system

Abstract

We provide the system. [Solution] A means of receiving communication data from a communication medium, analyzing its contents, and extracting problem information, A means of centralizing extracted problem information into an information set and classifying it based on importance and deadline, A means of visualizing and managing the progress of classified problem information, A means of creating and proposing solution strategies to problem information to users, A means by which robots in a factory integrate and collect communication data from other equipment and workers to construct an optimal work plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, numerous tasks and issues arise through communication means such as emails and chat tools. However, since this information is diverse, it is very difficult to efficiently manage individual tasks and issues and prioritize their processing. Also, individually investigating resources for finding solutions to issues is a significant time burden for users. There is a need to improve such a situation and streamline task management and solution proposal.

Means for Solving the Problems

[0005] This invention provides a system that analyzes communication data received from a communication platform, automatically extracts task and issue information, and centralizes it in a database. The extracted information is organized and classified based on its importance and deadline, and the user can see the progress visually. Furthermore, this system has the function to automatically generate and propose optimal solutions to issues by utilizing past information and external resources. In addition, by incorporating user feedback, the accuracy of solution proposals can be continuously improved. This provides a practical solution for efficiently managing individual tasks and responding quickly.

[0006] A "communication platform" refers to online tools, such as email and chat applications, that allow users to exchange information with each other.

[0007] "Communication data" refers to a collection of information, including the content of messages and conversations, that are sent and received through communication tools.

[0008] "Issue information" refers to information about tasks and problems that need to be solved, extracted from communication data.

[0009] A "database" is a digital information recording system that allows for the systematic storage, management, retrieval, and updating of information.

[0010] "Centralization" is the process of consolidating dispersed information and data into one location and managing them in a unified manner.

[0011] "Progress" refers to information that indicates the current state or degree of completion of a task or issue.

[0012] A "solution" refers to a feasible answer or approach to a specific challenge or problem.

[0013] "Natural language processing technology" refers to technologies that enable computers to understand, process, and generate language that humans use on a daily basis.

[0014] "Feedback" refers to opinions and evaluations provided by users based on their results and experiences using the product or service. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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.

[0019] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of this invention automatically collects and analyzes communication data from a user's communication platform to identify and manage tasks and issues. The system mainly consists of server, terminal, and user elements, and the specific processing is as follows.

[0037] Data collection and analysis

[0038] The server periodically crawls the email and chat applications used by users to collect new communication data. The collected data is analyzed using natural language processing techniques to extract information relevant to tasks and issues.

[0039] Information organization and management

[0040] The server stores and centralizes the extracted task information in a database. This information is automatically organized based on attributes such as importance, deadline, and relevant people. The server comprehensively manages this information and creates a list of all tasks currently held by the user.

[0041] Providing a user interface

[0042] The server generates an interface on the terminal that provides organized task and issue information. Through this interface, users can check the progress of tasks and update their status. The dashboard displayed on the terminal visually shows task details (deadline, requester, importance, etc.).

[0043] Generating and proposing solutions

[0044] The server generates appropriate solutions to problems. This process involves referencing a database of past cases and external resources to suggest the most suitable solution for the user. This allows users to address problems efficiently.

[0045] Utilizing feedback

[0046] Users can provide feedback on the effectiveness of the proposed solutions. This feedback is collected by the server and used to improve the accuracy of future solution generation.

[0047] For example, if a user is tasked with creating a project progress report, the system analyzes relevant communication data and suggests necessary information and report templates to the user. In this way, the system of the present invention provides a comprehensive platform to support the efficient management of tasks.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server obtains authentication credentials to access the user's communication platform and uses APIs or scraping techniques to retrieve new communication data from email and chat applications.

[0051] Step 2:

[0052] The server analyzes the acquired communication data using natural language processing technology and extracts keywords related to tasks and issues based on verbs and nouns. This identifies the actions (e.g., "create," "report") and objects (e.g., "report," "meeting") of the tasks.

[0053] Step 3:

[0054] The server stores the extracted task and issue information in a database. This includes metadata such as importance, deadline, and related people and projects.

[0055] Step 4:

[0056] The server displays a dashboard on the user's terminal based on organized task and issue information. The terminal visually shows the progress and status of tasks (e.g., not started, in progress, completed).

[0057] Step 5:

[0058] Users can manually update task statuses and add detailed information through the task management interface, allowing for real-time tracking of current progress.

[0059] Step 6:

[0060] The server references historical databases and external information sources to generate the optimal solution for the user's task or problem. The proposed solution is then displayed on the user's terminal.

[0061] Step 7:

[0062] Users can review the proposed solutions and provide feedback on their usefulness. This feedback is collected by the server and used to improve the accuracy of the proposed solutions.

[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 diverse communication platforms, efficiently and automatically organizing information and providing users with appropriate solutions is a challenging task. Traditional systems handle information collection and organization, as well as solution generation and presentation, separately, making comprehensive management difficult. Furthermore, there is a lack of means to quickly incorporate user feedback and deliver highly accurate suggestions.

[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 communication data from a communication platform, analyzing its content, and extracting information; means for centralizing the extracted information in an information management system and classifying it based on its attributes; and means for visualizing and managing the classified information. This enables efficient information management, the presentation of solutions, and the rapid application of feedback.

[0068] A "communication platform" refers to a digital-based environment for users to exchange information, and includes email and chat applications.

[0069] "Communication data" refers to the collection of messages and information sent and received through a communication platform.

[0070] An "information management system" refers to a digital infrastructure for efficiently storing, organizing, and managing large amounts of data.

[0071] "Generative architecture input statements" refer to the words or instructions given to an artificial intelligence model to generate a specific solution.

[0072] "Machine processing technology" refers to technologies for the automatic analysis and manipulation of data, including natural language processing.

[0073] This invention is a system that effectively manages information by combining multiple elements and presents appropriate solutions to users. Its main components include a server, a terminal, and a user.

[0074] Server operation

[0075] The server utilizes a specific programming language (e.g., Python) and automation tools (e.g., Selenium) to retrieve communication data from the communication platform. This data is analyzed using natural language processing techniques to extract information about tasks and issues. Natural language processing libraries such as SpaCy and NLTK are used for the analysis. The extracted information is stored in a database management system such as MySQL® or PostgreSQL and organized using a data frame library (e.g., Pandas).

[0076] Providing information to the device

[0077] The server generates an interface using a web framework (e.g., Django) and sends task and issue information to the device. This interface is composed of HTML / CSS and JavaScript®, allowing users to easily view and update information. The device's dashboard displays task details in a visually organized format.

[0078] User interaction

[0079] Users can check the progress of tasks and update their status through the provided interface via their terminal. They can also receive solutions presented by the server and provide feedback on their effectiveness. The server collects this feedback to improve the accuracy of future solution generation.

[0080] Solution generation

[0081] The server utilizes a generative AI model to automatically generate optimal solutions based on historical data and the current situation. In this process, suggestions are generated according to the input text for the generative architecture. For example, the following prompt might be used: "Please provide a template to help create a report summarizing project progress. Also, please generate advice based on successful case studies from similar past projects."

[0082] This system enables centralized information management and the rapid provision of solutions to users, which is expected to improve operational efficiency.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects communication data from the communication platform. In this process, the server uses Selenium to automatically crawl email and chat application data and extract new messages. Input requires user account information and access authentication, and output is the newly collected communication data.

[0086] Step 2:

[0087] The server analyzes the collected communication data using natural language processing techniques. The server tokenizes the text data using SpaCy and tags it with parts of speech. Furthermore, it extracts task- and issue-related information from the text. The input is the communication data obtained in step 1, and the output is the extracted task-related information.

[0088] Step 3:

[0089] The server stores the extracted information in an information management system. The server stores the information in a MySQL database and uses Pandas to organize the data by attributes such as importance and due date. The input is the task-related information obtained in step 2, and the output is the organized task information stored in the database.

[0090] Step 4:

[0091] The server uses Django to generate a user interface that displays task information. This generated interface is sent to the terminal, allowing the user to easily view the task information. The input is task information stored in a database, and the output is a visually organized interface.

[0092] Step 5:

[0093] The user checks the progress of a task and updates its status via a terminal. The terminal sends the user's input to the server, which updates the database. The input is the user's updated information, and the output is the updated task status.

[0094] Step 6:

[0095] The server uses a generative AI model to propose an appropriate solution for the task. The server calls the OpenAI® API and creates input sentences for the generative architecture based on the context extracted in step 2. The inputs are task information and user feedback, and the output is the solution presented to the user.

[0096] Step 7:

[0097] The user provides feedback on the proposed solution. The terminal sends the feedback to the server, which records this feedback in a database and incorporates it into future suggestions. The input is the user's feedback, and the output is an improvement to the suggestion system based on the feedback information.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In modern manufacturing, there is a demand for increased efficiency and optimization of production processes within factories. However, rapidly and accurately analyzing the vast amounts of data exchanged through communication media and constructing appropriate production plans is not easy. To overcome this challenge, there is a need for systems that enable efficient data processing and timely optimization of production schedules.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] This invention includes a server that receives communication data from a communication medium, analyzes its contents, and extracts problem information; a server that centralizes the extracted problem information into an information set and classifies it based on importance and deadline; and a robot in the factory that integrates and collects communication data from other equipment and workers to construct an optimal work plan. This enables the optimization of the manufacturing process and improvement of production efficiency.

[0103] A "communication medium" refers to a platform for information exchange that enables the sending and receiving of data, and includes email and chat applications.

[0104] "Communication data" refers to the collection of information exchanged through communication media, and includes the content of emails and messages.

[0105] "Problem information" refers to data related to tasks and issues that need to be resolved, extracted through the analysis of communication data.

[0106] An "information aggregate" refers to a database used to store and manage extracted problem information, centrally holding multiple pieces of information.

[0107] "Importance" refers to a criterion used to determine the priority of information, indicating the urgency and impact of a task.

[0108] A "deadline" refers to the time constraint that a task or issue must be completed, and indicates the target date for its completion.

[0109] "Integrated data collection" refers to the process by which robots within a factory collect data from various information sources and manage it centrally.

[0110] An "optimal work plan" refers to a schedule designed to streamline production processes and maximize effectiveness, based on collected and analyzed problem information.

[0111] This invention is a system aimed at optimizing production processes in a factory. This system is realized through the specific roles played by the server, terminals, and users.

[0112] The server first periodically collects communication data from various communication media, including emails and chat messages. Next, it uses natural language processing techniques to extract problem information from this communication data. The information is centralized into an information aggregate and automatically classified based on importance and deadlines. The server analyzes this information and performs integrated collection to ensure that robots in the factory operate efficiently and that production schedules are optimized.

[0113] The terminal receives data from the server and presents it to the user via a visual user interface. This interface displays progress, the importance of problem information, deadlines, and other relevant details. The user can use this information to consider changes or improvements to the production plan.

[0114] Users provide feedback on the proposed solutions. The server receives this feedback and uses it to improve the system, leading to further optimization.

[0115] As a concrete example, consider a situation where a product on the production line fails to meet certain quality standards. The server identifies the root cause and presents an adjustable schedule and solution. The user can then review this on their terminal and provide feedback, contributing to the rapid resolution of the problem.

[0116] Example prompt: "Please describe an algorithm that presents a specific solution for optimizing a factory's production process."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server periodically collects communication data from communication media such as email and chat messages. It receives this message data as input. The server stores this data and prepares it for subsequent processing.

[0120] Step 2:

[0121] The server analyzes the collected communication data using natural language processing techniques. In this step, the communication data is used as input to extract problem information and related issue information. Data processing includes language analysis and keyword extraction, and the output is extracted results with importance and deadline information added.

[0122] Step 3:

[0123] The server centralizes the extracted problem information into an information database and classifies it based on importance and deadline. The input is problem information. This step involves data processing, organizing the data and storing it in the information database. The output is a set of classified information.

[0124] Step 4:

[0125] The terminal receives classified problem information provided by the server and presents it visually to the user via a user interface. The input is information from the server. The terminal converts this information into a display dashboard, and the output is the displayed visualized information.

[0126] Step 5:

[0127] Users send feedback on proposed solutions to the server via their terminal. Input includes user ratings and opinions, which are sent to the server to generate feedback data. The output is feedback information, which can be used for further system improvements.

[0128] 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.

[0129] In addition to its basic function of analyzing user communication data to extract tasks and issues, the system of the present invention also analyzes the user's emotional state by incorporating an emotion engine. This allows task processing and solution suggestions to be provided in a way that is more optimized for the user.

[0130] Collection and analysis of communication data

[0131] The server collects emails and chat history from the communication platform with the user's permission. This data is analyzed using natural language processing techniques and passed through an emotion engine in the process of extracting tasks and issues. The emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) from the content of the communications and generates corresponding emotion data.

[0132] Information integration and management

[0133] The server stores extracted issue information and sentiment data in a database and centrally manages this information. Task and issue information is prioritized based on criteria such as importance and deadline, as well as the user's emotional state.

[0134] Providing a user interface

[0135] The server generates a dashboard on the terminal, displaying details of the tasks the user needs to manage. This dashboard also visually displays the user's recent emotional state, which is used as reference information for task progress.

[0136] Generating and proposing solutions

[0137] When generating optimal solutions from historical data and external sources, the server adjusts its suggestions based on sentiment data. For example, if a user is experiencing stress, it prioritizes suggesting simplified procedures and support information.

[0138] Utilizing feedback

[0139] Users can provide feedback on the solutions offered. This feedback, along with sentiment data, is stored on the server and used to improve accuracy through the sentiment engine.

[0140] For example, when a user is emotionally exhausted and processing a large volume of project emails, the system will list priority tasks and suggest simplified procedures that are appropriate for that emotional state. In this way, the present invention provides an innovative platform that supports task management and problem-solving according to the user's emotions.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server uses user authentication information to access the communication platform and periodically retrieves new communication data. This data includes emails and chat messages.

[0144] Step 2:

[0145] The server processes the acquired communication data using natural language processing techniques to extract keywords related to tasks and issues. The extracted information includes attributes such as task name, deadline, and requester.

[0146] Step 3:

[0147] The server analyzes the communication data using an emotion engine in parallel with the results of natural language processing to identify the user's emotions. Based on the context of the communication and the choice of words, it determines the emotional state (e.g., joy, anger, stress, calmness, etc.).

[0148] Step 4:

[0149] The server integrates extracted task information and sentiment data and stores it in a database. This allows tasks to be organized not only by their importance and deadline, but also according to the user's emotional state.

[0150] Step 5:

[0151] The server generates a dashboard based on task and sentiment data and displays it on the terminal. This visually shows the current task status and sentiment-based priorities.

[0152] Step 6:

[0153] Through this dashboard, users can check the progress of tasks and manually update the status as needed. Furthermore, they can use the displayed sentiment data to decide which tasks to prioritize.

[0154] Step 7:

[0155] The server generates solutions while taking the user's emotional state into consideration. If an emotion indicating stress is detected, it prioritizes suggesting concise solutions and supportive content.

[0156] Step 8:

[0157] Users provide feedback on the proposed solutions. The server collects this feedback along with sentiment data and uses it to improve the accuracy of future solution suggestions.

[0158] (Example 2)

[0159] 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".

[0160] With the advancement of modern information and communication technology, users need to process large amounts of communication data daily. Therefore, there is a need for systems that can efficiently extract and manage tasks and issues while providing personalized solutions that take into account the user's emotional state. However, conventional systems face the challenge of being unable to analyze the user's emotional state in real time and provide appropriate suggestions based on that analysis.

[0161] 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.

[0162] In this invention, the server includes means for receiving data from a communication system, analyzing its content, and extracting information; means for centralizing the extracted information into a collection and classifying it based on importance and deadline; means for generating solutions to the information and proposing them to the user; and means for analyzing the user's emotional state and adjusting the proposed solutions based on that state. This enables task management and solution proposals that are appropriate to the user's emotions.

[0163] A "communication system" refers to any platform that enables the transmission of information between users, and typically includes email services and chat applications.

[0164] "Data" refers to the collective term for all information obtained through communication systems, such as messages and documents, and includes text, images, and audio.

[0165] "Means of analyzing content and extracting information" refers to a function that uses natural language processing technology on received data to identify tasks and important information that the user should address.

[0166] "Means of centralizing and classifying information into a collection" refers to the function of organizing and storing extracted information based on certain criteria, and managing it in a sequential manner according to its importance and urgency.

[0167] "Means for generating solutions and proposing them to users" refers to a function that devises the optimal countermeasure based on extracted information and communicates the results to the user.

[0168] "Means for analyzing the emotional state of users" refers to a function that evaluates the emotional response of users from received data and generates data based on that evaluation, with the aim of optimizing the proposed content.

[0169] "Means for adjusting the content of suggestions" refers to a function that modifies the generated suggestions to the most effective form for individual users, taking into account the user's emotional state.

[0170] The system of this invention combines multiple elements to efficiently manage user communication data and provide optimized solutions based on emotional states.

[0171] The server uses APIs from email services and chat applications to retrieve data from the communication platform. Specifically, when a user receives the latest information via email or chat, it forwards it to the server through the API.

[0172] Next, the server applies natural language processing techniques to the acquired data to identify important tasks and issues within the text. This process involves extracting keywords and phrases using NLP libraries. For example, if a user is communicating about a project deadline, the server retrieves relevant information such as "task management" and "deadline."

[0173] In addition, the server utilizes an emotion analysis model to analyze the user's emotional state from the extracted communication data. This uses an emotion engine to evaluate the emotional tone of the text and determine what emotional state the user is currently in.

[0174] The terminal displays information sent from the server in a dashboard format. Here, users can check their tasks and their priorities. This dashboard uses HTML5, CSS3, and JavaScript to provide a visually intuitive interface.

[0175] Furthermore, by utilizing a generative AI model, the server references similar historical data and external databases to generate solutions based on the user's current emotional state. This proposal breaks down tasks into smaller steps to reduce the user's burden.

[0176] As a concrete example, consider a situation where a user is emotionally exhausted and has to process a large number of project-related emails. In this case, the system would suggest "prioritizing emails" and "simplified work procedures" that are appropriate for the user's emotional state. An example of a prompt would be, "Suggest how to efficiently manage a large volume of project emails when the user is mentally exhausted."

[0177] Thus, the present invention realizes a comprehensive platform for efficient data management and solution provision that takes user emotions into consideration.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] Collection of communication data

[0181] The server retrieves data through APIs of email services and chat applications used by users. Inputs include new emails and chat messages. Specifically, this data is passed to the server via API requests. Output is the communication data stored on the server in text format.

[0182] Step 2:

[0183] Data analysis and information extraction

[0184] The server applies natural language processing techniques to the communication data collected in Step 1 to extract important information. The input is the raw communication data. The server uses an NLP library (e.g., spaCy or NLTK) to identify keywords and phrases from the text. Specifically, the data processing identifies information related to tasks and deadlines within each message. The output is the extracted task information and related data.

[0185] Step 3:

[0186] Analysis of emotional states

[0187] The server uses an emotion engine to evaluate the user's emotional state based on the communication data collected in Step 1. The input is the communication data obtained in the previous step. Specifically, an emotion analysis model is applied to analyze the emotional nuances of the text. As output, the user's emotional state (e.g., stress, joy, etc.) is generated as data.

[0188] Step 4:

[0189] Information integration and prioritization

[0190] The server integrates the information obtained in steps 2 and 3 into a single entity and prioritizes tasks based on importance, deadline, and emotional state. Inputs include task information and emotional state data. Specifically, this information is stored in a database, and an algorithm is used to automatically set the priority for each task. The output is a prioritized task list.

[0191] Step 5:

[0192] Dashboard generation

[0193] The server builds a user dashboard on the terminal. The input is the prioritized task list generated in step 4. Specifically, it uses HTML5, CSS3, and JavaScript to build a visually intuitive screen. The output is a user-friendly administrative dashboard.

[0194] Step 6:

[0195] Generating and proposing solutions

[0196] The server uses a generative AI model to generate optimal solutions tailored to the user's situation. Inputs include a prioritized task list and the user's emotional state. Specifically, it automatically forms appropriate suggestions by referencing similar past data and external information. The output is a concrete solution that the user can refer to.

[0197] Step 7:

[0198] Collecting and utilizing feedback

[0199] Users provide feedback on the suggestions they receive. This feedback includes ratings and comments from the user. Specifically, feedback is collected from a dashboard, and this data is stored on a server. The output generates data that can be used to identify areas for improvement and to enhance the accuracy of future suggestions.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0202] In today's information society, effectively managing vast amounts of communication information and emotional data, and proposing optimal solutions for users based on this information, is challenging. Furthermore, in electronic transactions, optimizing the interface according to the user's mental state is necessary, but conventional methods have limited the technologies capable of achieving this. It is essential to address these challenges and improve the user experience.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes means for receiving communication information from a communication interface, analyzing its contents, and extracting issue data; means for centralizing the extracted issue data and emotional state into an information base and prioritizing them based on importance and deadline; means for generating solutions adjusted based on the emotional state and proposing them to the user; and means for optimizing the user interface of electronic transactions according to the user's emotional state. This enables accurate issue extraction from communication information, flexible solution proposals that take emotional data into consideration, and the provision of safe and secure electronic transactions that are tailored to the user's mental state.

[0205] A "communication interface" refers to a digital platform or device used for information exchange, and serves as a foundation for collecting communication information from users.

[0206] "Communication information" refers to the content of digital communications such as emails and chat messages exchanged between users, and problem data is extracted based on this information.

[0207] "Issue data" refers to tasks and problems extracted from communication information, and it serves as basic information for making appropriate suggestions to users based on this data.

[0208] "Emotional state" refers to the psychological state analyzed from the user's communication information, and includes data such as the user's stress, joy, and anxiety.

[0209] An "information base" is a data store where extracted issue data and emotional states are centrally managed, serving as a foundation for data classification and prioritization.

[0210] Prioritization is the process of rearranging tasks that require processing or proposals based on issue data and emotional states, according to their importance and urgency.

[0211] A "solution" refers to specific methods or suggestions provided in response to the extracted problem data, and is presented in a way that is tailored to the user's emotional state.

[0212] "User interface" refers to the appearance and operating environment that end users use to interact with a system, and in electronic transactions, it is optimized to improve user convenience.

[0213] "Electronic transactions" refer to commercial transaction activities conducted through digital platforms, and are a general term for the process by which users purchase or sell products and services.

[0214] The system for implementing the present invention includes a user terminal, a server, and a database. The user terminal is a mobile device such as a smartphone or tablet, and functions as a user input interface. This terminal is used when the user generates communication information.

[0215] After receiving communication information, the server analyzes its content using natural language processing techniques and extracts task data. Here, open-source natural language processing libraries (e.g., NLTK, SpaCy) are used to translate the communication information and process the data. The server also performs sentiment analysis and runs an emotion engine to determine the user's emotional state. For this purpose, AI frameworks for sentiment analysis (e.g., TENSORFLOW®, PyTorch) are used.

[0216] The analyzed issue data and emotional states are centralized in a database and prioritized based on importance and deadline. The server then generates user-optimized solutions based on this data. These solutions are presented to the user through a user interface.

[0217] A key feature of this system is its ability to dynamically optimize the user interface for electronic trading based on the user's emotional state. For example, if a user is feeling stressed, the system will provide a simpler interface to support faster transactions.

[0218] As a concrete example, when a user makes an electronic payment using their smartphone, if the emotion engine detects abnormal tension, the system displays only simple and clear instructions, creating an environment where the transaction can be completed quickly. Furthermore, by using prompts such as "If the user is in a hurry, suggest a quick payment option" for the generating AI model, an appropriate solution is provided. In this way, the present invention realizes a flexible and intuitive user experience that takes into account the user's emotional state.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The server receives communication information from the user's terminal. Text data generated by the user via email or chat applications is input. The server temporarily stores this information as digital data. This stored data forms the basis for future processing.

[0222] Step 2:

[0223] The server analyzes the received communication information using natural language processing (NLTK) techniques. Specifically, it uses natural language processing libraries such as NLTK and SpaCy to tokenize the text data and tag it with parts of speech. The input is the communication information, and the output is structured information of the analyzed text data. Based on this structured information, the task data is extracted.

[0224] Step 3:

[0225] The server performs sentiment analysis to determine the user's emotional state. An emotion engine using TensorFlow or PyTorch is executed using the analyzed text data as input. This engine analyzes keywords and expressions extracted from the text and classifies the user's emotional state into categories such as "stress," "joy," and "anxiety." The output is the user's current emotional state data.

[0226] Step 4:

[0227] The server stores the extracted issue data and emotional states in a database and uses this data to assign priorities. The inputs are issue data and emotional states. Based on these, the information is centrally organized in the database, and priorities are calculated according to importance and deadlines. The output is a list of prioritized issues.

[0228] Step 5:

[0229] The server generates and proposes solutions tailored to the user's emotional state. The input consists of a prioritized list of issues and the user's emotional state. Referencing a generation AI model and external data, the server generates the steps and solutions that the user deems most appropriate, and sends these to the user's terminal. The output is the solution presented to the user.

[0230] Step 6:

[0231] The user terminal displays the solution received from the server in the user interface. The interface is optimized for intuitive understanding on the spot. This process improves the user experience by visually organizing and concisely displaying the input solution. The output is a solution in a format that the user can visually confirm.

[0232] 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.

[0233] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] The system of this invention automatically collects and analyzes communication data from a user's communication platform to identify and manage tasks and issues. The system mainly consists of server, terminal, and user elements, and the specific processing is as follows.

[0249] Data collection and analysis

[0250] The server periodically crawls the email and chat applications used by users to collect new communication data. The collected data is analyzed using natural language processing techniques to extract information relevant to tasks and issues.

[0251] Information organization and management

[0252] The server stores and centralizes the extracted task information in a database. This information is automatically organized based on attributes such as importance, deadline, and relevant people. The server comprehensively manages this information and creates a list of all tasks currently held by the user.

[0253] Providing a user interface

[0254] The server generates an interface on the terminal that provides organized task and issue information. Through this interface, users can check the progress of tasks and update their status. The dashboard displayed on the terminal visually shows task details (deadline, requester, importance, etc.).

[0255] Generating and proposing solutions

[0256] The server generates appropriate solutions to problems. This process involves referencing a database of past cases and external resources to suggest the most suitable solution for the user. This allows users to address problems efficiently.

[0257] Utilizing feedback

[0258] Users can provide feedback on the effectiveness of the proposed solutions. This feedback is collected by the server and used to improve the accuracy of future solution generation.

[0259] For example, if a user is tasked with creating a project progress report, the system analyzes relevant communication data and suggests necessary information and report templates to the user. In this way, the system of the present invention provides a comprehensive platform to support the efficient management of tasks.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The server obtains authentication credentials to access the user's communication platform and uses APIs or scraping techniques to retrieve new communication data from email and chat applications.

[0263] Step 2:

[0264] The server analyzes the acquired communication data using natural language processing technology and extracts keywords related to tasks and issues based on verbs and nouns. This identifies the actions (e.g., "create," "report") and objects (e.g., "report," "meeting") of the tasks.

[0265] Step 3:

[0266] The server stores the extracted task and issue information in a database. This includes metadata such as importance, deadline, and related people and projects.

[0267] Step 4:

[0268] The server displays a dashboard on the user's terminal based on organized task and issue information. The terminal visually shows the progress and status of tasks (e.g., not started, in progress, completed).

[0269] Step 5:

[0270] Users can manually update task statuses and add detailed information through the task management interface, allowing for real-time tracking of current progress.

[0271] Step 6:

[0272] The server references historical databases and external information sources to generate the optimal solution for the user's task or problem. The proposed solution is then displayed on the user's terminal.

[0273] Step 7:

[0274] Users can review the proposed solutions and provide feedback on their usefulness. This feedback is collected by the server and used to improve the accuracy of the proposed solutions.

[0275] (Example 1)

[0276] 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".

[0277] In today's diverse communication platforms, efficiently and automatically organizing information and providing users with appropriate solutions is a challenging task. Traditional systems handle information collection and organization, as well as solution generation and presentation, separately, making comprehensive management difficult. Furthermore, there is a lack of means to quickly incorporate user feedback and deliver highly accurate suggestions.

[0278] 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.

[0279] In this invention, the server includes means for acquiring communication data from a communication platform, analyzing the content to extract information, means for unifying the extracted information in an information management system and classifying it based on attributes, and means for visualizing and managing the classified information. As a result, efficient information management, presentation of solutions, and rapid application of feedback become possible.

[0280] The "communication platform" refers to a digital-based environment for users to exchange information, including email, chat applications, etc.

[0281] "Communication data" refers to the collection of messages and information transmitted and received through a communication platform.

[0282] The "information management system" refers to a digital infrastructure for efficiently storing, organizing, and managing large amounts of data.

[0283] The "input sentence for generation architecture" refers to the text and instructions given to an artificial intelligence model to generate a specific solution.

[0284] "Machine processing technology" refers to technologies for automatically analyzing and operating data, including natural language processing.

[0285] This invention is a system that effectively manages information by combining multiple elements and presents appropriate solutions to users. The main components include a server, a terminal, and a user.

[0286] Server Operations

[0287] The server utilizes a specific programming language (e.g., Python) and automation tools (e.g., Selenium) to retrieve communication data from the communication platform. This data is analyzed using natural language processing techniques to extract information about tasks and issues. Natural language processing libraries such as SpaCy and NLTK are used for the analysis. The extracted information is stored in a database management system such as MySQL or PostgreSQL and organized using a dataframe library (e.g., Pandas).

[0288] Providing information to the device

[0289] The server generates an interface using a web framework (e.g., Django) and sends task and issue information to the device. This interface is composed of HTML / CSS and JavaScript, allowing users to easily view and update information. The device's dashboard displays task details in a visually organized manner.

[0290] User interaction

[0291] Users can check the progress of tasks and update their status through the provided interface via their terminal. They can also receive solutions presented by the server and provide feedback on their effectiveness. The server collects this feedback to improve the accuracy of future solution generation.

[0292] Solution generation

[0293] The server utilizes a generative AI model to automatically generate optimal solutions based on historical data and the current situation. In this process, suggestions are generated according to the input text for the generative architecture. For example, the following prompt might be used: "Please provide a template to help create a report summarizing project progress. Also, please generate advice based on successful case studies from similar past projects."

[0294] This system enables centralized information management and the rapid provision of solutions to users, which is expected to improve operational efficiency.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The server collects communication data from the communication platform. In this process, the server uses Selenium to automatically crawl email and chat application data and extract new messages. Input requires user account information and access authentication, and output is the newly collected communication data.

[0298] Step 2:

[0299] The server analyzes the collected communication data using natural language processing techniques. The server tokenizes the text data using SpaCy and tags it with parts of speech. Furthermore, it extracts task- and issue-related information from the text. The input is the communication data obtained in step 1, and the output is the extracted task-related information.

[0300] Step 3:

[0301] The server stores the extracted information in an information management system. The server stores the information in a MySQL database and uses Pandas to organize the data by attributes such as importance and due date. The input is the task-related information obtained in step 2, and the output is the organized task information stored in the database.

[0302] Step 4:

[0303] The server uses Django to generate a user interface for displaying task information. The generated interface is sent to the terminal so that users can easily check the task information. The input is the task information stored in the database, and the output is a visually organized interface.

[0304] Step 5:

[0305] The user checks the progress of the task via the terminal and updates the status. The terminal sends the user's input to the server, and the server updates the database. The input is the user's update information, and the output is the status of the updated task.

[0306] Step 6:

[0307] The server uses the generative AI model to propose appropriate solutions for the task. The server calls the OpenAI API and creates an input sentence for the generative architecture based on the context extracted in Step 2. The input is the task information and user feedback, and the output is the solution presented to the user.

[0308] Step 7:

[0309] The user provides feedback on the presented solution. The terminal sends the feedback to the server, and the server records this feedback in the database and reflects it in the next proposal. The input is the feedback from the user, and the output is the improvement of the proposal system based on the feedback information.

[0310] (Application Example 1)

[0311] 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".

[0312] In modern manufacturing, there is a demand for increased efficiency and optimization of production processes within factories. However, rapidly and accurately analyzing the vast amounts of data exchanged through communication media and constructing appropriate production plans is not easy. To overcome this challenge, there is a need for systems that enable efficient data processing and timely optimization of production schedules.

[0313] 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.

[0314] This invention includes a server that receives communication data from a communication medium, analyzes its contents, and extracts problem information; a server that centralizes the extracted problem information into an information set and classifies it based on importance and deadline; and a robot in the factory that integrates and collects communication data from other equipment and workers to construct an optimal work plan. This enables the optimization of the manufacturing process and improvement of production efficiency.

[0315] A "communication medium" refers to a platform for information exchange that enables the sending and receiving of data, and includes email and chat applications.

[0316] "Communication data" refers to the collection of information exchanged through communication media, and includes the content of emails and messages.

[0317] "Problem information" refers to data related to tasks and issues that need to be resolved, extracted through the analysis of communication data.

[0318] An "information aggregate" refers to a database used to store and manage extracted problem information, centrally holding multiple pieces of information.

[0319] "Importance" refers to a criterion used to determine the priority of information, indicating the urgency and impact of a task.

[0320] A "deadline" refers to the time constraint that a task or issue must be completed, and indicates the target date for its completion.

[0321] "Integrated data collection" refers to the process by which robots within a factory collect data from various information sources and manage it centrally.

[0322] An "optimal work plan" refers to a schedule designed to streamline production processes and maximize effectiveness, based on collected and analyzed problem information.

[0323] This invention is a system aimed at optimizing production processes in a factory. This system is realized through the specific roles played by the server, terminals, and users.

[0324] The server first periodically collects communication data from various communication media, including emails and chat messages. Next, it uses natural language processing techniques to extract problem information from this communication data. The information is centralized into an information aggregate and automatically classified based on importance and deadlines. The server analyzes this information and performs integrated collection to ensure that robots in the factory operate efficiently and that production schedules are optimized.

[0325] The terminal receives data from the server and presents it to the user via a visual user interface. This interface displays progress, the importance of problem information, deadlines, and other relevant details. The user can use this information to consider changes or improvements to the production plan.

[0326] Users provide feedback on the proposed solutions. The server receives this feedback and uses it to improve the system, leading to further optimization.

[0327] As a concrete example, consider a situation where a product on the production line fails to meet certain quality standards. The server identifies the root cause and presents an adjustable schedule and solution. The user can then review this on their terminal and provide feedback, contributing to the rapid resolution of the problem.

[0328] Example prompt: "Please describe an algorithm that presents a specific solution for optimizing a factory's production process."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The server periodically collects communication data from communication media such as email and chat messages. It receives this message data as input. The server stores this data and prepares it for subsequent processing.

[0332] Step 2:

[0333] The server analyzes the collected communication data using natural language processing techniques. In this step, the communication data is used as input to extract problem information and related issue information. Data processing includes language analysis and keyword extraction, and the output is extracted results with importance and deadline information added.

[0334] Step 3:

[0335] The server centralizes the extracted problem information into an information database and classifies it based on importance and deadline. The input is problem information. This step involves data processing, organizing the data and storing it in the information database. The output is a set of classified information.

[0336] Step 4:

[0337] The terminal receives classified problem information provided by the server and presents it visually to the user via a user interface. The input is information from the server. The terminal converts this information into a display dashboard, and the output is the displayed visualized information.

[0338] Step 5:

[0339] Users send feedback on proposed solutions to the server via their terminal. Input includes user ratings and opinions, which are sent to the server to generate feedback data. The output is feedback information, which can be used for further system improvements.

[0340] 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.

[0341] In addition to its basic function of analyzing user communication data to extract tasks and issues, the system of the present invention also analyzes the user's emotional state by incorporating an emotion engine. This allows task processing and solution suggestions to be provided in a way that is more optimized for the user.

[0342] Collection and analysis of communication data

[0343] The server collects emails and chat history from the communication platform with the user's permission. This data is analyzed using natural language processing techniques and passed through an emotion engine in the process of extracting tasks and issues. The emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) from the content of the communications and generates corresponding emotion data.

[0344] Information integration and management

[0345] The server stores extracted issue information and sentiment data in a database and centrally manages this information. Task and issue information is prioritized based on criteria such as importance and deadline, as well as the user's emotional state.

[0346] Providing a user interface

[0347] The server generates a dashboard on the terminal, displaying details of the tasks the user needs to manage. This dashboard also visually displays the user's recent emotional state, which is used as reference information for task progress.

[0348] Generating and proposing solutions

[0349] When generating optimal solutions from historical data and external sources, the server adjusts its suggestions based on sentiment data. For example, if a user is experiencing stress, it prioritizes suggesting simplified procedures and support information.

[0350] Utilizing feedback

[0351] Users can provide feedback on the solutions offered. This feedback, along with sentiment data, is stored on the server and used to improve accuracy through the sentiment engine.

[0352] For example, when a user is emotionally exhausted and processing a large volume of project emails, the system will list priority tasks and suggest simplified procedures that are appropriate for that emotional state. In this way, the present invention provides an innovative platform that supports task management and problem-solving according to the user's emotions.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The server uses user authentication information to access the communication platform and periodically retrieves new communication data. This data includes emails and chat messages.

[0356] Step 2:

[0357] The server processes the acquired communication data using natural language processing techniques to extract keywords related to tasks and issues. The extracted information includes attributes such as task name, deadline, and requester.

[0358] Step 3:

[0359] The server analyzes the communication data using an emotion engine in parallel with the results of natural language processing to identify the user's emotions. Based on the context of the communication and the choice of words, it determines the emotional state (e.g., joy, anger, stress, calmness, etc.).

[0360] Step 4:

[0361] The server integrates extracted task information and sentiment data and stores it in a database. This allows tasks to be organized not only by their importance and deadline, but also according to the user's emotional state.

[0362] Step 5:

[0363] The server generates a dashboard based on task and sentiment data and displays it on the terminal. This visually shows the current task status and sentiment-based priorities.

[0364] Step 6:

[0365] Through this dashboard, users can check the progress of tasks and manually update the status as needed. Furthermore, they can use the displayed sentiment data to decide which tasks to prioritize.

[0366] Step 7:

[0367] The server generates solutions while taking the user's emotional state into consideration. If an emotion indicating stress is detected, it prioritizes suggesting concise solutions and supportive content.

[0368] Step 8:

[0369] Users provide feedback on the proposed solutions. The server collects this feedback along with sentiment data and uses it to improve the accuracy of future solution suggestions.

[0370] (Example 2)

[0371] 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".

[0372] With the advancement of modern information and communication technology, users need to process large amounts of communication data daily. Therefore, there is a need for systems that can efficiently extract and manage tasks and issues while providing personalized solutions that take into account the user's emotional state. However, conventional systems face the challenge of being unable to analyze the user's emotional state in real time and provide appropriate suggestions based on that analysis.

[0373] 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.

[0374] In this invention, the server includes means for receiving data from a communication system, analyzing its content, and extracting information; means for centralizing the extracted information into a collection and classifying it based on importance and deadline; means for generating solutions to the information and proposing them to the user; and means for analyzing the user's emotional state and adjusting the proposed solutions based on that state. This enables task management and solution proposals that are appropriate to the user's emotions.

[0375] A "communication system" refers to any platform that enables the transmission of information between users, and typically includes email services and chat applications.

[0376] "Data" refers to the collective term for all information obtained through communication systems, such as messages and documents, and includes text, images, and audio.

[0377] "Means of analyzing content and extracting information" refers to a function that uses natural language processing technology on received data to identify tasks and important information that the user should address.

[0378] "Means of centralizing and classifying information into a collection" refers to the function of organizing and storing extracted information based on certain criteria, and managing it in a sequential manner according to its importance and urgency.

[0379] "Means for generating solutions and proposing them to users" refers to a function that devises the optimal countermeasure based on extracted information and communicates the results to the user.

[0380] "Means for analyzing the emotional state of users" refers to a function that evaluates the emotional response of users from received data and generates data based on that evaluation, with the aim of optimizing the proposed content.

[0381] "Means for adjusting the content of suggestions" refers to a function that modifies the generated suggestions to the most effective form for individual users, taking into account the user's emotional state.

[0382] The system of this invention combines multiple elements to efficiently manage user communication data and provide optimized solutions based on emotional states.

[0383] The server uses APIs from email services and chat applications to retrieve data from the communication platform. Specifically, when a user receives the latest information via email or chat, it forwards it to the server through the API.

[0384] Next, the server applies natural language processing techniques to the acquired data to identify important tasks and issues within the text. This process involves extracting keywords and phrases using NLP libraries. For example, if a user is communicating about a project deadline, the server retrieves relevant information such as "task management" and "deadline."

[0385] In addition, the server utilizes an emotion analysis model to analyze the user's emotional state from the extracted communication data. This uses an emotion engine to evaluate the emotional tone of the text and determine what emotional state the user is currently in.

[0386] The terminal displays information sent from the server in a dashboard format. Here, users can check their tasks and their priorities. This dashboard uses HTML5, CSS3, and JavaScript to provide a visually intuitive interface.

[0387] Furthermore, by utilizing a generative AI model, the server references similar historical data and external databases to generate solutions based on the user's current emotional state. This proposal breaks down tasks into smaller steps to reduce the user's burden.

[0388] As a concrete example, consider a situation where a user is emotionally exhausted and has to process a large number of project-related emails. In this case, the system would suggest "prioritizing emails" and "simplified work procedures" that are appropriate for the user's emotional state. An example of a prompt would be, "Suggest how to efficiently manage a large volume of project emails when the user is mentally exhausted."

[0389] Thus, the present invention realizes a comprehensive platform for efficient data management and solution provision that takes user emotions into consideration.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] Collection of communication data

[0393] The server retrieves data through APIs of email services and chat applications used by users. Inputs include new emails and chat messages. Specifically, this data is passed to the server via API requests. Output is the communication data stored on the server in text format.

[0394] Step 2:

[0395] Data analysis and information extraction

[0396] The server applies natural language processing techniques to the communication data collected in Step 1 to extract important information. The input is the raw communication data. The server uses an NLP library (e.g., spaCy or NLTK) to identify keywords and phrases from the text. Specifically, the data processing identifies information related to tasks and deadlines within each message. The output is the extracted task information and related data.

[0397] Step 3:

[0398] Analysis of emotional states

[0399] The server uses an emotion engine to evaluate the user's emotional state based on the communication data collected in Step 1. The input is the communication data obtained in the previous step. Specifically, an emotion analysis model is applied to analyze the emotional nuances of the text. As output, the user's emotional state (e.g., stress, joy, etc.) is generated as data.

[0400] Step 4:

[0401] Information integration and prioritization

[0402] The server integrates the information obtained in steps 2 and 3 into a single entity and prioritizes tasks based on importance, deadline, and emotional state. Inputs include task information and emotional state data. Specifically, this information is stored in a database, and an algorithm is used to automatically set the priority for each task. The output is a prioritized task list.

[0403] Step 5:

[0404] Dashboard generation

[0405] The server builds a user dashboard on the terminal. The input is the prioritized task list generated in step 4. Specifically, it uses HTML5, CSS3, and JavaScript to build a visually intuitive screen. The output is a user-friendly administrative dashboard.

[0406] Step 6:

[0407] Generating and proposing solutions

[0408] The server uses a generative AI model to generate optimal solutions tailored to the user's situation. Inputs include a prioritized task list and the user's emotional state. Specifically, it automatically forms appropriate suggestions by referencing similar past data and external information. The output is a concrete solution that the user can refer to.

[0409] Step 7:

[0410] Collecting and utilizing feedback

[0411] Users provide feedback on the suggestions they receive. This feedback includes ratings and comments from the user. Specifically, feedback is collected from a dashboard, and this data is stored on a server. The output generates data that can be used to identify areas for improvement and to enhance the accuracy of future suggestions.

[0412] (Application Example 2)

[0413] 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."

[0414] In today's information society, effectively managing vast amounts of communication information and emotional data, and proposing optimal solutions for users based on this information, is challenging. Furthermore, in electronic transactions, optimizing the interface according to the user's mental state is necessary, but conventional methods have limited the technologies capable of achieving this. It is essential to address these challenges and improve the user experience.

[0415] 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.

[0416] In this invention, the server includes means for receiving communication information from a communication interface, analyzing its contents, and extracting issue data; means for centralizing the extracted issue data and emotional state into an information base and prioritizing them based on importance and deadline; means for generating solutions adjusted based on the emotional state and proposing them to the user; and means for optimizing the user interface of electronic transactions according to the user's emotional state. This enables accurate issue extraction from communication information, flexible solution proposals that take emotional data into consideration, and the provision of safe and secure electronic transactions that are tailored to the user's mental state.

[0417] A "communication interface" refers to a digital platform or device used for information exchange, and serves as a foundation for collecting communication information from users.

[0418] "Communication information" refers to the content of digital communications such as emails and chat messages exchanged between users, and problem data is extracted based on this information.

[0419] "Issue data" refers to tasks and problems extracted from communication information, and it serves as basic information for making appropriate suggestions to users based on this data.

[0420] "Emotional state" refers to the psychological state analyzed from the user's communication information, and includes data such as the user's stress, joy, and anxiety.

[0421] An "information base" is a data store where extracted issue data and emotional states are centrally managed, serving as a foundation for data classification and prioritization.

[0422] Prioritization is the process of rearranging tasks that require processing or proposals based on issue data and emotional states, according to their importance and urgency.

[0423] A "solution" refers to specific methods or suggestions provided in response to the extracted problem data, and is presented in a way that is tailored to the user's emotional state.

[0424] "User interface" refers to the appearance and operating environment that end users use to interact with a system, and in electronic transactions, it is optimized to improve user convenience.

[0425] "Electronic transactions" refer to commercial transaction activities conducted through digital platforms, and are a general term for the process by which users purchase or sell products and services.

[0426] The system for implementing the present invention includes a user terminal, a server, and a database. The user terminal is a mobile device such as a smartphone or tablet, and functions as a user input interface. This terminal is used when the user generates communication information.

[0427] After receiving communication information, the server analyzes its content using natural language processing techniques and extracts task data. Here, open-source natural language processing libraries (e.g., NLTK, SpaCy) are used to translate the communication information and process the data. The server also performs sentiment analysis and runs an emotion engine to determine the user's emotional state. For this purpose, AI frameworks for sentiment analysis (e.g., TensorFlow, PyTorch) are used.

[0428] The analyzed issue data and emotional states are centralized in a database and prioritized based on importance and deadline. The server then generates user-optimized solutions based on this data. These solutions are presented to the user through a user interface.

[0429] A key feature of this system is its ability to dynamically optimize the user interface for electronic trading based on the user's emotional state. For example, if a user is feeling stressed, the system will provide a simpler interface to support faster transactions.

[0430] As a concrete example, when a user makes an electronic payment using their smartphone, if the emotion engine detects abnormal tension, the system displays only simple and clear instructions, creating an environment where the transaction can be completed quickly. Furthermore, by using prompts such as "If the user is in a hurry, suggest a quick payment option" for the generating AI model, an appropriate solution is provided. In this way, the present invention realizes a flexible and intuitive user experience that takes into account the user's emotional state.

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

[0432] Step 1:

[0433] The server receives communication information from the user's terminal. Text data generated by the user via email or chat applications is input. The server temporarily stores this information as digital data. This stored data forms the basis for future processing.

[0434] Step 2:

[0435] The server analyzes the received communication information using natural language processing (NLTK) techniques. Specifically, it uses natural language processing libraries such as NLTK and SpaCy to tokenize the text data and tag it with parts of speech. The input is the communication information, and the output is structured information of the analyzed text data. Based on this structured information, the task data is extracted.

[0436] Step 3:

[0437] The server performs sentiment analysis to determine the user's emotional state. An emotion engine using TensorFlow or PyTorch is executed using the analyzed text data as input. This engine analyzes keywords and expressions extracted from the text and classifies the user's emotional state into categories such as "stress," "joy," and "anxiety." The output is the user's current emotional state data.

[0438] Step 4:

[0439] The server stores the extracted issue data and emotional states in a database and uses this data to assign priorities. The inputs are issue data and emotional states. Based on these, the information is centrally organized in the database, and priorities are calculated according to importance and deadlines. The output is a list of prioritized issues.

[0440] Step 5:

[0441] The server generates and proposes solutions tailored to the user's emotional state. The input consists of a prioritized list of issues and the user's emotional state. Referencing a generation AI model and external data, the server generates the steps and solutions that the user deems most appropriate, and sends these to the user's terminal. The output is the solution presented to the user.

[0442] Step 6:

[0443] The user terminal displays the solution received from the server in the user interface. The interface is optimized for intuitive understanding on the spot. This process improves the user experience by visually organizing and concisely displaying the input solution. The output is a solution in a format that the user can visually confirm.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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".

[0460] The system of this invention automatically collects and analyzes communication data from a user's communication platform to identify and manage tasks and issues. The system mainly consists of server, terminal, and user elements, and the specific processing is as follows.

[0461] Data collection and analysis

[0462] The server periodically crawls the email and chat applications used by users to collect new communication data. The collected data is analyzed using natural language processing techniques to extract information relevant to tasks and issues.

[0463] Information organization and management

[0464] The server stores and centralizes the extracted task information in a database. This information is automatically organized based on attributes such as importance, deadline, and relevant people. The server comprehensively manages this information and creates a list of all tasks currently held by the user.

[0465] Providing a user interface

[0466] The server generates an interface on the terminal that provides organized task and issue information. Through this interface, users can check the progress of tasks and update their status. The dashboard displayed on the terminal visually shows task details (deadline, requester, importance, etc.).

[0467] Generating and proposing solutions

[0468] The server generates appropriate solutions to problems. This process involves referencing a database of past cases and external resources to suggest the most suitable solution for the user. This allows users to address problems efficiently.

[0469] Utilizing feedback

[0470] Users can provide feedback on the effectiveness of the proposed solutions. This feedback is collected by the server and used to improve the accuracy of future solution generation.

[0471] For example, if a user is tasked with creating a project progress report, the system analyzes relevant communication data and suggests necessary information and report templates to the user. In this way, the system of the present invention provides a comprehensive platform to support the efficient management of tasks.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The server obtains authentication credentials to access the user's communication platform and uses APIs or scraping techniques to retrieve new communication data from email and chat applications.

[0475] Step 2:

[0476] The server analyzes the acquired communication data using natural language processing technology and extracts keywords related to tasks and issues based on verbs and nouns. This identifies the actions (e.g., "create," "report") and objects (e.g., "report," "meeting") of the tasks.

[0477] Step 3:

[0478] The server stores the extracted task and issue information in a database. This includes metadata such as importance, deadline, and related people and projects.

[0479] Step 4:

[0480] The server displays a dashboard on the user's terminal based on organized task and issue information. The terminal visually shows the progress and status of tasks (e.g., not started, in progress, completed).

[0481] Step 5:

[0482] Users can manually update task statuses and add detailed information through the task management interface, allowing for real-time tracking of current progress.

[0483] Step 6:

[0484] The server references historical databases and external information sources to generate the optimal solution for the user's task or problem. The proposed solution is then displayed on the user's terminal.

[0485] Step 7:

[0486] Users can review the proposed solutions and provide feedback on their usefulness. This feedback is collected by the server and used to improve the accuracy of the proposed solutions.

[0487] (Example 1)

[0488] 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."

[0489] In today's diverse communication platforms, efficiently and automatically organizing information and providing users with appropriate solutions is a challenging task. Traditional systems handle information collection and organization, as well as solution generation and presentation, separately, making comprehensive management difficult. Furthermore, there is a lack of means to quickly incorporate user feedback and deliver highly accurate suggestions.

[0490] 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.

[0491] In this invention, the server includes means for acquiring communication data from a communication platform, analyzing its content, and extracting information; means for centralizing the extracted information in an information management system and classifying it based on its attributes; and means for visualizing and managing the classified information. This enables efficient information management, the presentation of solutions, and the rapid application of feedback.

[0492] A "communication platform" refers to a digital-based environment for users to exchange information, and includes email and chat applications.

[0493] "Communication data" refers to the collection of messages and information sent and received through a communication platform.

[0494] An "information management system" refers to a digital infrastructure for efficiently storing, organizing, and managing large amounts of data.

[0495] "Generative architecture input statements" refer to the words or instructions given to an artificial intelligence model to generate a specific solution.

[0496] "Machine processing technology" refers to technologies for the automatic analysis and manipulation of data, including natural language processing.

[0497] This invention is a system that effectively manages information by combining multiple elements and presents appropriate solutions to users. Its main components include a server, a terminal, and a user.

[0498] Server operation

[0499] The server utilizes a specific programming language (e.g., Python) and automation tools (e.g., Selenium) to retrieve communication data from the communication platform. This data is analyzed using natural language processing techniques to extract information about tasks and issues. Natural language processing libraries such as SpaCy and NLTK are used for the analysis. The extracted information is stored in a database management system such as MySQL or PostgreSQL and organized using a dataframe library (e.g., Pandas).

[0500] Providing information to the device

[0501] The server generates an interface using a web framework (e.g., Django) and sends task and issue information to the device. This interface is composed of HTML / CSS and JavaScript, allowing users to easily view and update information. The device's dashboard displays task details in a visually organized manner.

[0502] User interaction

[0503] Users can check the progress of tasks and update their status through the provided interface via their terminal. They can also receive solutions presented by the server and provide feedback on their effectiveness. The server collects this feedback to improve the accuracy of future solution generation.

[0504] Solution generation

[0505] The server utilizes a generative AI model to automatically generate optimal solutions based on historical data and the current situation. In this process, suggestions are generated according to the input text for the generative architecture. For example, the following prompt might be used: "Please provide a template to help create a report summarizing project progress. Also, please generate advice based on successful case studies from similar past projects."

[0506] This system enables centralized information management and the rapid provision of solutions to users, which is expected to improve operational efficiency.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] The server collects communication data from the communication platform. In this process, the server uses Selenium to automatically crawl email and chat application data and extract new messages. Input requires user account information and access authentication, and output is the newly collected communication data.

[0510] Step 2:

[0511] The server analyzes the collected communication data using natural language processing techniques. The server tokenizes the text data using SpaCy and tags it with parts of speech. Furthermore, it extracts task- and issue-related information from the text. The input is the communication data obtained in step 1, and the output is the extracted task-related information.

[0512] Step 3:

[0513] The server stores the extracted information in an information management system. The server stores the information in a MySQL database and uses Pandas to organize the data by attributes such as importance and due date. The input is the task-related information obtained in step 2, and the output is the organized task information stored in the database.

[0514] Step 4:

[0515] The server uses Django to generate a user interface that displays task information. This generated interface is sent to the terminal, allowing the user to easily view the task information. The input is task information stored in a database, and the output is a visually organized interface.

[0516] Step 5:

[0517] The user checks the progress of a task and updates its status via a terminal. The terminal sends the user's input to the server, which updates the database. The input is the user's updated information, and the output is the updated task status.

[0518] Step 6:

[0519] The server uses a generative AI model to propose an appropriate solution for the task. The server calls the OpenAI API and creates input sentences for the generative architecture based on the context extracted in step 2. The inputs are task information and user feedback, and the output is the solution presented to the user.

[0520] Step 7:

[0521] The user provides feedback on the proposed solution. The terminal sends the feedback to the server, which records this feedback in a database and incorporates it into future suggestions. The input is the user's feedback, and the output is an improvement to the suggestion system based on the feedback information.

[0522] (Application Example 1)

[0523] 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."

[0524] In modern manufacturing, there is a demand for increased efficiency and optimization of production processes within factories. However, rapidly and accurately analyzing the vast amounts of data exchanged through communication media and constructing appropriate production plans is not easy. To overcome this challenge, there is a need for systems that enable efficient data processing and timely optimization of production schedules.

[0525] 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.

[0526] This invention includes a server that receives communication data from a communication medium, analyzes its contents, and extracts problem information; a server that centralizes the extracted problem information into an information set and classifies it based on importance and deadline; and a robot in the factory that integrates and collects communication data from other equipment and workers to construct an optimal work plan. This enables the optimization of the manufacturing process and improvement of production efficiency.

[0527] A "communication medium" refers to a platform for information exchange that enables the sending and receiving of data, and includes email and chat applications.

[0528] "Communication data" refers to the collection of information exchanged through communication media, and includes the content of emails and messages.

[0529] "Problem information" refers to data related to tasks and issues that need to be resolved, extracted through the analysis of communication data.

[0530] An "information aggregate" refers to a database used to store and manage extracted problem information, centrally holding multiple pieces of information.

[0531] "Importance" refers to a criterion used to determine the priority of information, indicating the urgency and impact of a task.

[0532] A "deadline" refers to the time constraint that a task or issue must be completed, and indicates the target date for its completion.

[0533] "Integrated data collection" refers to the process by which robots within a factory collect data from various information sources and manage it centrally.

[0534] An "optimal work plan" refers to a schedule designed to streamline production processes and maximize effectiveness, based on collected and analyzed problem information.

[0535] This invention is a system aimed at optimizing production processes in a factory. This system is realized through the specific roles played by the server, terminals, and users.

[0536] The server first periodically collects communication data from various communication media, including emails and chat messages. Next, it uses natural language processing techniques to extract problem information from this communication data. The information is centralized into an information aggregate and automatically classified based on importance and deadlines. The server analyzes this information and performs integrated collection to ensure that robots in the factory operate efficiently and that production schedules are optimized.

[0537] The terminal receives data from the server and presents it to the user via a visual user interface. This interface displays progress, the importance of problem information, deadlines, and other relevant details. The user can use this information to consider changes or improvements to the production plan.

[0538] Users provide feedback on the proposed solutions. The server receives this feedback and uses it to improve the system, leading to further optimization.

[0539] As a concrete example, consider a situation where a product on the production line fails to meet certain quality standards. The server identifies the root cause and presents an adjustable schedule and solution. The user can then review this on their terminal and provide feedback, contributing to the rapid resolution of the problem.

[0540] Example prompt: "Please describe an algorithm that presents a specific solution for optimizing a factory's production process."

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] The server periodically collects communication data from communication media such as email and chat messages. It receives this message data as input. The server stores this data and prepares it for subsequent processing.

[0544] Step 2:

[0545] The server analyzes the collected communication data using natural language processing techniques. In this step, the communication data is used as input to extract problem information and related issue information. Data processing includes language analysis and keyword extraction, and the output is extracted results with importance and deadline information added.

[0546] Step 3:

[0547] The server centralizes the extracted problem information into an information database and classifies it based on importance and deadline. The input is problem information. This step involves data processing, organizing the data and storing it in the information database. The output is a set of classified information.

[0548] Step 4:

[0549] The terminal receives classified problem information provided by the server and presents it visually to the user via a user interface. The input is information from the server. The terminal converts this information into a display dashboard, and the output is the displayed visualized information.

[0550] Step 5:

[0551] Users send feedback on proposed solutions to the server via their terminal. Input includes user ratings and opinions, which are sent to the server to generate feedback data. The output is feedback information, which can be used for further system improvements.

[0552] 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.

[0553] In addition to its basic function of analyzing user communication data to extract tasks and issues, the system of the present invention also analyzes the user's emotional state by incorporating an emotion engine. This allows task processing and solution suggestions to be provided in a way that is more optimized for the user.

[0554] Collection and analysis of communication data

[0555] The server collects emails and chat history from the communication platform with the user's permission. This data is analyzed using natural language processing techniques and passed through an emotion engine in the process of extracting tasks and issues. The emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) from the content of the communications and generates corresponding emotion data.

[0556] Information integration and management

[0557] The server stores extracted issue information and sentiment data in a database and centrally manages this information. Task and issue information is prioritized based on criteria such as importance and deadline, as well as the user's emotional state.

[0558] Providing a user interface

[0559] The server generates a dashboard on the terminal, displaying details of the tasks the user needs to manage. This dashboard also visually displays the user's recent emotional state, which is used as reference information for task progress.

[0560] Generating and proposing solutions

[0561] When generating optimal solutions from historical data and external sources, the server adjusts its suggestions based on sentiment data. For example, if a user is experiencing stress, it prioritizes suggesting simplified procedures and support information.

[0562] Utilizing feedback

[0563] Users can provide feedback on the solutions offered. This feedback, along with sentiment data, is stored on the server and used to improve accuracy through the sentiment engine.

[0564] For example, when a user is emotionally exhausted and processing a large volume of project emails, the system will list priority tasks and suggest simplified procedures that are appropriate for that emotional state. In this way, the present invention provides an innovative platform that supports task management and problem-solving according to the user's emotions.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The server uses user authentication information to access the communication platform and periodically retrieves new communication data. This data includes emails and chat messages.

[0568] Step 2:

[0569] The server processes the acquired communication data using natural language processing techniques to extract keywords related to tasks and issues. The extracted information includes attributes such as task name, deadline, and requester.

[0570] Step 3:

[0571] The server analyzes the communication data using an emotion engine in parallel with the results of natural language processing to identify the user's emotions. Based on the context of the communication and the choice of words, it determines the emotional state (e.g., joy, anger, stress, calmness, etc.).

[0572] Step 4:

[0573] The server integrates extracted task information and sentiment data and stores it in a database. This allows tasks to be organized not only by their importance and deadline, but also according to the user's emotional state.

[0574] Step 5:

[0575] The server generates a dashboard based on task and sentiment data and displays it on the terminal. This visually shows the current task status and sentiment-based priorities.

[0576] Step 6:

[0577] Through this dashboard, users can check the progress of tasks and manually update the status as needed. Furthermore, they can use the displayed sentiment data to decide which tasks to prioritize.

[0578] Step 7:

[0579] The server generates solutions while taking the user's emotional state into consideration. If an emotion indicating stress is detected, it prioritizes suggesting concise solutions and supportive content.

[0580] Step 8:

[0581] Users provide feedback on the proposed solutions. The server collects this feedback along with sentiment data and uses it to improve the accuracy of future solution suggestions.

[0582] (Example 2)

[0583] 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."

[0584] With the advancement of modern information and communication technology, users need to process large amounts of communication data daily. Therefore, there is a need for systems that can efficiently extract and manage tasks and issues while providing personalized solutions that take into account the user's emotional state. However, conventional systems face the challenge of being unable to analyze the user's emotional state in real time and provide appropriate suggestions based on that analysis.

[0585] 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.

[0586] In this invention, the server includes means for receiving data from a communication system, analyzing its content, and extracting information; means for centralizing the extracted information into a collection and classifying it based on importance and deadline; means for generating solutions to the information and proposing them to the user; and means for analyzing the user's emotional state and adjusting the proposed solutions based on that state. This enables task management and solution proposals that are appropriate to the user's emotions.

[0587] A "communication system" refers to any platform that enables the transmission of information between users, and typically includes email services and chat applications.

[0588] "Data" refers to the collective term for all information obtained through communication systems, such as messages and documents, and includes text, images, and audio.

[0589] "Means of analyzing content and extracting information" refers to a function that uses natural language processing technology on received data to identify tasks and important information that the user should address.

[0590] "Means of centralizing and classifying information into a collection" refers to the function of organizing and storing extracted information based on certain criteria, and managing it in a sequential manner according to its importance and urgency.

[0591] "Means for generating solutions and proposing them to users" refers to a function that devises the optimal countermeasure based on extracted information and communicates the results to the user.

[0592] "Means for analyzing the emotional state of users" refers to a function that evaluates the emotional response of users from received data and generates data based on that evaluation, with the aim of optimizing the proposed content.

[0593] "Means for adjusting the content of suggestions" refers to a function that modifies the generated suggestions to the most effective form for individual users, taking into account the user's emotional state.

[0594] The system of this invention combines multiple elements to efficiently manage user communication data and provide optimized solutions based on emotional states.

[0595] The server uses APIs from email services and chat applications to retrieve data from the communication platform. Specifically, when a user receives the latest information via email or chat, it forwards it to the server through the API.

[0596] Next, the server applies natural language processing techniques to the acquired data to identify important tasks and issues within the text. This process involves extracting keywords and phrases using NLP libraries. For example, if a user is communicating about a project deadline, the server retrieves relevant information such as "task management" and "deadline."

[0597] In addition, the server utilizes an emotion analysis model to analyze the user's emotional state from the extracted communication data. This uses an emotion engine to evaluate the emotional tone of the text and determine what emotional state the user is currently in.

[0598] The terminal displays information sent from the server in a dashboard format. Here, users can check their tasks and their priorities. This dashboard uses HTML5, CSS3, and JavaScript to provide a visually intuitive interface.

[0599] Furthermore, by utilizing a generative AI model, the server references similar historical data and external databases to generate solutions based on the user's current emotional state. This proposal breaks down tasks into smaller steps to reduce the user's burden.

[0600] As a concrete example, consider a situation where a user is emotionally exhausted and has to process a large number of project-related emails. In this case, the system would suggest "prioritizing emails" and "simplified work procedures" that are appropriate for the user's emotional state. An example of a prompt would be, "Suggest how to efficiently manage a large volume of project emails when the user is mentally exhausted."

[0601] Thus, the present invention realizes a comprehensive platform for efficient data management and solution provision that takes user emotions into consideration.

[0602] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0603] Step 1:

[0604] Collection of communication data

[0605] The server retrieves data through APIs of email services and chat applications used by users. Inputs include new emails and chat messages. Specifically, this data is passed to the server via API requests. Output is the communication data stored on the server in text format.

[0606] Step 2:

[0607] Data analysis and information extraction

[0608] The server applies natural language processing techniques to the communication data collected in Step 1 to extract important information. The input is the raw communication data. The server uses an NLP library (e.g., spaCy or NLTK) to identify keywords and phrases from the text. Specifically, the data processing identifies information related to tasks and deadlines within each message. The output is the extracted task information and related data.

[0609] Step 3:

[0610] Analysis of emotional states

[0611] The server uses an emotion engine to evaluate the user's emotional state based on the communication data collected in Step 1. The input is the communication data obtained in the previous step. Specifically, an emotion analysis model is applied to analyze the emotional nuances of the text. As output, the user's emotional state (e.g., stress, joy, etc.) is generated as data.

[0612] Step 4:

[0613] Information integration and prioritization

[0614] The server integrates the information obtained in steps 2 and 3 into a single entity and prioritizes tasks based on importance, deadline, and emotional state. Inputs include task information and emotional state data. Specifically, this information is stored in a database, and an algorithm is used to automatically set the priority for each task. The output is a prioritized task list.

[0615] Step 5:

[0616] Dashboard generation

[0617] The server builds a user dashboard on the terminal. The input is the prioritized task list generated in step 4. Specifically, it uses HTML5, CSS3, and JavaScript to build a visually intuitive screen. The output is a user-friendly administrative dashboard.

[0618] Step 6:

[0619] Generating and proposing solutions

[0620] The server uses a generative AI model to generate optimal solutions tailored to the user's situation. Inputs include a prioritized task list and the user's emotional state. Specifically, it automatically forms appropriate suggestions by referencing similar past data and external information. The output is a concrete solution that the user can refer to.

[0621] Step 7:

[0622] Collecting and utilizing feedback

[0623] Users provide feedback on the suggestions they receive. This feedback includes ratings and comments from the user. Specifically, feedback is collected from a dashboard, and this data is stored on a server. The output generates data that can be used to identify areas for improvement and to enhance the accuracy of future suggestions.

[0624] (Application Example 2)

[0625] 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."

[0626] In today's information society, effectively managing vast amounts of communication information and emotional data, and proposing optimal solutions for users based on this information, is challenging. Furthermore, in electronic transactions, optimizing the interface according to the user's mental state is necessary, but conventional methods have limited the technologies capable of achieving this. It is essential to address these challenges and improve the user experience.

[0627] 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.

[0628] In this invention, the server includes means for receiving communication information from a communication interface, analyzing its contents, and extracting issue data; means for centralizing the extracted issue data and emotional state into an information base and prioritizing them based on importance and deadline; means for generating solutions adjusted based on the emotional state and proposing them to the user; and means for optimizing the user interface of electronic transactions according to the user's emotional state. This enables accurate issue extraction from communication information, flexible solution proposals that take emotional data into consideration, and the provision of safe and secure electronic transactions that are tailored to the user's mental state.

[0629] A "communication interface" refers to a digital platform or device used for information exchange, and serves as a foundation for collecting communication information from users.

[0630] "Communication information" refers to the content of digital communications such as emails and chat messages exchanged between users, and problem data is extracted based on this information.

[0631] "Issue data" refers to tasks and problems extracted from communication information, and it serves as basic information for making appropriate suggestions to users based on this data.

[0632] "Emotional state" refers to the psychological state analyzed from the user's communication information, and includes data such as the user's stress, joy, and anxiety.

[0633] An "information base" is a data store where extracted issue data and emotional states are centrally managed, serving as a foundation for data classification and prioritization.

[0634] Prioritization is the process of rearranging tasks that require processing or proposals based on issue data and emotional states, according to their importance and urgency.

[0635] A "solution" refers to specific methods or suggestions provided in response to the extracted problem data, and is presented in a way that is tailored to the user's emotional state.

[0636] "User interface" refers to the appearance and operating environment that end users use to interact with a system, and in electronic transactions, it is optimized to improve user convenience.

[0637] "Electronic transactions" refer to commercial transaction activities conducted through digital platforms, and are a general term for the process by which users purchase or sell products and services.

[0638] The system for implementing the present invention includes a user terminal, a server, and a database. The user terminal is a mobile device such as a smartphone or tablet, and functions as a user input interface. This terminal is used when the user generates communication information.

[0639] After receiving communication information, the server analyzes its content using natural language processing techniques and extracts task data. Here, open-source natural language processing libraries (e.g., NLTK, SpaCy) are used to translate the communication information and process the data. The server also performs sentiment analysis and runs an emotion engine to determine the user's emotional state. For this purpose, AI frameworks for sentiment analysis (e.g., TensorFlow, PyTorch) are used.

[0640] The analyzed issue data and emotional states are centralized in a database and prioritized based on importance and deadline. The server then generates user-optimized solutions based on this data. These solutions are presented to the user through a user interface.

[0641] A key feature of this system is its ability to dynamically optimize the user interface for electronic trading based on the user's emotional state. For example, if a user is feeling stressed, the system will provide a simpler interface to support faster transactions.

[0642] As a concrete example, when a user makes an electronic payment using their smartphone, if the emotion engine detects abnormal tension, the system displays only simple and clear instructions, creating an environment where the transaction can be completed quickly. Furthermore, by using prompts such as "If the user is in a hurry, suggest a quick payment option" for the generating AI model, an appropriate solution is provided. In this way, the present invention realizes a flexible and intuitive user experience that takes into account the user's emotional state.

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] The server receives communication information from the user's terminal. Text data generated by the user via email or chat applications is input. The server temporarily stores this information as digital data. This stored data forms the basis for future processing.

[0646] Step 2:

[0647] The server analyzes the received communication information using natural language processing (NLTK) techniques. Specifically, it uses natural language processing libraries such as NLTK and SpaCy to tokenize the text data and tag it with parts of speech. The input is the communication information, and the output is structured information of the analyzed text data. Based on this structured information, the task data is extracted.

[0648] Step 3:

[0649] The server performs sentiment analysis to determine the user's emotional state. An emotion engine using TensorFlow or PyTorch is executed using the analyzed text data as input. This engine analyzes keywords and expressions extracted from the text and classifies the user's emotional state into categories such as "stress," "joy," and "anxiety." The output is the user's current emotional state data.

[0650] Step 4:

[0651] The server stores the extracted issue data and emotional states in a database and uses this data to assign priorities. The inputs are issue data and emotional states. Based on these, the information is centrally organized in the database, and priorities are calculated according to importance and deadlines. The output is a list of prioritized issues.

[0652] Step 5:

[0653] The server generates and proposes solutions tailored to the user's emotional state. The input consists of a prioritized list of issues and the user's emotional state. Referencing a generation AI model and external data, the server generates the steps and solutions that the user deems most appropriate, and sends these to the user's terminal. The output is the solution presented to the user.

[0654] Step 6:

[0655] The user terminal displays the solution received from the server in the user interface. The interface is optimized for intuitive understanding on the spot. This process improves the user experience by visually organizing and concisely displaying the input solution. The output is a solution in a format that the user can visually confirm.

[0656] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0658] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0659] [Fourth Embodiment]

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

[0661] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0662] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0663] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0664] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0665] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0666] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0667] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0668] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0669] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0670] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0671] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0672] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] The system of this invention automatically collects and analyzes communication data from a user's communication platform to identify and manage tasks and issues. The system mainly consists of server, terminal, and user elements, and the specific processing is as follows.

[0674] Data collection and analysis

[0675] The server periodically crawls the email and chat applications used by users to collect new communication data. The collected data is analyzed using natural language processing techniques to extract information relevant to tasks and issues.

[0676] Information organization and management

[0677] The server stores and centralizes the extracted task information in a database. This information is automatically organized based on attributes such as importance, deadline, and relevant people. The server comprehensively manages this information and creates a list of all tasks currently held by the user.

[0678] Providing a user interface

[0679] The server generates an interface on the terminal that provides organized task and issue information. Through this interface, users can check the progress of tasks and update their status. The dashboard displayed on the terminal visually shows task details (deadline, requester, importance, etc.).

[0680] Generating and proposing solutions

[0681] The server generates appropriate solutions to problems. This process involves referencing a database of past cases and external resources to suggest the most suitable solution for the user. This allows users to address problems efficiently.

[0682] Utilizing feedback

[0683] Users can provide feedback on the effectiveness of the proposed solutions. This feedback is collected by the server and used to improve the accuracy of future solution generation.

[0684] For example, if a user is tasked with creating a project progress report, the system analyzes relevant communication data and suggests necessary information and report templates to the user. In this way, the system of the present invention provides a comprehensive platform to support the efficient management of tasks.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The server obtains authentication credentials to access the user's communication platform and uses APIs or scraping techniques to retrieve new communication data from email and chat applications.

[0688] Step 2:

[0689] The server analyzes the acquired communication data using natural language processing technology and extracts keywords related to tasks and issues based on verbs and nouns. This identifies the actions (e.g., "create," "report") and objects (e.g., "report," "meeting") of the tasks.

[0690] Step 3:

[0691] The server stores the extracted task and issue information in a database. This includes metadata such as importance, deadline, and related people and projects.

[0692] Step 4:

[0693] The server displays a dashboard on the user's terminal based on organized task and issue information. The terminal visually shows the progress and status of tasks (e.g., not started, in progress, completed).

[0694] Step 5:

[0695] Users can manually update task statuses and add detailed information through the task management interface, allowing for real-time tracking of current progress.

[0696] Step 6:

[0697] The server references historical databases and external information sources to generate the optimal solution for the user's task or problem. The proposed solution is then displayed on the user's terminal.

[0698] Step 7:

[0699] Users can review the proposed solutions and provide feedback on their usefulness. This feedback is collected by the server and used to improve the accuracy of the proposed solutions.

[0700] (Example 1)

[0701] 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".

[0702] In today's diverse communication platforms, efficiently and automatically organizing information and providing users with appropriate solutions is a challenging task. Traditional systems handle information collection and organization, as well as solution generation and presentation, separately, making comprehensive management difficult. Furthermore, there is a lack of means to quickly incorporate user feedback and deliver highly accurate suggestions.

[0703] 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.

[0704] In this invention, the server includes means for acquiring communication data from a communication platform, analyzing its content, and extracting information; means for centralizing the extracted information in an information management system and classifying it based on its attributes; and means for visualizing and managing the classified information. This enables efficient information management, the presentation of solutions, and the rapid application of feedback.

[0705] A "communication platform" refers to a digital-based environment for users to exchange information, and includes email and chat applications.

[0706] "Communication data" refers to the collection of messages and information sent and received through a communication platform.

[0707] An "information management system" refers to a digital infrastructure for efficiently storing, organizing, and managing large amounts of data.

[0708] "Generative architecture input statements" refer to the words or instructions given to an artificial intelligence model to generate a specific solution.

[0709] "Machine processing technology" refers to technologies for the automatic analysis and manipulation of data, including natural language processing.

[0710] This invention is a system that effectively manages information by combining multiple elements and presents appropriate solutions to users. Its main components include a server, a terminal, and a user.

[0711] Server operation

[0712] The server utilizes a specific programming language (e.g., Python) and automation tools (e.g., Selenium) to retrieve communication data from the communication platform. This data is analyzed using natural language processing techniques to extract information about tasks and issues. Natural language processing libraries such as SpaCy and NLTK are used for the analysis. The extracted information is stored in a database management system such as MySQL or PostgreSQL and organized using a dataframe library (e.g., Pandas).

[0713] Providing information to the device

[0714] The server generates an interface using a web framework (e.g., Django) and sends task and issue information to the device. This interface is composed of HTML / CSS and JavaScript, allowing users to easily view and update information. The device's dashboard displays task details in a visually organized manner.

[0715] User interaction

[0716] Users can check the progress of tasks and update their status through the provided interface via their terminal. They can also receive solutions presented by the server and provide feedback on their effectiveness. The server collects this feedback to improve the accuracy of future solution generation.

[0717] Solution generation

[0718] The server utilizes a generative AI model to automatically generate optimal solutions based on historical data and the current situation. In this process, suggestions are generated according to the input text for the generative architecture. For example, the following prompt might be used: "Please provide a template to help create a report summarizing project progress. Also, please generate advice based on successful case studies from similar past projects."

[0719] This system enables centralized information management and the rapid provision of solutions to users, which is expected to improve operational efficiency.

[0720] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0721] Step 1:

[0722] The server collects communication data from the communication platform. In this process, the server uses Selenium to automatically crawl email and chat application data and extract new messages. Input requires user account information and access authentication, and output is the newly collected communication data.

[0723] Step 2:

[0724] The server analyzes the collected communication data using natural language processing techniques. The server tokenizes the text data using SpaCy and tags it with parts of speech. Furthermore, it extracts task- and issue-related information from the text. The input is the communication data obtained in step 1, and the output is the extracted task-related information.

[0725] Step 3:

[0726] The server stores the extracted information in an information management system. The server stores the information in a MySQL database and uses Pandas to organize the data by attributes such as importance and due date. The input is the task-related information obtained in step 2, and the output is the organized task information stored in the database.

[0727] Step 4:

[0728] The server uses Django to generate a user interface that displays task information. This generated interface is sent to the terminal, allowing the user to easily view the task information. The input is task information stored in a database, and the output is a visually organized interface.

[0729] Step 5:

[0730] The user checks the progress of a task and updates its status via a terminal. The terminal sends the user's input to the server, which updates the database. The input is the user's updated information, and the output is the updated task status.

[0731] Step 6:

[0732] The server uses a generative AI model to propose an appropriate solution for the task. The server calls the OpenAI API and creates input sentences for the generative architecture based on the context extracted in step 2. The inputs are task information and user feedback, and the output is the solution presented to the user.

[0733] Step 7:

[0734] The user provides feedback on the proposed solution. The terminal sends the feedback to the server, which records this feedback in a database and incorporates it into future suggestions. The input is the user's feedback, and the output is an improvement to the suggestion system based on the feedback information.

[0735] (Application Example 1)

[0736] 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".

[0737] In modern manufacturing, there is a demand for increased efficiency and optimization of production processes within factories. However, rapidly and accurately analyzing the vast amounts of data exchanged through communication media and constructing appropriate production plans is not easy. To overcome this challenge, there is a need for systems that enable efficient data processing and timely optimization of production schedules.

[0738] 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.

[0739] This invention includes a server that receives communication data from a communication medium, analyzes its contents, and extracts problem information; a server that centralizes the extracted problem information into an information set and classifies it based on importance and deadline; and a robot in the factory that integrates and collects communication data from other equipment and workers to construct an optimal work plan. This enables the optimization of the manufacturing process and improvement of production efficiency.

[0740] A "communication medium" refers to a platform for information exchange that enables the sending and receiving of data, and includes email and chat applications.

[0741] "Communication data" refers to the collection of information exchanged through communication media, and includes the content of emails and messages.

[0742] "Problem information" refers to data related to tasks and issues that need to be resolved, extracted through the analysis of communication data.

[0743] An "information aggregate" refers to a database used to store and manage extracted problem information, centrally holding multiple pieces of information.

[0744] "Importance" refers to a criterion used to determine the priority of information, indicating the urgency and impact of a task.

[0745] A "deadline" refers to the time constraint that a task or issue must be completed, and indicates the target date for its completion.

[0746] "Integrated data collection" refers to the process by which robots within a factory collect data from various information sources and manage it centrally.

[0747] An "optimal work plan" refers to a schedule designed to streamline production processes and maximize effectiveness, based on collected and analyzed problem information.

[0748] This invention is a system aimed at optimizing production processes in a factory. This system is realized through the specific roles played by the server, terminals, and users.

[0749] The server first periodically collects communication data from various communication media, including emails and chat messages. Next, it uses natural language processing techniques to extract problem information from this communication data. The information is centralized into an information aggregate and automatically classified based on importance and deadlines. The server analyzes this information and performs integrated collection to ensure that robots in the factory operate efficiently and that production schedules are optimized.

[0750] The terminal receives data from the server and presents it to the user via a visual user interface. This interface displays progress, the importance of problem information, deadlines, and other relevant details. The user can use this information to consider changes or improvements to the production plan.

[0751] Users provide feedback on the proposed solutions. The server receives this feedback and uses it to improve the system, leading to further optimization.

[0752] As a concrete example, consider a situation where a product on the production line fails to meet certain quality standards. The server identifies the root cause and presents an adjustable schedule and solution. The user can then review this on their terminal and provide feedback, contributing to the rapid resolution of the problem.

[0753] Example prompt: "Please describe an algorithm that presents a specific solution for optimizing a factory's production process."

[0754] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0755] Step 1:

[0756] The server periodically collects communication data from communication media such as email and chat messages. It receives this message data as input. The server stores this data and prepares it for subsequent processing.

[0757] Step 2:

[0758] The server analyzes the collected communication data using natural language processing techniques. In this step, the communication data is used as input to extract problem information and related issue information. Data processing includes language analysis and keyword extraction, and the output is extracted results with importance and deadline information added.

[0759] Step 3:

[0760] The server centralizes the extracted problem information into an information database and classifies it based on importance and deadline. The input is problem information. This step involves data processing, organizing the data and storing it in the information database. The output is a set of classified information.

[0761] Step 4:

[0762] The terminal receives classified problem information provided by the server and presents it visually to the user via a user interface. The input is information from the server. The terminal converts this information into a display dashboard, and the output is the displayed visualized information.

[0763] Step 5:

[0764] Users send feedback on proposed solutions to the server via their terminal. Input includes user ratings and opinions, which are sent to the server to generate feedback data. The output is feedback information, which can be used for further system improvements.

[0765] 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.

[0766] In addition to its basic function of analyzing user communication data to extract tasks and issues, the system of the present invention also analyzes the user's emotional state by incorporating an emotion engine. This allows task processing and solution suggestions to be provided in a way that is more optimized for the user.

[0767] Collection and analysis of communication data

[0768] The server collects emails and chat history from the communication platform with the user's permission. This data is analyzed using natural language processing techniques and passed through an emotion engine in the process of extracting tasks and issues. The emotion engine analyzes the user's emotions (e.g., joy, anger, sadness) from the content of the communications and generates corresponding emotion data.

[0769] Information integration and management

[0770] The server stores extracted issue information and sentiment data in a database and centrally manages this information. Task and issue information is prioritized based on criteria such as importance and deadline, as well as the user's emotional state.

[0771] Providing a user interface

[0772] The server generates a dashboard on the terminal, displaying details of the tasks the user needs to manage. This dashboard also visually displays the user's recent emotional state, which is used as reference information for task progress.

[0773] Generating and proposing solutions

[0774] When generating optimal solutions from historical data and external sources, the server adjusts its suggestions based on sentiment data. For example, if a user is experiencing stress, it prioritizes suggesting simplified procedures and support information.

[0775] Utilizing feedback

[0776] Users can provide feedback on the solutions offered. This feedback, along with sentiment data, is stored on the server and used to improve accuracy through the sentiment engine.

[0777] For example, when a user is emotionally exhausted and processing a large volume of project emails, the system will list priority tasks and suggest simplified procedures that are appropriate for that emotional state. In this way, the present invention provides an innovative platform that supports task management and problem-solving according to the user's emotions.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The server uses user authentication information to access the communication platform and periodically retrieves new communication data. This data includes emails and chat messages.

[0781] Step 2:

[0782] The server processes the acquired communication data using natural language processing techniques to extract keywords related to tasks and issues. The extracted information includes attributes such as task name, deadline, and requester.

[0783] Step 3:

[0784] The server analyzes the communication data using an emotion engine in parallel with the results of natural language processing to identify the user's emotions. Based on the context of the communication and the choice of words, it determines the emotional state (e.g., joy, anger, stress, calmness, etc.).

[0785] Step 4:

[0786] The server integrates extracted task information and sentiment data and stores it in a database. This allows tasks to be organized not only by their importance and deadline, but also according to the user's emotional state.

[0787] Step 5:

[0788] The server generates a dashboard based on task and sentiment data and displays it on the terminal. This visually shows the current task status and sentiment-based priorities.

[0789] Step 6:

[0790] Through this dashboard, users can check the progress of tasks and manually update the status as needed. Furthermore, they can use the displayed sentiment data to decide which tasks to prioritize.

[0791] Step 7:

[0792] The server generates solutions while taking the user's emotional state into consideration. If an emotion indicating stress is detected, it prioritizes suggesting concise solutions and supportive content.

[0793] Step 8:

[0794] Users provide feedback on the proposed solutions. The server collects this feedback along with sentiment data and uses it to improve the accuracy of future solution suggestions.

[0795] (Example 2)

[0796] 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".

[0797] With the advancement of modern information and communication technology, users need to process large amounts of communication data daily. Therefore, there is a need for systems that can efficiently extract and manage tasks and issues while providing personalized solutions that take into account the user's emotional state. However, conventional systems face the challenge of being unable to analyze the user's emotional state in real time and provide appropriate suggestions based on that analysis.

[0798] 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.

[0799] In this invention, the server includes means for receiving data from a communication system, analyzing its content, and extracting information; means for centralizing the extracted information into a collection and classifying it based on importance and deadline; means for generating solutions to the information and proposing them to the user; and means for analyzing the user's emotional state and adjusting the proposed solutions based on that state. This enables task management and solution proposals that are appropriate to the user's emotions.

[0800] A "communication system" refers to any platform that enables the transmission of information between users, and typically includes email services and chat applications.

[0801] "Data" refers to the collective term for all information obtained through communication systems, such as messages and documents, and includes text, images, and audio.

[0802] "Means of analyzing content and extracting information" refers to a function that uses natural language processing technology on received data to identify tasks and important information that the user should address.

[0803] "Means of centralizing and classifying information into a collection" refers to the function of organizing and storing extracted information based on certain criteria, and managing it in a sequential manner according to its importance and urgency.

[0804] "Means for generating solutions and proposing them to users" refers to a function that devises the optimal countermeasure based on extracted information and communicates the results to the user.

[0805] "Means for analyzing the emotional state of users" refers to a function that evaluates the emotional response of users from received data and generates data based on that evaluation, with the aim of optimizing the proposed content.

[0806] "Means for adjusting the content of suggestions" refers to a function that modifies the generated suggestions to the most effective form for individual users, taking into account the user's emotional state.

[0807] The system of this invention combines multiple elements to efficiently manage user communication data and provide optimized solutions based on emotional states.

[0808] The server uses APIs from email services and chat applications to retrieve data from the communication platform. Specifically, when a user receives the latest information via email or chat, it forwards it to the server through the API.

[0809] Next, the server applies natural language processing techniques to the acquired data to identify important tasks and issues within the text. This process involves extracting keywords and phrases using NLP libraries. For example, if a user is communicating about a project deadline, the server retrieves relevant information such as "task management" and "deadline."

[0810] In addition, the server utilizes an emotion analysis model to analyze the user's emotional state from the extracted communication data. This uses an emotion engine to evaluate the emotional tone of the text and determine what emotional state the user is currently in.

[0811] The terminal displays information sent from the server in a dashboard format. Here, users can check their tasks and their priorities. This dashboard uses HTML5, CSS3, and JavaScript to provide a visually intuitive interface.

[0812] Furthermore, by utilizing a generative AI model, the server references similar historical data and external databases to generate solutions based on the user's current emotional state. This proposal breaks down tasks into smaller steps to reduce the user's burden.

[0813] As a concrete example, consider a situation where a user is emotionally exhausted and has to process a large number of project-related emails. In this case, the system would suggest "prioritizing emails" and "simplified work procedures" that are appropriate for the user's emotional state. An example of a prompt would be, "Suggest how to efficiently manage a large volume of project emails when the user is mentally exhausted."

[0814] Thus, the present invention realizes a comprehensive platform for efficient data management and solution provision that takes user emotions into consideration.

[0815] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0816] Step 1:

[0817] Collection of communication data

[0818] The server retrieves data through APIs of email services and chat applications used by users. Inputs include new emails and chat messages. Specifically, this data is passed to the server via API requests. Output is the communication data stored on the server in text format.

[0819] Step 2:

[0820] Data analysis and information extraction

[0821] The server applies natural language processing techniques to the communication data collected in Step 1 to extract important information. The input is the raw communication data. The server uses an NLP library (e.g., spaCy or NLTK) to identify keywords and phrases from the text. Specifically, the data processing identifies information related to tasks and deadlines within each message. The output is the extracted task information and related data.

[0822] Step 3:

[0823] Analysis of emotional states

[0824] The server uses an emotion engine to evaluate the user's emotional state based on the communication data collected in Step 1. The input is the communication data obtained in the previous step. Specifically, an emotion analysis model is applied to analyze the emotional nuances of the text. As output, the user's emotional state (e.g., stress, joy, etc.) is generated as data.

[0825] Step 4:

[0826] Information integration and prioritization

[0827] The server integrates the information obtained in steps 2 and 3 into a single entity and prioritizes tasks based on importance, deadline, and emotional state. Inputs include task information and emotional state data. Specifically, this information is stored in a database, and an algorithm is used to automatically set the priority for each task. The output is a prioritized task list.

[0828] Step 5:

[0829] Dashboard generation

[0830] The server builds a user dashboard on the terminal. The input is the prioritized task list generated in step 4. Specifically, it uses HTML5, CSS3, and JavaScript to build a visually intuitive screen. The output is a user-friendly administrative dashboard.

[0831] Step 6:

[0832] Generating and proposing solutions

[0833] The server uses a generative AI model to generate optimal solutions tailored to the user's situation. Inputs include a prioritized task list and the user's emotional state. Specifically, it automatically forms appropriate suggestions by referencing similar past data and external information. The output is a concrete solution that the user can refer to.

[0834] Step 7:

[0835] Collecting and utilizing feedback

[0836] Users provide feedback on the suggestions they receive. This feedback includes ratings and comments from the user. Specifically, feedback is collected from a dashboard, and this data is stored on a server. The output generates data that can be used to identify areas for improvement and to enhance the accuracy of future suggestions.

[0837] (Application Example 2)

[0838] 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".

[0839] In today's information society, effectively managing vast amounts of communication information and emotional data, and proposing optimal solutions for users based on this information, is challenging. Furthermore, in electronic transactions, optimizing the interface according to the user's mental state is necessary, but conventional methods have limited the technologies capable of achieving this. It is essential to address these challenges and improve the user experience.

[0840] 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.

[0841] In this invention, the server includes means for receiving communication information from a communication interface, analyzing its contents, and extracting issue data; means for centralizing the extracted issue data and emotional state into an information base and prioritizing them based on importance and deadline; means for generating solutions adjusted based on the emotional state and proposing them to the user; and means for optimizing the user interface of electronic transactions according to the user's emotional state. This enables accurate issue extraction from communication information, flexible solution proposals that take emotional data into consideration, and the provision of safe and secure electronic transactions that are tailored to the user's mental state.

[0842] A "communication interface" refers to a digital platform or device used for information exchange, and serves as a foundation for collecting communication information from users.

[0843] "Communication information" refers to the content of digital communications such as emails and chat messages exchanged between users, and problem data is extracted based on this information.

[0844] "Issue data" refers to tasks and problems extracted from communication information, and it serves as basic information for making appropriate suggestions to users based on this data.

[0845] "Emotional state" refers to the psychological state analyzed from the user's communication information, and includes data such as the user's stress, joy, and anxiety.

[0846] An "information base" is a data store where extracted issue data and emotional states are centrally managed, serving as a foundation for data classification and prioritization.

[0847] Prioritization is the process of rearranging tasks that require processing or proposals based on issue data and emotional states, according to their importance and urgency.

[0848] A "solution" refers to specific methods or suggestions provided in response to the extracted problem data, and is presented in a way that is tailored to the user's emotional state.

[0849] "User interface" refers to the appearance and operating environment that end users use to interact with a system, and in electronic transactions, it is optimized to improve user convenience.

[0850] "Electronic transactions" refer to commercial transaction activities conducted through digital platforms, and are a general term for the process by which users purchase or sell products and services.

[0851] The system for implementing the present invention includes a user terminal, a server, and a database. The user terminal is a mobile device such as a smartphone or tablet, and functions as a user input interface. This terminal is used when the user generates communication information.

[0852] After receiving communication information, the server analyzes its content using natural language processing techniques and extracts task data. Here, open-source natural language processing libraries (e.g., NLTK, SpaCy) are used to translate the communication information and process the data. The server also performs sentiment analysis and runs an emotion engine to determine the user's emotional state. For this purpose, AI frameworks for sentiment analysis (e.g., TensorFlow, PyTorch) are used.

[0853] The analyzed issue data and emotional states are centralized in a database and prioritized based on importance and deadline. The server then generates user-optimized solutions based on this data. These solutions are presented to the user through a user interface.

[0854] A key feature of this system is its ability to dynamically optimize the user interface for electronic trading based on the user's emotional state. For example, if a user is feeling stressed, the system will provide a simpler interface to support faster transactions.

[0855] As a concrete example, when a user makes an electronic payment using their smartphone, if the emotion engine detects abnormal tension, the system displays only simple and clear instructions, creating an environment where the transaction can be completed quickly. Furthermore, by using prompts such as "If the user is in a hurry, suggest a quick payment option" for the generating AI model, an appropriate solution is provided. In this way, the present invention realizes a flexible and intuitive user experience that takes into account the user's emotional state.

[0856] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0857] Step 1:

[0858] The server receives communication information from the user's terminal. Text data generated by the user via email or chat applications is input. The server temporarily stores this information as digital data. This stored data forms the basis for future processing.

[0859] Step 2:

[0860] The server analyzes the received communication information using natural language processing (NLTK) techniques. Specifically, it uses natural language processing libraries such as NLTK and SpaCy to tokenize the text data and tag it with parts of speech. The input is the communication information, and the output is structured information of the analyzed text data. Based on this structured information, the task data is extracted.

[0861] Step 3:

[0862] The server performs sentiment analysis to determine the user's emotional state. An emotion engine using TensorFlow or PyTorch is executed using the analyzed text data as input. This engine analyzes keywords and expressions extracted from the text and classifies the user's emotional state into categories such as "stress," "joy," and "anxiety." The output is the user's current emotional state data.

[0863] Step 4:

[0864] The server stores the extracted issue data and emotional states in a database and uses this data to assign priorities. The inputs are issue data and emotional states. Based on these, the information is centrally organized in the database, and priorities are calculated according to importance and deadlines. The output is a list of prioritized issues.

[0865] Step 5:

[0866] The server generates and proposes solutions tailored to the user's emotional state. The input consists of a prioritized list of issues and the user's emotional state. Referencing a generation AI model and external data, the server generates the steps and solutions that the user deems most appropriate, and sends these to the user's terminal. The output is the solution presented to the user.

[0867] Step 6:

[0868] The user terminal displays the solution received from the server in the user interface. The interface is optimized for intuitive understanding on the spot. This process improves the user experience by visually organizing and concisely displaying the input solution. The output is a solution in a format that the user can visually confirm.

[0869] 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.

[0870] 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.

[0871] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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."

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] The following is further disclosed regarding the embodiments described above.

[0891] (Claim 1)

[0892] A means of receiving communication data from a communication platform, analyzing its content, and extracting problem information,

[0893] A method for centralizing extracted issue information into a database and classifying it based on importance and deadline,

[0894] A means of displaying and managing the progress of classified task information,

[0895] A means of generating solutions to problem information and proposing them to users,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising means for receiving user feedback and using it to improve the accuracy of solution suggestions.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising means of using natural language processing technology when extracting issue information from communication data.

[0901] "Example 1"

[0902] (Claim 1)

[0903] A means of acquiring communication data from a communication platform, analyzing its content, and extracting information,

[0904] A means of centralizing the extracted information in an information management system and classifying it based on attributes,

[0905] A means of visualizing and managing classified information,

[0906] A means of generating solutions based on information and presenting them to the user,

[0907] A means of presenting new solutions using input statements for a generative architecture,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, comprising means for obtaining user feedback and using it to improve the accuracy of providing solutions.

[0911] (Claim 3)

[0912] The system according to claim 1, comprising means for using machine processing technology when extracting information from communication data.

[0913] "Application Example 1"

[0914] (Claim 1)

[0915] A means of receiving communication data from a communication medium, analyzing its contents, and extracting problem information,

[0916] A means of centralizing extracted problem information into an information set and classifying it based on importance and deadline,

[0917] A means of visualizing and managing the progress of classified problem information,

[0918] A means of creating and proposing solution strategies to problem information to users,

[0919] A means by which robots in a factory integrate and collect communication data from other equipment and workers to construct an optimal work plan,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, further comprising means for receiving user feedback and using it to improve the accuracy of solution suggestions.

[0923] (Claim 3)

[0924] The system according to claim 1, comprising means of using natural language processing technology when extracting problem information from communication data.

[0925] "Example 2 of combining an emotion engine"

[0926] (Claim 1)

[0927] A means of receiving data from a communication system, analyzing its content, and extracting information,

[0928] A means of centralizing extracted information into a collection and classifying it based on importance and deadline,

[0929] A means of displaying and managing the progress of classified information,

[0930] A means of generating solutions to information and proposing them to users,

[0931] A means of analyzing the emotional state of the user and adjusting the proposed content based on that state,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, comprising means for receiving evaluation information from users and using it to improve the accuracy of solution proposals.

[0935] (Claim 3)

[0936] The system according to claim 1, comprising means of using natural language processing technology when extracting information from data.

[0937] "Application example 2 when combining with an emotional engine"

[0938] (Claim 1)

[0939] A means of receiving communication information from a communication interface, analyzing its contents, and extracting problem data,

[0940] Extracted issue data and emotional states are unified into an information base, and a means is used to prioritize them based on importance and deadline.

[0941] A means of visualizing and managing the progress of prioritized task data,

[0942] A means of generating and proposing solutions tailored to the user's emotional state,

[0943] A means for optimizing the user interface of electronic transactions according to the emotional state of the user,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, further comprising means for receiving user feedback and using it to improve the accuracy of solution suggestions.

[0947] (Claim 3)

[0948] The system according to claim 1, comprising means of using natural language processing techniques when extracting task data from communication information. [Explanation of symbols]

[0949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving communication data from a communication medium, analyzing its contents, and extracting problem information, A means of centralizing extracted problem information into an information set and classifying it based on importance and deadline, A means of visualizing and managing the progress of classified problem information, A means of creating and proposing solution strategies to problem information to users, A means by which robots in a factory integrate and collect communication data from other equipment and workers to construct an optimal work plan, A system that includes this.

2. The system according to claim 1, further comprising means for receiving user feedback and using it to improve the accuracy of proposed solutions.

3. The system according to claim 1, further comprising means for using natural language processing technology when extracting problem information from communication data.

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