system

Abstract

We provide systems that improve operational efficiency and enhance customer satisfaction. [Solution] A system comprising: means for acquiring data from an information source via a communication network; means for analyzing the acquired data and automatically extracting relevant task information; means for transferring the extracted task information to a spreadsheet program; means for monitoring progress based on the transferred task information and automatically sending reminders for tasks with approaching deadlines; means for visually displaying the task information on an interface; means for sharing user updates to task information in real time; and means for issuing warnings based on specific criteria from the acquired data.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, due to the dispersion of information in various communication tools, issues such as task omission and difficulty in deadline management have become problems. Also, because this information is not properly managed, the management burden increases, and there is a possibility of a delay in response. Furthermore, delays in information sharing with relevant parties and overlooking important information such as claims are also serious problems. Such situations can reduce the business efficiency of a company and also have an adverse impact on customer satisfaction.

Means for Solving the Problems

[0005] This invention provides a system that enables centralized task management by acquiring data from information sources via a communication network, extracting task information through automatic analysis, and transferring it to spreadsheet software. Furthermore, it reduces the management burden by monitoring progress based on the extracted task information and sending automatic reminders according to deadlines. In addition, it facilitates information sharing among stakeholders by visualizing task information on an interface and sharing user updates in real time. Moreover, a warning function allows for immediate identification and sharing of important information such as complaints, enabling rapid response. This aims to improve operational efficiency and customer satisfaction.

[0006] A "communication network" is a means of interconnecting multiple devices in order to send and receive data.

[0007] "Information source" refers to the source or medium from which data is generated or provided.

[0008] "Data acquisition" is the act of collecting necessary data from information sources.

[0009] "Analysis" refers to the process of thoroughly examining acquired data and understanding its meaning and relationships.

[0010] "Task information" refers to detailed information related to a specific task or operation, such as the person responsible, the deadline, and the level of importance.

[0011] A "spreadsheet program" refers to a computer program used to input and manage numerical and textual data in a matrix format, and to perform calculations and analyses.

[0012] "Transcribing" refers to the act of moving or copying data from one location to another.

[0013] "Progress status" refers to information indicating the progress and degree of completion of a task or project.

[0014] A "reminder" is a notification or warning set to prevent forgetting schedules or deadlines.

[0015] An "interface" refers to the screen or operating means for a user to interact with a computer system.

[0016] A "warning" is a signal or message for notifying a user of some abnormality or important event.

Brief Explanation of Drawings

[0017] [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0021] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to a system that acquires data from an information source, analyzes that data to extract task information, and then transfers it to a spreadsheet and monitors its progress. This system mainly consists of three elements: a server, a terminal, and a user.

[0039] Server roles and processing

[0040] The server continuously accesses information sources (e.g., mail servers and chat systems) via a communication network to acquire new data. This data is temporarily stored within the server and automatically analyzed. The analysis uses natural language processing techniques to extract task information (such as responsible parties, deadlines, and importance levels) from the data. The server has a function to automatically transfer the extracted task information to spreadsheet software such as Google Sheets.

[0041] The server also monitors the progress of tasks and sends progress reminders according to configured conditions. For example, the server facilitates progress checks by sending emails to the assigned person when the task deadline is approaching.

[0042] Furthermore, the server assigns warnings to the received data based on specific criteria. When important claim information or messages indicating high urgency are detected, the server flags them and promptly notifies the relevant personnel or administrators.

[0043] Terminal role and user interface

[0044] The device receives notifications and data from the server and provides the user with relevant information visually. Through the device, users can access detailed task information and check their progress. The device features an intuitive interface, allowing users to easily manage their tasks.

[0045] User interaction and task management

[0046] Users can access spreadsheets via their devices to check and update the status of their assigned tasks. Information updates by users are immediately sent to the server and reflected in the spreadsheet software, enabling real-time information sharing. This system allows administrators and other team members to always understand the work status based on the latest information.

[0047] As a concrete example, when a sales team handles customer inquiries, this system automatically analyzes each customer's inquiry and assigns it to the appropriate person. As deadlines approach, progress checks are automatically sent, and if a serious complaint arises, an immediate warning is issued, prompting a quick response. This significantly improves operational efficiency and enhances customer satisfaction.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server retrieves new data from information sources via the communication network. It uses APIs from mail servers and chat systems to retrieve messages and stores that data in an internal database.

[0051] Step 2:

[0052] The server analyzes the acquired data and automatically extracts task information using natural language processing technology. It analyzes email and chat content, identifies task-related information such as the person responsible, deadline, and importance level, and organizes it as structured data.

[0053] Step 3:

[0054] The server transfers organized task information to a Google Spreadsheet. Using an API, the extracted data is entered into the corresponding columns in the spreadsheet, and the data is updated while verifying that it does not match existing data.

[0055] Step 4:

[0056] The server monitors the progress of each task based on a spreadsheet. For tasks nearing their deadline, it creates progress check reminders at set time intervals and automatically sends reminder emails to the assigned person.

[0057] Step 5:

[0058] The device receives reminder emails sent from the server and notifies the user of their contents. The user interface visually displays the task progress, allowing the user to operate intuitively.

[0059] Step 6:

[0060] Users can check the status of their assigned tasks through an interface on their device and update progress and actions as needed. The updated information is sent to the server in real time and immediately reflected in the spreadsheet.

[0061] Step 7:

[0062] The server flags complaints and messages with a high level of intensity based on specific criteria within the data it receives. It also sends notifications to the relevant personnel to enable prompt action.

[0063] (Example 1)

[0064] 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."

[0065] In today's information-saturated environment, efficient task management is essential for business and daily operations. However, manual task allocation and progress tracking can lead to overlooking important work information, potentially resulting in delays and errors. Furthermore, while rapid response to critical information based on specific criteria is required, this has been difficult to achieve with conventional systems.

[0066] 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.

[0067] In this invention, the server includes means for acquiring information from an information source via a communication medium, means for analyzing work information from the information using natural language processing technology, and means for issuing warnings based on specific criteria from the acquired information. This makes it possible to automatically and efficiently extract and transcribe work information and to reliably monitor and notify the progress of important tasks.

[0068] "Communication medium" is a general term for means such as networks and the internet used to send and receive data.

[0069] "Information source" refers to the systems or services from which data and information are transmitted, such as email servers or chat platforms.

[0070] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0071] "Work information" refers to data that includes specific information about the progress of a task, such as the name of the person in charge, the deadline, and attributes such as importance.

[0072] "Calculation sheet" refers to software used for performing calculations in a digital format, such as a spreadsheet.

[0073] A "notification" is information sent to a user when important information or events occur, and is primarily delivered via email or in-app messages.

[0074] A "warning" is a flag or message that a system assigns to indicate a situation or matter that requires special attention.

[0075] This invention relates to a system that acquires information from information sources via a communication medium and performs progress management and notification based on the extracted work information. This system mainly consists of a server, terminals, and users, each playing a specific role.

[0076] The server continuously accesses the network to retrieve data from information sources. Specifically, the server periodically retrieves emails and messages from mail servers and chat systems. Communication protocols such as IMAP and HTTP can be used for this purpose. After retrieving the data, the server uses natural language processing techniques to analyze the data for work information. For example, spaCy or NLTK could be used as natural language processing libraries. The analyzed data is structured as work information and automatically transferred to a spreadsheet (e.g., Google Sheets).

[0077] The server further monitors progress based on work information in the spreadsheet and automatically sends notifications for tasks nearing their deadlines. These notifications are often sent as emails using the Gmail API. The server also issues alerts based on specific criteria from the received information. For example, if the content is of high urgency, it is immediately flagged and a notification is sent to the person responsible.

[0078] The terminal is responsible for providing users with visual information using notifications and data received from the server. The terminal provides an intuitive user interface so that users can easily manage their tasks.

[0079] Users can access the spreadsheet through their terminal, check the task status in real time, and update information as needed. User actions are immediately reflected on the server, and the contents of the spreadsheet are updated in real time, enabling work based on the latest information.

[0080] As a concrete example of its use, when a sales representative receives an inquiry from a new customer, they can use a prompt message such as "Analyze the new customer inquiry, register the task in a spreadsheet, and monitor the progress." The server will then automatically analyze the information, reflect the results in a spreadsheet, and enable frequent progress monitoring. This improves work efficiency and prevents problems such as overlooking details or delays in response.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server retrieves data from information sources via a communication medium. Inputs include email and chat messages. The server periodically accesses the information sources using IMAP or HTTP protocols to retrieve new messages and updates. Output consists of the retrieved raw data stored in temporary storage.

[0084] Step 2:

[0085] The server analyzes the acquired data using natural language processing techniques. The input is the raw data saved in step 1. The server analyzes the text using libraries such as spaCy or NLTK, and extracts work information (such as the person responsible, deadline, and importance level). The output is the analyzed structured data, which is organized as work information.

[0086] Step 3:

[0087] The server transfers the extracted work information to a spreadsheet. The input is the structured data obtained in step 2. The server uses functions such as the Google Sheets API to input the work information into the specified cells and update the spreadsheet. The output is the updated spreadsheet, which contains the new work information.

[0088] Step 4:

[0089] The server monitors progress based on the updated calculation sheet. The input is the calculation sheet updated in step 3. The server monitors whether the task is progressing on time based on the configured conditions (e.g., number of days remaining until the deadline). If necessary, it sends email notifications to the person in charge via the Gmail API, etc. The output is the progress confirmation notification delivered to the person in charge.

[0090] Step 5:

[0091] The server issues alerts based on specific criteria from the received data. The input consists of the raw data obtained in step 1 and the information parsed in step 2. The server evaluates the data content and flags high-urgency information. If notification is required, it sends an alert to the relevant personnel via Gmail or other notification systems. The output is a notification message with an alert.

[0092] Step 6:

[0093] The terminal receives notifications and update data from the server and provides information to the user visually. Input is notifications and update information from the server. The terminal provides an environment where the user can view and manage work information through a user interface. Output is the latest dashboard accessible to the user.

[0094] Step 7:

[0095] Users access the spreadsheet by operating a terminal and update work information as needed. Input is the work information confirmed by the user. When a user enters new information, the data is immediately sent to the server and reflected in the spreadsheet. Output is real-time updated work information, shared with the entire team.

[0096] (Application Example 1)

[0097] 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."

[0098] Conventional factory management systems struggle to detect equipment and device malfunctions in real time and respond immediately. Furthermore, they lack sufficient automated management of operational information and efficient monitoring of progress, highlighting the need for improved operational efficiency and faster response to anomalies.

[0099] 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.

[0100] In this invention, the server includes means for acquiring information from information sources via a communication path, means for analyzing the acquired information and automatically extracting relevant business information, and means for acquiring information from equipment and devices operating within the factory, detecting anomalies, and responding quickly. This enables efficient management of business information, as well as real-time detection of anomalies within the factory and immediate response.

[0101] A "communication path" is the network infrastructure used to send and receive information.

[0102] An "information source" is a device or system that serves as the starting point for providing data or information.

[0103] "Business information" refers to information related to tasks, progress, importance, etc., in business activities.

[0104] A "spreadsheet application" is software that allows you to input data in a table format and perform analysis and graphing.

[0105] "Progress status" refers to information indicating the degree of progress of a task or project.

[0106] An "operation screen" is a display unit used by users to operate information and systems.

[0107] A "user" is an individual or group that utilizes the system's functions and information.

[0108] An "alarm" is a notice or signal issued to draw attention when specific conditions occur.

[0109] "Equipment and devices operating within a factory" refers to all machinery and equipment used in production or manufacturing processes.

[0110] "Anomaly detection" is the process of identifying and specifying a state that deviates from the normal operating state.

[0111] The system based on this invention is designed to streamline the operation of equipment and devices within a factory. The server analyzes information acquired through the communication path and extracts relevant business information. This makes it possible to detect anomalies using data acquired from equipment and devices operating within the factory. When an anomaly is detected, the server issues an alarm to facilitate a rapid response.

[0112] The server automatically transfers the analyzed business information to a spreadsheet application. As a result, administrators can monitor business information in real time through the user interface. In particular, business information is categorized by recipient, deadline, and importance, making it easy to evaluate progress. This process improves operational efficiency and enables faster response to anomalies.

[0113] As a concrete example, in an automotive parts manufacturing plant, the operation data of a robot arm is monitored, and if an abnormal operation is detected, a notification is sent to the administrator. This notification allows the administrator to take countermeasures quickly. Another example of a prompt message generated by the AI ​​model is the instruction, "Create a program that analyzes the operation logs obtained from the factory robots, detects abnormal patterns, and transfers them to a Google Spreadsheet. Include a function to send an alert to the administrator if an abnormality occurs." In this way, the system helps to operate efficiently as a whole.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server acquires sensor information in real time from equipment and devices within the factory. The input is raw data sent from the sensors, and the output is the storage of this data. The server uses the network to collect data and stores this data in its internal database.

[0117] Step 2:

[0118] The server performs analysis for anomaly detection based on acquired sensor information. The input is real-time accumulated sensor data. The output is the analysis results used to identify anomalies. The server uses natural language processing technology and machine learning algorithms to analyze the data and detect abnormal operating patterns.

[0119] Step 3:

[0120] If the server detects an anomaly based on the analysis results, it sends a notification to the designated operation screen. The input is the analysis result of the anomaly detection, and the output is an alert sent to the administrator. Based on the detected anomaly, the server takes action to send an email or pop-up notification to the administrator's terminal.

[0121] Step 4:

[0122] The server transfers the analyzed business information to a spreadsheet application. The input is business information obtained from anomaly detection, and the output is the updated data in the spreadsheet application. The server uses the Google Sheets API, etc., to automatically input the business information into the spreadsheet as structured data.

[0123] Step 5:

[0124] Users check the spreadsheet application through the user interface to monitor progress. Inputs are transcribed business information, and output is the user's understanding of current work. Users visualize the information on the spreadsheet and evaluate the progress.

[0125] 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.

[0126] This invention is a system that combines data acquisition and analysis with an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user.

[0127] Server roles and processing

[0128] When a server retrieves data from information sources via a communication network, it collects messages from mail servers and chat systems. Furthermore, the server can utilize an emotion engine to analyze the user's emotional state from the retrieved data and highlight task information accordingly. By combining natural language processing techniques and emotion analysis algorithms, it identifies the emotions expressed in each message and re-evaluates the importance of tasks based on these findings.

[0129] The extracted task information is automatically transferred to a Google Sheets spreadsheet, where progress is then monitored. The emotion engine leverages emotions to dynamically adjust task priorities and optimize the recipients and timing of reminders.

[0130] Furthermore, the server identifies the user's emotions from messages containing emotionally charged complaints and, based on that, issues warnings or promptly notifies users of information that should be shared.

[0131] Terminal role and user interface

[0132] The device receives sentiment analysis results and task information sent from the server and presents them to the user in an easy-to-understand manner. Users can check the progress of tasks and related sentiment information on the device, and tasks and messages of particular importance are visually highlighted.

[0133] User interaction and task management

[0134] Users can access spreadsheets through their devices, update their assigned tasks in real time, and take appropriate action based on the displayed sentiment information. For example, if a highly sensitive complaint is highlighted, users can strive to respond more quickly. This system enables users to perform advanced task management that takes sentiment information into account.

[0135] As a concrete example, consider a scenario where a customer support team receives customer inquiries. Using this system, messages that express anger or dissatisfaction are given high priority and immediately notified to the support staff. The staff can then use this emotion-based information to quickly begin addressing the issue and improve customer satisfaction.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server retrieves new data from information sources via the communication network. It collects messages using APIs from mail servers and chat systems and stores them in an internal database.

[0139] Step 2:

[0140] The server analyzes the acquired data. Using natural language processing techniques, task information (responsible party, deadline, importance level) is automatically extracted from the messages. The task information is then structured based on these analysis results.

[0141] Step 3:

[0142] The server uses an emotion engine to analyze the user's emotions from the acquired data. Based on the message content, it identifies emotional states such as positive, negative, or neutral, and re-evaluates the importance of the task.

[0143] Step 4:

[0144] The server extracts task information and sentiment information and transfers it to a Google Spreadsheet. Using an API, task information is added to the spreadsheet, and priorities are set according to sentiment.

[0145] Step 5:

[0146] The server monitors progress based on information from the spreadsheet. It creates reminders and automatically sends email notifications for tasks that are nearing their deadline or that are deemed emotionally important.

[0147] Step 6:

[0148] The device notifies the user of reminders and task information received from the server. The device interface highlights high-priority tasks, particularly those based on emotions.

[0149] Step 7:

[0150] Users check the status and sentiment information of tasks on their devices. Users check the progress and update task responses as needed. Updates are sent to the server in real time and reflected in the spreadsheet.

[0151] Step 8:

[0152] The server uses an emotion engine to identify complaints and messages with high emotional intensity, and assigns a warning to those messages. This information is then shared with the relevant personnel to encourage a prompt response.

[0153] (Example 2)

[0154] 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".

[0155] Conventional information processing systems struggle to efficiently analyze collected data and dynamically adjust task priorities based on emotional information. Furthermore, they lack sufficient functionality to quickly send reminders for tasks with approaching deadlines, preventing users from responding in the appropriate time. Additionally, the difficulty for users to update data in real time and share it immediately with others hinders efficient collaborative work.

[0156] 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.

[0157] In this invention, the server includes means for acquiring information from information sources via a communication network, means for determining the emotional state within the acquired information using sentiment analysis, and means for dynamically adjusting task priorities based on the determined emotional state. This enables efficient analysis of collected data and dynamic optimization of task management based on emotional information. Furthermore, it allows for the sending of quick and appropriate reminders for tasks with approaching deadlines, enabling users to respond in a timely manner. In addition, it improves the efficiency of collaborative work by allowing users to update information in real time and efficiently share it with other users.

[0158] A "communication network" is a network infrastructure used for sending and receiving data, and includes the internet and local networks.

[0159] "Information sources" refer to external recording media or systems that provide data, including mail servers and chat systems.

[0160] "Information" refers to data and messages expressed in digital format, which are the objects of analysis and processing.

[0161] "Business information" refers to data related to tasks and activities extracted from information sources, and it is necessary to manage and process this data.

[0162] "Spreadsheet software" is software used for organizing and aggregating data, and for recording and managing data in a tabular format.

[0163] "Progress" is an indicator that shows the status of work or tasks toward completion, and its management affects work efficiency.

[0164] "Sentiment analysis" is a technology that identifies a person's emotional state from text data, and is implemented using natural language processing and machine learning.

[0165] A "display device" is a device used to present information visually, and includes displays and monitors.

[0166] A "user" is an individual or organization that operates and utilizes this system, and updates and manages information according to their purpose.

[0167] A "warning" is a notice issued based on specific criteria, indicating risks and priorities related to work or tasks.

[0168] This invention is a system that analyzes the user's emotional state and enables the dynamic adjustment of task priorities. The system consists of three elements: a server, a terminal, and a user, each playing the following role.

[0169] The server retrieves necessary information from information sources via the communication network. The server utilizes POP3, IMAP protocols, and REST APIs to collect data from mail servers and chat systems. The collected information is preprocessed using natural language processing libraries (e.g., NLTK or SpaCy) for sentiment analysis.

[0170] The server applies sentiment analysis algorithms to the collected information to determine emotional states. This analysis utilizes sentiment dictionaries and machine learning models. Based on the sentiment scores obtained from the analysis, the server dynamically prioritizes work tasks and extracts important work information. This work information is automatically transferred to spreadsheet software and used for monitoring progress.

[0171] The terminal receives sentiment analysis results and business information sent from the server and presents them to the user visually. The terminal communicates with the server in real time via WebSocket to receive the latest data. UI frameworks (such as React or Vue.js) are used for display, and the information is provided in a format that is easy for the user to understand.

[0172] Users access spreadsheet software from their terminals to manage their work. They can prioritize tasks and update data using an intuitive interface. Updated data is immediately shared with other users, facilitating efficient collaboration.

[0173] A concrete example is a customer support team handling customer inquiries. Using this system, messages from customers exhibiting particularly heightened emotions are prioritized and immediately notified to support staff. These staff members can then leverage emotional information to initiate a quicker response, thereby improving customer satisfaction.

[0174] An example of a prompt for a generative AI model is: "Please describe the sentiment recognition system you use in customer support. In particular, please explain in detail how it analyzes the sentiment of messages and improves customer service."

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server retrieves data from information sources via the communication network. Specifically, it uses POP3 and IMAP protocols from mail servers and REST APIs to collect messages from chat systems. The data input in this retrieval process is text data such as emails and messages. The output is raw, unprocessed data that can be used for analysis.

[0178] Step 2:

[0179] The server preprocesses the acquired message data using natural language processing libraries (e.g., NLTK or SpaCy). Specific preprocessing steps include tokenization, stop word removal, and stemming. The input is raw message data, and the output, after preprocessing, is text data formatted for easier sentiment analysis.

[0180] Step 3:

[0181] The server applies a sentiment analysis algorithm to the preprocessed data. In this step, a sentiment dictionary or machine learning model is used to calculate a sentiment score for each message. The input is preprocessed text data, and the analysis results are output as sentiment scores such as positive, negative, or neutral.

[0182] Step 4:

[0183] The server extracts business information based on the sentiment analysis results and re-evaluates its importance. Keyword extraction technology is used to select information related to specific tasks, and its importance is set by comparing it to the sentiment score. The input is data with sentiment scores, and the output is prioritized business information. This business information is automatically transferred to spreadsheet software.

[0184] Step 5:

[0185] The terminal receives sentiment analysis results and business information sent from the server. It receives data in real time using WebSocket and presents it visually using a UI framework (e.g., React or Vue.js). In this step, the input is analyzed data from the server, and the output is information formatted for user display.

[0186] Step 6:

[0187] Users access and manage spreadsheet software displayed on their terminals. Data can be manipulated through an intuitive interface, and updated information is shared immediately. Input consists of business information updated by the user, while output is shared in real time with other users via the terminal.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] Traditional task management systems often process tasks based on a uniform priority without considering the user's emotional state, potentially leading to decreased customer satisfaction. Furthermore, there is a lack of mechanisms to quickly recognize and respond to customer emotions during in-store interactions. This creates a challenge in improving the customer experience in physical stores.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for acquiring data from an information source via a communication network, means for issuing warnings based on specific criteria from the acquired data, and means for detecting a person's emotional state based on information displayed on a terminal device and providing relevant action recommendations in real time. This makes it possible to dynamically adjust task priorities according to the user's emotional state, enabling faster and more appropriate customer service.

[0193] A "communication network" is an infrastructure consisting of lines and protocols for sending and receiving data.

[0194] A "terminal device" is a computing device that allows a user to directly operate and retrieve information.

[0195] "Emotional state" refers to a classification of a person's internal psychological reactions and emotions expressed through facial expressions and voice.

[0196] "Action recommendations" are instructions or advice for taking appropriate action in specific situations.

[0197] "Issuing a warning" means issuing a notification or alert to draw attention to information that meets specific conditions.

[0198] "Task information" refers to data about a specific activity or task, including details such as deadlines and priorities.

[0199] A "server" is a computer system that processes and stores data and provides services to other devices.

[0200] This invention is a system designed to support customer service in physical stores. It detects the emotional state of customers and provides services based on that state. By having the server, terminal, and user each play a specific role, it aims to provide a better customer experience.

[0201] The server retrieves data from information sources via a communication network. This data may include customer facial expressions and voice information. The retrieved data is then subjected to an algorithm to analyze emotions based on specific criteria. This algorithm uses Google Cloud's "Natural Language API" and "Cloud Vision API" to identify emotional states from the data. Based on the identification results, warnings are issued or action recommendations are generated as needed.

[0202] The terminal receives sentiment analysis results and recommendations sent from the server and displays them to the user in real time. Specifically, these are displayed on wearable devices such as smart glasses, providing customers with optimal product suggestions and service information. If the customer's sentiment is positive, additional products are recommended; if it is negative, careful guidance is provided.

[0203] Users adjust their customer interactions based on information gathered through their devices. For example, if a customer is hesitant about making a purchase, the system can understand their feelings and offer additional information or discounts. This allows customers to make a purchase decision with confidence, leading to increased customer satisfaction.

[0204] As a concrete example, suppose a customer asks a question about a product and displays a confused expression. This information is immediately analyzed by the server for sentiment, and a message appears on the terminal's display saying, "Please provide additional information about this product." The store clerk then provides further details according to the instructions, allowing the customer to make a purchase decision with confidence.

[0205] Examples of prompts used include, "Generate advice on what information to provide if this customer is feeling anxious," and "Based on the customer's emotional state as determined from the current conversation, suggest any relevant products." This allows the generative AI model to suggest appropriate actions.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server receives customer facial expression and audio data acquired from terminal devices via a communication network. The input is digital data of the customer's video and audio. This data is sent to Google Cloud's "Cloud Vision API" and "Natural Language API" to analyze the data and identify the customer's emotional state. As a result, data classified as an emotional state is output.

[0209] Step 2:

[0210] The server prepares prompt messages that use a generative AI model to generate action recommendations based on emotional state data. The input is identified emotional state data. Using this data, the server inputs a prompt message to the generative AI model such as, "Generate advice on what information to provide if this customer is feeling anxious." The output is the action recommendation obtained from the AI ​​model.

[0211] Step 3:

[0212] The server sends the generated action recommendations to the terminal. The input is the recommendations from the AI ​​model. The terminal receives these recommendations and displays them in a user-friendly format. Based on this information, the user can adjust their response to the customer. For example, the display might show "Please provide additional information about the product." As output, visual instructions are displayed on the terminal's screen.

[0213] Step 4:

[0214] The user provides actual customer support based on instructions displayed on the terminal. The input is the instructions shown on the terminal's display, and the user provides additional information and takes appropriate actions based on these instructions. The user observes whether the customer makes a purchase decision and provides further support as needed. The output is either that the customer's inquiry is resolved or their satisfaction level improves.

[0215] 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.

[0216] 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 those described above. 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 shown 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention relates to a system that acquires data from an information source, analyzes that data to extract task information, and then transfers it to a spreadsheet and monitors its progress. This system mainly consists of three elements: a server, a terminal, and a user.

[0232] Server roles and processing

[0233] The server continuously accesses information sources (e.g., email servers and chat systems) via a communication network to acquire new data. This data is temporarily stored within the server and automatically analyzed. The analysis uses natural language processing techniques to extract task information (such as responsible parties, deadlines, and importance levels) from the data. The server has a function to automatically transfer the extracted task information to spreadsheet software such as Google Sheets.

[0234] The server also monitors the progress of tasks and sends progress reminders according to configured conditions. For example, the server facilitates progress checks by sending emails to the assigned person when the task deadline is approaching.

[0235] Furthermore, the server assigns warnings to the received data based on specific criteria. When important claim information or messages indicating high urgency are detected, the server flags them and promptly notifies the relevant personnel or administrators.

[0236] Terminal role and user interface

[0237] The device receives notifications and data from the server and provides the user with relevant information visually. Through the device, users can access detailed task information and check their progress. The device features an intuitive interface, allowing users to easily manage their tasks.

[0238] User interaction and task management

[0239] Users can access spreadsheets via their devices to check and update the status of their assigned tasks. Information updates by users are immediately sent to the server and reflected in the spreadsheet software, enabling real-time information sharing. This system allows administrators and other team members to always understand the work status based on the latest information.

[0240] As a concrete example, when a sales team handles customer inquiries, this system automatically analyzes each customer's inquiry and assigns it to the appropriate person. As deadlines approach, progress checks are automatically sent, and if a serious complaint arises, an immediate warning is issued, prompting a quick response. This significantly improves operational efficiency and enhances customer satisfaction.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server retrieves new data from information sources via the communication network. It uses APIs from mail servers and chat systems to retrieve messages and stores that data in an internal database.

[0244] Step 2:

[0245] The server analyzes the acquired data and automatically extracts task information using natural language processing technology. It analyzes email and chat content, identifies task-related information such as the person responsible, deadline, and importance level, and organizes it as structured data.

[0246] Step 3:

[0247] The server transfers organized task information to a Google Spreadsheet. Using an API, the extracted data is entered into the corresponding columns in the spreadsheet, and the data is updated while verifying that it does not match existing data.

[0248] Step 4:

[0249] The server monitors the progress of each task based on a spreadsheet. For tasks nearing their deadline, it creates progress check reminders at set time intervals and automatically sends reminder emails to the assigned person.

[0250] Step 5:

[0251] The device receives reminder emails sent from the server and notifies the user of their contents. The user interface visually displays the task progress, allowing the user to operate intuitively.

[0252] Step 6:

[0253] Users can check the status of their assigned tasks through an interface on their device and update progress and actions as needed. The updated information is sent to the server in real time and immediately reflected in the spreadsheet.

[0254] Step 7:

[0255] The server flags complaints and messages with a high level of intensity based on specific criteria within the data it receives. It also sends notifications to the relevant personnel to enable prompt action.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In today's information-saturated environment, efficient task management is essential for business and daily operations. However, manual task allocation and progress tracking can lead to overlooking important work information, potentially resulting in delays and errors. Furthermore, while rapid response to critical information based on specific criteria is required, this has been difficult to achieve with conventional systems.

[0259] 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.

[0260] In this invention, the server includes means for acquiring information from an information source via a communication medium, means for analyzing work information from the information using natural language processing technology, and means for issuing warnings based on specific criteria from the acquired information. This makes it possible to automatically and efficiently extract and transcribe work information and to reliably monitor and notify the progress of important tasks.

[0261] "Communication medium" is a general term for means such as networks and the internet used to send and receive data.

[0262] "Information source" refers to the systems or services from which data and information are transmitted, such as email servers or chat platforms.

[0263] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0264] "Work information" refers to data that includes specific information about the progress of a task, such as the name of the person in charge, the deadline, and attributes such as importance.

[0265] "Calculation sheet" refers to software used for performing calculations in a digital format, such as a spreadsheet.

[0266] A "notification" is information sent to a user when important information or events occur, and is primarily delivered via email or in-app messages.

[0267] A "warning" is a flag or message that a system assigns to indicate a situation or matter that requires special attention.

[0268] This invention relates to a system that acquires information from information sources via a communication medium and performs progress management and notification based on the extracted work information. This system mainly consists of a server, terminals, and users, each playing a specific role.

[0269] The server continuously accesses the network to retrieve data from information sources. Specifically, the server periodically retrieves emails and messages from mail servers and chat systems. Communication protocols such as IMAP and HTTP can be used for this purpose. After retrieving the data, the server uses natural language processing techniques to analyze the data for work information. For example, spaCy or NLTK could be used as natural language processing libraries. The analyzed data is structured as work information and automatically transferred to a spreadsheet (e.g., Google Sheets).

[0270] The server further monitors progress based on work information in the spreadsheet and automatically sends notifications for tasks nearing their deadlines. These notifications are often sent as emails using the Gmail API. The server also issues alerts based on specific criteria from the received information. For example, if the content is of high urgency, it is immediately flagged and a notification is sent to the person responsible.

[0271] The terminal is responsible for providing users with visual information using notifications and data received from the server. The terminal provides an intuitive user interface so that users can easily manage their tasks.

[0272] Users can access the spreadsheet through their terminal, check the task status in real time, and update information as needed. User actions are immediately reflected on the server, and the contents of the spreadsheet are updated in real time, enabling work based on the latest information.

[0273] As a concrete example of its use, when a sales representative receives an inquiry from a new customer, they can use a prompt message such as "Analyze the new customer inquiry, register the task in a spreadsheet, and monitor the progress." The server will then automatically analyze the information, reflect the results in a spreadsheet, and enable frequent progress monitoring. This improves work efficiency and prevents problems such as overlooking details or delays in response.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server retrieves data from information sources via a communication medium. Inputs include email and chat messages. The server periodically accesses the information sources using IMAP or HTTP protocols to retrieve new messages and updates. Output consists of the retrieved raw data stored in temporary storage.

[0277] Step 2:

[0278] The server analyzes the acquired data using natural language processing techniques. The input is the raw data saved in step 1. The server analyzes the text using libraries such as spaCy or NLTK, and extracts work information (such as the person responsible, deadline, and importance level). The output is the analyzed structured data, which is organized as work information.

[0279] Step 3:

[0280] The server transfers the extracted work information to a spreadsheet. The input is the structured data obtained in step 2. The server uses functions such as the Google Sheets API to input the work information into the specified cells and update the spreadsheet. The output is the updated spreadsheet, which contains the new work information.

[0281] Step 4:

[0282] The server monitors progress based on the updated calculation sheet. The input is the calculation sheet updated in step 3. The server monitors whether the task is progressing on time based on the configured conditions (e.g., number of days remaining until the deadline). If necessary, it sends email notifications to the person in charge via the Gmail API, etc. The output is the progress confirmation notification delivered to the person in charge.

[0283] Step 5:

[0284] The server assigns warnings based on specific criteria from the received data. The input is the raw data obtained in Step 1 and the information analyzed in Step 2. The server evaluates the content of the data and flags high-urgency information. If a notification is required, an alert is sent to the relevant responsible person via Gmail or other notification systems. The output is a notification message with a warning.

[0285] Step 6:

[0286] The terminal receives the notification and updated data from the server and provides information visually to the user. The input is the notification and updated information from the server. The terminal provides an environment through the user interface where the user can view and manage work information. The output is the latest dashboard accessible to the user.

[0287] Step 7:

[0288] The user operates the terminal to access the spreadsheet and updates the work information as needed. The input is the work information confirmed by the user. When the user enters new information, the data is immediately sent to the server and reflected in the spreadsheet. The output is the work information updated in real time and shared across the team.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In a conventional factory management system, it is difficult to detect anomalies in equipment and devices in real time and respond immediately. Also, the automatic management of business information and the efficient monitoring of progress are insufficient, and there is a demand for improving business efficiency and responding quickly to anomalies.

[0292] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes means for acquiring information from information sources via a communication path, means for analyzing the acquired information and automatically extracting relevant business information, and means for acquiring information from equipment and devices operating within the factory, detecting anomalies, and responding quickly. This enables efficient management of business information, as well as real-time detection of anomalies within the factory and immediate response.

[0294] A "communication path" is the network infrastructure used to send and receive information.

[0295] An "information source" is a device or system that serves as the starting point for providing data or information.

[0296] "Business information" refers to information related to tasks, progress, importance, etc., in business activities.

[0297] A "spreadsheet application" is software that allows you to input data in a table format and perform analysis and graphing.

[0298] "Progress status" refers to information indicating the degree of progress of a task or project.

[0299] An "operation screen" is a display unit used by users to operate information and systems.

[0300] A "user" is an individual or group that utilizes the system's functions and information.

[0301] An "alarm" is a notice or signal issued to draw attention when specific conditions occur.

[0302] "Equipment and devices operating within a factory" refers to all machinery and equipment used in production or manufacturing processes.

[0303] "Abnormality detection" is a process of identifying and specifying a state that deviates from the normal operating state.

[0304] The system according to this invention is for improving the operation efficiency of equipment and devices in a factory. The server analyzes the information obtained through the communication path and extracts relevant business information. Thereby, it is possible to detect abnormalities using the data obtained from the equipment and devices operating in the factory. When an abnormality is detected, the server issues an alarm to promote a prompt response.

[0305] The server automatically posts the analyzed business information to a spreadsheet application. As a result, the administrator can monitor the business information in real time through the operation screen. In particular, the business information is classified into categories such as recipients, delivery dates, and importance, making it easier to evaluate the progress. Through this process, an improvement in business efficiency and a quick response to abnormalities are achieved.

[0306] As a specific example, in an automobile parts manufacturing factory, when monitoring the operation data of a robotic arm and detecting an operation abnormality, a notification is sent to the administrator. With this notification, the administrator can quickly take countermeasures. Also, as an example of a prompt sentence by a generative AI model, there is an instruction such as "Create a program that analyzes the operation logs obtained from the factory robots, detects abnormal patterns, and posts them to a Google spreadsheet. Include a function to send an alert to the administrator when an abnormality occurs." In this way, it supports the efficient operation of the entire system.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server acquires sensor information in real time from equipment and devices within the factory. The input is raw data sent from the sensors, and the output is the storage of this data. The server uses the network to collect data and stores this data in its internal database.

[0310] Step 2:

[0311] The server performs analysis for anomaly detection based on acquired sensor information. The input is real-time accumulated sensor data. The output is the analysis results used to identify anomalies. The server uses natural language processing technology and machine learning algorithms to analyze the data and detect abnormal operating patterns.

[0312] Step 3:

[0313] If the server detects an anomaly based on the analysis results, it sends a notification to the designated operation screen. The input is the analysis result of the anomaly detection, and the output is an alert sent to the administrator. Based on the detected anomaly, the server takes action to send an email or pop-up notification to the administrator's terminal.

[0314] Step 4:

[0315] The server transfers the analyzed business information to a spreadsheet application. The input is business information obtained from anomaly detection, and the output is the updated data in the spreadsheet application. The server uses the Google Sheets API, etc., to automatically input the business information into the spreadsheet as structured data.

[0316] Step 5:

[0317] Users check the spreadsheet application through the user interface to monitor progress. Inputs are transcribed business information, and output is the user's understanding of current work. Users visualize the information on the spreadsheet and evaluate the progress.

[0318] 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.

[0319] This invention is a system that combines data acquisition and analysis with an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user.

[0320] Server roles and processing

[0321] When a server retrieves data from information sources via a communication network, it collects messages from mail servers and chat systems. Furthermore, the server can utilize an emotion engine to analyze the user's emotional state from the retrieved data and highlight task information accordingly. By combining natural language processing techniques and emotion analysis algorithms, it identifies the emotions expressed in each message and re-evaluates the importance of tasks based on these findings.

[0322] The extracted task information is automatically transferred to a Google Sheets spreadsheet, where progress is then monitored. The emotion engine leverages emotions to dynamically adjust task priorities and optimize the recipients and timing of reminders.

[0323] Furthermore, the server identifies the user's emotions from messages containing emotionally charged complaints and, based on that, issues warnings or promptly notifies users of information that should be shared.

[0324] Terminal role and user interface

[0325] The device receives sentiment analysis results and task information sent from the server and presents them to the user in an easy-to-understand manner. Users can check the progress of tasks and related sentiment information on the device, and tasks and messages of particular importance are visually highlighted.

[0326] User interaction and task management

[0327] Users can access spreadsheets through their devices, update their assigned tasks in real time, and take appropriate action based on the displayed sentiment information. For example, if a highly sensitive complaint is highlighted, users can strive to respond more quickly. This system enables users to perform advanced task management that takes sentiment information into account.

[0328] As a concrete example, consider a scenario where a customer support team receives customer inquiries. Using this system, messages that express anger or dissatisfaction are given high priority and immediately notified to the support staff. The staff can then use this emotion-based information to quickly begin addressing the issue and improve customer satisfaction.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server retrieves new data from information sources via the communication network. It collects messages using APIs from mail servers and chat systems and stores them in an internal database.

[0332] Step 2:

[0333] The server analyzes the acquired data. Using natural language processing techniques, task information (responsible party, deadline, importance level) is automatically extracted from the messages. The task information is then structured based on these analysis results.

[0334] Step 3:

[0335] The server uses an emotion engine to analyze the user's emotions from the acquired data. Based on the message content, it identifies emotional states such as positive, negative, or neutral, and re-evaluates the importance of the task.

[0336] Step 4:

[0337] The server extracts task information and sentiment information and transfers it to a Google Spreadsheet. Using an API, task information is added to the spreadsheet, and priorities are set according to sentiment.

[0338] Step 5:

[0339] The server monitors progress based on information from the spreadsheet. It creates reminders and automatically sends email notifications for tasks that are nearing their deadline or that are deemed emotionally important.

[0340] Step 6:

[0341] The device notifies the user of reminders and task information received from the server. The device interface highlights high-priority tasks, particularly those based on emotions.

[0342] Step 7:

[0343] Users check the status and sentiment information of tasks on their devices. Users check the progress and update task responses as needed. Updates are sent to the server in real time and reflected in the spreadsheet.

[0344] Step 8:

[0345] The server uses an emotion engine to identify complaints and messages with high emotional intensity, and assigns a warning to those messages. This information is then shared with the relevant personnel to encourage a prompt response.

[0346] (Example 2)

[0347] 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".

[0348] Conventional information processing systems struggle to efficiently analyze collected data and dynamically adjust task priorities based on emotional information. Furthermore, they lack sufficient functionality to quickly send reminders for tasks with approaching deadlines, preventing users from responding in the appropriate time. Additionally, the difficulty for users to update data in real time and share it immediately with others hinders efficient collaborative work.

[0349] 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.

[0350] In this invention, the server includes means for acquiring information from information sources via a communication network, means for determining the emotional state within the acquired information using sentiment analysis, and means for dynamically adjusting task priorities based on the determined emotional state. This enables efficient analysis of collected data and dynamic optimization of task management based on emotional information. Furthermore, it allows for the sending of quick and appropriate reminders for tasks with approaching deadlines, enabling users to respond in a timely manner. In addition, it improves the efficiency of collaborative work by allowing users to update information in real time and efficiently share it with other users.

[0351] A "communication network" is a network infrastructure used for sending and receiving data, and includes the internet and local networks.

[0352] "Information sources" refer to external recording media or systems that provide data, including mail servers and chat systems.

[0353] "Information" refers to data and messages expressed in digital format, which are the objects of analysis and processing.

[0354] "Business information" refers to data related to tasks and activities extracted from information sources, and it is necessary to manage and process this data.

[0355] "Spreadsheet software" is software used for organizing and aggregating data, and for recording and managing data in a tabular format.

[0356] "Progress" is an indicator that shows the status of work or tasks toward completion, and its management affects work efficiency.

[0357] "Sentiment analysis" is a technology that identifies a person's emotional state from text data, and is implemented using natural language processing and machine learning.

[0358] A "display device" is a device used to present information visually, and includes displays and monitors.

[0359] A "user" is an individual or organization that operates and utilizes this system, and updates and manages information according to their purpose.

[0360] A "warning" is a notice issued based on specific criteria, indicating risks and priorities related to work or tasks.

[0361] This invention is a system that analyzes the user's emotional state and enables the dynamic adjustment of task priorities. The system consists of three elements: a server, a terminal, and a user, each playing the following role.

[0362] The server retrieves necessary information from information sources via the communication network. The server utilizes POP3, IMAP protocols, and REST APIs to collect data from mail servers and chat systems. The collected information is preprocessed using natural language processing libraries (e.g., NLTK or SpaCy) for sentiment analysis.

[0363] The server applies sentiment analysis algorithms to the collected information to determine emotional states. This analysis utilizes sentiment dictionaries and machine learning models. Based on the sentiment scores obtained from the analysis, the server dynamically prioritizes work tasks and extracts important work information. This work information is automatically transferred to spreadsheet software and used for monitoring progress.

[0364] The terminal receives sentiment analysis results and business information sent from the server and presents them to the user visually. The terminal communicates with the server in real time via WebSocket to receive the latest data. UI frameworks (such as React or Vue.js) are used for display, and the information is provided in a format that is easy for the user to understand.

[0365] Users access spreadsheet software from their terminals to manage their work. They can prioritize tasks and update data using an intuitive interface. Updated data is immediately shared with other users, facilitating efficient collaboration.

[0366] A concrete example is a customer support team handling customer inquiries. Using this system, messages from customers exhibiting particularly heightened emotions are prioritized and immediately notified to support staff. These staff members can then leverage emotional information to initiate a quicker response, thereby improving customer satisfaction.

[0367] An example of a prompt for a generative AI model is: "Please describe the sentiment recognition system you use in customer support. In particular, please explain in detail how it analyzes the sentiment of messages and improves customer service."

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server retrieves data from information sources via the communication network. Specifically, it uses POP3 and IMAP protocols from mail servers and REST APIs to collect messages from chat systems. The data input in this retrieval process is text data such as emails and messages. The output is raw, unprocessed data that can be used for analysis.

[0371] Step 2:

[0372] The server preprocesses the acquired message data using natural language processing libraries (e.g., NLTK or SpaCy). Specific preprocessing steps include tokenization, stop word removal, and stemming. The input is raw message data, and the output, after preprocessing, is text data formatted for easier sentiment analysis.

[0373] Step 3:

[0374] The server applies a sentiment analysis algorithm to the preprocessed data. In this step, a sentiment dictionary or machine learning model is used to calculate a sentiment score for each message. The input is preprocessed text data, and the analysis results are output as sentiment scores such as positive, negative, or neutral.

[0375] Step 4:

[0376] The server extracts business information based on the sentiment analysis results and re-evaluates its importance. Keyword extraction technology is used to select information related to specific tasks, and its importance is set by comparing it to the sentiment score. The input is data with sentiment scores, and the output is prioritized business information. This business information is automatically transferred to spreadsheet software.

[0377] Step 5:

[0378] The terminal receives sentiment analysis results and business information sent from the server. It receives data in real time using WebSocket and presents it visually using a UI framework (e.g., React or Vue.js). In this step, the input is analyzed data from the server, and the output is information formatted for user display.

[0379] Step 6:

[0380] Users access and manage spreadsheet software displayed on their terminals. Data can be manipulated through an intuitive interface, and updated information is shared immediately. Input consists of business information updated by the user, while output is shared in real time with other users via the terminal.

[0381] (Application Example 2)

[0382] 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."

[0383] Traditional task management systems often process tasks based on a uniform priority without considering the user's emotional state, potentially leading to decreased customer satisfaction. Furthermore, there is a lack of mechanisms to quickly recognize and respond to customer emotions during in-store interactions. This creates a challenge in improving the customer experience in physical stores.

[0384] 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.

[0385] In this invention, the server includes means for acquiring data from an information source via a communication network, means for issuing warnings based on specific criteria from the acquired data, and means for detecting a person's emotional state based on information displayed on a terminal device and providing relevant action recommendations in real time. This makes it possible to dynamically adjust task priorities according to the user's emotional state, enabling faster and more appropriate customer service.

[0386] A "communication network" is an infrastructure consisting of lines and protocols for sending and receiving data.

[0387] A "terminal device" is a computing device that allows a user to directly operate and retrieve information.

[0388] "Emotional state" refers to a classification of a person's internal psychological reactions and emotions expressed through facial expressions and voice.

[0389] "Action recommendations" are instructions or advice for taking appropriate action in specific situations.

[0390] "Issuing a warning" means issuing a notification or alert to draw attention to information that meets specific conditions.

[0391] "Task information" refers to data about a specific activity or task, including details such as deadlines and priorities.

[0392] A "server" is a computer system that processes and stores data and provides services to other devices.

[0393] This invention is a system designed to support customer service in physical stores. It detects the emotional state of customers and provides services based on that state. By having the server, terminal, and user each play a specific role, it aims to provide a better customer experience.

[0394] The server retrieves data from information sources via a communication network. This data may include customer facial expressions and voice information. The retrieved data is then subjected to an algorithm to analyze emotions based on specific criteria. This algorithm uses Google Cloud's "Natural Language API" and "Cloud Vision API" to identify emotional states from the data. Based on the identification results, warnings are issued or action recommendations are generated as needed.

[0395] The terminal receives sentiment analysis results and recommendations sent from the server and displays them to the user in real time. Specifically, these are displayed on wearable devices such as smart glasses, providing customers with optimal product suggestions and service information. If the customer's sentiment is positive, additional products are recommended; if it is negative, careful guidance is provided.

[0396] Users adjust their customer interactions based on information gathered through their devices. For example, if a customer is hesitant about making a purchase, the system can understand their feelings and offer additional information or discounts. This allows customers to make a purchase decision with confidence, leading to increased customer satisfaction.

[0397] As a concrete example, suppose a customer asks a question about a product and displays a confused expression. This information is immediately analyzed by the server for sentiment, and a message appears on the terminal's display saying, "Please provide additional information about this product." The store clerk then provides further details according to the instructions, allowing the customer to make a purchase decision with confidence.

[0398] Examples of prompts used include, "Generate advice on what information to provide if this customer is feeling anxious," and "Based on the customer's emotional state as determined from the current conversation, suggest any relevant products." This allows the generative AI model to suggest appropriate actions.

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The server receives customer facial expression and audio data acquired from terminal devices via a communication network. The input is digital data of the customer's video and audio. This data is sent to Google Cloud's "Cloud Vision API" and "Natural Language API" to analyze the data and identify the customer's emotional state. As a result, data classified as an emotional state is output.

[0402] Step 2:

[0403] The server prepares prompt messages that use a generative AI model to generate action recommendations based on emotional state data. The input is identified emotional state data. Using this data, the server inputs a prompt message to the generative AI model such as, "Generate advice on what information to provide if this customer is feeling anxious." The output is the action recommendation obtained from the AI ​​model.

[0404] Step 3:

[0405] The server sends the generated action recommendations to the terminal. The input is the recommendations from the AI ​​model. The terminal receives these recommendations and displays them in a user-friendly format. Based on this information, the user can adjust their response to the customer. For example, the display might show "Please provide additional information about the product." As output, visual instructions are displayed on the terminal's screen.

[0406] Step 4:

[0407] The user provides actual customer support based on instructions displayed on the terminal. The input is the instructions shown on the terminal's display, and the user provides additional information and takes appropriate actions based on these instructions. The user observes whether the customer makes a purchase decision and provides further support as needed. The output is either that the customer's inquiry is resolved or their satisfaction level improves.

[0408] 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.

[0409] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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 shown 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention relates to a system that acquires data from an information source, analyzes that data to extract task information, and then transfers it to a spreadsheet and monitors its progress. This system mainly consists of three elements: a server, a terminal, and a user.

[0425] Server roles and processing

[0426] The server continuously accesses information sources (e.g., email servers and chat systems) via a communication network to acquire new data. This data is temporarily stored within the server and automatically analyzed. The analysis uses natural language processing techniques to extract task information (such as responsible parties, deadlines, and importance levels) from the data. The server has a function to automatically transfer the extracted task information to spreadsheet software such as Google Sheets.

[0427] The server also monitors the progress of tasks and sends progress reminders according to configured conditions. For example, the server facilitates progress checks by sending emails to the assigned person when the task deadline is approaching.

[0428] Furthermore, the server assigns warnings to the received data based on specific criteria. When important claim information or messages indicating high urgency are detected, the server flags them and promptly notifies the relevant personnel or administrators.

[0429] Terminal role and user interface

[0430] The device receives notifications and data from the server and provides the user with relevant information visually. Through the device, users can access detailed task information and check their progress. The device features an intuitive interface, allowing users to easily manage their tasks.

[0431] User interaction and task management

[0432] Users can access spreadsheets via their devices to check and update the status of their assigned tasks. Information updates by users are immediately sent to the server and reflected in the spreadsheet software, enabling real-time information sharing. This system allows administrators and other team members to always understand the work status based on the latest information.

[0433] As a concrete example, when a sales team handles customer inquiries, this system automatically analyzes each customer's inquiry and assigns it to the appropriate person. As deadlines approach, progress checks are automatically sent, and if a serious complaint arises, an immediate warning is issued, prompting a quick response. This significantly improves operational efficiency and enhances customer satisfaction.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The server retrieves new data from information sources via the communication network. It uses APIs from mail servers and chat systems to retrieve messages and stores that data in an internal database.

[0437] Step 2:

[0438] The server analyzes the acquired data and automatically extracts task information using natural language processing technology. It analyzes email and chat content, identifies task-related information such as the person responsible, deadline, and importance level, and organizes it as structured data.

[0439] Step 3:

[0440] The server transfers organized task information to a Google Spreadsheet. Using an API, the extracted data is entered into the corresponding columns in the spreadsheet, and the data is updated while verifying that it does not match existing data.

[0441] Step 4:

[0442] The server monitors the progress of each task based on a spreadsheet. For tasks nearing their deadline, it creates progress check reminders at set time intervals and automatically sends reminder emails to the assigned person.

[0443] Step 5:

[0444] The device receives reminder emails sent from the server and notifies the user of their contents. The user interface visually displays the task progress, allowing the user to operate intuitively.

[0445] Step 6:

[0446] Users can check the status of their assigned tasks through an interface on their device and update progress and actions as needed. The updated information is sent to the server in real time and immediately reflected in the spreadsheet.

[0447] Step 7:

[0448] The server flags complaints and messages with a high level of intensity based on specific criteria within the data it receives. It also sends notifications to the relevant personnel to enable prompt action.

[0449] (Example 1)

[0450] 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."

[0451] In today's information-saturated environment, efficient task management is essential for business and daily operations. However, manual task allocation and progress tracking can lead to overlooking important work information, potentially resulting in delays and errors. Furthermore, while rapid response to critical information based on specific criteria is required, this has been difficult to achieve with conventional systems.

[0452] 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.

[0453] In this invention, the server includes means for acquiring information from an information source via a communication medium, means for analyzing work information from the information using natural language processing technology, and means for issuing warnings based on specific criteria from the acquired information. This makes it possible to automatically and efficiently extract and transcribe work information and to reliably monitor and notify the progress of important tasks.

[0454] "Communication medium" is a general term for means such as networks and the internet used to send and receive data.

[0455] "Information source" refers to the systems or services from which data and information are transmitted, such as email servers or chat platforms.

[0456] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0457] "Work information" refers to data that includes specific information about the progress of a task, such as the name of the person in charge, the deadline, and attributes such as importance.

[0458] "Calculation sheet" refers to software used for performing calculations in a digital format, such as a spreadsheet.

[0459] A "notification" is information sent to a user when important information or events occur, and is primarily delivered via email or in-app messages.

[0460] A "warning" is a flag or message that a system assigns to indicate a situation or matter that requires special attention.

[0461] This invention relates to a system that acquires information from information sources via a communication medium and performs progress management and notification based on the extracted work information. This system mainly consists of a server, terminals, and users, each playing a specific role.

[0462] The server continuously accesses the network to retrieve data from information sources. Specifically, the server periodically retrieves emails and messages from mail servers and chat systems. Communication protocols such as IMAP and HTTP can be used for this purpose. After retrieving the data, the server uses natural language processing techniques to analyze the data for work information. For example, spaCy or NLTK could be used as natural language processing libraries. The analyzed data is structured as work information and automatically transferred to a spreadsheet (e.g., Google Sheets).

[0463] The server further monitors progress based on work information in the spreadsheet and automatically sends notifications for tasks nearing their deadlines. These notifications are often sent as emails using the Gmail API. The server also issues alerts based on specific criteria from the received information. For example, if the content is of high urgency, it is immediately flagged and a notification is sent to the person responsible.

[0464] The terminal is responsible for providing users with visual information using notifications and data received from the server. The terminal provides an intuitive user interface so that users can easily manage their tasks.

[0465] Users can access the spreadsheet through their terminal, check the task status in real time, and update information as needed. User actions are immediately reflected on the server, and the contents of the spreadsheet are updated in real time, enabling work based on the latest information.

[0466] As a concrete example of its use, when a sales representative receives an inquiry from a new customer, they can use a prompt message such as "Analyze the new customer inquiry, register the task in a spreadsheet, and monitor the progress." The server will then automatically analyze the information, reflect the results in a spreadsheet, and enable frequent progress monitoring. This improves work efficiency and prevents problems such as overlooking details or delays in response.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The server retrieves data from information sources via a communication medium. Inputs include email and chat messages. The server periodically accesses the information sources using IMAP or HTTP protocols to retrieve new messages and updates. Output consists of the retrieved raw data stored in temporary storage.

[0470] Step 2:

[0471] The server analyzes the acquired data using natural language processing techniques. The input is the raw data saved in step 1. The server analyzes the text using libraries such as spaCy or NLTK, and extracts work information (such as the person responsible, deadline, and importance level). The output is the analyzed structured data, which is organized as work information.

[0472] Step 3:

[0473] The server transfers the extracted work information to a spreadsheet. The input is the structured data obtained in step 2. The server uses functions such as the Google Sheets API to input the work information into the specified cells and update the spreadsheet. The output is the updated spreadsheet, which contains the new work information.

[0474] Step 4:

[0475] The server monitors progress based on the updated calculation sheet. The input is the calculation sheet updated in step 3. The server monitors whether the task is progressing on time based on the configured conditions (e.g., number of days remaining until the deadline). If necessary, it sends email notifications to the person in charge via the Gmail API, etc. The output is the progress confirmation notification delivered to the person in charge.

[0476] Step 5:

[0477] The server issues alerts based on specific criteria from the received data. The input consists of the raw data obtained in step 1 and the information parsed in step 2. The server evaluates the data content and flags high-urgency information. If notification is required, it sends an alert to the relevant personnel via Gmail or other notification systems. The output is a notification message with an alert.

[0478] Step 6:

[0479] The terminal receives notifications and update data from the server and provides information to the user visually. Input is notifications and update information from the server. The terminal provides an environment where the user can view and manage work information through a user interface. Output is the latest dashboard accessible to the user.

[0480] Step 7:

[0481] Users access the spreadsheet by operating a terminal and update work information as needed. Input is the work information confirmed by the user. When a user enters new information, the data is immediately sent to the server and reflected in the spreadsheet. Output is real-time updated work information, shared with the entire team.

[0482] (Application Example 1)

[0483] 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."

[0484] Conventional factory management systems struggle to detect equipment and device malfunctions in real time and respond immediately. Furthermore, they lack sufficient automated management of operational information and efficient monitoring of progress, highlighting the need for improved operational efficiency and faster response to anomalies.

[0485] 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.

[0486] In this invention, the server includes means for acquiring information from information sources via a communication path, means for analyzing the acquired information and automatically extracting relevant business information, and means for acquiring information from equipment and devices operating within the factory, detecting anomalies, and responding quickly. This enables efficient management of business information, as well as real-time detection of anomalies within the factory and immediate response.

[0487] A "communication path" is the network infrastructure used to send and receive information.

[0488] An "information source" is a device or system that serves as the starting point for providing data or information.

[0489] "Business information" refers to information related to tasks, progress, importance, etc., in business activities.

[0490] A "spreadsheet application" is software that allows you to input data in a table format and perform analysis and graphing.

[0491] "Progress status" refers to information indicating the degree of progress of a task or project.

[0492] An "operation screen" is a display unit used by users to operate information and systems.

[0493] A "user" is an individual or group that utilizes the system's functions and information.

[0494] An "alarm" is a notice or signal issued to draw attention when specific conditions occur.

[0495] "Equipment and devices operating within a factory" refers to all machinery and equipment used in production or manufacturing processes.

[0496] "Anomaly detection" is the process of identifying and specifying a state that deviates from the normal operating state.

[0497] The system based on this invention is designed to streamline the operation of equipment and devices within a factory. The server analyzes information acquired through the communication path and extracts relevant business information. This makes it possible to detect anomalies using data acquired from equipment and devices operating within the factory. When an anomaly is detected, the server issues an alarm to facilitate a rapid response.

[0498] The server automatically transfers the analyzed business information to a spreadsheet application. As a result, administrators can monitor business information in real time through the user interface. In particular, business information is categorized by recipient, deadline, and importance, making it easy to evaluate progress. This process improves operational efficiency and enables faster response to anomalies.

[0499] As a concrete example, in an automotive parts manufacturing plant, the operation data of a robot arm is monitored, and if an abnormal operation is detected, a notification is sent to the administrator. This notification allows the administrator to take countermeasures quickly. Another example of a prompt message generated by the AI ​​model is the instruction, "Create a program that analyzes the operation logs obtained from the factory robots, detects abnormal patterns, and transfers them to a Google Spreadsheet. Include a function to send an alert to the administrator if an abnormality occurs." In this way, the system helps to operate efficiently as a whole.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server acquires sensor information in real time from equipment and devices within the factory. The input is raw data sent from the sensors, and the output is the storage of this data. The server uses the network to collect data and stores this data in its internal database.

[0503] Step 2:

[0504] The server performs analysis for anomaly detection based on acquired sensor information. The input is real-time accumulated sensor data. The output is the analysis results used to identify anomalies. The server uses natural language processing technology and machine learning algorithms to analyze the data and detect abnormal operating patterns.

[0505] Step 3:

[0506] If the server detects an anomaly based on the analysis results, it sends a notification to the designated operation screen. The input is the analysis result of the anomaly detection, and the output is an alert sent to the administrator. Based on the detected anomaly, the server takes action to send an email or pop-up notification to the administrator's terminal.

[0507] Step 4:

[0508] The server transfers the analyzed business information to a spreadsheet application. The input is business information obtained from anomaly detection, and the output is the updated data in the spreadsheet application. The server uses the Google Sheets API, etc., to automatically input the business information into the spreadsheet as structured data.

[0509] Step 5:

[0510] Users check the spreadsheet application through the user interface to monitor progress. Inputs are transcribed business information, and output is the user's understanding of current work. Users visualize the information on the spreadsheet and evaluate the progress.

[0511] 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.

[0512] This invention is a system that combines data acquisition and analysis with an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user.

[0513] Server roles and processing

[0514] When a server retrieves data from information sources via a communication network, it collects messages from mail servers and chat systems. Furthermore, the server can utilize an emotion engine to analyze the user's emotional state from the retrieved data and highlight task information accordingly. By combining natural language processing techniques and emotion analysis algorithms, it identifies the emotions expressed in each message and re-evaluates the importance of tasks based on these findings.

[0515] The extracted task information is automatically transferred to a Google Sheets spreadsheet, where progress is then monitored. The emotion engine leverages emotions to dynamically adjust task priorities and optimize the recipients and timing of reminders.

[0516] Furthermore, the server identifies the user's emotions from messages containing emotionally charged complaints and, based on that, issues warnings or promptly notifies users of information that should be shared.

[0517] Terminal role and user interface

[0518] The device receives sentiment analysis results and task information sent from the server and presents them to the user in an easy-to-understand manner. Users can check the progress of tasks and related sentiment information on the device, and tasks and messages of particular importance are visually highlighted.

[0519] User interaction and task management

[0520] Users can access spreadsheets through their devices, update their assigned tasks in real time, and take appropriate action based on the displayed sentiment information. For example, if a highly sensitive complaint is highlighted, users can strive to respond more quickly. This system enables users to perform advanced task management that takes sentiment information into account.

[0521] As a concrete example, consider a scenario where a customer support team receives customer inquiries. Using this system, messages that express anger or dissatisfaction are given high priority and immediately notified to the support staff. The staff can then use this emotion-based information to quickly begin addressing the issue and improve customer satisfaction.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The server retrieves new data from information sources via the communication network. It collects messages using APIs from mail servers and chat systems and stores them in an internal database.

[0525] Step 2:

[0526] The server analyzes the acquired data. Using natural language processing techniques, task information (responsible party, deadline, importance level) is automatically extracted from the messages. The task information is then structured based on these analysis results.

[0527] Step 3:

[0528] The server uses an emotion engine to analyze the user's emotions from the acquired data. Based on the message content, it identifies emotional states such as positive, negative, or neutral, and re-evaluates the importance of the task.

[0529] Step 4:

[0530] The server extracts task information and sentiment information and transfers it to a Google Spreadsheet. Using an API, task information is added to the spreadsheet, and priorities are set according to sentiment.

[0531] Step 5:

[0532] The server monitors progress based on information from the spreadsheet. It creates reminders and automatically sends email notifications for tasks that are nearing their deadline or that are deemed emotionally important.

[0533] Step 6:

[0534] The device notifies the user of reminders and task information received from the server. The device interface highlights high-priority tasks, particularly those based on emotions.

[0535] Step 7:

[0536] Users check the status and sentiment information of tasks on their devices. Users check the progress and update task responses as needed. Updates are sent to the server in real time and reflected in the spreadsheet.

[0537] Step 8:

[0538] The server uses an emotion engine to identify complaints and messages with high emotional intensity, and assigns a warning to those messages. This information is then shared with the relevant personnel to encourage a prompt response.

[0539] (Example 2)

[0540] 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."

[0541] Conventional information processing systems struggle to efficiently analyze collected data and dynamically adjust task priorities based on emotional information. Furthermore, they lack sufficient functionality to quickly send reminders for tasks with approaching deadlines, preventing users from responding in the appropriate time. Additionally, the difficulty for users to update data in real time and share it immediately with others hinders efficient collaborative work.

[0542] 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.

[0543] In this invention, the server includes means for acquiring information from information sources via a communication network, means for determining the emotional state within the acquired information using sentiment analysis, and means for dynamically adjusting task priorities based on the determined emotional state. This enables efficient analysis of collected data and dynamic optimization of task management based on emotional information. Furthermore, it allows for the sending of quick and appropriate reminders for tasks with approaching deadlines, enabling users to respond in a timely manner. In addition, it improves the efficiency of collaborative work by allowing users to update information in real time and efficiently share it with other users.

[0544] A "communication network" is a network infrastructure used for sending and receiving data, and includes the internet and local networks.

[0545] "Information sources" refer to external recording media or systems that provide data, including mail servers and chat systems.

[0546] "Information" refers to data and messages expressed in digital format, which are the objects of analysis and processing.

[0547] "Business information" refers to data related to tasks and activities extracted from information sources, and it is necessary to manage and process this data.

[0548] "Spreadsheet software" is software used for organizing and aggregating data, and for recording and managing data in a tabular format.

[0549] "Progress" is an indicator that shows the status of work or tasks toward completion, and its management affects work efficiency.

[0550] "Sentiment analysis" is a technology that identifies a person's emotional state from text data, and is implemented using natural language processing and machine learning.

[0551] A "display device" is a device used to present information visually, and includes displays and monitors.

[0552] A "user" is an individual or organization that operates and utilizes this system, and updates and manages information according to their purpose.

[0553] A "warning" is a notice issued based on specific criteria, indicating risks and priorities related to work or tasks.

[0554] This invention is a system that analyzes the user's emotional state and enables the dynamic adjustment of task priorities. The system consists of three elements: a server, a terminal, and a user, each playing the following role.

[0555] The server retrieves necessary information from information sources via the communication network. The server utilizes POP3, IMAP protocols, and REST APIs to collect data from mail servers and chat systems. The collected information is preprocessed using natural language processing libraries (e.g., NLTK or SpaCy) for sentiment analysis.

[0556] The server applies sentiment analysis algorithms to the collected information to determine emotional states. This analysis utilizes sentiment dictionaries and machine learning models. Based on the sentiment scores obtained from the analysis, the server dynamically prioritizes work tasks and extracts important work information. This work information is automatically transferred to spreadsheet software and used for monitoring progress.

[0557] The terminal receives sentiment analysis results and business information sent from the server and presents them to the user visually. The terminal communicates with the server in real time via WebSocket to receive the latest data. UI frameworks (such as React or Vue.js) are used for display, and the information is provided in a format that is easy for the user to understand.

[0558] Users access spreadsheet software from their terminals to manage their work. They can prioritize tasks and update data using an intuitive interface. Updated data is immediately shared with other users, facilitating efficient collaboration.

[0559] A concrete example is a customer support team handling customer inquiries. Using this system, messages from customers exhibiting particularly heightened emotions are prioritized and immediately notified to support staff. These staff members can then leverage emotional information to initiate a quicker response, thereby improving customer satisfaction.

[0560] An example of a prompt for a generative AI model is: "Please describe the sentiment recognition system you use in customer support. In particular, please explain in detail how it analyzes the sentiment of messages and improves customer service."

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The server retrieves data from information sources via the communication network. Specifically, it uses POP3 and IMAP protocols from mail servers and REST APIs to collect messages from chat systems. The data input in this retrieval process is text data such as emails and messages. The output is raw, unprocessed data that can be used for analysis.

[0564] Step 2:

[0565] The server preprocesses the acquired message data using natural language processing libraries (e.g., NLTK or SpaCy). Specific preprocessing steps include tokenization, stop word removal, and stemming. The input is raw message data, and the output, after preprocessing, is text data formatted for easier sentiment analysis.

[0566] Step 3:

[0567] The server applies a sentiment analysis algorithm to the preprocessed data. In this step, a sentiment dictionary or machine learning model is used to calculate a sentiment score for each message. The input is preprocessed text data, and the analysis results are output as sentiment scores such as positive, negative, or neutral.

[0568] Step 4:

[0569] The server extracts business information based on the sentiment analysis results and re-evaluates its importance. Keyword extraction technology is used to select information related to specific tasks, and its importance is set by comparing it to the sentiment score. The input is data with sentiment scores, and the output is prioritized business information. This business information is automatically transferred to spreadsheet software.

[0570] Step 5:

[0571] The terminal receives sentiment analysis results and business information sent from the server. It receives data in real time using WebSocket and presents it visually using a UI framework (e.g., React or Vue.js). In this step, the input is analyzed data from the server, and the output is information formatted for user display.

[0572] Step 6:

[0573] Users access and manage spreadsheet software displayed on their terminals. Data can be manipulated through an intuitive interface, and updated information is shared immediately. Input consists of business information updated by the user, while output is shared in real time with other users via the terminal.

[0574] (Application Example 2)

[0575] 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."

[0576] Traditional task management systems often process tasks based on a uniform priority without considering the user's emotional state, potentially leading to decreased customer satisfaction. Furthermore, there is a lack of mechanisms to quickly recognize and respond to customer emotions during in-store interactions. This creates a challenge in improving the customer experience in physical stores.

[0577] 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.

[0578] In this invention, the server includes means for acquiring data from an information source via a communication network, means for issuing warnings based on specific criteria from the acquired data, and means for detecting a person's emotional state based on information displayed on a terminal device and providing relevant action recommendations in real time. This makes it possible to dynamically adjust task priorities according to the user's emotional state, enabling faster and more appropriate customer service.

[0579] A "communication network" is an infrastructure consisting of lines and protocols for sending and receiving data.

[0580] A "terminal device" is a computing device that allows a user to directly operate and retrieve information.

[0581] "Emotional state" refers to a classification of a person's internal psychological reactions and emotions expressed through facial expressions and voice.

[0582] "Action recommendations" are instructions or advice for taking appropriate action in specific situations.

[0583] "Issuing a warning" means issuing a notification or alert to draw attention to information that meets specific conditions.

[0584] "Task information" refers to data about a specific activity or task, including details such as deadlines and priorities.

[0585] A "server" is a computer system that processes and stores data and provides services to other devices.

[0586] This invention is a system designed to support customer service in physical stores. It detects the emotional state of customers and provides services based on that state. By having the server, terminal, and user each play a specific role, it aims to provide a better customer experience.

[0587] The server retrieves data from information sources via a communication network. This data may include customer facial expressions and voice information. The retrieved data is then subjected to an algorithm to analyze emotions based on specific criteria. This algorithm uses Google Cloud's "Natural Language API" and "Cloud Vision API" to identify emotional states from the data. Based on the identification results, warnings are issued or action recommendations are generated as needed.

[0588] The terminal receives sentiment analysis results and recommendations sent from the server and displays them to the user in real time. Specifically, these are displayed on wearable devices such as smart glasses, providing customers with optimal product suggestions and service information. If the customer's sentiment is positive, additional products are recommended; if it is negative, careful guidance is provided.

[0589] Users adjust their customer interactions based on information gathered through their devices. For example, if a customer is hesitant about making a purchase, the system can understand their feelings and offer additional information or discounts. This allows customers to make a purchase decision with confidence, leading to increased customer satisfaction.

[0590] As a concrete example, suppose a customer asks a question about a product and displays a confused expression. This information is immediately analyzed by the server for sentiment, and a message appears on the terminal's display saying, "Please provide additional information about this product." The store clerk then provides further details according to the instructions, allowing the customer to make a purchase decision with confidence.

[0591] Examples of prompts used include, "Generate advice on what information to provide if this customer is feeling anxious," and "Based on the customer's emotional state as determined from the current conversation, suggest any relevant products." This allows the generative AI model to suggest appropriate actions.

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The server receives customer facial expression and audio data acquired from terminal devices via a communication network. The input is digital data of the customer's video and audio. This data is sent to Google Cloud's "Cloud Vision API" and "Natural Language API" to analyze the data and identify the customer's emotional state. As a result, data classified as an emotional state is output.

[0595] Step 2:

[0596] The server prepares prompt messages that use a generative AI model to generate action recommendations based on emotional state data. The input is identified emotional state data. Using this data, the server inputs a prompt message to the generative AI model such as, "Generate advice on what information to provide if this customer is feeling anxious." The output is the action recommendation obtained from the AI ​​model.

[0597] Step 3:

[0598] The server sends the generated action recommendations to the terminal. The input is the recommendations from the AI ​​model. The terminal receives these recommendations and displays them in a user-friendly format. Based on this information, the user can adjust their response to the customer. For example, the display might show "Please provide additional information about the product." As output, visual instructions are displayed on the terminal's screen.

[0599] Step 4:

[0600] The user provides actual customer support based on instructions displayed on the terminal. The input is the instructions shown on the terminal's display, and the user provides additional information and takes appropriate actions based on these instructions. The user observes whether the customer makes a purchase decision and provides further support as needed. The output is either that the customer's inquiry is resolved or their satisfaction level improves.

[0601] 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.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] 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.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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".

[0618] This invention relates to a system that acquires data from an information source, analyzes that data to extract task information, and then transfers it to a spreadsheet and monitors its progress. This system mainly consists of three elements: a server, a terminal, and a user.

[0619] Server roles and processing

[0620] The server continuously accesses information sources (e.g., email servers and chat systems) via a communication network to acquire new data. This data is temporarily stored within the server and automatically analyzed. The analysis uses natural language processing techniques to extract task information (such as responsible parties, deadlines, and importance levels) from the data. The server has a function to automatically transfer the extracted task information to spreadsheet software such as Google Sheets.

[0621] The server also monitors the progress of tasks and sends progress reminders according to configured conditions. For example, the server facilitates progress checks by sending emails to the assigned person when the task deadline is approaching.

[0622] Furthermore, the server assigns warnings to the received data based on specific criteria. When important claim information or messages indicating high urgency are detected, the server flags them and promptly notifies the relevant personnel or administrators.

[0623] Terminal role and user interface

[0624] The device receives notifications and data from the server and provides the user with relevant information visually. Through the device, the user can access detailed task information and check its progress. The device features an intuitive interface, allowing users to easily manage their tasks.

[0625] User interaction and task management

[0626] Users can access spreadsheets via their devices to check and update the status of their assigned tasks. Information updates by users are immediately sent to the server and reflected in the spreadsheet software, enabling real-time information sharing. This system allows administrators and other team members to always understand the work status based on the latest information.

[0627] As a concrete example, when a sales team handles customer inquiries, this system automatically analyzes each customer's inquiry and assigns it to the appropriate person. As deadlines approach, progress checks are automatically sent, and if a serious complaint arises, an immediate warning is issued, prompting a quick response. This significantly improves operational efficiency and enhances customer satisfaction.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server retrieves new data from information sources via the communication network. It uses APIs from mail servers and chat systems to retrieve messages and stores that data in an internal database.

[0631] Step 2:

[0632] The server analyzes the acquired data and automatically extracts task information using natural language processing technology. It analyzes email and chat content, identifies task-related information such as the person responsible, deadline, and importance level, and organizes it as structured data.

[0633] Step 3:

[0634] The server transfers organized task information to a Google Spreadsheet. Using an API, the extracted data is entered into the corresponding columns in the spreadsheet, and the data is updated while verifying that it does not match existing data.

[0635] Step 4:

[0636] The server monitors the progress of each task based on a spreadsheet. For tasks nearing their deadline, it creates progress check reminders at set time intervals and automatically sends reminder emails to the assigned person.

[0637] Step 5:

[0638] The device receives reminder emails sent from the server and notifies the user of their contents. The user interface visually displays the task progress, allowing the user to operate intuitively.

[0639] Step 6:

[0640] Users can check the status of their assigned tasks through an interface on their device and update progress and actions as needed. The updated information is sent to the server in real time and immediately reflected in the spreadsheet.

[0641] Step 7:

[0642] The server flags complaints and messages with a high level of intensity based on specific criteria within the data it receives. It also sends notifications to the relevant personnel to enable prompt action.

[0643] (Example 1)

[0644] 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".

[0645] In today's information-saturated environment, efficient task management is essential for business and daily operations. However, manual task allocation and progress tracking can lead to overlooking important work information, potentially resulting in delays and errors. Furthermore, while rapid response to critical information based on specific criteria is required, this has been difficult to achieve with conventional systems.

[0646] 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.

[0647] In this invention, the server includes means for acquiring information from an information source via a communication medium, means for analyzing work information from the information using natural language processing technology, and means for issuing warnings based on specific criteria from the acquired information. This makes it possible to automatically and efficiently extract and transcribe work information and to reliably monitor and notify the progress of important tasks.

[0648] "Communication medium" is a general term for means such as networks and the internet used to send and receive data.

[0649] "Information source" refers to the systems or services from which data and information are transmitted, such as email servers or chat platforms.

[0650] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0651] "Work information" refers to data that includes specific information about the progress of a task, such as the name of the person in charge, the deadline, and attributes such as importance.

[0652] "Calculation sheet" refers to software used for performing calculations in a digital format, such as a spreadsheet.

[0653] A "notification" is information sent to a user when important information or events occur, and is primarily delivered via email or in-app messages.

[0654] A "warning" is a flag or message that a system assigns to indicate a situation or matter that requires special attention.

[0655] This invention relates to a system that acquires information from information sources via a communication medium and performs progress management and notification based on the extracted work information. This system mainly consists of a server, terminals, and users, each playing a specific role.

[0656] The server continuously accesses the network to retrieve data from information sources. Specifically, the server periodically retrieves emails and messages from mail servers and chat systems. Communication protocols such as IMAP and HTTP can be used for this purpose. After retrieving the data, the server uses natural language processing techniques to analyze the data for work information. For example, spaCy or NLTK could be used as natural language processing libraries. The analyzed data is structured as work information and automatically transferred to a spreadsheet (e.g., Google Sheets).

[0657] The server further monitors progress based on work information in the spreadsheet and automatically sends notifications for tasks nearing their deadlines. These notifications are often sent as emails using the Gmail API. The server also issues alerts based on specific criteria from the received information. For example, if the content is of high urgency, it is immediately flagged and a notification is sent to the person responsible.

[0658] The terminal is responsible for providing users with visual information using notifications and data received from the server. The terminal provides an intuitive user interface so that users can easily manage their tasks.

[0659] Users can access the spreadsheet through their terminal, check the task status in real time, and update information as needed. User actions are immediately reflected on the server, and the contents of the spreadsheet are updated in real time, enabling work based on the latest information.

[0660] As a concrete example of its use, when a sales representative receives an inquiry from a new customer, they can use a prompt message such as "Analyze the new customer inquiry, register the task in a spreadsheet, and monitor the progress." The server will then automatically analyze the information, reflect the results in a spreadsheet, and enable frequent progress monitoring. This improves work efficiency and prevents problems such as overlooking details or delays in response.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The server retrieves data from information sources via a communication medium. Inputs include email and chat messages. The server periodically accesses the information sources using IMAP or HTTP protocols to retrieve new messages and updates. Output consists of the retrieved raw data stored in temporary storage.

[0664] Step 2:

[0665] The server analyzes the acquired data using natural language processing techniques. The input is the raw data saved in step 1. The server analyzes the text using libraries such as spaCy or NLTK, and extracts work information (such as the person responsible, deadline, and importance level). The output is the analyzed structured data, which is organized as work information.

[0666] Step 3:

[0667] The server transfers the extracted work information to a spreadsheet. The input is the structured data obtained in step 2. The server uses functions such as the Google Sheets API to input the work information into the specified cells and update the spreadsheet. The output is the updated spreadsheet, which contains the new work information.

[0668] Step 4:

[0669] The server monitors progress based on the updated calculation sheet. The input is the calculation sheet updated in step 3. The server monitors whether the task is progressing on time based on the configured conditions (e.g., number of days remaining until the deadline). If necessary, it sends email notifications to the person in charge via the Gmail API, etc. The output is the progress confirmation notification delivered to the person in charge.

[0670] Step 5:

[0671] The server issues alerts based on specific criteria from the received data. The input consists of the raw data obtained in step 1 and the information parsed in step 2. The server evaluates the data content and flags high-urgency information. If notification is required, it sends an alert to the relevant personnel via Gmail or other notification systems. The output is a notification message with an alert.

[0672] Step 6:

[0673] The terminal receives notifications and update data from the server and provides information to the user visually. Input is notifications and update information from the server. The terminal provides an environment where the user can view and manage work information through a user interface. Output is the latest dashboard accessible to the user.

[0674] Step 7:

[0675] Users access the spreadsheet by operating a terminal and update work information as needed. Input is the work information confirmed by the user. When a user enters new information, the data is immediately sent to the server and reflected in the spreadsheet. Output is real-time updated work information, shared with the entire team.

[0676] (Application Example 1)

[0677] 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".

[0678] Conventional factory management systems struggle to detect equipment and device malfunctions in real time and respond immediately. Furthermore, they lack sufficient automated management of operational information and efficient monitoring of progress, highlighting the need for improved operational efficiency and faster response to anomalies.

[0679] 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.

[0680] In this invention, the server includes means for acquiring information from information sources via a communication path, means for analyzing the acquired information and automatically extracting relevant business information, and means for acquiring information from equipment and devices operating within the factory, detecting anomalies, and responding quickly. This enables efficient management of business information, as well as real-time detection of anomalies within the factory and immediate response.

[0681] A "communication path" is the network infrastructure used to send and receive information.

[0682] An "information source" is a device or system that serves as the starting point for providing data or information.

[0683] "Business information" refers to information related to tasks, progress, importance, etc., in business activities.

[0684] A "spreadsheet application" is software that allows you to input data in a table format and perform analysis and graphing.

[0685] "Progress status" refers to information indicating the degree of progress of a task or project.

[0686] An "operation screen" is a display unit used by users to operate information and systems.

[0687] A "user" is an individual or group that utilizes the system's functions and information.

[0688] An "alarm" is a notice or signal issued to draw attention when specific conditions occur.

[0689] "Equipment and devices operating within a factory" refers to all machinery and equipment used in production or manufacturing processes.

[0690] "Anomaly detection" is the process of identifying and specifying a state that deviates from the normal operating state.

[0691] The system based on this invention is designed to streamline the operation of equipment and devices within a factory. The server analyzes information acquired through the communication path and extracts relevant business information. This makes it possible to detect anomalies using data acquired from equipment and devices operating within the factory. When an anomaly is detected, the server issues an alarm to facilitate a rapid response.

[0692] The server automatically transfers the analyzed business information to a spreadsheet application. As a result, administrators can monitor business information in real time through the user interface. In particular, business information is categorized by recipient, deadline, and importance, making it easy to evaluate progress. This process improves operational efficiency and enables faster response to anomalies.

[0693] As a concrete example, in an automotive parts manufacturing plant, the operation data of a robot arm is monitored, and if an abnormal operation is detected, a notification is sent to the administrator. This notification allows the administrator to take countermeasures quickly. Another example of a prompt message generated by the AI ​​model is the instruction, "Create a program that analyzes the operation logs obtained from the factory robots, detects abnormal patterns, and transfers them to a Google Spreadsheet. Include a function to send an alert to the administrator if an abnormality occurs." In this way, the system helps to operate efficiently as a whole.

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The server acquires sensor information in real time from equipment and devices within the factory. The input is raw data sent from the sensors, and the output is the storage of this data. The server uses the network to collect data and stores this data in its internal database.

[0697] Step 2:

[0698] The server performs analysis for anomaly detection based on acquired sensor information. The input is real-time accumulated sensor data. The output is the analysis results used to identify anomalies. The server uses natural language processing technology and machine learning algorithms to analyze the data and detect abnormal operating patterns.

[0699] Step 3:

[0700] If the server detects an anomaly based on the analysis results, it sends a notification to the designated operation screen. The input is the analysis result of the anomaly detection, and the output is an alert sent to the administrator. Based on the detected anomaly, the server takes action to send an email or pop-up notification to the administrator's terminal.

[0701] Step 4:

[0702] The server transfers the analyzed business information to a spreadsheet application. The input is business information obtained from anomaly detection, and the output is the updated data in the spreadsheet application. The server uses the Google Sheets API, etc., to automatically input the business information into the spreadsheet as structured data.

[0703] Step 5:

[0704] Users check the spreadsheet application through the user interface to monitor progress. Inputs are transcribed business information, and outputs are the user's understanding of current tasks. Users visualize the information on the spreadsheet and evaluate the progress.

[0705] 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.

[0706] This invention is a system that combines data acquisition and analysis with an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user.

[0707] Server roles and processing

[0708] When a server retrieves data from information sources via a communication network, it collects messages from mail servers and chat systems. Furthermore, the server can utilize an emotion engine to analyze the user's emotional state from the retrieved data and highlight task information accordingly. By combining natural language processing techniques and emotion analysis algorithms, it identifies the emotions expressed in each message and re-evaluates the importance of tasks based on these findings.

[0709] The extracted task information is automatically transferred to a Google Sheets spreadsheet, where progress is then monitored. The emotion engine leverages emotions to dynamically adjust task priorities and optimize the recipients and timing of reminders.

[0710] Furthermore, the server identifies the user's emotions from messages containing emotionally charged complaints and, based on that, issues warnings or promptly notifies users of information that should be shared.

[0711] Terminal role and user interface

[0712] The device receives sentiment analysis results and task information sent from the server and presents them to the user in an easy-to-understand manner. Users can check the progress of tasks and related sentiment information on the device, and tasks and messages of particular importance are visually highlighted.

[0713] User interaction and task management

[0714] Users can access spreadsheets through their devices, update their assigned tasks in real time, and take appropriate action based on the displayed sentiment information. For example, if a highly sensitive complaint is highlighted, users can strive to respond more quickly. This system enables users to perform advanced task management that takes sentiment information into account.

[0715] As a concrete example, consider a scenario where a customer support team receives customer inquiries. Using this system, messages that express anger or dissatisfaction are given high priority and immediately notified to the support staff. The staff can then use this emotion-based information to quickly begin addressing the issue and improve customer satisfaction.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The server retrieves new data from information sources via the communication network. It collects messages using APIs from mail servers and chat systems and stores them in an internal database.

[0719] Step 2:

[0720] The server analyzes the acquired data. Using natural language processing techniques, task information (responsible party, deadline, importance level) is automatically extracted from the messages. The task information is then structured based on these analysis results.

[0721] Step 3:

[0722] The server uses an emotion engine to analyze the user's emotions from the acquired data. Based on the message content, it identifies emotional states such as positive, negative, and neutral, and re-evaluates the importance of the task.

[0723] Step 4:

[0724] The server extracts task information and sentiment information and transfers it to a Google Spreadsheet. Using an API, task information is added to the spreadsheet, and priorities are set according to sentiment.

[0725] Step 5:

[0726] The server monitors progress based on information from the spreadsheet. It creates reminders and automatically sends email notifications for tasks that are nearing their deadline or that are deemed emotionally important.

[0727] Step 6:

[0728] The device notifies the user of reminders and task information received from the server. The device interface highlights high-priority tasks, particularly those based on emotions.

[0729] Step 7:

[0730] Users check the status and sentiment information of tasks on their devices. Users check the progress and update task responses as needed. Updates are sent to the server in real time and reflected in the spreadsheet.

[0731] Step 8:

[0732] The server uses an emotion engine to identify complaints and messages with high emotional intensity, and adds a warning to those messages. This information is then shared with the relevant personnel to encourage a quick response.

[0733] (Example 2)

[0734] 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".

[0735] Conventional information processing systems struggle to efficiently analyze collected data and dynamically adjust task priorities based on emotional information. Furthermore, they lack sufficient functionality to quickly send reminders for tasks with approaching deadlines, preventing users from responding in the appropriate time. Additionally, the difficulty for users to update data in real time and share it immediately with others hinders efficient collaborative work.

[0736] 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.

[0737] In this invention, the server includes means for acquiring information from information sources via a communication network, means for determining the emotional state within the acquired information using sentiment analysis, and means for dynamically adjusting task priorities based on the determined emotional state. This enables efficient analysis of collected data and dynamic optimization of task management based on emotional information. Furthermore, it allows for the sending of quick and appropriate reminders for tasks with approaching deadlines, enabling users to respond in a timely manner. In addition, it improves the efficiency of collaborative work by allowing users to update information in real time and efficiently share it with other users.

[0738] A "communication network" is a network infrastructure used for sending and receiving data, and includes the internet and local networks.

[0739] "Information sources" refer to external recording media or systems that provide data, including mail servers and chat systems.

[0740] "Information" refers to data and messages expressed in digital format, which are the objects of analysis and processing.

[0741] "Business information" refers to data related to tasks and activities extracted from information sources, and it is necessary to manage and process this data.

[0742] "Spreadsheet software" is software used for organizing and aggregating data, and for recording and managing data in a tabular format.

[0743] "Progress" is an indicator that shows the status of work or tasks toward completion, and its management affects work efficiency.

[0744] "Sentiment analysis" is a technology that identifies a person's emotional state from text data, and is implemented using natural language processing and machine learning.

[0745] A "display device" is a device used to present information visually, and includes displays and monitors.

[0746] A "user" is an individual or organization that operates and utilizes this system, and updates and manages information according to their purpose.

[0747] A "warning" is a notice issued based on specific criteria, indicating risks and priorities related to work or tasks.

[0748] This invention is a system that analyzes the user's emotional state and enables the dynamic adjustment of task priorities. The system consists of three elements: a server, a terminal, and a user, each playing the following role.

[0749] The server retrieves necessary information from information sources via the communication network. The server utilizes POP3, IMAP protocols, and REST APIs to collect data from mail servers and chat systems. The collected information is preprocessed using natural language processing libraries (e.g., NLTK or SpaCy) for sentiment analysis.

[0750] The server applies sentiment analysis algorithms to the collected information to determine emotional states. This analysis utilizes sentiment dictionaries and machine learning models. Based on the sentiment scores obtained from the analysis, the server dynamically prioritizes work tasks and extracts important work information. This work information is automatically transferred to spreadsheet software and used for monitoring progress.

[0751] The terminal receives sentiment analysis results and business information sent from the server and presents them to the user visually. The terminal communicates with the server in real time via WebSocket to receive the latest data. UI frameworks (such as React or Vue.js) are used for display, and the information is provided in a format that is easy for the user to understand.

[0752] Users access spreadsheet software from their terminals to manage their work. They can prioritize tasks and update data using an intuitive interface. Updated data is immediately shared with other users, facilitating efficient collaboration.

[0753] A concrete example is a customer support team handling customer inquiries. Using this system, messages from customers exhibiting particularly heightened emotions are prioritized and immediately notified to support staff. These staff members can then leverage emotional information to initiate a quicker response, thereby improving customer satisfaction.

[0754] An example of a prompt for a generative AI model is: "Please describe the sentiment recognition system you use in customer support. In particular, please explain in detail how it analyzes the sentiment of messages and improves customer service."

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The server retrieves data from information sources via the communication network. Specifically, it uses POP3 and IMAP protocols from mail servers and REST APIs to collect messages from chat systems. The data input in this retrieval process is text data such as emails and messages. The output is raw, unprocessed data that can be used for analysis.

[0758] Step 2:

[0759] The server preprocesses the acquired message data using natural language processing libraries (e.g., NLTK or SpaCy). Specific preprocessing steps include tokenization, stop word removal, and stemming. The input is raw message data, and the output, after preprocessing, is text data formatted for easier sentiment analysis.

[0760] Step 3:

[0761] The server applies a sentiment analysis algorithm to the preprocessed data. In this step, a sentiment dictionary or machine learning model is used to calculate a sentiment score for each message. The input is preprocessed text data, and the analysis results are output as sentiment scores such as positive, negative, or neutral.

[0762] Step 4:

[0763] The server extracts business information based on the sentiment analysis results and re-evaluates its importance. Keyword extraction technology is used to select information related to specific tasks, and its importance is set by comparing it to the sentiment score. The input is data with sentiment scores, and the output is prioritized business information. This business information is automatically transferred to spreadsheet software.

[0764] Step 5:

[0765] The terminal receives sentiment analysis results and business information sent from the server. It receives data in real time using WebSocket and presents it visually using a UI framework (e.g., React or Vue.js). In this step, the input is analyzed data from the server, and the output is information formatted for user display.

[0766] Step 6:

[0767] Users access and manage spreadsheet software displayed on their terminals. Data can be manipulated through an intuitive interface, and updated information is shared immediately. Input consists of business information updated by the user, while output is shared in real time with other users via the terminal.

[0768] (Application Example 2)

[0769] 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".

[0770] Traditional task management systems often process tasks based on a uniform priority without considering the user's emotional state, potentially leading to decreased customer satisfaction. Furthermore, there is a lack of mechanisms to quickly recognize and respond to customer emotions during in-store interactions. This creates a challenge in improving the customer experience in physical stores.

[0771] 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.

[0772] In this invention, the server includes means for acquiring data from an information source via a communication network, means for issuing warnings based on specific criteria from the acquired data, and means for detecting a person's emotional state based on information displayed on a terminal device and providing relevant action recommendations in real time. This makes it possible to dynamically adjust task priorities according to the user's emotional state, enabling faster and more appropriate customer service.

[0773] A "communication network" is an infrastructure consisting of lines and protocols for sending and receiving data.

[0774] A "terminal device" is a computing device that allows a user to directly operate and retrieve information.

[0775] "Emotional state" refers to a classification of a person's internal psychological reactions and emotions expressed through facial expressions and voice.

[0776] "Action recommendations" are instructions or advice for taking appropriate action in specific situations.

[0777] "Issuing a warning" means issuing a notification or alert to draw attention to information that meets specific conditions.

[0778] "Task information" refers to data about a specific activity or task, including details such as deadlines and priorities.

[0779] A "server" is a computer system that processes and stores data and provides services to other devices.

[0780] This invention is a system designed to support customer service in physical stores. It detects the emotional state of customers and provides services based on that state. By having the server, terminal, and user each play a specific role, it aims to provide a better customer experience.

[0781] The server retrieves data from information sources via a communication network. This data may include customer facial expressions and voice information. The retrieved data is then subjected to an algorithm to analyze emotions based on specific criteria. This algorithm uses Google Cloud's "Natural Language API" and "Cloud Vision API" to identify emotional states from the data. Based on the identification results, warnings are issued or action recommendations are generated as needed.

[0782] The terminal receives sentiment analysis results and recommendations sent from the server and displays them to the user in real time. Specifically, these are displayed on wearable devices such as smart glasses, providing customers with optimal product suggestions and service information. If the customer's sentiment is positive, additional products are recommended; if it is negative, careful guidance is provided.

[0783] Users adjust their customer interactions based on information gathered through their devices. For example, if a customer is hesitant about making a purchase, the system can understand their feelings and offer additional information or discounts. This allows customers to make a purchase decision with confidence, leading to increased customer satisfaction.

[0784] As a concrete example, suppose a customer asks a question about a product and displays a confused expression. This information is immediately analyzed by the server for sentiment, and a message appears on the terminal's display saying, "Please provide additional information about this product." The store clerk then provides further details according to the instructions, allowing the customer to make a purchase decision with confidence.

[0785] Examples of prompts used include, "Generate advice on what information to provide if this customer is feeling anxious," and "Based on the customer's emotional state as determined from the current conversation, suggest any relevant products." This allows the generative AI model to suggest appropriate actions.

[0786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0787] Step 1:

[0788] The server receives customer facial expression and audio data acquired from terminal devices via a communication network. The input is digital data of the customer's video and audio. This data is sent to Google Cloud's "Cloud Vision API" and "Natural Language API" to analyze the data and identify the customer's emotional state. As a result, data classified as an emotional state is output.

[0789] Step 2:

[0790] The server prepares prompt messages that use a generative AI model to generate action recommendations based on emotional state data. The input is identified emotional state data. Using this data, the server inputs a prompt message to the generative AI model such as, "Generate advice on what information to provide if this customer is feeling anxious." The output is the action recommendation obtained from the AI ​​model.

[0791] Step 3:

[0792] The server sends the generated action recommendations to the terminal. The input is the recommendations from the AI ​​model. The terminal receives these recommendations and displays them in a user-friendly format. Based on this information, the user can adjust their response to the customer. For example, the display might show "Please provide additional information about the product." As output, visual instructions are displayed on the terminal's screen.

[0793] Step 4:

[0794] The user provides actual customer support based on instructions displayed on the terminal. The input is the instructions shown on the terminal's display, and the user provides additional information and takes appropriate actions based on these instructions. The user observes whether the customer makes a purchase decision and provides further support as needed. The output is either that the customer's inquiry is resolved or their satisfaction level improves.

[0795] 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.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] 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 robot 414.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] Means for obtaining data from information sources via a communication network,

[0819] A means of analyzing acquired data and automatically extracting relevant task information,

[0820] A method for transferring the extracted task information to a spreadsheet program,

[0821] A means of monitoring progress based on transcribed task information and automatically sending reminders for tasks with approaching deadlines,

[0822] A means of visually displaying task information on the interface,

[0823] A means of sharing user-generated task information updates in real time,

[0824] A means of issuing warnings based on specific criteria from acquired data,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the extracted task information is classified based on categories of respondent, deadline, and importance.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the aforementioned warning is applied to data containing claims or content that conveys a sense of high temperature.

[0830] "Example 1"

[0831] (Claim 1)

[0832] Means of obtaining information from a source via a communication medium,

[0833] A means of analyzing acquired information and automatically extracting relevant work information,

[0834] A means of transferring the extracted work information to a calculation sheet,

[0835] A means of monitoring progress based on transcribed work information and automatically sending notifications for tasks with approaching deadlines,

[0836] A means of visually displaying work information on an interface,

[0837] A means of instantly sharing user-generated work information updates,

[0838] A means of issuing warnings based on specific criteria from the acquired information,

[0839] A means of analyzing work information from information using natural language processing technology,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, wherein the extracted work information is classified based on categories of person in charge, deadline, and importance.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the warning is issued for information containing a complaint or of high urgency.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] Means for obtaining information from an information source via a communication path,

[0848] A means of analyzing acquired information and automatically extracting relevant business information,

[0849] A means of transferring extracted business information to a spreadsheet application,

[0850] A means of monitoring progress based on transcribed work information and automatically sending notifications for tasks with approaching deadlines,

[0851] A means of visually displaying business information on the operation screen,

[0852] A means of instantly sharing updates to business information made by users,

[0853] A means of issuing an alarm based on specific conditions from the acquired information,

[0854] A means of acquiring information from equipment and devices operating within a factory, detecting anomalies, and responding quickly,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the extracted business information is classified based on the type of recipient, deadline, and importance.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the alarm is issued to information containing assertive or high-temperature content.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] Means of obtaining information from information sources via a communication network,

[0863] A means of analyzing acquired information and automatically extracting relevant business information,

[0864] A means of transferring the extracted business information to spreadsheet software,

[0865] A means of monitoring progress based on transcribed work information and automatically sending reminders for tasks with approaching deadlines,

[0866] A means of determining the emotional state within information obtained using emotion analysis,

[0867] A means of dynamically adjusting the priority of tasks based on the identified emotional state,

[0868] A means of providing business information visually on a display device,

[0869] A means of instantly sharing updates to business information made by users,

[0870] A means of issuing warnings based on specific criteria from the acquired information,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the extracted business information is classified based on categories of person in charge, deadline, and importance.

[0874] (Claim 3)

[0875] The system according to claim 1, wherein the warning is given to information containing complaints or content that conveys a strong sense of anger.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] Means for obtaining data from information sources via a communication network,

[0879] A means of analyzing acquired data and automatically extracting relevant task information,

[0880] A method for transferring the extracted task information to a spreadsheet program,

[0881] A means of monitoring progress based on transcribed task information and automatically sending reminders for tasks with approaching deadlines,

[0882] A means of visually displaying task information on the interface,

[0883] A means of sharing user-generated task information updates in real time,

[0884] A means of issuing warnings based on specific criteria from acquired data,

[0885] A means of detecting a person's emotional state based on information displayed on a terminal device and providing relevant behavioral recommendations in real time,

[0886] A means of suggesting products or services according to the emotional state and providing visual alerts on the display screen of a terminal device,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the extracted task information is classified based on categories of respondent, deadline, and importance.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the aforementioned warning is applied to data containing claims or content that conveys a sense of high temperature. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for obtaining data from information sources via a communication network, A means of analyzing acquired data and automatically extracting relevant task information, A method for transferring the extracted task information to a spreadsheet program, A means of monitoring progress based on transcribed task information and automatically sending reminders for tasks with approaching deadlines, A means of visually displaying task information on the interface, A means of sharing user-generated task information updates in real time, A means of issuing warnings based on specific criteria from acquired data, A system that includes this.

2. The system according to claim 1, wherein the extracted task information is classified based on categories of respondent, deadline, and importance.

3. The system according to claim 1, wherein the aforementioned warning is applied to data containing claims or content that conveys a sense of high temperature.

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