic decision-making support that takes user emotions into account. This system is realized through a combination of server, terminal, and user interaction, along with an emotion engine.

[0346] System configuration:

[0347] This system analyzes electronic communications to extract important information, and based on that, it has the means to identify and prioritize tasks. It can also convert audio data into text to extract key points from meetings and present this information visually to the user. Furthermore, it evaluates the user's emotional state in real time through an emotion engine and reflects this in task management.

[0348] Specific example 1:

[0349] When an email is received, the server analyzes its contents to extract important tasks and set priorities. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if the user is stressed, the server will design the task list to postpone high-priority and complex tasks and start with easier ones.

[0350] Specific example 2:

[0351] Audio data generated during meetings is converted into text in real time by the server, and the key points are extracted. The emotion engine records emotional fluctuations during the meeting based on the user's reactions. When following up on the meeting later, the user's emotional history is referred to, and task allocation that minimizes stress for the user is suggested.

[0352] Operating instructions:

[0353] Users can visually review tasks and meeting information through their devices, and accept or modify emotionally-based suggestions. Task management that reflects user emotions is expected to improve work efficiency and reduce stress.

[0354] By combining these emotional engines, we can achieve flexible task management that takes user emotions into account, thereby supporting knowledge work more comfortably and effectively.

[0355] The following describes the processing flow.

[0356] Step 1:

[0357] The server accesses the designated mailbox and retrieves new electronic communications.

[0358] Step 2:

[0359] The server analyzes the acquired electronic communications using natural language processing algorithms to extract important information. The extraction criteria depend on whether specific keywords or phrases are included.

[0360] Step 3:

[0361] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is calculated based on the importance and urgency of each task.

[0362] Step 4:

[0363] The terminal displays a prioritized task list received from the server to the user, presenting it in a visual and interactive format.

[0364] Step 5:

[0365] Users provide their facial expressions and voice through their devices as input to the emotion engine, allowing the system to recognize their emotional state.

[0366] Step 6:

[0367] The server uses an emotion engine to analyze the user's emotional state. Based on this analysis, tasks are adjusted according to the user's emotions.

[0368] Step 7:

[0369] The server dynamically adjusts the priority of the task list to reflect the user's emotional state. For example, if the user is stressed, complex and important tasks will be postponed, and simpler tasks will be placed first.

[0370] Step 8:

[0371] The device then presents the user with a revised task list, displaying tasks in an order that reflects their emotions.

[0372] Step 9:

[0373] Users review the task list displayed on their device and make final adjustments to suit their work style and emotional state.

[0374] Step 10:

[0375] Daily work activities are carried out based on the information confirmed by the user. Emotion-based task management allows for work to be performed while reducing stress.

[0376] (Example 2)

[0377] 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".

[0378] In today's information and communication environment, it is difficult for users to efficiently manage large amounts of information and make optimal decisions. In particular, there is a need for a means to accurately analyze data from electronic communications and meetings, and to enable flexible work management that responds to users' emotional states. Providing a system that enables rapid information analysis and emotion-based management is a key challenge.

[0379] 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.

[0380] In this invention, the server includes means for analyzing electronic communications to extract important information, means for identifying tasks and setting priorities based on the extracted information, means for converting audio information into text information and extracting key points of meetings, and means for evaluating the user's emotional state and reflecting this in task management. As a result, users can efficiently process large amounts of information and make optimal decisions according to their emotional state.

[0381] "Electronic communications" refers to all activities involving the sending and receiving of information in digital format, including email and messaging applications.

[0382] "Important information" refers to data elements extracted from acquired data that have value and directly contribute to business operations and decision-making.

[0383] "Tasks" refer to specific tasks or projects that users are required to accomplish, and they play a role as part of organizational activities.

[0384] Prioritization is the process of determining the order in which tasks and work should be carried out, based on their importance and urgency.

[0385] "Speech information" refers to sound wave data obtained from speech, which is recorded as an acoustic signal.

[0386] "Textual information" refers to data obtained by converting audio or video data into character codes, and is treated as text.

[0387] "Meeting summaries" are a summary of the main points discussed and decisions made during a meeting, and their purpose is to facilitate efficient information dissemination.

[0388] "User" refers to an individual or organization that uses the system for information management or business operations.

[0389] "Emotional state" refers to the psychological and emotional attitudes that a user exhibits in specific situations, and is evaluated based on facial expressions, tone of voice, word choice, and other factors.

[0390] One embodiment of this invention is realized through a system in which a server, a terminal, and a user collaborate to perform electronic communication analysis, extraction of important information, transcription and summarization of voice data, and evaluation of emotional states.

[0391] Program Description

[0392] The server acquires electronic communications (e.g., emails and messages) and analyzes their content. This analysis uses natural language processing techniques to extract important information and keywords. The server leverages generative AI models to identify important tasks from the user's electronic communications and set task priorities. For audio information, the server converts audio data into text in real time and extracts the key points. For example, AI speech recognition technology is implemented as an API to support the text conversion process.

[0393] A server equipped with an emotion engine evaluates the user's emotional state in real time based on input data from cameras and microphones. By using machine learning libraries in this process, it is possible to estimate the user's psychological state from their facial expressions and tone of voice, and reflect this in business management.

[0394] The device presents this information to the user through a visual interface. By utilizing a GUI and displaying the schedule in an easy-to-understand format, the user can confirm the order of the suggested tasks and provide further feedback.

[0395] Specific example

[0396] For example, suppose the server analyzes the content of an email and extracts tasks that the user should prioritize. If the emotion engine detects a high stress level based on the user's facial expressions, the server can suggest postponing more complex tasks and starting with simpler ones.

[0397] Examples of prompts for the generative AI model include, "Generate optimal task priorities based on the user's emotional state," and "Analyze emotional fluctuations during a meeting and suggest appropriate follow-up strategies." In this way, the system can provide flexible task management support through the suggested language.

[0398] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0399] Step 1:

[0400] The server acquires electronic communications. In this process, it receives new emails from the mail server and imports them as text data. The input is the raw email data, which is then parsed to extract its content. Data processing includes analyzing the email metadata and converting the content to text. The output is the parsed email content.

[0401] Step 2:

[0402] The server analyzes the content of emails and extracts important information. The input is the analyzed email content obtained in the previous step. Using a generative AI model, keywords and tasks are identified using natural language processing techniques. Specifically, this involves applying an algorithm to identify high-priority information. The output is the extracted important tasks and related information.

[0403] Step 3:

[0404] The server uses an emotion engine to evaluate the user's emotional state. Input consists of real-time data from the camera and microphone, including facial expressions and voice tone data. The server performs emotion analysis using a machine learning model and outputs the results. Specific operations include emotion estimation through facial recognition and voice analysis. The output is an indicator of the user's emotional state.

[0405] Step 4:

[0406] The server prioritizes tasks based on extracted key information and emotional states. Inputs include key task information and emotional state indicators. Data processing utilizes a priority determination algorithm to determine the order of tasks according to the user's stress level. The specific actions involve restructuring the task list and adjusting priorities. The output is a prioritized task list presented to the user.

[0407] Step 5:

[0408] The terminal visually presents information to the user. The input is a prioritized task list sent from the server. The information is formatted into an easily viewable format and displayed on the screen. Specific operations include GUI design and application. The output is the visual interface provided to the user.

[0409] Step 6:

[0410] The user provides feedback based on visualized task information, either correcting the task order or adding new tasks. The input for this step is the task list presented from the terminal and the user's judgment. The output is the corrected task list and feedback data, which the system uses for subsequent processing. Specific actions include operating and changing settings in task management software.

[0411] (Application Example 2)

[0412] 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."

[0413] Traditional task management systems simply set priorities without considering the user's emotional state. This made it difficult to manage tasks efficiently while reducing user stress. Furthermore, there was a need for a flexible task management approach that accommodated individual emotions in daily life.

[0414] 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.

[0415] In this invention, the server includes means for analyzing electronic data and extracting important information, means for identifying and prioritizing tasks based on the extracted information, and means for evaluating the user's emotional state and adjusting tasks based on those emotions. This enables flexible task management that takes the user's emotions into consideration.

[0416] "Means for analyzing electronic data" refers to methods or devices for analyzing electronically recorded information and extracting necessary information.

[0417] "Means for extracting important information" refers to methods or devices for extracting valuable data from a large amount of data that is relevant to a particular situation or purpose.

[0418] "Means for identifying tasks" refers to methods or devices for clarifying specific action items using extracted information.

[0419] "Means of prioritizing" refers to a method or apparatus for ordering identified tasks based on their importance or urgency.

[0420] "Means for converting audio data into documents" refers to a method or apparatus for analyzing audio information and converting it into text information.

[0421] "Means for extracting the essentials of information" refers to a method or apparatus for extracting core information from documented data.

[0422] "Means of visual presentation" refers to methods or devices for displaying generated information in a way that is easily understood visually by the user.

[0423] "Means for evaluating a user's emotional state" refers to a method or device for detecting and evaluating a user's emotions from their facial expressions, voice, etc.

[0424] "Means of adjusting tasks based on emotions" refers to methods or devices for changing the priority or order of tasks, taking into account the user's emotional state.

[0425] This invention realizes a task management system that takes user emotions into consideration. The system uses a server, terminals, and an emotion engine to effectively support the user's task management.

[0426] The server receives electronic data sent by users, analyzes it, and extracts important information. This process utilizes natural language processing techniques and includes algorithms to efficiently extract targeted information from large amounts of data. The server also uses a speech recognition API to convert audio data into text, recording key points from meetings and communications as text. This allows users to easily review past information.

[0427] The terminal visually presents information generated by the server to the user. This uses display devices such as screens and smartphones, and the information can be intuitively manipulated through the interface. Furthermore, by receiving input from the terminal, the user can manually adjust priorities.

[0428] The emotion engine evaluates the user's emotional state in real time using sensors and voice analysis technology. Specifically, it uses facial recognition cameras and voice tone analysis software to determine the user's emotional state and transmits this information to the server. Based on this information, the server rearranges the order of tasks to suit the user's emotions, thereby reducing the burden on the user.

[0429] For example, if a user is relaxed after lunch, they can prioritize and complete important but complex tasks. On the other hand, if the user is stressed, the system will adjust to prioritize relatively easy tasks.

[0430] Examples of prompt messages include, "What are your tasks for today? How are you feeling?" Using prompts tailored to the user's situation enables effective task management. This allows users to smoothly carry out their daily activities through the system.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The server receives electronic data sent by users. This includes emails and document data. The server analyzes the input data and extracts important information using natural language processing techniques. As a result, the identified important information is output.

[0434] Step 2:

[0435] The server identifies and prioritizes tasks based on the extracted information. The input is the extracted key information, and its numerical priority is output. Machine learning algorithms are used in this process to ensure efficient prioritization.

[0436] Step 3:

[0437] The terminal visually presents information generated by the server to the user. To display the information on the user's screen, it utilizes a graphical user interface (GUI) to convert the information into a format that is easily understandable to the user.

[0438] Step 4:

[0439] The user's emotional state is evaluated through an emotion engine. The system takes the user's emotional data as input and analyzes the emotions in real time based on data from sensors and voice analysis technology, then sends the resulting state to the server. The detected emotional state is then output.

[0440] Step 5:

[0441] The server adjusts task priorities based on emotional data received from the emotion engine. Inputs include emotional state and a prioritized list of tasks, and based on this, it outputs a task order appropriate to the user's emotions. This adjustment is performed by an algorithm based on psychological data.

[0442] Step 6:

[0443] Users review the visualized information on their device and manually adjust priorities as needed. The user's input is output as the new priority, and the system uses this information in the next task management cycle.

[0444] Through the above series of steps, effective task management that takes into account the user's emotional state is achieved.

[0445] 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.

[0446] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention is a task management and strategic decision support system designed to improve the operational efficiency of users. This system is implemented through the interaction of servers, terminals, and users.

[0462] System configuration:

[0463] This system primarily offers the following functions: First, the server analyzes electronic communications and extracts important elements from the information. This makes it easy to find necessary actions from a large volume of daily emails. Second, the server converts audio data into text and summarizes the meeting content. This allows users to efficiently review the key points.

[0464] Specific example 1:

[0465] When a user receives a new email, the server analyzes its contents and extracts important events such as contract deadlines and meeting schedules. The device instantly notifies the user of this information and visually lists them as high-priority tasks. This list is dynamically updated according to the urgency and importance of the tasks.

[0466] Specific example 2:

[0467] During remote meetings, the server uses a feature that converts audio conversations to text in real time. It automatically summarizes key decisions and next steps from the meeting and presents them to the user via their device. This eliminates the need for users to take extensive notes after the meeting, allowing them to quickly plan their next actions.

[0468] User roles:

[0469] Users can determine the order in which tasks are executed based on the information presented by their device. Furthermore, they can accept or adjust schedule optimization suggestions to effectively carry out their work.

[0470] Thus, the present invention supports improved work efficiency and faster decision-making by extracting necessary tasks from a large amount of information and presenting them visually in an easy-to-understand manner for the user.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The server accesses a specific mailbox and retrieves new electronic communications.

[0474] Step 2:

[0475] The server analyzes the acquired electronic communications using natural language processing algorithms and extracts important information. This extraction is performed based on pre-configured keywords and phrases.

[0476] Step 3:

[0477] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is set based on the importance and urgency of each task.

[0478] Step 4:

[0479] The terminal visually displays a prioritized task list received from the server to the user and provides notifications.

[0480] Step 5:

[0481] Users can review the displayed task list and adjust the task priority according to its content.

[0482] Step 6:

[0483] The device transmits audio from the remote meeting to the server in real time.

[0484] Step 7:

[0485] The server converts the received audio data into text using speech recognition technology.

[0486] Step 8:

[0487] The server analyzes the converted text data and automatically extracts the key points of the meeting.

[0488] Step 9:

[0489] The device presents the extracted key points to the user to help them decide on their next action.

[0490] Step 10:

[0491] Based on the information provided, users review their schedule, incorporate newly assigned tasks, and proceed with their work.

[0492] (Example 1)

[0493] 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."

[0494] In today's information-saturated society, it is difficult for users to quickly and efficiently extract important tasks and decisions from the information they receive daily through electronic communications and meetings. Furthermore, manual prioritization and task management in schedule management is time-consuming and labor-intensive, hindering work efficiency. Solving these issues is essential to improving user work efficiency and providing a comfortable work environment.

[0495] 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.

[0496] In this invention, the server includes means for processing electronic data and extracting important items, means for converting audio data into text information and extracting key points from meetings, etc., and means for analyzing the user's personal schedule information and suggesting the optimal order for executing tasks. This enables the user to make informed, efficient prioritizations and quick decisions.

[0497] "Electronic data" refers to information such as text and emails that are sent and received over computers and networks.

[0498] "Means" refers to the apparatus, method, or function used to achieve a particular purpose.

[0499] "Extracting items" refers to the process of identifying and extracting important or relevant information from the original data.

[0500] "Audio data" refers to information in the form of recorded conversations or voices.

[0501] "Converting to text information" refers to the process of analyzing audio data and converting it into a corresponding string of characters.

[0502] "Meeting summaries" refer to the important topics and decisions discussed during the meeting.

[0503] "Personal schedule information" refers to information related to a user's personal schedule and time management.

[0504] "Suggesting a work order" refers to proposing a recommended sequence of tasks for efficient work execution.

[0505] To implement this invention, a system is constructed in which users, servers, and terminals work together.

[0506] When the server receives electronic communications accessible by the user, it analyzes them. Specifically, it uses software available as a natural language processing engine, such as Python's NLTK library or Google's Natural Language API, to extract important items from the electronic data. In this process, the server examines the content of emails and identifies important information such as contract deadlines and meeting schedules.

[0507] Furthermore, the server acquires the audio data and converts it into text using speech recognition technologies such as Google Cloud Speech-to-Text and IBM Watson Speech to Text. This allows it to extract the key points of meetings and discussions and organize them into a summary.

[0508] The terminal visually presents tasks to the user based on information provided by the server. Suitable task management software for this purpose includes Asana and Trello. Through this software, the terminal displays a list of tasks to the user that reflects their priority.

[0509] Users can efficiently manage their work tasks based on recommendations from their devices. Furthermore, users can receive suggestions for optimizing their schedules via their devices and use them as a basis for decision-making.

[0510] As a concrete example, a user might enter the prompt, "Please review the important documents in preparation for tomorrow's meeting." The system then immediately searches the server for meeting-related materials and sends them to the user's terminal to support their preparation.

[0511] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0512] Step 1:

[0513] The server accesses the user's mailbox and retrieves new electronic communications. Based on this input data, it analyzes the text using natural language processing techniques. The analysis extracts important event information from the email (e.g., "meeting schedule," "document deadline") and stores this information in a database. The output is metadata for the extracted important items.

[0514] Step 2:

[0515] The server receives audio data acquired during the meeting as input. Using speech recognition software such as Google Cloud Speech-to-Text, this audio data is converted into text. Next, the converted information is summarized, and the key points of the meeting are extracted. The output of this process is text information containing a summary of the meeting.

[0516] Step 3:

[0517] The terminal receives important information and meeting summaries sent from the server. Based on this data, task management software (e.g., Asana, Trello) is used to visually display a list of tasks to the user based on their priority. The input is the analysis results sent from the server, and the output is a task list that the user can visually review.

[0518] Step 4:

[0519] The user reviews the task list displayed on the device and decides on an action. Based on the user's input, the device adds new tasks or updates the completion status of existing tasks. It also generates an updated task list and schedule suggestions as output, based on whether the user accepts or adjusts schedule optimization suggestions.

[0520] (Application Example 1)

[0521] 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."

[0522] In today's business environment, users receive a massive influx of information daily in the form of emails and audio data, increasing the risk of overlooking important information or delaying the execution of urgent tasks. Furthermore, monitoring activities require the rapid identification of events that warrant immediate attention. This invention aims to solve these problems and provide an environment that enables users to perform their tasks efficiently and effectively and make quick decisions.

[0523] 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.

[0524] In this invention, the server includes means for analyzing electronic information and extracting key elements, means for identifying tasks and setting priorities based on the extracted elements, means for converting audio information into text and extracting key points of meetings, and means for analyzing alarms and monitoring audio and notifying users of high-priority events. This enables users to receive important information with dynamically optimized priorities and take immediate action as needed.

[0525] "Electronic information" refers to messages and data expressed in digital format, including information such as emails and text messages.

[0526] "Key elements" are pieces of information extracted from electronic or audio data that directly and immediately influence user behavior and decision-making.

[0527] "Work" refers to work activities that consist of tasks and responsibilities that a user must accomplish.

[0528] "Priority" is a measure that indicates the urgency or importance of an action in processing a particular task or piece of information.

[0529] "Audio information" refers to information conveyed orally, such as in meetings or conversations, and is recorded as audio data.

[0530] A "meeting" is a gathering where discussions and decisions are made among multiple participants.

[0531] An "alarm" is a notification or warning information that alerts the system to an anomaly or danger detected by the system.

[0532] "Surveillance audio" refers to audio recordings detected through surveillance equipment, and is information used for security and safety management.

[0533] A "notification" is a communication or message intended to convey specific information or warnings to a user.

[0534] The system based on this invention can analyze electronic information to extract key elements and set priorities based on them, in order to provide users with an environment in which they can perform their tasks efficiently. The system is realized through interaction between a server, a terminal, and the user.

[0535] The server uses Amazon Web Services (AWS) for data management and, after receiving electronic information, applies OpenAI's generative AI model as a natural language processing tool to extract important elements. Audio information is converted to text using the Google Cloud Speech-to-Text API to summarize the key points of meetings. Furthermore, the server analyzes alarm and monitoring audio and notifies users of high-priority events in real time.

[0536] The device runs on iOS or Android mobile devices, and users can receive information visually through the application. This allows users to always understand their work priorities in an optimized state and supports quick decision-making.

[0537] As a concrete example, if a user receives urgent electronic information while at work, the system automatically analyzes it, extracts the necessary action items, and notifies the user's terminal. This is achieved through prompts such as "Analyze the surveillance camera audio and provide a text summary of the key points of suspicious activity" or "Analyze the received security report and summarize the most urgent incidents." This allows users to take immediate action or prioritize tasks.

[0538] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0539] Step 1:

[0540] The server receives electronic information. It receives digital information such as emails and text messages as input and places it in a state ready for analysis. As output, a set of analyzable electronic information is constructed.

[0541] Step 2:

[0542] The server performs natural language processing on the received electronic information and extracts important elements. Specifically, it uses a generative AI model to extract important elements from the data based on prompt phrases such as "contract deadline" and "urgent matters." The output is a list of the extracted important elements.

[0543] Step 3:

[0544] The terminal receives a list of important elements from the server and presents the information visually through the user interface. The input is a list of important elements, and based on this, a task list is output with priorities assigned according to urgency and importance.

[0545] Step 4:

[0546] The server receives audio information and converts it to text using the Google Cloud Speech-to-Text API. It takes audio recordings of meetings or voice conversations as input and produces transcribed data as output.

[0547] Step 5:

[0548] The server analyzes the transcribed audio data and summarizes the key points. Using a generative AI model, it extracts essential information such as "meeting conclusions" and "next steps" from a series of conversational data. The output is a summarized text.

[0549] Step 6:

[0550] The device notifies the user of the summarized meeting points. Based on the received summary text, the user interface displays the content for review.

[0551] Step 7:

[0552] The server receives alarms and monitoring audio, analyzes them, and identifies high-priority events. It organizes the identified events, such as "detection of abnormal sounds" or "suspicious person's movements," as data. The output is a list of high-priority events.

[0553] Step 8:

[0554] The device will push notifications to the user regarding high-priority issues. Based on the list of issues received as input, it will display a notification prompting the user to take immediate action.

[0555] 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.

[0556] This invention provides a system for task management and strategic decision-making support that takes user emotions into account. This system is realized through a combination of server, terminal, and user interaction, along with an emotion engine.

[0557] System configuration:

[0558] This system analyzes electronic communications to extract important information, and based on that, it has the means to identify and prioritize tasks. It can also convert audio data into text to extract key points from meetings and present this information visually to the user. Furthermore, it evaluates the user's emotional state in real time through an emotion engine and reflects this in task management.

[0559] Specific example 1:

[0560] When an email is received, the server analyzes its contents to extract important tasks and set priorities. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if the user is stressed, the server will design the task list to postpone high-priority and complex tasks and start with easier ones.

[0561] Specific example 2:

[0562] Audio data generated during meetings is converted into text in real time by the server, and the key points are extracted. The emotion engine records emotional fluctuations during the meeting based on the user's reactions. When following up on the meeting later, the user's emotional history is referred to, and task allocation that minimizes stress for the user is suggested.

[0563] Operating instructions:

[0564] Users can visually review tasks and meeting information through their devices, and accept or modify emotionally-based suggestions. Task management that reflects user emotions is expected to improve work efficiency and reduce stress.

[0565] By combining these emotional engines, we can achieve flexible task management that takes user emotions into account, thereby supporting knowledge work more comfortably and effectively.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The server accesses the designated mailbox and retrieves new electronic communications.

[0569] Step 2:

[0570] The server analyzes the acquired electronic communications using natural language processing algorithms to extract important information. The extraction criteria depend on whether specific keywords or phrases are included.

[0571] Step 3:

[0572] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is calculated based on the importance and urgency of each task.

[0573] Step 4:

[0574] The terminal displays a prioritized task list received from the server to the user, presenting it in a visual and interactive format.

[0575] Step 5:

[0576] Users provide their facial expressions and voice through their devices as input to the emotion engine, allowing the system to recognize their emotional state.

[0577] Step 6:

[0578] The server uses an emotion engine to analyze the user's emotional state. Based on this analysis, tasks are adjusted according to the user's emotions.

[0579] Step 7:

[0580] The server dynamically adjusts the priority of the task list to reflect the user's emotional state. For example, if the user is stressed, complex and important tasks will be postponed, and simpler tasks will be placed first.

[0581] Step 8:

[0582] The device then presents the user with a revised task list, displaying tasks in an order that reflects their emotions.

[0583] Step 9:

[0584] Users review the task list displayed on their device and make final adjustments to suit their work style and emotional state.

[0585] Step 10:

[0586] Daily work activities are carried out based on the information confirmed by the user. Emotion-based task management allows for work to be performed while reducing stress.

[0587] (Example 2)

[0588] 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."

[0589] In today's information and communication environment, it is difficult for users to efficiently manage large amounts of information and make optimal decisions. In particular, there is a need for a means to accurately analyze data from electronic communications and meetings, and to enable flexible work management that responds to users' emotional states. Providing a system that enables rapid information analysis and emotion-based management is a key challenge.

[0590] 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.

[0591] In this invention, the server includes means for analyzing electronic communications to extract important information, means for identifying tasks and setting priorities based on the extracted information, means for converting audio information into text information and extracting key points of meetings, and means for evaluating the user's emotional state and reflecting this in task management. As a result, users can efficiently process large amounts of information and make optimal decisions according to their emotional state.

[0592] "Electronic communications" refers to all activities involving the sending and receiving of information in digital format, including email and messaging applications.

[0593] "Important information" refers to data elements extracted from acquired data that have value and directly contribute to business operations and decision-making.

[0594] "Tasks" refer to specific tasks or projects that users are required to accomplish, and they play a role as part of organizational activities.

[0595] Prioritization is the process of determining the order in which tasks and work should be carried out, based on their importance and urgency.

[0596] "Speech information" refers to sound wave data obtained from speech, which is recorded as an acoustic signal.

[0597] "Textual information" refers to data obtained by converting audio or video data into character codes, and is treated as text.

[0598] "Meeting summaries" are a summary of the main points discussed and decisions made during a meeting, and their purpose is to facilitate efficient information dissemination.

[0599] "User" refers to an individual or organization that uses the system for information management or business operations.

[0600] "Emotional state" refers to the psychological and emotional attitudes that a user exhibits in specific situations, and is evaluated based on facial expressions, tone of voice, word choice, and other factors.

[0601] One embodiment of this invention is realized through a system in which a server, a terminal, and a user collaborate to perform electronic communication analysis, extraction of important information, transcription and summarization of voice data, and evaluation of emotional states.

[0602] Program Description

[0603] The server acquires electronic communications (e.g., emails and messages) and analyzes their content. This analysis uses natural language processing techniques to extract important information and keywords. The server leverages generative AI models to identify important tasks from the user's electronic communications and set task priorities. For audio information, the server converts audio data into text in real time and extracts the key points. For example, AI speech recognition technology is implemented as an API to support the text conversion process.

[0604] A server equipped with an emotion engine evaluates the user's emotional state in real time based on input data from cameras and microphones. By using machine learning libraries in this process, it is possible to estimate the user's psychological state from their facial expressions and tone of voice, and reflect this in business management.

[0605] The device presents this information to the user through a visual interface. By utilizing a GUI and displaying the schedule in an easy-to-understand format, the user can confirm the order of the suggested tasks and provide further feedback.

[0606] Specific example

[0607] For example, suppose the server analyzes the content of an email and extracts tasks that the user should prioritize. If the emotion engine detects a high stress level based on the user's facial expressions, the server can suggest postponing more complex tasks and starting with simpler ones.

[0608] Examples of prompts for the generative AI model include, "Generate optimal task priorities based on the user's emotional state," and "Analyze emotional fluctuations during a meeting and suggest appropriate follow-up strategies." In this way, the system can provide flexible task management support through the suggested language.

[0609] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0610] Step 1:

[0611] The server acquires electronic communications. In this process, it receives new emails from the mail server and imports them as text data. The input is the raw email data, which is then parsed to extract its content. Data processing includes analyzing the email metadata and converting the content to text. The output is the parsed email content.

[0612] Step 2:

[0613] The server analyzes the content of emails and extracts important information. The input is the analyzed email content obtained in the previous step. Using a generative AI model, keywords and tasks are identified using natural language processing techniques. Specifically, this involves applying an algorithm to identify high-priority information. The output is the extracted important tasks and related information.

[0614] Step 3:

[0615] The server uses an emotion engine to evaluate the user's emotional state. Input consists of real-time data from the camera and microphone, including facial expressions and voice tone data. The server performs emotion analysis using a machine learning model and outputs the results. Specific operations include emotion estimation through facial recognition and voice analysis. The output is an indicator of the user's emotional state.

[0616] Step 4:

[0617] The server prioritizes tasks based on extracted key information and emotional states. Inputs include key task information and emotional state indicators. Data processing utilizes a priority determination algorithm to determine the order of tasks according to the user's stress level. The specific actions involve restructuring the task list and adjusting priorities. The output is a prioritized task list presented to the user.

[0618] Step 5:

[0619] The terminal visually presents information to the user. The input is a prioritized task list sent from the server. The information is formatted into an easily viewable format and displayed on the screen. Specific operations include GUI design and application. The output is the visual interface provided to the user.

[0620] Step 6:

[0621] The user provides feedback based on visualized task information, either correcting the task order or adding new tasks. The input for this step is the task list presented from the terminal and the user's judgment. The output is the corrected task list and feedback data, which the system uses for subsequent processing. Specific actions include operating and changing settings in task management software.

[0622] (Application Example 2)

[0623] 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."

[0624] Traditional task management systems simply set priorities without considering the user's emotional state. This made it difficult to manage tasks efficiently while reducing user stress. Furthermore, there was a need for a flexible task management approach that accommodated individual emotions in daily life.

[0625] 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.

[0626] In this invention, the server includes means for analyzing electronic data and extracting important information, means for identifying and prioritizing tasks based on the extracted information, and means for evaluating the user's emotional state and adjusting tasks based on those emotions. This enables flexible task management that takes the user's emotions into consideration.

[0627] "Means for analyzing electronic data" refers to methods or devices for analyzing electronically recorded information and extracting necessary information.

[0628] "Means for extracting important information" refers to methods or devices for extracting valuable data from a large amount of data that is relevant to a particular situation or purpose.

[0629] "Means for identifying tasks" refers to methods or devices for clarifying specific action items using extracted information.

[0630] "Means of prioritizing" refers to a method or apparatus for ordering identified tasks based on their importance or urgency.

[0631] "Means for converting audio data into documents" refers to a method or apparatus for analyzing audio information and converting it into text information.

[0632] "Means for extracting the essentials of information" refers to a method or apparatus for extracting core information from documented data.

[0633] "Means of visual presentation" refers to methods or devices for displaying generated information in a way that is easily understood visually by the user.

[0634] "Means for evaluating a user's emotional state" refers to a method or device for detecting and evaluating a user's emotions from their facial expressions, voice, etc.

[0635] "Means of adjusting tasks based on emotions" refers to methods or devices for changing the priority or order of tasks, taking into account the user's emotional state.

[0636] This invention realizes a task management system that takes user emotions into consideration. The system uses a server, terminals, and an emotion engine to effectively support the user's task management.

[0637] The server receives electronic data sent by users, analyzes it, and extracts important information. This process utilizes natural language processing techniques and includes algorithms to efficiently extract targeted information from large amounts of data. The server also uses a speech recognition API to convert audio data into text, recording key points from meetings and communications as text. This allows users to easily review past information.

[0638] The terminal visually presents information generated by the server to the user. This uses display devices such as screens and smartphones, and the information can be intuitively manipulated through the interface. Furthermore, by receiving input from the terminal, the user can manually adjust priorities.

[0639] The emotion engine evaluates the user's emotional state in real time using sensors and voice analysis technology. Specifically, it uses facial recognition cameras and voice tone analysis software to determine the user's emotional state and transmits this information to the server. Based on this information, the server rearranges the order of tasks to suit the user's emotions, thereby reducing the burden on the user.

[0640] For example, if a user is relaxed after lunch, they can prioritize and complete important but complex tasks. On the other hand, if the user is stressed, the system will adjust to prioritize relatively easy tasks.

[0641] Examples of prompt messages include, "What are your tasks for today? How are you feeling?" Using prompts tailored to the user's situation enables effective task management. This allows users to smoothly carry out their daily activities through the system.

[0642] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0643] Step 1:

[0644] The server receives electronic data sent by users. This includes emails and document data. The server analyzes the input data and extracts important information using natural language processing techniques. As a result, the identified important information is output.

[0645] Step 2:

[0646] The server identifies and prioritizes tasks based on the extracted information. The input is the extracted key information, and its numerical priority is output. Machine learning algorithms are used in this process to ensure efficient prioritization.

[0647] Step 3:

[0648] The terminal visually presents information generated by the server to the user. To display the information on the user's screen, it utilizes a graphical user interface (GUI) to convert the information into a format that is easily understandable to the user.

[0649] Step 4:

[0650] The user's emotional state is evaluated through an emotion engine. The system takes the user's emotional data as input and analyzes the emotions in real time based on data from sensors and voice analysis technology, then sends the resulting state to the server. The detected emotional state is then output.

[0651] Step 5:

[0652] The server adjusts task priorities based on emotional data received from the emotion engine. Inputs include emotional state and a prioritized list of tasks, and based on this, it outputs a task order appropriate to the user's emotions. This adjustment is performed by an algorithm based on psychological data.

[0653] Step 6:

[0654] Users review the visualized information on their device and manually adjust priorities as needed. The user's input is output as the new priority, and the system uses this information in the next task management cycle.

[0655] Through the above series of steps, effective task management that takes into account the user's emotional state is achieved.

[0656] 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.

[0657] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0658] 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.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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".

[0673] This invention is a task management and strategic decision support system designed to improve the operational efficiency of users. This system is implemented through the interaction of servers, terminals, and users.

[0674] System configuration:

[0675] This system primarily offers the following functions: First, the server analyzes electronic communications and extracts important elements from the information. This makes it easy to find necessary actions from a large volume of daily emails. Second, the server converts audio data into text and summarizes the meeting content. This allows users to efficiently review the key points.

[0676] Specific example 1:

[0677] When a user receives a new email, the server analyzes its contents and extracts important events such as contract deadlines and meeting schedules. The device instantly notifies the user of this information and visually lists them as high-priority tasks. This list is dynamically updated according to the urgency and importance of the tasks.

[0678] Specific example 2:

[0679] During remote meetings, the server uses a feature that converts audio conversations to text in real time. It automatically summarizes key decisions and next steps from the meeting and presents them to the user via their device. This eliminates the need for users to take extensive notes after the meeting, allowing them to quickly plan their next actions.

[0680] User roles:

[0681] Users can determine the order in which tasks are executed based on the information presented by their device. Furthermore, they can accept or adjust schedule optimization suggestions to effectively carry out their work.

[0682] Thus, the present invention supports improved work efficiency and faster decision-making by extracting necessary tasks from a large amount of information and presenting them visually in an easy-to-understand manner for the user.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The server accesses a specific mailbox and retrieves new electronic communications.

[0686] Step 2:

[0687] The server analyzes the acquired electronic communications using natural language processing algorithms and extracts important information. This extraction is performed based on pre-configured keywords and phrases.

[0688] Step 3:

[0689] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is set based on the importance and urgency of each task.

[0690] Step 4:

[0691] The terminal visually displays a prioritized task list received from the server to the user and provides notifications.

[0692] Step 5:

[0693] Users can review the displayed task list and adjust the task priority according to its content.

[0694] Step 6:

[0695] The device transmits audio from the remote meeting to the server in real time.

[0696] Step 7:

[0697] The server converts the received audio data into text using speech recognition technology.

[0698] Step 8:

[0699] The server analyzes the converted text data and automatically extracts the key points of the meeting.

[0700] Step 9:

[0701] The device presents the extracted key points to the user to help them decide on their next action.

[0702] Step 10:

[0703] Based on the information provided, users review their schedule, incorporate newly assigned tasks, and proceed with their work.

[0704] (Example 1)

[0705] 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".

[0706] In today's information-saturated society, it is difficult for users to quickly and efficiently extract important tasks and decisions from the information they receive daily through electronic communications and meetings. Furthermore, manual prioritization and task management in schedule management is time-consuming and labor-intensive, hindering work efficiency. Solving these issues is essential to improving user work efficiency and providing a comfortable work environment.

[0707] 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.

[0708] In this invention, the server includes means for processing electronic data and extracting important items, means for converting audio data into text information and extracting key points from meetings, etc., and means for analyzing the user's personal schedule information and suggesting the optimal order for executing tasks. This enables the user to make informed, efficient prioritizations and quick decisions.

[0709] "Electronic data" refers to information such as text and emails that are sent and received over computers and networks.

[0710] "Means" refers to the apparatus, method, or function used to achieve a particular purpose.

[0711] "Extracting items" refers to the process of identifying and extracting important or relevant information from the original data.

[0712] "Audio data" refers to information in the form of recorded conversations or voices.

[0713] "Converting to text information" refers to the process of analyzing audio data and converting it into a corresponding string of characters.

[0714] "Meeting summaries" refer to the important topics and decisions discussed during the meeting.

[0715] "Personal schedule information" refers to information related to a user's personal schedule and time management.

[0716] "Suggesting a work order" refers to proposing a recommended sequence of tasks for efficient work execution.

[0717] To implement this invention, a system is constructed in which users, servers, and terminals work together.

[0718] When the server receives electronic communications accessible by the user, it analyzes them. Specifically, it uses software available as a natural language processing engine, such as Python's NLTK library or Google's Natural Language API, to extract important items from the electronic data. In this process, the server examines the content of emails and identifies important information such as contract deadlines and meeting schedules.

[0719] Furthermore, the server acquires the audio data and converts it into text using speech recognition technologies such as Google Cloud Speech-to-Text and IBM Watson Speech to Text. This allows it to extract the key points of meetings and discussions and organize them into a summary.

[0720] The terminal visually presents tasks to the user based on information provided by the server. Suitable task management software for this purpose includes Asana and Trello. Through this software, the terminal displays a list of tasks to the user that reflects their priority.

[0721] Users can efficiently manage their work tasks based on recommendations from their devices. Furthermore, users can receive suggestions for optimizing their schedules via their devices and use them as a basis for decision-making.

[0722] As a concrete example, a user might enter the prompt, "Please review the important documents in preparation for tomorrow's meeting." The system then immediately searches the server for meeting-related materials and sends them to the user's terminal to support their preparation.

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] The server accesses the user's mailbox and retrieves new electronic communications. Based on this input data, it analyzes the text using natural language processing techniques. The analysis extracts important event information from the email (e.g., "meeting schedule," "document deadline") and stores this information in a database. The output is metadata for the extracted important items.

[0726] Step 2:

[0727] The server receives audio data acquired during the meeting as input. Using speech recognition software such as Google Cloud Speech-to-Text, this audio data is converted into text. Next, the converted information is summarized, and the key points of the meeting are extracted. The output of this process is text information containing a summary of the meeting.

[0728] Step 3:

[0729] The terminal receives important information and meeting summaries sent from the server. Based on this data, task management software (e.g., Asana, Trello) is used to visually display a list of tasks to the user based on their priority. The input is the analysis results sent from the server, and the output is a task list that the user can visually review.

[0730] Step 4:

[0731] The user reviews the task list displayed on the device and decides on an action. Based on the user's input, the device adds new tasks or updates the completion status of existing tasks. It also generates an updated task list and schedule suggestions as output, based on whether the user accepts or adjusts schedule optimization suggestions.

[0732] (Application Example 1)

[0733] 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".

[0734] In today's business environment, users receive a massive influx of information daily in the form of emails and audio data, increasing the risk of overlooking important information or delaying the execution of urgent tasks. Furthermore, monitoring activities require the rapid identification of events that warrant immediate attention. This invention aims to solve these problems and provide an environment that enables users to perform their tasks efficiently and effectively and make quick decisions.

[0735] 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.

[0736] In this invention, the server includes means for analyzing electronic information and extracting key elements, means for identifying tasks and setting priorities based on the extracted elements, means for converting audio information into text and extracting key points of meetings, and means for analyzing alarms and monitoring audio and notifying users of high-priority events. This enables users to receive important information with dynamically optimized priorities and take immediate action as needed.

[0737] "Electronic information" refers to messages and data expressed in digital format, including information such as emails and text messages.

[0738] "Key elements" are pieces of information extracted from electronic or audio data that directly and immediately influence user behavior and decision-making.

[0739] "Work" refers to work activities that consist of tasks and responsibilities that a user must accomplish.

[0740] "Priority" is a measure that indicates the urgency or importance of an action in processing a particular task or piece of information.

[0741] "Audio information" refers to information conveyed orally, such as in meetings or conversations, and is recorded as audio data.

[0742] A "meeting" is a gathering where discussions and decisions are made among multiple participants.

[0743] An "alarm" is a notification or warning information that alerts the system to an anomaly or danger detected by the system.

[0744] "Surveillance audio" refers to audio recordings detected through surveillance equipment, and is information used for security and safety management.

[0745] A "notification" is a communication or message intended to convey specific information or warnings to a user.

[0746] The system based on this invention can analyze electronic information to extract key elements and set priorities based on them, in order to provide users with an environment in which they can perform their tasks efficiently. The system is realized through interaction between a server, a terminal, and the user.

[0747] The server uses Amazon Web Services (AWS) for data management and, after receiving electronic information, applies OpenAI's generative AI model as a natural language processing tool to extract important elements. Audio information is converted to text using the Google Cloud Speech-to-Text API to summarize the key points of meetings. Furthermore, the server analyzes alarm and monitoring audio and notifies users of high-priority events in real time.

[0748] The device runs on iOS or Android mobile devices, and users can receive information visually through the application. This allows users to always understand their work priorities in an optimized state and supports quick decision-making.

[0749] As a concrete example, if a user receives urgent electronic information while at work, the system automatically analyzes it, extracts the necessary action items, and notifies the user's terminal. This is achieved through prompts such as "Analyze the surveillance camera audio and provide a text summary of the key points of suspicious activity" or "Analyze the received security report and summarize the most urgent incidents." This allows users to take immediate action or prioritize tasks.

[0750] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0751] Step 1:

[0752] The server receives electronic information. It receives digital information such as emails and text messages as input and places it in a state ready for analysis. As output, a set of analyzable electronic information is constructed.

[0753] Step 2:

[0754] The server performs natural language processing on the received electronic information and extracts important elements. Specifically, it uses a generative AI model to extract important elements from the data based on prompt phrases such as "contract deadline" and "urgent matters." The output is a list of the extracted important elements.

[0755] Step 3:

[0756] The terminal receives a list of important elements from the server and presents the information visually through the user interface. The input is a list of important elements, and based on this, a task list is output with priorities assigned according to urgency and importance.

[0757] Step 4:

[0758] The server receives audio information and converts it to text using the Google Cloud Speech-to-Text API. It takes audio recordings of meetings or voice conversations as input and produces transcribed data as output.

[0759] Step 5:

[0760] The server analyzes the transcribed audio data and summarizes the key points. Using a generative AI model, it extracts essential information such as "meeting conclusions" and "next steps" from a series of conversational data. The output is a summarized text.

[0761] Step 6:

[0762] The device notifies the user of the summarized meeting points. Based on the received summary text, the user interface displays the content for review.

[0763] Step 7:

[0764] The server receives alarms and monitoring audio, analyzes them, and identifies high-priority events. It organizes the identified events, such as "detection of abnormal sounds" or "suspicious person's movements," as data. The output is a list of high-priority events.

[0765] Step 8:

[0766] The device will push notifications to the user regarding high-priority issues. Based on the list of issues received as input, it will display a notification prompting the user to take immediate action.

[0767] 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.

[0768] This invention provides a system for task management and strategic decision-making support that takes user emotions into account. This system is realized through a combination of server, terminal, and user interaction, along with an emotion engine.

[0769] System configuration:

[0770] This system analyzes electronic communications to extract important information, and based on that, it has the means to identify and prioritize tasks. It can also convert audio data into text to extract key points from meetings and present this information visually to the user. Furthermore, it evaluates the user's emotional state in real time through an emotion engine and reflects this in task management.

[0771] Specific example 1:

[0772] When an email is received, the server analyzes its contents to extract important tasks and set priorities. The emotion engine analyzes the user's facial expressions and tone of voice to understand their current emotional state. For example, if the user is stressed, the server will design the task list to postpone high-priority and complex tasks and start with easier ones.

[0773] Specific example 2:

[0774] Audio data generated during meetings is converted into text in real time by the server, and the key points are extracted. The emotion engine records emotional fluctuations during the meeting based on the user's reactions. When following up on the meeting later, the user's emotional history is referred to, and task allocation that minimizes stress for the user is suggested.

[0775] Operating instructions:

[0776] Users can visually review tasks and meeting information through their devices, and accept or modify emotionally-based suggestions. Task management that reflects user emotions is expected to improve work efficiency and reduce stress.

[0777] By combining these emotional engines, we can achieve flexible task management that takes user emotions into account, thereby supporting knowledge work more comfortably and effectively.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The server accesses the designated mailbox and retrieves new electronic communications.

[0781] Step 2:

[0782] The server analyzes the acquired electronic communications using natural language processing algorithms to extract important information. The extraction criteria depend on whether specific keywords or phrases are included.

[0783] Step 3:

[0784] The server generates a task list based on the extracted information and assigns a priority to each task. This priority is calculated based on the importance and urgency of each task.

[0785] Step 4:

[0786] The terminal displays a prioritized task list received from the server to the user, presenting it in a visual and interactive format.

[0787] Step 5:

[0788] Users provide their facial expressions and voice through their devices as input to the emotion engine, allowing the system to recognize their emotional state.

[0789] Step 6:

[0790] The server uses an emotion engine to analyze the user's emotional state. Based on this analysis, tasks are adjusted according to the user's emotions.

[0791] Step 7:

[0792] The server dynamically adjusts the priority of the task list to reflect the user's emotional state. For example, if the user is stressed, complex and important tasks will be postponed, and simpler tasks will be placed first.

[0793] Step 8:

[0794] The device then presents the user with a revised task list, displaying tasks in an order that reflects their emotions.

[0795] Step 9:

[0796] Users review the task list displayed on their device and make final adjustments to suit their work style and emotional state.

[0797] Step 10:

[0798] Daily work activities are carried out based on the information confirmed by the user. Emotion-based task management allows for work to be performed while reducing stress.

[0799] (Example 2)

[0800] 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".

[0801] In today's information and communication environment, it is difficult for users to efficiently manage large amounts of information and make optimal decisions. In particular, there is a need for a means to accurately analyze data from electronic communications and meetings, and to enable flexible work management that responds to users' emotional states. Providing a system that enables rapid information analysis and emotion-based management is a key challenge.

[0802] 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.

[0803] In this invention, the server includes means for analyzing electronic communications to extract important information, means for identifying tasks and setting priorities based on the extracted information, means for converting audio information into text information and extracting key points of meetings, and means for evaluating the user's emotional state and reflecting this in task management. As a result, users can efficiently process large amounts of information and make optimal decisions according to their emotional state.

[0804] "Electronic communications" refers to all activities involving the sending and receiving of information in digital format, including email and messaging applications.

[0805] "Important information" refers to data elements extracted from acquired data that have value and directly contribute to business operations and decision-making.

[0806] "Tasks" refer to specific tasks or projects that users are required to accomplish, and they play a role as part of organizational activities.

[0807] Prioritization is the process of determining the order in which tasks and work should be carried out, based on their importance and urgency.

[0808] "Speech information" refers to sound wave data obtained from speech, which is recorded as an acoustic signal.

[0809] "Textual information" refers to data obtained by converting audio or video data into character codes, and is treated as text.

[0810] "Meeting summaries" are a summary of the main points discussed and decisions made during a meeting, and their purpose is to facilitate efficient information dissemination.

[0811] "User" refers to an individual or organization that uses the system for information management or business operations.

[0812] "Emotional state" refers to the psychological and emotional attitudes that a user exhibits in specific situations, and is evaluated based on facial expressions, tone of voice, word choice, and other factors.

[0813] One embodiment of this invention is realized through a system in which a server, a terminal, and a user collaborate to perform electronic communication analysis, extraction of important information, transcription and summarization of voice data, and evaluation of emotional states.

[0814] Program Description

[0815] The server acquires electronic communications (e.g., emails and messages) and analyzes their content. This analysis uses natural language processing techniques to extract important information and keywords. The server leverages generative AI models to identify important tasks from the user's electronic communications and set task priorities. For audio information, the server converts audio data into text in real time and extracts the key points. For example, AI speech recognition technology is implemented as an API to support the text conversion process.

[0816] A server equipped with an emotion engine evaluates the user's emotional state in real time based on input data from cameras and microphones. By using machine learning libraries in this process, it is possible to estimate the user's psychological state from their facial expressions and tone of voice, and reflect this in business management.

[0817] The device presents this information to the user through a visual interface. By utilizing a GUI and displaying the schedule in an easy-to-understand format, the user can confirm the order of the suggested tasks and provide further feedback.

[0818] Specific example

[0819] For example, suppose the server analyzes the content of an email and extracts tasks that the user should prioritize. If the emotion engine detects a high stress level based on the user's facial expressions, the server can suggest postponing more complex tasks and starting with simpler ones.

[0820] Examples of prompts for the generative AI model include, "Generate optimal task priorities based on the user's emotional state," and "Analyze emotional fluctuations during a meeting and suggest appropriate follow-up strategies." In this way, the system can provide flexible task management support through the suggested language.

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1:

[0823] The server acquires electronic communications. In this process, it receives new emails from the mail server and imports them as text data. The input is the raw email data, which is then parsed to extract its content. Data processing includes analyzing the email metadata and converting the content to text. The output is the parsed email content.

[0824] Step 2:

[0825] The server analyzes the content of emails and extracts important information. The input is the analyzed email content obtained in the previous step. Using a generative AI model, keywords and tasks are identified using natural language processing techniques. Specifically, this involves applying an algorithm to identify high-priority information. The output is the extracted important tasks and related information.

[0826] Step 3:

[0827] The server uses an emotion engine to evaluate the user's emotional state. Input consists of real-time data from the camera and microphone, including facial expressions and voice tone data. The server performs emotion analysis using a machine learning model and outputs the results. Specific operations include emotion estimation through facial recognition and voice analysis. The output is an indicator of the user's emotional state.

[0828] Step 4:

[0829] The server prioritizes tasks based on extracted key information and emotional states. Inputs include key task information and emotional state indicators. Data processing utilizes a priority determination algorithm to determine the order of tasks according to the user's stress level. The specific actions involve restructuring the task list and adjusting priorities. The output is a prioritized task list presented to the user.

[0830] Step 5:

[0831] The terminal visually presents information to the user. The input is a prioritized task list sent from the server. The information is formatted into an easily viewable format and displayed on the screen. Specific operations include GUI design and application. The output is the visual interface provided to the user.

[0832] Step 6:

[0833] The user provides feedback based on visualized task information, either correcting the task order or adding new tasks. The input for this step is the task list presented from the terminal and the user's judgment. The output is the corrected task list and feedback data, which the system uses for subsequent processing. Specific actions include operating and changing settings in task management software.

[0834] (Application Example 2)

[0835] 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".

[0836] Traditional task management systems simply set priorities without considering the user's emotional state. This made it difficult to manage tasks efficiently while reducing user stress. Furthermore, there was a need for a flexible task management approach that accommodated individual emotions in daily life.

[0837] 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.

[0838] In this invention, the server includes means for analyzing electronic data and extracting important information, means for identifying and prioritizing tasks based on the extracted information, and means for evaluating the user's emotional state and adjusting tasks based on those emotions. This enables flexible task management that takes the user's emotions into consideration.

[0839] "Means for analyzing electronic data" refers to methods or devices for analyzing electronically recorded information and extracting necessary information.

[0840] "Means for extracting important information" refers to methods or devices for extracting valuable data from a large amount of data that is relevant to a particular situation or purpose.

[0841] "Means for identifying tasks" refers to methods or devices for clarifying specific action items using extracted information.

[0842] "Means of prioritizing" refers to a method or apparatus for ordering identified tasks based on their importance or urgency.

[0843] "Means for converting audio data into documents" refers to a method or apparatus for analyzing audio information and converting it into text information.

[0844] "Means for extracting the essentials of information" refers to a method or apparatus for extracting core information from documented data.

[0845] "Means of visual presentation" refers to methods or devices for displaying generated information in a way that is easily understood visually by the user.

[0846] "Means for evaluating a user's emotional state" refers to a method or device for detecting and evaluating a user's emotions from their facial expressions, voice, etc.

[0847] "Means of adjusting tasks based on emotions" refers to methods or devices for changing the priority or order of tasks, taking into account the user's emotional state.

[0848] This invention realizes a task management system that takes user emotions into consideration. The system uses a server, terminals, and an emotion engine to effectively support the user's task management.

[0849] The server receives electronic data sent by users, analyzes it, and extracts important information. This process utilizes natural language processing techniques and includes algorithms to efficiently extract targeted information from large amounts of data. The server also uses a speech recognition API to convert audio data into text, recording key points from meetings and communications as text. This allows users to easily review past information.

[0850] The terminal visually presents information generated by the server to the user. This uses display devices such as screens and smartphones, and the information can be intuitively manipulated through the interface. Furthermore, by receiving input from the terminal, the user can manually adjust priorities.

[0851] The emotion engine evaluates the user's emotional state in real time using sensors and voice analysis technology. Specifically, it uses facial recognition cameras and voice tone analysis software to determine the user's emotional state and transmits this information to the server. Based on this information, the server rearranges the order of tasks to suit the user's emotions, thereby reducing the burden on the user.

[0852] For example, if a user is relaxed after lunch, they can prioritize and complete important but complex tasks. On the other hand, if the user is stressed, the system will adjust to prioritize relatively easy tasks.

[0853] Examples of prompt messages include, "What are your tasks for today? How are you feeling?" Using prompts tailored to the user's situation enables effective task management. This allows users to smoothly carry out their daily activities through the system.

[0854] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0855] Step 1:

[0856] The server receives electronic data sent by users. This includes emails and document data. The server analyzes the input data and extracts important information using natural language processing techniques. As a result, the identified important information is output.

[0857] Step 2:

[0858] The server identifies and prioritizes tasks based on the extracted information. The input is the extracted key information, and its numerical priority is output. Machine learning algorithms are used in this process to ensure efficient prioritization.

[0859] Step 3:

[0860] The terminal visually presents information generated by the server to the user. To display the information on the user's screen, it utilizes a graphical user interface (GUI) to convert the information into a format that is easily understandable to the user.

[0861] Step 4:

[0862] The user's emotional state is evaluated through an emotion engine. The system takes the user's emotional data as input and analyzes the emotions in real time based on data from sensors and voice analysis technology, then sends the resulting state to the server. The detected emotional state is then output.

[0863] Step 5:

[0864] The server adjusts task priorities based on emotional data received from the emotion engine. Inputs include emotional state and a prioritized list of tasks, and based on this, it outputs a task order appropriate to the user's emotions. This adjustment is performed by an algorithm based on psychological data.

[0865] Step 6:

[0866] Users review the visualized information on their device and manually adjust priorities as needed. The user's input is output as the new priority, and the system uses this information in the next task management cycle.

[0867] Through the above series of steps, effective task management that takes into account the user's emotional state is achieved.

[0868] 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.

[0869] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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."

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] A means of analyzing electronic communications to extract important information,

[0892] A means of identifying and prioritizing tasks based on extracted information,

[0893] A method for converting audio data into text and extracting key points from a meeting,

[0894] A means of visually presenting the generated information to the user,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, further comprising means for adjusting priorities through user input.

[0898] (Claim 3)

[0899] The system according to claim 1, further comprising means for analyzing user schedule information and proposing an optimal task execution order.

[0900] "Example 1"

[0901] (Claim 1)

[0902] A means of processing electronic data and extracting important items,

[0903] A means of identifying and prioritizing business tasks based on the extracted items,

[0904] A method for converting audio data into text information and extracting key points from meetings, etc.

[0905] A means of visually presenting processed information via a terminal,

[0906] A means of recording user behavior and improving system performance based on feedback,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising means for adjusting priority through user input.

[0910] (Claim 3)

[0911] The system according to claim 1, further comprising means for analyzing the user's personal schedule information and suggesting the optimal order for performing tasks.

[0912] "Application Example 1"

[0913] (Claim 1)

[0914] A means of analyzing electronic information and extracting important elements,

[0915] A means of identifying tasks and setting priorities based on extracted elements,

[0916] A means of converting audio information into text and extracting the key points of a meeting,

[0917] A means of visually presenting the generated information to the user,

[0918] A means of analyzing alarms and monitoring sounds to notify of high-priority events,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, further comprising means for adjusting priority through user input.

[0922] (Claim 3)

[0923] The system according to claim 1, further comprising means for analyzing user planning information and proposing the optimal order of task execution.

[0924] "Example 2 of combining an emotion engine"

[0925] (Claim 1)

[0926] A means of analyzing electronic communications to extract important information,

[0927] A means of identifying tasks and setting priorities based on extracted information,

[0928] A method for converting audio information into text information and extracting key points from a meeting,

[0929] A means of visually presenting the generated information to the user,

[0930] A means of evaluating the emotional state of users and reflecting it in work management,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1, further comprising means for adjusting priorities through user input.

[0934] (Claim 3)

[0935] The system according to claim 1, further comprising means for analyzing user schedule information and proposing the optimal order of task execution.

[0936] "Application example 2 when combining with an emotional engine"

[0937] (Claim 1)

[0938] A means of analyzing electronic data to extract important information,

[0939] A means of identifying and prioritizing tasks based on extracted information,

[0940] A means of converting audio data into documents and extracting the key points of the information,

[0941] Means for visually presenting the generated information,

[0942] A means of evaluating the user's emotional state and adjusting tasks based on those emotions,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, further comprising means for adjusting priorities through user input.

[0946] (Claim 3)

[0947] The system according to claim 1, further comprising means for analyzing user activity information and proposing the optimal order of tasks. [Explanation of symbols]

[0948] 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. A means of analyzing electronic information and extracting important elements, A means of identifying tasks and setting priorities based on extracted elements, A means of converting audio information into text and extracting the key points of a meeting, A means of visually presenting the generated information to the user, A means of analyzing alarms and monitoring sounds to notify of high-priority events, A system that includes this.

2. The system according to claim 1, further comprising means for adjusting priority through user input.

3. The system according to claim 1, further comprising means for analyzing user planning information and proposing the optimal order of task execution.

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