system

Abstract

We provide the system. [Solution] A data integration method that collects information from each department and manages it centrally, A task prioritization method that automatically evaluates and adjusts priorities using integrated information, A monitoring system that monitors the project's progress in real time and notifies when an anomaly is detected, A real-time report generation means that generates the latest status as a report in response to user requests, Task automation methods for automatically performing regular tasks, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern enterprises, since each department manages a wide variety of information and tasks independently, the business process often becomes inefficient. In particular, due to insufficient information sharing between departments and inappropriate task priorities, there are problems such as delays in project progress and wasteful consumption of resources. Also, it is difficult to grasp the real-time status of projects, which hinders rapid decision-making. Therefore, in order to maintain and improve the productivity of the entire enterprise, a new system that comprehensively solves these problems is required.

Means for Solving the Problems

[0005] This invention streamlines information sharing between departments by providing a data integration means that collects and centrally manages information from each department. Furthermore, it enables optimal resource allocation through a task prioritization means that automatically evaluates and adjusts task priorities based on the integrated information. It also facilitates rapid problem resolution by providing a monitoring means that monitors project progress in real time and immediately notifies when anomalies are detected. In addition, it supports faster management decision-making by using a real-time report generation means that generates reports of the latest status in real time according to user requests. Finally, it reduces the burden on human resources by constructing a task automation means that automatically executes periodic tasks. Through these means, the invention optimizes cross-departmental operations within a company and achieves increased productivity.

[0006] A "data integration tool" is a function that converts information in different formats obtained from each department into a standardized format and manages it centrally.

[0007] A "task prioritization tool" is a function that evaluates the importance and urgency of tasks based on integrated information and automatically adjusts schedules and resource allocations.

[0008] A "monitoring method" is a function that monitors the progress of a project in real time and immediately notifies the user when an anomaly is detected.

[0009] The "real-time report generation method" is a function that quickly generates reports containing the latest project status and analysis information in response to user requests.

[0010] "Task automation means" are functions that automatically execute routine tasks that are performed regularly, thereby reducing the burden of human work. [Brief explanation of the drawing]

[0011] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system of this invention consists of multiple functions performed in cooperation with a server, terminals, and users. Each function is designed to optimize cross-departmental operations within an organization and improve productivity. The specific operation of each function is described below.

[0033] First, the server periodically retrieves operational data from each department. This includes various types of information, such as manufacturing data, sales data, and customer feedback. After receiving this data, the server uses data integration tools to standardize the data in different formats. This ensures consistent information management across departments.

[0034] Next, the server uses the integrated data to perform analysis using AI algorithms. Based on the results of this analysis, it automatically sets task priorities. The task prioritization method takes into account the resources and progress of each department to create an optimal schedule.

[0035] Furthermore, the server monitors whether the project is progressing smoothly. If delays or problems occur during project progress, the monitoring system immediately detects the problem and sends an alert to the relevant personnel. This allows for early problem resolution and supports the smooth execution of the project.

[0036] Furthermore, the terminal generates reports in real time in response to user requests. When a user wants to check the latest status of a project, the terminal accepts the request, and the server creates a report based on the latest data. This report is displayed on the terminal in a visually easy-to-understand format, supporting quick decision-making.

[0037] Finally, the server automates routine and repetitive tasks. This task automation includes, for example, scheduling regular meetings and automatically generating monthly reports. This reduces the efficiency of manual tasks.

[0038] As a concrete example, consider a product development project. A server can integrate inventory data from the manufacturing department and market feedback from the sales department to optimize the product schedule. As a result, users can understand the project's progress in real time based on data and make strategic decisions.

[0039] Thus, the present invention is a system that revolutionizes the productivity of an entire company by consistently performing tasks from data integration to automation.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server connects to each department's database to collect the latest business data. This collected data includes manufacturing data, sales data, customer feedback, and more. This data is then stored in temporary storage.

[0043] Step 2:

[0044] The server standardizes the data stored in storage using data integration means. It converts data in different formats into a unified format and organizes it in a state where it can be analyzed. This integrated data is then stored in the main database.

[0045] Step 3:

[0046] The server inputs integrated data into an AI algorithm to analyze the business situation. Based on the analysis results, it evaluates the importance and urgency of each task and sets priorities using a task prioritization method.

[0047] Step 4:

[0048] The server monitors the project's progress. It checks whether the project is progressing according to the set schedule, and if delays or problems occur, it issues alerts through the monitoring system and notifies the relevant departments.

[0049] Step 5:

[0050] When a user requests to check the project's status, the terminal forwards the request to the server. The server gathers the latest information based on the received request, generates a real-time report, and sends it to the terminal. The terminal then displays the report to the user.

[0051] Step 6:

[0052] The server automates regular routine tasks. For example, it improves operational efficiency by executing tasks based on pre-configured conditions, such as generating monthly reports and adjusting schedules.

[0053] (Example 1)

[0054] 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."

[0055] It is necessary to improve productivity by efficiently integrating data managed individually by each department within a company and automating task prioritization and scheduling. Furthermore, a system is needed to monitor project progress in real time, detect anomalies early, and respond accordingly. Additionally, there is a need for means to enable users to instantly obtain the information they need and to streamline routine tasks.

[0056] 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.

[0057] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and a monitoring means for monitoring the status of ongoing tasks in real time and notifying the relevant personnel when an anomaly is detected. This makes it possible to effectively manage data across the entire company and improve productivity.

[0058] "Information integration means" refers to technology that centrally manages information collected from various departments, converts data in different formats into a standardized format, and makes it analyzable.

[0059] A "task prioritization method" is a technology that uses integrated information and artificial intelligence algorithms to automatically evaluate the priority of tasks and dynamically adjust plans and resources.

[0060] "Monitoring measures" refer to technologies that observe the status of ongoing operations in real time and immediately notify the relevant personnel when an anomaly is detected.

[0061] An "instant report generation method" is a technology that quickly gathers the latest information in response to user requests and generates and presents it as an easy-to-understand report.

[0062] "Task automation methods" refer to technologies that automatically process regularly scheduled tasks using programs, eliminating the need for human intervention.

[0063] A "generative AI model" is a model that artificial intelligence uses to learn from input data and perform predictions and analyses.

[0064] To implement this invention, a system is required in which a server, terminals, and users work in coordination. The server collects information from various departments within the company and uses a database server and API technology to centrally manage it. The collected information is standardized from different formats through an information integration means, achieving consistent data management.

[0065] The server analyzes the integrated data using an analysis method that employs a generative AI model. The generative AI model used at this stage leverages machine learning algorithms to effectively prioritize tasks, thereby improving overall business efficiency.

[0066] The server monitors the project's progress in real time using monitoring mechanisms. If an anomaly is detected, the responsible party is immediately notified. This enables a swift response and supports the smooth execution of the project.

[0067] The terminal uses an instant report generation mechanism in response to user requests to create reports based on the latest information. The generated reports are presented to the user in a visually easy-to-understand format to support strategic decision-making. For example, if the user enters a prompt such as "Please tell me the sales forecast for the next quarter," the terminal will immediately generate the necessary report.

[0068] Furthermore, the server automates regularly scheduled tasks through automated processes. This automation significantly improves the efficiency of manual tasks, saving time.

[0069] In this way, servers, terminals, and users work together, utilizing various technologies and methods to seamlessly execute everything from data management to business process automation, thereby building a system that improves corporate productivity.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server collects information from various departments within the company via APIs. This input information includes manufacturing data, sales data, and customer feedback. The server stores the collected data in a temporary database, maintaining different formats for each department.

[0073] Step 2:

[0074] The server standardizes information stored in the temporary database using data integration means. This involves converting different data formats into a common format, for example, unifying different unit systems. The server then arranges this standardized data in a form that allows for more efficient analysis and stores it in the integrated database.

[0075] Step 3:

[0076] The server analyzes the integrated data using a generative AI model. The input data for the AI ​​model is the newly integrated, standardized data. The server uses the AI ​​model to automatically prioritize tasks. For example, it performs predictive analytics to optimize production schedules. The output is a prioritized task list.

[0077] Step 4:

[0078] The server monitors the project's progress in real time using monitoring tools. The server monitors task lists and progress information, and if anomalies or delays occur, it detects the problem from the real-time data input and sends alerts to the relevant personnel.

[0079] Step 5:

[0080] The terminal activates an instant report generation mechanism based on user prompts. When a user inputs something like, "Please tell me the sales forecast for the next quarter," the terminal sends a request to the server, which generates a corresponding report based on the latest data and sends it to the terminal. The user then makes decisions based on the graphs and reports displayed on the terminal.

[0081] Step 6:

[0082] The server automates the execution of regular tasks using automation tools. For example, it runs a process to automatically generate monthly reports and send them via email to the relevant personnel. The server runs scripts at scheduled times, thereby streamlining tasks that are understaffed.

[0083] (Application Example 1)

[0084] 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."

[0085] This invention aims to optimize company-wide operations in today's diverse business environment, where efficient aggregation of information between departments is required, enabling real-time prioritization of activities and rapid detection of anomalies. It also aims to meet the needs of factory operations, where optimization of maintenance and increased operational efficiency based on operational information are required in the operation of manufacturing equipment.

[0086] 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.

[0087] In this invention, the server includes a data aggregation means for centrally managing information from each department, an activity prioritization means for analyzing the aggregated information and automatically adjusting the priority of activities, a monitoring means for monitoring the progress of the business in real time and immediately notifying when an anomaly is detected, and an optimization means for aggregating and analyzing the operation information of manufacturing equipment to optimize maintenance timing and operation. This makes it possible to promote overall business efficiency and optimize the operation of manufacturing equipment.

[0088] A "data aggregation method" is a function that provides a foundation for centrally managing information collected from each department and using it for subsequent analysis and processing.

[0089] A "activity prioritization method" is a method that uses aggregated information to automatically evaluate and adjust the priority of each activity, thereby achieving efficient resource allocation.

[0090] A "monitoring method" is a technique that tracks the progress of a project in real time and immediately notifies the user when an anomaly is detected.

[0091] The "real-time report generation method" is a function that instantly generates reports in an easy-to-understand format, based on the user's request, showing the latest business status.

[0092] "Task automation methods" are systems that automatically perform regular tasks and promote operational efficiency.

[0093] An "optimization method" is a method for optimizing maintenance timing and operating conditions by aggregating operational information of manufacturing equipment and analyzing it.

[0094] In an embodiment of this invention, the server first centrally manages diverse information collected from various departments using data aggregation means. Since the collected data is often provided in different formats, it is converted into a standardized format. Database software and data conversion tools are used for this purpose. For example, the Pandas library is used to integrate the data.

[0095] Next, the server activates an activity prioritization mechanism based on the aggregated data. This mechanism uses machine learning algorithms to analyze and automatically adjust the priority of each activity. The Scikit-learn library is used here for data analysis and dynamic scheduling. Once task priorities are determined, optimal resource allocation becomes possible.

[0096] Furthermore, the server employs monitoring mechanisms to track the progress of the project. Project status is tracked in real time, and if an anomaly is detected, an immediate notification is sent to the responsible person. This enables a rapid response.

[0097] The terminal visualizes the latest work status through a real-time report generation mechanism according to the user's requests. Furthermore, the server automates periodic tasks, leading to increased efficiency.

[0098] Finally, the server aggregates operational information from the manufacturing equipment and provides optimization tools to optimize maintenance timing and operating status through analysis. For example, it can automatically determine the necessary maintenance schedule based on robot operation data. This leads to improved operational efficiency.

[0099] For example, if a robot malfunctions and stops in a manufacturing plant, the server immediately sends a notification to the responsible person, prompting a quick response. This makes it possible to optimize maintenance timing based on operational data to maintain the efficiency of the manufacturing line.

[0100] Example of a prompt:

[0101] "Please tell me how to integrate inventory data from the manufacturing department with feedback from the sales department to propose the optimal production schedule."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server collects information from each department. Inputs are departmental databases and files, and output is integrated raw data. Database software is used to retrieve the data by executing SQL queries, and this data is then centrally aggregated.

[0105] Step 2:

[0106] The server standardizes the collected raw data using data aggregation methods. The input is raw data in various formats, and the output is data in a standardized format. The Pandas library is used to clean the data and extract and transform the necessary information.

[0107] Step 3:

[0108] The server performs an activity prioritization mechanism using standardized data. The input is a standardized dataset, and the output is a priority list for each activity. A machine learning model is trained using Scikit-learn to run an algorithm that dynamically determines priorities.

[0109] Step 4:

[0110] The server tracks the project's progress in real time using monitoring tools. Input is progress data obtained from the project management system, and output is the detection results of anomalies. When an anomaly is detected, it sends a notification email to the responsible party.

[0111] Step 5:

[0112] The terminal operates a real-time report generation system based on user instructions to create a report. The input is the latest status data provided by the server, and the output is a visualized report. The report is generated in HTML or PDF format and presented visually to the user.

[0113] Step 6:

[0114] The server performs routine tasks using automated methods. The input is the schedule of the recurring tasks, and the output is a record of completed tasks. This automates tasks such as scheduling meetings and generating reports.

[0115] Step 7:

[0116] The server analyzes the operational information of the manufacturing equipment using optimization techniques and proposes the optimal operation. The input is the operational data of the manufacturing equipment, and the output is an optimized maintenance schedule. Based on the analysis results, the server adjusts the necessary maintenance plan.

[0117] 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.

[0118] This invention aims to improve overall corporate productivity by incorporating an emotion engine into a system that optimizes cross-departmental business operations within a company. This engine recognizes user emotions and further optimizes business processes. The main components of the system include various means consisting of servers, terminals, and users.

[0119] The server provides a data integration mechanism that collects and centrally manages data from each department. It converts and analyzes information obtained from different data sources into a standardized format, enabling a comprehensive understanding of ongoing projects.

[0120] The server analyzes integrated data using AI algorithms to dynamically evaluate task priorities. This task prioritization method optimizes resource allocation based on importance and urgency, thereby improving operational efficiency.

[0121] Furthermore, the server is equipped with monitoring capabilities to track project progress in real time and immediately notifies users of any anomalies. By providing users with information at the appropriate time, it supports quick and accurate decision-making.

[0122] To take into account the user's emotional state, the system incorporates an emotion engine to optimize user interaction. The emotion engine, for example, analyzes the user's voice data to identify their emotional state. The server then adjusts the content and urgency of notifications based on this emotional state, supporting the user in carrying out their work in a less stressful way.

[0123] As a concrete example, suppose a notification is issued indicating that the project's progress is unsatisfactory. If the emotion engine determines that the user is feeling stressed, the server adjusts the tone and level of detail of the notification to present it in a way that is most appropriate for the user. This allows the user to efficiently understand the situation and consider countermeasures without feeling unnecessarily pressured.

[0124] Thus, the system of the present invention, by combining data integration and emotion recognition, enables more human-centric business operations and enhances the overall performance of the organization.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server accesses each department's database to collect the latest operational data. This data includes manufacturing information, sales reports, and customer feedback. The retrieved data is stored in temporary storage.

[0128] Step 2:

[0129] The server converts data stored in temporary storage into a standard format using data integration means. Unifying data in different formats enables consistent data analysis. This integrated data is then stored in the main database.

[0130] Step 3:

[0131] The server analyzes integrated data using AI algorithms to evaluate work progress and resource utilization. Based on the analysis results, it determines the importance and urgency of tasks and sets priorities using task prioritization tools.

[0132] Step 4:

[0133] The server monitors the project's progress in real time. If delays or problems occur, it uses the monitoring system to issue notifications, immediately informing the relevant departments.

[0134] Step 5:

[0135] The user sends a request from their terminal to the system to check the project status. The terminal forwards that request to the server.

[0136] Step 6:

[0137] The server processes the received request and generates a real-time report using the latest data. This report is then sent to the terminal, which displays the information to the user.

[0138] Step 7:

[0139] The emotion engine analyzes the user's voice data to recognize their emotional state. This analysis result is then fed back to the server.

[0140] Step 8:

[0141] The server adjusts system notifications and interfaces based on the user's emotional state. For example, if the server detects that the user is stressed, it softens the tone and content of notifications, providing information in a way that is best suited to the user.

[0142] Through this series of processes, the system leverages centralized data management and emotion recognition to enable efficient and ergonomic work execution.

[0143] (Example 2)

[0144] 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".

[0145] Modern businesses face problems such as inconsistent data and prioritization across departments, as well as increased user stress. These issues lead to decreased productivity and delayed decision-making, ultimately undermining overall business efficiency. In particular, there is a need for systems that can adjust operations in real time while reducing the emotional burden on users.

[0146] 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.

[0147] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and an emotion recognition means for identifying the user's emotional state and adjusting the content and tone of notifications based on this. This enables cross-departmental data management, appropriate resource allocation, and information provision that reduces stress on users.

[0148] "Information integration means" refers to a method for centrally collecting information obtained from different departments and converting it into a standardized format.

[0149] A "task prioritization method" is a means of analyzing aggregated information and dynamically determining priorities based on the importance and urgency of the tasks.

[0150] A "situation monitoring system" is a means of monitoring the progress of a project in real time and taking immediate action if an anomaly is detected.

[0151] "Emotion recognition means" refers to a means of identifying the user's emotional state and optimizing the content and tone of notifications based on that information.

[0152] "Interaction optimization means" are methods that support users in receiving information in a stress-reduced state.

[0153] This invention is a system for optimizing cross-departmental business processes within a company and realizing corporate management that takes user emotions into consideration. The main components are a server, terminals, and users.

[0154] The server provides an information integration mechanism for collecting and centrally managing data from various departments. This involves database management systems and ETL tools, specifically including MySQL® and Apache® NiFi. This standardizes and integrates data in different formats.

[0155] The server analyzes integrated data to determine task priorities. AI algorithms are used for analysis, leveraging machine learning platforms such as TENSORFLOW® and PyTorch. This allows for optimal resource allocation and improved task efficiency.

[0156] Furthermore, the server monitors the project's progress in real time and immediately notifies users if any anomalies are detected. Prometheus and Zabbix are used as the monitoring system.

[0157] The device utilizes speech recognition technology to collect user voice data. Speech analysis software such as Google® Cloud Speech-to-Text is used to extract emotional states from the user's utterances and transmit the data to the server via emotion recognition.

[0158] Through this system, users can receive timely and necessary information and make business decisions. A specific example of its use is when, in the event of insufficient project progress, the system adjusts notifications in a way that reduces user stress.

[0159] An example of a prompt would be, "Please tell me how to collect departmental data and build a notification system that takes user sentiment into consideration." Based on such prompts, the system uses a generative AI model to provide an appropriate solution.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server collects data from each department. The input data comes in different formats depending on the department. Specific data collection operations include API calls and file transfers. The server converts this data into a standardized format and outputs it as integrated data. Data conversion tools are used for this process.

[0163] Step 2:

[0164] The server analyzes the integrated data. The input here is the integrated data formatted in Step 1. The server uses machine learning algorithms to analyze the data and determine the priority of the tasks. Specifically, it performs data model training and predictive calculations, and the result is output as a priority level.

[0165] Step 3:

[0166] The server monitors the project's progress in real time. Its input is the current status information of each process running within the system. The server tracks this using monitoring tools and outputs alert notifications if an anomaly occurs. Specific operations include threshold checks and alert settings.

[0167] Step 4:

[0168] The device collects the user's voice data and sends it to the server. The input is the user's voice data. The device uses speech recognition technology to convert this voice data into text format. The output is sent to the server as text data and used for subsequent sentiment analysis. This specific operation involves capturing voice input and converting it to text.

[0169] Step 5:

[0170] The server analyzes the text extracted from the audio data to determine the user's emotional state. The input is the text data generated in step 4. The server analyzes this data using an emotion analysis model and outputs the emotional state. The specific operations include natural language processing and emotion score calculation.

[0171] Step 6:

[0172] The server adjusts the content and tone of notifications based on the user's emotional state and sends them to the user. Inputs include the emotional state obtained in step 5 and the monitoring information from step 3. Based on this, the server generates appropriate notification content and sends it to the user. Specific actions include information filtering and notification message optimization.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0175] In modern work environments, workers' emotional states significantly impact productivity, but traditional management systems struggle to prioritize and adjust tasks while considering individual emotions. In such cases, there is a need to achieve an efficient and comfortable work environment while harmonizing human interaction with machines.

[0176] 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.

[0177] In this invention, the server includes an information integration means for aggregating and managing data from each area, a task prioritization evaluation means for dynamically calculating priorities based on the integrated data, and an emotion analysis means for analyzing the user's emotional state and adjusting notification content. This enables flexible and efficient work adjustments that take into account the emotional state of workers.

[0178] An "information integration tool" is a function for centralizing multiple data collected from various domains, converting them into a unified format, and managing them.

[0179] A "task prioritization evaluation tool" is a function that dynamically calculates and adjusts task priorities based on integrated data.

[0180] The "monitoring function" is a function that continuously observes the progress of work and promptly notifies the user when an anomaly is detected.

[0181] "Emotional analysis methods" are technologies that analyze a user's voice and behavioral data to determine their emotional state.

[0182] "Task automation functionality" refers to a function that autonomously performs regular tasks according to pre-set rules.

[0183] In the system for realizing this invention, a server plays a central role. The server uses information integration means to collect data from various domains, converts it into a unified format, and manages it. This eliminates data inconsistencies and enables consistent data analysis. Based on this integrated data, the server uses work prioritization evaluation means to dynamically calculate priorities.

[0184] Furthermore, the server operates sentiment analysis tools and evaluates the user's voice and behavioral data in real time using analysis tools such as Google Cloud's Natural Language API. Based on these evaluation results, it automatically adjusts the content of work notifications and schedules to ensure the user can work in the most optimal state.

[0185] A concrete example is when a server sends commands to robots operating on a factory production line. It analyzes the emotional state of the workers and notifies them to prioritize less burdensome tasks. This reduces worker stress and enables more efficient production.

[0186] As an example of a prompt, the system might input, "Analyze the emotional state of this audio and determine whether the worker is experiencing stress." Based on this prompt, the server performs an emotional analysis and optimizes the task according to the results obtained.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The server collects data from various domains and converts it into a unified format. The input is raw data obtained from different data sources, and the output is a dataset converted to a unified format. This process extracts and standardizes the data, performing data processing to ensure consistency in subsequent analysis.

[0190] Step 2:

[0191] The server dynamically calculates task priorities using work prioritization evaluation tools based on integrated data. The input is a dataset converted to a unified format, and the output is a prioritized task list. AI algorithms are used to analyze the data, assess the importance and urgency of tasks, and optimize resource allocation based on the results.

[0192] Step 3:

[0193] The server analyzes the user's emotional state by applying emotion analysis to user voice and behavioral data. Voice data from the user is provided as input, and analysis results indicating the emotional state are generated as output. Here, Google Cloud's Natural Language API is used to identify emotional components in the voice data and measure states such as stress and fatigue.

[0194] Step 4:

[0195] The server adjusts the task list content and notifications based on the sentiment analysis results. The input is the sentiment analysis results and a prioritized task list; the output is the adjusted task list. Based on these analysis results, the server flexibly changes the tone and content of notifications to the worker to create an optimal work environment.

[0196] Step 5:

[0197] Ultimately, the server sends the adjusted task list and notifications to the terminal. The input is the adjusted task list and notification settings, and the output is the optimized information displayed on the terminal. The information displayed on the terminal is designed to reduce user stress and support efficient work performance.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] The system of this invention consists of multiple functions performed in cooperation with a server, terminals, and users. Each function is designed to optimize cross-departmental operations within an organization and improve productivity. The specific operation of each function is described below.

[0215] First, the server periodically retrieves operational data from each department. This includes various types of information, such as manufacturing data, sales data, and customer feedback. After receiving this data, the server uses data integration tools to standardize the data in different formats. This ensures consistent information management across departments.

[0216] Next, the server uses the integrated data to perform analysis using AI algorithms. Based on the results of this analysis, it automatically sets task priorities. The task prioritization method takes into account the resources and progress of each department to create an optimal schedule.

[0217] Furthermore, the server monitors whether the project is progressing smoothly. If delays or problems occur during project progress, the monitoring system immediately detects the problem and sends an alert to the relevant personnel. This allows for early problem resolution and supports the smooth execution of the project.

[0218] Furthermore, the terminal generates reports in real time in response to user requests. When a user wants to check the latest status of a project, the terminal accepts the request, and the server creates a report based on the latest data. This report is displayed on the terminal in a visually easy-to-understand format, supporting quick decision-making.

[0219] Finally, the server automates routine and repetitive tasks. This task automation includes, for example, scheduling regular meetings and automatically generating monthly reports. This reduces the efficiency of manual tasks.

[0220] As a concrete example, consider a product development project. A server can integrate inventory data from the manufacturing department and market feedback from the sales department to optimize the product schedule. As a result, users can understand the project's progress in real time based on data and make strategic decisions.

[0221] Thus, the present invention is a system that revolutionizes the productivity of an entire company by consistently performing tasks from data integration to automation.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The server connects to each department's database to collect the latest business data. This collected data includes manufacturing data, sales data, customer feedback, and more. This data is then stored in temporary storage.

[0225] Step 2:

[0226] The server standardizes the data stored in storage using data integration means. It converts data in different formats into a unified format and organizes it in a state where it can be analyzed. This integrated data is then stored in the main database.

[0227] Step 3:

[0228] The server inputs integrated data into an AI algorithm to analyze the business situation. Based on the analysis results, it evaluates the importance and urgency of each task and sets priorities using a task prioritization method.

[0229] Step 4:

[0230] The server monitors the project's progress. It checks whether the project is progressing according to the set schedule, and if delays or problems occur, it issues alerts through the monitoring system and notifies the relevant departments.

[0231] Step 5:

[0232] When a user requests to check the project's status, the terminal forwards the request to the server. The server gathers the latest information based on the received request, generates a real-time report, and sends it to the terminal. The terminal then displays the report to the user.

[0233] Step 6:

[0234] The server automates regular routine tasks. For example, it improves operational efficiency by executing tasks based on pre-configured conditions, such as generating monthly reports and adjusting schedules.

[0235] (Example 1)

[0236] 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."

[0237] It is necessary to improve productivity by efficiently integrating data managed individually by each department within a company and automating task prioritization and scheduling. Furthermore, a system is needed to monitor project progress in real time, detect anomalies early, and respond accordingly. Additionally, there is a need for means to enable users to instantly obtain the information they need and to streamline routine tasks.

[0238] 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.

[0239] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and a monitoring means for monitoring the status of ongoing tasks in real time and notifying the relevant personnel when an anomaly is detected. This makes it possible to effectively manage data across the entire company and improve productivity.

[0240] "Information integration means" refers to technology that centrally manages information collected from various departments, converts data in different formats into a standardized format, and makes it analyzable.

[0241] A "task prioritization method" is a technology that uses integrated information and artificial intelligence algorithms to automatically evaluate the priority of tasks and dynamically adjust plans and resources.

[0242] "Monitoring measures" refer to technologies that observe the status of ongoing operations in real time and immediately notify the relevant personnel when an anomaly is detected.

[0243] An "instant report generation method" is a technology that quickly gathers the latest information in response to user requests and generates and presents it as an easy-to-understand report.

[0244] "Task automation methods" refer to technologies that automatically process regularly scheduled tasks using programs, eliminating the need for human intervention.

[0245] A "generative AI model" is a model that artificial intelligence uses to learn from input data and perform predictions and analyses.

[0246] To implement this invention, a system is required in which a server, terminals, and users work in coordination. The server collects information from various departments within the company and uses a database server and API technology to centrally manage it. The collected information is standardized from different formats through an information integration means, achieving consistent data management.

[0247] The server analyzes the integrated data using an analysis method that employs a generative AI model. The generative AI model used at this stage leverages machine learning algorithms to effectively prioritize tasks, thereby improving overall business efficiency.

[0248] The server monitors the project's progress in real time using monitoring mechanisms. If an anomaly is detected, the responsible party is immediately notified. This enables a swift response and supports the smooth execution of the project.

[0249] The terminal uses an instant report generation mechanism in response to user requests to create reports based on the latest information. The generated reports are presented to the user in a visually easy-to-understand format to support strategic decision-making. For example, if the user enters a prompt such as "Please tell me the sales forecast for the next quarter," the terminal will immediately generate the necessary report.

[0250] Furthermore, the server automates regularly scheduled tasks through automated processes. This automation significantly improves the efficiency of manual tasks, saving time.

[0251] In this way, servers, terminals, and users work together, utilizing various technologies and methods to seamlessly execute everything from data management to business process automation, thereby building a system that improves corporate productivity.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The server collects information from various departments within the company via APIs. This input information includes manufacturing data, sales data, and customer feedback. The server stores the collected data in a temporary database, maintaining different formats for each department.

[0255] Step 2:

[0256] The server standardizes information stored in the temporary database using data integration means. This involves converting different data formats into a common format, for example, unifying different unit systems. The server then arranges this standardized data in a form that allows for more efficient analysis and stores it in the integrated database.

[0257] Step 3:

[0258] The server analyzes the integrated data using a generative AI model. The input data for the AI ​​model is the newly integrated, standardized data. The server uses the AI ​​model to automatically prioritize tasks. For example, it performs predictive analytics to optimize production schedules. The output is a prioritized task list.

[0259] Step 4:

[0260] The server monitors the project's progress in real time using monitoring tools. The server monitors task lists and progress information, and if anomalies or delays occur, it detects the problem from the real-time data input and sends alerts to the relevant personnel.

[0261] Step 5:

[0262] The terminal activates an instant report generation mechanism based on user prompts. When a user inputs something like, "Please tell me the sales forecast for the next quarter," the terminal sends a request to the server, which generates a corresponding report based on the latest data and sends it to the terminal. The user then makes decisions based on the graphs and reports displayed on the terminal.

[0263] Step 6:

[0264] The server automates the execution of regular tasks using automation tools. For example, it runs a process to automatically generate monthly reports and send them via email to the relevant personnel. The server runs scripts at scheduled times, thereby streamlining tasks that are understaffed.

[0265] (Application Example 1)

[0266] 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 glasses 214 will be referred to as the "terminal."

[0267] This invention aims to optimize company-wide operations in today's diverse business environment, where efficient aggregation of information between departments is required, enabling real-time prioritization of activities and rapid detection of anomalies. It also aims to meet the needs of factory operations, where optimization of maintenance and increased operational efficiency based on operational information are required in the operation of manufacturing equipment.

[0268] 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.

[0269] In this invention, the server includes a data aggregation means for centrally managing information from each department, an activity prioritization means for analyzing the aggregated information and automatically adjusting the priority of activities, a monitoring means for monitoring the progress of the business in real time and immediately notifying when an anomaly is detected, and an optimization means for aggregating and analyzing the operation information of manufacturing equipment to optimize maintenance timing and operation. This makes it possible to promote overall business efficiency and optimize the operation of manufacturing equipment.

[0270] A "data aggregation method" is a function that provides a foundation for centrally managing information collected from each department and using it for subsequent analysis and processing.

[0271] A "activity prioritization method" is a method that uses aggregated information to automatically evaluate and adjust the priority of each activity, thereby achieving efficient resource allocation.

[0272] A "monitoring method" is a technique that tracks the progress of a project in real time and immediately notifies the user when an anomaly is detected.

[0273] The "real-time report generation method" is a function that instantly generates reports in an easy-to-understand format, based on the user's request, showing the latest business status.

[0274] "Task automation methods" are systems that automatically perform regular tasks and promote operational efficiency.

[0275] An "optimization method" is a method for optimizing maintenance timing and operating conditions by aggregating operational information of manufacturing equipment and analyzing it.

[0276] In an embodiment of this invention, the server first centrally manages diverse information collected from various departments using data aggregation means. Since the collected data is often provided in different formats, it is converted into a standardized format. Database software and data conversion tools are used for this purpose. For example, the Pandas library is used to integrate the data.

[0277] Next, the server activates an activity prioritization mechanism based on the aggregated data. This mechanism uses machine learning algorithms to analyze and automatically adjust the priority of each activity. The Scikit-learn library is used here for data analysis and dynamic scheduling. Once task priorities are determined, optimal resource allocation becomes possible.

[0278] Furthermore, the server employs monitoring mechanisms to track the progress of the project. Project status is tracked in real time, and if an anomaly is detected, an immediate notification is sent to the responsible person. This enables a rapid response.

[0279] The terminal visualizes the latest work status through a real-time report generation mechanism according to the user's requests. Furthermore, the server automates periodic tasks, leading to increased efficiency.

[0280] Finally, the server aggregates operational information from the manufacturing equipment and provides optimization tools to optimize maintenance timing and operating status through analysis. For example, it can automatically determine the necessary maintenance schedule based on robot operation data. This leads to improved operational efficiency.

[0281] As a specific example, when a robot stops abnormally in a manufacturing factory, the server immediately sends a notification to the person in charge to prompt a quick response. In order to maintain the efficiency of the production line, it becomes possible to optimize the maintenance timing based on the operation data.

[0282] Example of a prompt sentence:

[0283] "Please teach me how to integrate the inventory data from the manufacturing department and the feedback from the sales department and propose an optimal production schedule."

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server collects information from each department. The input is the database or file for each department, and the output is the integrated raw data. Use database software to execute SQL queries to obtain data and aggregate this data centrally.

[0287] Step 2:

[0288] The server standardizes the collected raw data by means of data aggregation. The input is raw data in different formats, and the output is data in a standardized format. Use the Pandas library to clean the data and extract and transform the necessary information.

[0289] Step 3:

[0290] The server executes activity prioritization means using the standardized data. The input is a standardized data set, and the output is a list of priorities for each activity. Use Scikit-learn to train a machine learning model and execute an algorithm for dynamically determining priorities.

[0291] Step 4:

[0292] The server tracks the project's progress in real time using monitoring tools. Input is progress data obtained from the project management system, and output is the detection results of anomalies. When an anomaly is detected, it sends a notification email to the responsible party.

[0293] Step 5:

[0294] The terminal operates a real-time report generation system based on user instructions to create a report. The input is the latest status data provided by the server, and the output is a visualized report. The report is generated in HTML or PDF format and presented visually to the user.

[0295] Step 6:

[0296] The server performs routine tasks using automated methods. The input is the schedule of the recurring tasks, and the output is a record of completed tasks. This automates tasks such as scheduling meetings and generating reports.

[0297] Step 7:

[0298] The server analyzes the operational information of the manufacturing equipment using optimization techniques and proposes the optimal operation. The input is the operational data of the manufacturing equipment, and the output is an optimized maintenance schedule. Based on the analysis results, the server adjusts the necessary maintenance plan.

[0299] 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.

[0300] This invention aims to improve overall corporate productivity by incorporating an emotion engine into a system that optimizes cross-departmental business operations within a company. This engine recognizes user emotions and further optimizes business processes. The main components of the system include various means consisting of servers, terminals, and users.

[0301] The server provides data integration means for collecting data from each department and centrally managing it. It converts and analyzes the information obtained from different data sources into a standardized format so as to grasp the overall picture of the ongoing project.

[0302] The server analyzes the integrated data using AI algorithms and dynamically evaluates the priority of tasks. The task prioritization means realizes the optimal allocation of resources according to importance and urgency, and improves the efficiency of operations.

[0303] Furthermore, the server has monitoring means for monitoring the progress of the project in real time and immediately notifies when an abnormality is detected. By providing information to the user at an appropriate timing, it supports quick and accurate decision-making.

[0304] In order to consider the emotional state of the user, the system incorporates an emotion engine to optimize the interaction with the user. The emotion engine analyzes, for example, the user's voice data to identify the emotional state. The server adjusts the content and urgency of the notification according to this emotional state, and supports the user to proceed with the work in a form that is less likely to feel stressed.

[0305] As a specific example, assume that a notification indicating that the progress of the project is not satisfactory is issued. At this time, if the emotion engine determines that the user is feeling stressed, the server adjusts the tone and detail level of the notification and reports it to the user in an optimal form. As a result, the user can efficiently grasp the situation and consider countermeasures without feeling excessive pressure.

[0306] In this way, the system of the present invention combines data integration and emotion recognition to enable more human-centric corporate management and improve the performance of the entire organization.

[0307] The processing flow will be described below.

[0308] Step 1:

[0309] The server accesses each department's database to collect the latest operational data. This data includes manufacturing information, sales reports, and customer feedback. The retrieved data is stored in temporary storage.

[0310] Step 2:

[0311] The server converts data stored in temporary storage into a standard format using data integration means. Unifying data in different formats enables consistent data analysis. This integrated data is then stored in the main database.

[0312] Step 3:

[0313] The server analyzes integrated data using AI algorithms to evaluate work progress and resource utilization. Based on the analysis results, it determines the importance and urgency of tasks and sets priorities using task prioritization tools.

[0314] Step 4:

[0315] The server monitors the project's progress in real time. If delays or problems occur, it uses the monitoring system to issue notifications, immediately informing the relevant departments.

[0316] Step 5:

[0317] The user sends a request from their terminal to the system to check the project status. The terminal forwards that request to the server.

[0318] Step 6:

[0319] The server processes the received request and generates a real-time report using the latest data. This report is then sent to the terminal, which displays the information to the user.

[0320] Step 7:

[0321] The emotion engine analyzes the user's voice data to recognize their emotional state. This analysis result is then fed back to the server.

[0322] Step 8:

[0323] The server adjusts system notifications and interfaces based on the user's emotional state. For example, if the server detects that the user is stressed, it softens the tone and content of notifications, providing information in a way that is best suited to the user.

[0324] Through this series of processes, the system leverages centralized data management and emotion recognition to enable efficient and ergonomic work execution.

[0325] (Example 2)

[0326] 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".

[0327] Modern businesses face problems such as inconsistent data and prioritization across departments, as well as increased user stress. These issues lead to decreased productivity and delayed decision-making, ultimately undermining overall business efficiency. In particular, there is a need for systems that can adjust operations in real time while reducing the emotional burden on users.

[0328] 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.

[0329] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and an emotion recognition means for identifying the user's emotional state and adjusting the content and tone of notifications based on this. This enables cross-departmental data management, appropriate resource allocation, and information provision that reduces stress on users.

[0330] "Information integration means" refers to a method for centrally collecting information obtained from different departments and converting it into a standardized format.

[0331] A "task prioritization method" is a means of analyzing aggregated information and dynamically determining priorities based on the importance and urgency of the tasks.

[0332] A "situation monitoring system" is a means of monitoring the progress of a project in real time and taking immediate action if an anomaly is detected.

[0333] "Emotion recognition means" refers to a means of identifying the user's emotional state and optimizing the content and tone of notifications based on that information.

[0334] "Interaction optimization means" are methods that support users in receiving information in a stress-reduced state.

[0335] This invention is a system for optimizing cross-departmental business processes within a company and realizing corporate management that takes user emotions into consideration. The main components are a server, terminals, and users.

[0336] The server provides a means of information integration for collecting and centrally managing data from various departments. This involves using database management systems and ETL tools, specifically including MySQL and Apache NiFi. This standardizes and integrates data in different formats.

[0337] The server analyzes the integrated data to determine task priorities. AI algorithms are used for analysis, leveraging machine learning platforms such as TensorFlow and PyTorch. This allows for optimal resource allocation and improved task efficiency.

[0338] Furthermore, the server monitors the project's progress in real time and immediately notifies users if any anomalies are detected. Prometheus and Zabbix are used as the monitoring system.

[0339] The device utilizes speech recognition technology to collect user voice data. Speech analysis software such as Google Cloud Speech-to-Text is used to extract emotional states from the user's utterances and transmit the data to a server via emotion recognition.

[0340] Through this system, users can receive timely and necessary information and make business decisions. A specific example of its use is when, in the event of insufficient project progress, the system adjusts notifications in a way that reduces user stress.

[0341] An example of a prompt would be, "Please tell me how to collect departmental data and build a notification system that takes user sentiment into consideration." Based on such prompts, the system uses a generative AI model to provide an appropriate solution.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The server collects data from each department. The input data comes in different formats depending on the department. Specific data collection operations include API calls and file transfers. The server converts this data into a standardized format and outputs it as integrated data. Data conversion tools are used for this process.

[0345] Step 2:

[0346] The server analyzes the integrated data. The input here is the integrated data formatted in Step 1. The server uses machine learning algorithms to analyze the data and determine the priority of the tasks. Specifically, it performs data model training and predictive calculations, and the result is output as a priority level.

[0347] Step 3:

[0348] The server monitors the project's progress in real time. Its input is the current status information of each process running within the system. The server tracks this using monitoring tools and outputs alert notifications if an anomaly occurs. Specific operations include threshold checks and alert settings.

[0349] Step 4:

[0350] The device collects the user's voice data and sends it to the server. The input is the user's voice data. The device uses speech recognition technology to convert this voice data into text format. The output is sent to the server as text data and used for subsequent sentiment analysis. This specific operation involves capturing voice input and converting it to text.

[0351] Step 5:

[0352] The server analyzes the text extracted from the audio data to determine the user's emotional state. The input is the text data generated in step 4. The server analyzes this data using an emotion analysis model and outputs the emotional state. The specific operations include natural language processing and emotion score calculation.

[0353] Step 6:

[0354] The server adjusts the content and tone of notifications based on the user's emotional state and sends them to the user. Inputs include the emotional state obtained in step 5 and the monitoring information from step 3. Based on this, the server generates appropriate notification content and sends it to the user. Specific actions include information filtering and notification message optimization.

[0355] (Application Example 2)

[0356] 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."

[0357] In modern work environments, workers' emotional states significantly impact productivity, but traditional management systems struggle to prioritize and adjust tasks while considering individual emotions. In such cases, there is a need to achieve an efficient and comfortable work environment while harmonizing human interaction with machines.

[0358] 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.

[0359] In this invention, the server includes an information integration means for aggregating and managing data from each area, a task prioritization evaluation means for dynamically calculating priorities based on the integrated data, and an emotion analysis means for analyzing the user's emotional state and adjusting notification content. This enables flexible and efficient work adjustments that take into account the emotional state of workers.

[0360] An "information integration tool" is a function for centralizing multiple data collected from various domains, converting them into a unified format, and managing them.

[0361] A "task prioritization evaluation tool" is a function that dynamically calculates and adjusts task priorities based on integrated data.

[0362] The "monitoring function" is a function that continuously observes the progress of work and promptly notifies the user when an anomaly is detected.

[0363] "Emotional analysis methods" are technologies that analyze a user's voice and behavioral data to determine their emotional state.

[0364] "Task automation functionality" refers to a function that autonomously performs regular tasks according to pre-set rules.

[0365] In the system for realizing this invention, a server plays a central role. The server uses information integration means to collect data from various domains, converts it into a unified format, and manages it. This eliminates data inconsistencies and enables consistent data analysis. Based on this integrated data, the server uses work prioritization evaluation means to dynamically calculate priorities.

[0366] Furthermore, the server operates sentiment analysis tools and evaluates the user's voice and behavioral data in real time using analysis tools such as Google Cloud's Natural Language API. Based on these evaluation results, it automatically adjusts the content of work notifications and schedules to ensure the user can work in the most optimal state.

[0367] A concrete example is when a server sends commands to robots operating on a factory production line. It analyzes the emotional state of the workers and notifies them to prioritize less burdensome tasks. This reduces worker stress and enables more efficient production.

[0368] As an example of a prompt, the system might input, "Analyze the emotional state of this audio and determine whether the worker is experiencing stress." Based on this prompt, the server performs an emotional analysis and optimizes the task according to the results obtained.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server collects data from various domains and converts it into a unified format. The input is raw data obtained from different data sources, and the output is a dataset converted to a unified format. This process extracts and standardizes the data, performing data processing to ensure consistency in subsequent analysis.

[0372] Step 2:

[0373] The server dynamically calculates task priorities using work prioritization evaluation tools based on integrated data. The input is a dataset converted to a unified format, and the output is a prioritized task list. AI algorithms are used to analyze the data, assess the importance and urgency of tasks, and optimize resource allocation based on the results.

[0374] Step 3:

[0375] The server analyzes the user's emotional state by applying emotion analysis to user voice and behavioral data. Voice data from the user is provided as input, and analysis results indicating the emotional state are generated as output. Here, Google Cloud's Natural Language API is used to identify emotional components in the voice data and measure states such as stress and fatigue.

[0376] Step 4:

[0377] The server adjusts the task list content and notifications based on the sentiment analysis results. The input is the sentiment analysis results and a prioritized task list; the output is the adjusted task list. Based on these analysis results, the server flexibly changes the tone and content of notifications to the worker to create an optimal work environment.

[0378] Step 5:

[0379] Ultimately, the server sends the adjusted task list and notifications to the terminal. The input is the adjusted task list and notification settings, and the output is the optimized information displayed on the terminal. The information displayed on the terminal is designed to reduce user stress and support efficient work performance.

[0380] 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.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] The system of this invention consists of multiple functions performed in cooperation with a server, terminals, and users. Each function is designed to optimize cross-departmental operations within an organization and improve productivity. The specific operation of each function is described below.

[0397] First, the server periodically retrieves operational data from each department. This includes various types of information, such as manufacturing data, sales data, and customer feedback. After receiving this data, the server uses data integration tools to standardize the data in different formats. This ensures consistent information management across departments.

[0398] Next, the server uses the integrated data to perform analysis using AI algorithms. Based on the results of this analysis, it automatically sets task priorities. The task prioritization method takes into account the resources and progress of each department to create an optimal schedule.

[0399] Furthermore, the server monitors whether the project is progressing smoothly. If delays or problems occur during project progress, the monitoring system immediately detects the problem and sends an alert to the relevant personnel. This allows for early problem resolution and supports the smooth execution of the project.

[0400] Furthermore, the terminal generates reports in real time in response to user requests. When a user wants to check the latest status of a project, the terminal accepts the request, and the server creates a report based on the latest data. This report is displayed on the terminal in a visually easy-to-understand format, supporting quick decision-making.

[0401] Finally, the server automates routine and repetitive tasks. This task automation includes, for example, scheduling regular meetings and automatically generating monthly reports. This reduces the efficiency of manual tasks.

[0402] As a concrete example, consider a product development project. A server can integrate inventory data from the manufacturing department and market feedback from the sales department to optimize the product schedule. As a result, users can understand the project's progress in real time based on data and make strategic decisions.

[0403] Thus, the present invention is a system that revolutionizes the productivity of an entire company by consistently performing tasks from data integration to automation.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The server connects to each department's database to collect the latest business data. This collected data includes manufacturing data, sales data, customer feedback, and more. This data is then stored in temporary storage.

[0407] Step 2:

[0408] The server standardizes the data stored in storage using data integration means. It converts data in different formats into a unified format and organizes it in a state where it can be analyzed. This integrated data is then stored in the main database.

[0409] Step 3:

[0410] The server inputs integrated data into an AI algorithm to analyze the business situation. Based on the analysis results, it evaluates the importance and urgency of each task and sets priorities using a task prioritization method.

[0411] Step 4:

[0412] The server monitors the project's progress. It checks whether the project is progressing according to the set schedule, and if delays or problems occur, it issues alerts through the monitoring system and notifies the relevant departments.

[0413] Step 5:

[0414] When a user requests to check the project's status, the terminal forwards the request to the server. The server gathers the latest information based on the received request, generates a real-time report, and sends it to the terminal. The terminal then displays the report to the user.

[0415] Step 6:

[0416] The server automates regular routine tasks. For example, it improves operational efficiency by executing tasks based on pre-configured conditions, such as generating monthly reports and adjusting schedules.

[0417] (Example 1)

[0418] 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."

[0419] It is necessary to improve productivity by efficiently integrating data managed individually by each department within a company and automating task prioritization and scheduling. Furthermore, a system is needed to monitor project progress in real time, detect anomalies early, and respond accordingly. Additionally, there is a need for means to enable users to instantly obtain the information they need and to streamline routine tasks.

[0420] 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.

[0421] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and a monitoring means for monitoring the status of ongoing tasks in real time and notifying the relevant personnel when an anomaly is detected. This makes it possible to effectively manage data across the entire company and improve productivity.

[0422] "Information integration means" refers to technology that centrally manages information collected from various departments, converts data in different formats into a standardized format, and makes it analyzable.

[0423] A "task prioritization method" is a technology that uses integrated information and artificial intelligence algorithms to automatically evaluate the priority of tasks and dynamically adjust plans and resources.

[0424] "Monitoring measures" refer to technologies that observe the status of ongoing operations in real time and immediately notify the relevant personnel when an anomaly is detected.

[0425] An "instant report generation method" is a technology that quickly gathers the latest information in response to user requests and generates and presents it as an easy-to-understand report.

[0426] "Task automation methods" refer to technologies that automatically process regularly scheduled tasks using programs, eliminating the need for human intervention.

[0427] A "generative AI model" is a model that artificial intelligence uses to learn from input data and perform predictions and analyses.

[0428] To implement this invention, a system is required in which a server, terminals, and users work in coordination. The server collects information from various departments within the company and uses a database server and API technology to centrally manage it. The collected information is standardized from different formats through an information integration means, achieving consistent data management.

[0429] The server analyzes the integrated data using an analysis method that employs a generative AI model. The generative AI model used at this stage leverages machine learning algorithms to effectively prioritize tasks, thereby improving overall business efficiency.

[0430] The server monitors the project's progress in real time using monitoring mechanisms. If an anomaly is detected, the responsible party is immediately notified. This enables a swift response and supports the smooth execution of the project.

[0431] The terminal uses an instant report generation mechanism in response to user requests to create reports based on the latest information. The generated reports are presented to the user in a visually easy-to-understand format to support strategic decision-making. For example, if the user enters a prompt such as "Please tell me the sales forecast for the next quarter," the terminal will immediately generate the necessary report.

[0432] Furthermore, the server automates regularly scheduled tasks through automated processes. This automation significantly improves the efficiency of manual tasks, saving time.

[0433] In this way, servers, terminals, and users work together, utilizing various technologies and methods to seamlessly execute everything from data management to business process automation, thereby building a system that improves corporate productivity.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The server collects information from various departments within the company via APIs. This input information includes manufacturing data, sales data, and customer feedback. The server stores the collected data in a temporary database, maintaining different formats for each department.

[0437] Step 2:

[0438] The server standardizes information stored in the temporary database using data integration means. This involves converting different data formats into a common format, for example, unifying different unit systems. The server then arranges this standardized data in a form that allows for more efficient analysis and stores it in the integrated database.

[0439] Step 3:

[0440] The server analyzes the integrated data using a generative AI model. The input data for the AI ​​model is the newly integrated, standardized data. The server uses the AI ​​model to automatically prioritize tasks. For example, it performs predictive analytics to optimize production schedules. The output is a prioritized task list.

[0441] Step 4:

[0442] The server monitors the project's progress in real time using monitoring tools. The server monitors task lists and progress information, and if anomalies or delays occur, it detects the problem from the real-time data input and sends alerts to the relevant personnel.

[0443] Step 5:

[0444] The terminal activates an instant report generation mechanism based on user prompts. When a user inputs something like, "Please tell me the sales forecast for the next quarter," the terminal sends a request to the server, which generates a corresponding report based on the latest data and sends it to the terminal. The user then makes decisions based on the graphs and reports displayed on the terminal.

[0445] Step 6:

[0446] The server automates the execution of regular tasks using automation tools. For example, it runs a process to automatically generate monthly reports and send them via email to the relevant personnel. The server runs scripts at scheduled times, thereby streamlining tasks that are understaffed.

[0447] (Application Example 1)

[0448] 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."

[0449] This invention aims to optimize company-wide operations in today's diverse business environment, where efficient aggregation of information between departments is required, enabling real-time prioritization of activities and rapid detection of anomalies. It also aims to meet the needs of factory operations, where optimization of maintenance and increased operational efficiency based on operational information are required in the operation of manufacturing equipment.

[0450] 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.

[0451] In this invention, the server includes a data aggregation means for centrally managing information from each department, an activity prioritization means for analyzing the aggregated information and automatically adjusting the priority of activities, a monitoring means for monitoring the progress of the business in real time and immediately notifying when an anomaly is detected, and an optimization means for aggregating and analyzing the operation information of manufacturing equipment to optimize maintenance timing and operation. This makes it possible to promote overall business efficiency and optimize the operation of manufacturing equipment.

[0452] A "data aggregation method" is a function that provides a foundation for centrally managing information collected from each department and using it for subsequent analysis and processing.

[0453] A "activity prioritization method" is a method that uses aggregated information to automatically evaluate and adjust the priority of each activity, thereby achieving efficient resource allocation.

[0454] A "monitoring method" is a technique that tracks the progress of a project in real time and immediately notifies the user when an anomaly is detected.

[0455] The "real-time report generation method" is a function that instantly generates reports in an easy-to-understand format, based on the user's request, showing the latest business status.

[0456] "Task automation methods" are systems that automatically perform regular tasks and promote operational efficiency.

[0457] An "optimization method" is a method for optimizing maintenance timing and operating conditions by aggregating operational information of manufacturing equipment and analyzing it.

[0458] In an embodiment of this invention, the server first centrally manages diverse information collected from various departments using data aggregation means. Since the collected data is often provided in different formats, it is converted into a standardized format. Database software and data conversion tools are used for this purpose. For example, the Pandas library is used to integrate the data.

[0459] Next, the server activates an activity prioritization mechanism based on the aggregated data. This mechanism uses machine learning algorithms to analyze and automatically adjust the priority of each activity. The Scikit-learn library is used here for data analysis and dynamic scheduling. Once task priorities are determined, optimal resource allocation becomes possible.

[0460] Furthermore, the server employs monitoring mechanisms to track the progress of the project. Project status is tracked in real time, and if an anomaly is detected, an immediate notification is sent to the responsible person. This enables a rapid response.

[0461] The terminal visualizes the latest work status through a real-time report generation mechanism according to the user's requests. Furthermore, the server automates periodic tasks, leading to increased efficiency.

[0462] Finally, the server aggregates operational information from the manufacturing equipment and provides optimization tools to optimize maintenance timing and operating status through analysis. For example, it can automatically determine the necessary maintenance schedule based on robot operation data. This leads to improved operational efficiency.

[0463] For example, if a robot malfunctions and stops in a manufacturing plant, the server immediately sends a notification to the responsible person, prompting a quick response. This makes it possible to optimize maintenance timing based on operational data to maintain the efficiency of the manufacturing line.

[0464] Example of a prompt:

[0465] "Please tell me how to integrate inventory data from the manufacturing department with feedback from the sales department to propose the optimal production schedule."

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The server collects information from each department. Inputs are departmental databases and files, and output is integrated raw data. Database software is used to retrieve the data by executing SQL queries, and this data is then centrally aggregated.

[0469] Step 2:

[0470] The server standardizes the collected raw data using data aggregation methods. The input is raw data in various formats, and the output is data in a standardized format. The Pandas library is used to clean the data and extract and transform the necessary information.

[0471] Step 3:

[0472] The server performs an activity prioritization mechanism using standardized data. The input is a standardized dataset, and the output is a priority list for each activity. A machine learning model is trained using Scikit-learn to run an algorithm that dynamically determines priorities.

[0473] Step 4:

[0474] The server tracks the project's progress in real time using monitoring tools. Input is progress data obtained from the project management system, and output is the detection results of anomalies. When an anomaly is detected, it sends a notification email to the responsible party.

[0475] Step 5:

[0476] The terminal operates a real-time report generation system based on user instructions to create a report. The input is the latest status data provided by the server, and the output is a visualized report. The report is generated in HTML or PDF format and presented visually to the user.

[0477] Step 6:

[0478] The server performs routine tasks using automated methods. The input is the schedule of the recurring tasks, and the output is a record of completed tasks. This automates tasks such as scheduling meetings and generating reports.

[0479] Step 7:

[0480] The server analyzes the operational information of the manufacturing equipment using optimization techniques and proposes the optimal operation. The input is the operational data of the manufacturing equipment, and the output is an optimized maintenance schedule. Based on the analysis results, the server adjusts the necessary maintenance plan.

[0481] 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.

[0482] This invention aims to improve overall corporate productivity by incorporating an emotion engine into a system that optimizes cross-departmental business operations within a company. This engine recognizes user emotions and further optimizes business processes. The main components of the system include various means consisting of servers, terminals, and users.

[0483] The server provides a data integration mechanism that collects and centrally manages data from each department. It converts and analyzes information obtained from different data sources into a standardized format, enabling a comprehensive understanding of ongoing projects.

[0484] The server analyzes integrated data using AI algorithms to dynamically evaluate task priorities. This task prioritization method optimizes resource allocation based on importance and urgency, thereby improving operational efficiency.

[0485] Furthermore, the server is equipped with monitoring capabilities to track project progress in real time and immediately notifies users of any anomalies. By providing users with information at the appropriate time, it supports quick and accurate decision-making.

[0486] To take into account the user's emotional state, the system incorporates an emotion engine to optimize user interaction. The emotion engine, for example, analyzes the user's voice data to identify their emotional state. The server then adjusts the content and urgency of notifications based on this emotional state, supporting the user in carrying out their work in a less stressful way.

[0487] As a concrete example, suppose a notification is issued indicating that the project's progress is unsatisfactory. If the emotion engine determines that the user is feeling stressed, the server adjusts the tone and level of detail of the notification to present it in a way that is most appropriate for the user. This allows the user to efficiently understand the situation and consider countermeasures without feeling unnecessarily pressured.

[0488] Thus, the system of the present invention, by combining data integration and emotion recognition, enables more human-centric business operations and enhances the overall performance of the organization.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The server accesses each department's database to collect the latest operational data. This data includes manufacturing information, sales reports, and customer feedback. The retrieved data is stored in temporary storage.

[0492] Step 2:

[0493] The server converts data stored in temporary storage into a standard format using data integration means. Unifying data in different formats enables consistent data analysis. This integrated data is then stored in the main database.

[0494] Step 3:

[0495] The server analyzes integrated data using AI algorithms to evaluate work progress and resource utilization. Based on the analysis results, it determines the importance and urgency of tasks and sets priorities using task prioritization tools.

[0496] Step 4:

[0497] The server monitors the project's progress in real time. If delays or problems occur, it uses the monitoring system to issue notifications, immediately informing the relevant departments.

[0498] Step 5:

[0499] The user sends a request from their terminal to the system to check the project status. The terminal forwards that request to the server.

[0500] Step 6:

[0501] The server processes the received request and generates a real-time report using the latest data. This report is then sent to the terminal, which displays the information to the user.

[0502] Step 7:

[0503] The emotion engine analyzes the user's voice data to recognize their emotional state. This analysis result is then fed back to the server.

[0504] Step 8:

[0505] The server adjusts system notifications and interfaces based on the user's emotional state. For example, if the server detects that the user is stressed, it softens the tone and content of notifications, providing information in a way that is best suited to the user.

[0506] Through this series of processes, the system leverages centralized data management and emotion recognition to enable efficient and ergonomic work execution.

[0507] (Example 2)

[0508] 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."

[0509] Modern businesses face problems such as inconsistent data and prioritization across departments, as well as increased user stress. These issues lead to decreased productivity and delayed decision-making, ultimately undermining overall business efficiency. In particular, there is a need for systems that can adjust operations in real time while reducing the emotional burden on users.

[0510] 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.

[0511] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and an emotion recognition means for identifying the user's emotional state and adjusting the content and tone of notifications based on this. This enables cross-departmental data management, appropriate resource allocation, and information provision that reduces stress on users.

[0512] "Information integration means" refers to a method for centrally collecting information obtained from different departments and converting it into a standardized format.

[0513] A "task prioritization method" is a means of analyzing aggregated information and dynamically determining priorities based on the importance and urgency of the tasks.

[0514] A "situation monitoring system" is a means of monitoring the progress of a project in real time and taking immediate action if an anomaly is detected.

[0515] "Emotion recognition means" refers to a means of identifying the user's emotional state and optimizing the content and tone of notifications based on that information.

[0516] "Interaction optimization means" are methods that support users in receiving information in a stress-reduced state.

[0517] This invention is a system for optimizing cross-departmental business processes within a company and realizing corporate management that takes user emotions into consideration. The main components are a server, terminals, and users.

[0518] The server provides a means of information integration for collecting and centrally managing data from various departments. This involves using database management systems and ETL tools, specifically including MySQL and Apache NiFi. This standardizes and integrates data in different formats.

[0519] The server analyzes the integrated data to determine task priorities. AI algorithms are used for analysis, leveraging machine learning platforms such as TensorFlow and PyTorch. This allows for optimal resource allocation and improved task efficiency.

[0520] Furthermore, the server monitors the project's progress in real time and immediately notifies users if any anomalies are detected. Prometheus and Zabbix are used as the monitoring system.

[0521] The device utilizes speech recognition technology to collect user voice data. Speech analysis software such as Google Cloud Speech-to-Text is used to extract emotional states from the user's utterances and transmit the data to a server via emotion recognition.

[0522] Through this system, users can receive timely and necessary information and make business decisions. A specific example of its use is when, in the event of insufficient project progress, the system adjusts notifications in a way that reduces user stress.

[0523] An example of a prompt would be, "Please tell me how to collect departmental data and build a notification system that takes user sentiment into consideration." Based on such prompts, the system uses a generative AI model to provide an appropriate solution.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The server collects data from each department. The input data comes in different formats depending on the department. Specific data collection operations include API calls and file transfers. The server converts this data into a standardized format and outputs it as integrated data. Data conversion tools are used for this process.

[0527] Step 2:

[0528] The server analyzes the integrated data. The input here is the integrated data formatted in Step 1. The server uses machine learning algorithms to analyze the data and determine the priority of the tasks. Specifically, it performs data model training and predictive calculations, and the result is output as a priority level.

[0529] Step 3:

[0530] The server monitors the project's progress in real time. Its input is the current status information of each process running within the system. The server tracks this using monitoring tools and outputs alert notifications if an anomaly occurs. Specific operations include threshold checks and alert settings.

[0531] Step 4:

[0532] The device collects the user's voice data and sends it to the server. The input is the user's voice data. The device uses speech recognition technology to convert this voice data into text format. The output is sent to the server as text data and used for subsequent sentiment analysis. This specific operation involves capturing voice input and converting it to text.

[0533] Step 5:

[0534] The server analyzes the text extracted from the audio data to determine the user's emotional state. The input is the text data generated in step 4. The server analyzes this data using an emotion analysis model and outputs the emotional state. The specific operations include natural language processing and emotion score calculation.

[0535] Step 6:

[0536] The server adjusts the content and tone of notifications based on the user's emotional state and sends them to the user. Inputs include the emotional state obtained in step 5 and the monitoring information from step 3. Based on this, the server generates appropriate notification content and sends it to the user. Specific actions include information filtering and notification message optimization.

[0537] (Application Example 2)

[0538] 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."

[0539] In modern work environments, workers' emotional states significantly impact productivity, but traditional management systems struggle to prioritize and adjust tasks while considering individual emotions. In such cases, there is a need to achieve an efficient and comfortable work environment while harmonizing human interaction with machines.

[0540] 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.

[0541] In this invention, the server includes an information integration means for aggregating and managing data from each area, a task prioritization evaluation means for dynamically calculating priorities based on the integrated data, and an emotion analysis means for analyzing the user's emotional state and adjusting notification content. This enables flexible and efficient work adjustments that take into account the emotional state of workers.

[0542] An "information integration tool" is a function for centralizing multiple data collected from various domains, converting them into a unified format, and managing them.

[0543] A "task prioritization evaluation tool" is a function that dynamically calculates and adjusts task priorities based on integrated data.

[0544] The "monitoring function" is a function that continuously observes the progress of work and promptly notifies the user when an anomaly is detected.

[0545] "Emotional analysis methods" are technologies that analyze a user's voice and behavioral data to determine their emotional state.

[0546] "Task automation functionality" refers to a function that autonomously performs regular tasks according to pre-set rules.

[0547] In the system for realizing this invention, a server plays a central role. The server uses information integration means to collect data from various domains, converts it into a unified format, and manages it. This eliminates data inconsistencies and enables consistent data analysis. Based on this integrated data, the server uses work prioritization evaluation means to dynamically calculate priorities.

[0548] Furthermore, the server operates sentiment analysis tools and evaluates the user's voice and behavioral data in real time using analysis tools such as Google Cloud's Natural Language API. Based on these evaluation results, it automatically adjusts the content of work notifications and schedules to ensure the user can work in the most optimal state.

[0549] A concrete example is when a server sends commands to robots operating on a factory production line. It analyzes the emotional state of the workers and notifies them to prioritize less burdensome tasks. This reduces worker stress and enables more efficient production.

[0550] As an example of a prompt, the system might input, "Analyze the emotional state of this audio and determine whether the worker is experiencing stress." Based on this prompt, the server performs an emotional analysis and optimizes the task according to the results obtained.

[0551] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0552] Step 1:

[0553] The server collects data from various domains and converts it into a unified format. The input is raw data obtained from different data sources, and the output is a dataset converted to a unified format. This process extracts and standardizes the data, performing data processing to ensure consistency in subsequent analysis.

[0554] Step 2:

[0555] The server dynamically calculates task priorities using work prioritization evaluation tools based on integrated data. The input is a dataset converted to a unified format, and the output is a prioritized task list. AI algorithms are used to analyze the data, assess the importance and urgency of tasks, and optimize resource allocation based on the results.

[0556] Step 3:

[0557] The server analyzes the user's emotional state by applying emotion analysis to user voice and behavioral data. Voice data from the user is provided as input, and analysis results indicating the emotional state are generated as output. Here, Google Cloud's Natural Language API is used to identify emotional components in the voice data and measure states such as stress and fatigue.

[0558] Step 4:

[0559] The server adjusts the task list content and notifications based on the sentiment analysis results. The input is the sentiment analysis results and a prioritized task list; the output is the adjusted task list. Based on these analysis results, the server flexibly changes the tone and content of notifications to the worker to create an optimal work environment.

[0560] Step 5:

[0561] Ultimately, the server sends the adjusted task list and notifications to the terminal. The input is the adjusted task list and notification settings, and the output is the optimized information displayed on the terminal. The information displayed on the terminal is designed to reduce user stress and support efficient work performance.

[0562] 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.

[0563] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0564] 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.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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".

[0579] The system of this invention consists of multiple functions performed in cooperation with a server, terminals, and users. Each function is designed to optimize cross-departmental operations within an organization and improve productivity. The specific operation of each function is described below.

[0580] First, the server periodically retrieves operational data from each department. This includes various types of information, such as manufacturing data, sales data, and customer feedback. After receiving this data, the server uses data integration tools to standardize the data in different formats. This ensures consistent information management across departments.

[0581] Next, the server uses the integrated data to perform analysis using AI algorithms. Based on the results of this analysis, it automatically sets task priorities. The task prioritization method takes into account the resources and progress of each department to create an optimal schedule.

[0582] Furthermore, the server monitors whether the project is progressing smoothly. If delays or problems occur during project progress, the monitoring system immediately detects the problem and sends an alert to the relevant personnel. This allows for early problem resolution and supports the smooth execution of the project.

[0583] Furthermore, the terminal generates reports in real time in response to user requests. When a user wants to check the latest status of a project, the terminal accepts the request, and the server creates a report based on the latest data. This report is displayed on the terminal in a visually easy-to-understand format, supporting quick decision-making.

[0584] Finally, the server automates routine and repetitive tasks. This task automation includes, for example, scheduling regular meetings and automatically generating monthly reports. This reduces the efficiency of manual tasks.

[0585] As a concrete example, consider a product development project. A server can integrate inventory data from the manufacturing department and market feedback from the sales department to optimize the product schedule. As a result, users can understand the project's progress in real time based on data and make strategic decisions.

[0586] Thus, the present invention is a system that revolutionizes the productivity of an entire company by consistently performing tasks from data integration to automation.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The server connects to each department's database to collect the latest business data. This collected data includes manufacturing data, sales data, customer feedback, and more. This data is then stored in temporary storage.

[0590] Step 2:

[0591] The server standardizes the data stored in storage using data integration means. It converts data in different formats into a unified format and organizes it in a state where it can be analyzed. This integrated data is then stored in the main database.

[0592] Step 3:

[0593] The server inputs integrated data into an AI algorithm to analyze the business situation. Based on the analysis results, it evaluates the importance and urgency of each task and sets priorities using a task prioritization method.

[0594] Step 4:

[0595] The server monitors the project's progress. It checks whether the project is progressing according to the set schedule, and if delays or problems occur, it issues alerts through the monitoring system and notifies the relevant departments.

[0596] Step 5:

[0597] When a user requests to check the project's status, the terminal forwards the request to the server. The server gathers the latest information based on the received request, generates a real-time report, and sends it to the terminal. The terminal then displays the report to the user.

[0598] Step 6:

[0599] The server automates regular routine tasks. For example, it improves operational efficiency by executing tasks based on pre-configured conditions, such as generating monthly reports and adjusting schedules.

[0600] (Example 1)

[0601] 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".

[0602] It is necessary to improve productivity by efficiently integrating data managed individually by each department within a company and automating task prioritization and scheduling. Furthermore, a system is needed to monitor project progress in real time, detect anomalies early, and respond accordingly. Additionally, there is a need for means to enable users to instantly obtain the information they need and to streamline routine tasks.

[0603] 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.

[0604] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and a monitoring means for monitoring the status of ongoing tasks in real time and notifying the relevant personnel when an anomaly is detected. This makes it possible to effectively manage data across the entire company and improve productivity.

[0605] "Information integration means" refers to technology that centrally manages information collected from various departments, converts data in different formats into a standardized format, and makes it analyzable.

[0606] A "task prioritization method" is a technology that uses integrated information and artificial intelligence algorithms to automatically evaluate the priority of tasks and dynamically adjust plans and resources.

[0607] "Monitoring measures" refer to technologies that observe the status of ongoing operations in real time and immediately notify the relevant personnel when an anomaly is detected.

[0608] An "instant report generation method" is a technology that quickly gathers the latest information in response to user requests and generates and presents it as an easy-to-understand report.

[0609] "Task automation methods" refer to technologies that automatically process regularly scheduled tasks using programs, eliminating the need for human intervention.

[0610] A "generative AI model" is a model that artificial intelligence uses to learn from input data and perform predictions and analyses.

[0611] To implement this invention, a system is required in which a server, terminals, and users work in coordination. The server collects information from various departments within the company and uses a database server and API technology to centrally manage it. The collected information is standardized from different formats through an information integration means, achieving consistent data management.

[0612] The server analyzes the integrated data using an analysis method that employs a generative AI model. The generative AI model used at this stage leverages machine learning algorithms to effectively prioritize tasks, thereby improving overall business efficiency.

[0613] The server monitors the project's progress in real time using monitoring mechanisms. If an anomaly is detected, the responsible party is immediately notified. This enables a swift response and supports the smooth execution of the project.

[0614] The terminal uses an instant report generation mechanism in response to user requests to create reports based on the latest information. The generated reports are presented to the user in a visually easy-to-understand format to support strategic decision-making. For example, if the user enters a prompt such as "Please tell me the sales forecast for the next quarter," the terminal will immediately generate the necessary report.

[0615] Furthermore, the server automates regularly scheduled tasks through automated processes. This automation significantly improves the efficiency of manual tasks, saving time.

[0616] In this way, servers, terminals, and users work together, utilizing various technologies and methods to seamlessly execute everything from data management to business process automation, thereby building a system that improves corporate productivity.

[0617] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0618] Step 1:

[0619] The server collects information from various departments within the company via APIs. This input information includes manufacturing data, sales data, and customer feedback. The server stores the collected data in a temporary database, maintaining different formats for each department.

[0620] Step 2:

[0621] The server standardizes information stored in the temporary database using data integration means. This involves converting different data formats into a common format, for example, unifying different unit systems. The server then arranges this standardized data in a form that allows for more efficient analysis and stores it in the integrated database.

[0622] Step 3:

[0623] The server analyzes the integrated data using a generative AI model. The input data for the AI ​​model is the newly integrated, standardized data. The server uses the AI ​​model to automatically prioritize tasks. For example, it performs predictive analytics to optimize production schedules. The output is a prioritized task list.

[0624] Step 4:

[0625] The server monitors the project's progress in real time using monitoring tools. The server monitors task lists and progress information, and if anomalies or delays occur, it detects the problem from the real-time data input and sends alerts to the relevant personnel.

[0626] Step 5:

[0627] The terminal activates an instant report generation mechanism based on user prompts. When a user inputs something like, "Please tell me the sales forecast for the next quarter," the terminal sends a request to the server, which generates a corresponding report based on the latest data and sends it to the terminal. The user then makes decisions based on the graphs and reports displayed on the terminal.

[0628] Step 6:

[0629] The server automates the execution of regular tasks using automation tools. For example, it runs a process to automatically generate monthly reports and send them via email to the relevant personnel. The server runs scripts at scheduled times, thereby streamlining tasks that are understaffed.

[0630] (Application Example 1)

[0631] 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".

[0632] This invention aims to optimize company-wide operations in today's diverse business environment, where efficient aggregation of information between departments is required, enabling real-time prioritization of activities and rapid detection of anomalies. It also aims to meet the needs of factory operations, where optimization of maintenance and increased operational efficiency based on operational information are required in the operation of manufacturing equipment.

[0633] 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.

[0634] In this invention, the server includes a data aggregation means for centrally managing information from each department, an activity prioritization means for analyzing the aggregated information and automatically adjusting the priority of activities, a monitoring means for monitoring the progress of the business in real time and immediately notifying when an anomaly is detected, and an optimization means for aggregating and analyzing the operation information of manufacturing equipment to optimize maintenance timing and operation. This makes it possible to promote overall business efficiency and optimize the operation of manufacturing equipment.

[0635] A "data aggregation method" is a function that provides a foundation for centrally managing information collected from each department and using it for subsequent analysis and processing.

[0636] A "activity prioritization method" is a method that uses aggregated information to automatically evaluate and adjust the priority of each activity, thereby achieving efficient resource allocation.

[0637] A "monitoring method" is a technique that tracks the progress of a project in real time and immediately notifies the user when an anomaly is detected.

[0638] The "real-time report generation method" is a function that instantly generates reports in an easy-to-understand format, based on the user's request, showing the latest business status.

[0639] "Task automation methods" are systems that automatically perform regular tasks and promote operational efficiency.

[0640] An "optimization method" is a method for optimizing maintenance timing and operating conditions by aggregating operational information of manufacturing equipment and analyzing it.

[0641] In an embodiment of this invention, the server first centrally manages diverse information collected from various departments using data aggregation means. Since the collected data is often provided in different formats, it is converted into a standardized format. Database software and data conversion tools are used for this purpose. For example, the Pandas library is used to integrate the data.

[0642] Next, the server activates an activity prioritization mechanism based on the aggregated data. This mechanism uses machine learning algorithms to analyze and automatically adjust the priority of each activity. The Scikit-learn library is used here for data analysis and dynamic scheduling. Once task priorities are determined, optimal resource allocation becomes possible.

[0643] Furthermore, the server employs monitoring mechanisms to track the progress of the project. Project status is tracked in real time, and if an anomaly is detected, an immediate notification is sent to the responsible person. This enables a rapid response.

[0644] The terminal visualizes the latest work status through a real-time report generation mechanism according to the user's requests. Furthermore, the server automates periodic tasks, leading to increased efficiency.

[0645] Finally, the server aggregates operational information from the manufacturing equipment and provides optimization tools to optimize maintenance timing and operating status through analysis. For example, it can automatically determine the necessary maintenance schedule based on robot operation data. This leads to improved operational efficiency.

[0646] For example, if a robot malfunctions and stops in a manufacturing plant, the server immediately sends a notification to the responsible person, prompting a quick response. This makes it possible to optimize maintenance timing based on operational data to maintain the efficiency of the manufacturing line.

[0647] Example of a prompt:

[0648] "Please tell me how to integrate inventory data from the manufacturing department with feedback from the sales department to propose the optimal production schedule."

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The server collects information from each department. Inputs are departmental databases and files, and output is integrated raw data. Database software is used to retrieve the data by executing SQL queries, and this data is then centrally aggregated.

[0652] Step 2:

[0653] The server standardizes the collected raw data using data aggregation methods. The input is raw data in various formats, and the output is data in a standardized format. The Pandas library is used to clean the data and extract and transform the necessary information.

[0654] Step 3:

[0655] The server performs an activity prioritization mechanism using standardized data. The input is a standardized dataset, and the output is a priority list for each activity. A machine learning model is trained using Scikit-learn to run an algorithm that dynamically determines priorities.

[0656] Step 4:

[0657] The server tracks the project's progress in real time using monitoring tools. Input is progress data obtained from the project management system, and output is the detection results of anomalies. When an anomaly is detected, it sends a notification email to the responsible party.

[0658] Step 5:

[0659] The terminal operates a real-time report generation system based on user instructions to create a report. The input is the latest status data provided by the server, and the output is a visualized report. The report is generated in HTML or PDF format and presented visually to the user.

[0660] Step 6:

[0661] The server performs routine tasks using automated methods. The input is the schedule of the recurring tasks, and the output is a record of completed tasks. This automates tasks such as scheduling meetings and generating reports.

[0662] Step 7:

[0663] The server analyzes the operational information of the manufacturing equipment using optimization techniques and proposes the optimal operation. The input is the operational data of the manufacturing equipment, and the output is an optimized maintenance schedule. Based on the analysis results, the server adjusts the necessary maintenance plan.

[0664] 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.

[0665] This invention aims to improve overall corporate productivity by incorporating an emotion engine into a system that optimizes cross-departmental business operations within a company. This engine recognizes user emotions and further optimizes business processes. The main components of the system include various means consisting of servers, terminals, and users.

[0666] The server provides a data integration mechanism that collects and centrally manages data from each department. It converts and analyzes information obtained from different data sources into a standardized format, enabling a comprehensive understanding of ongoing projects.

[0667] The server analyzes integrated data using AI algorithms to dynamically evaluate task priorities. This task prioritization method optimizes resource allocation based on importance and urgency, thereby improving operational efficiency.

[0668] Furthermore, the server is equipped with monitoring capabilities to track project progress in real time and immediately notifies users of any anomalies. By providing users with information at the appropriate time, it supports quick and accurate decision-making.

[0669] To take into account the user's emotional state, the system incorporates an emotion engine to optimize user interaction. The emotion engine, for example, analyzes the user's voice data to identify their emotional state. The server then adjusts the content and urgency of notifications based on this emotional state, supporting the user in carrying out their work in a less stressful way.

[0670] As a concrete example, suppose a notification is issued indicating that the project's progress is unsatisfactory. If the emotion engine determines that the user is feeling stressed, the server adjusts the tone and level of detail of the notification to present it in a way that is most appropriate for the user. This allows the user to efficiently understand the situation and consider countermeasures without feeling unnecessarily pressured.

[0671] Thus, the system of the present invention, by combining data integration and emotion recognition, enables more human-centric business operations and enhances the overall performance of the organization.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The server accesses each department's database to collect the latest operational data. This data includes manufacturing information, sales reports, and customer feedback. The retrieved data is stored in temporary storage.

[0675] Step 2:

[0676] The server converts data stored in temporary storage into a standard format using data integration means. Unifying data in different formats enables consistent data analysis. This integrated data is then stored in the main database.

[0677] Step 3:

[0678] The server analyzes integrated data using AI algorithms to evaluate work progress and resource utilization. Based on the analysis results, it determines the importance and urgency of tasks and sets priorities using task prioritization tools.

[0679] Step 4:

[0680] The server monitors the project's progress in real time. If delays or problems occur, it uses the monitoring system to issue notifications, immediately informing the relevant departments.

[0681] Step 5:

[0682] The user sends a request from their terminal to the system to check the project status. The terminal forwards that request to the server.

[0683] Step 6:

[0684] The server processes the received request and generates a real-time report using the latest data. This report is then sent to the terminal, which displays the information to the user.

[0685] Step 7:

[0686] The emotion engine analyzes the user's voice data to recognize their emotional state. This analysis result is then fed back to the server.

[0687] Step 8:

[0688] The server adjusts system notifications and interfaces based on the user's emotional state. For example, if the server detects that the user is stressed, it softens the tone and content of notifications, providing information in a way that is best suited to the user.

[0689] Through this series of processes, the system leverages centralized data management and emotion recognition to enable efficient and ergonomic work execution.

[0690] (Example 2)

[0691] 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".

[0692] Modern businesses face problems such as inconsistent data and prioritization across departments, as well as increased user stress. These issues lead to decreased productivity and delayed decision-making, ultimately undermining overall business efficiency. In particular, there is a need for systems that can adjust operations in real time while reducing the emotional burden on users.

[0693] 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.

[0694] In this invention, the server includes an information integration means for collecting and centrally managing information from each department, a task prioritization means for automatically evaluating and adjusting priorities using the integrated information, and an emotion recognition means for identifying the user's emotional state and adjusting the content and tone of notifications based on this. This enables cross-departmental data management, appropriate resource allocation, and information provision that reduces stress on users.

[0695] "Information integration means" refers to a method for centrally collecting information obtained from different departments and converting it into a standardized format.

[0696] A "task prioritization method" is a means of analyzing aggregated information and dynamically determining priorities based on the importance and urgency of the tasks.

[0697] A "situation monitoring system" is a means of monitoring the progress of a project in real time and taking immediate action if an anomaly is detected.

[0698] "Emotion recognition means" refers to a means of identifying the user's emotional state and optimizing the content and tone of notifications based on that information.

[0699] "Interaction optimization means" are methods that support users in receiving information in a stress-reduced state.

[0700] This invention is a system for optimizing cross-departmental business processes within a company and realizing corporate management that takes user emotions into consideration. The main components are a server, terminals, and users.

[0701] The server provides a means of information integration for collecting and centrally managing data from various departments. This involves using database management systems and ETL tools, specifically including MySQL and Apache NiFi. This standardizes and integrates data in different formats.

[0702] The server analyzes the integrated data to determine task priorities. AI algorithms are used for analysis, leveraging machine learning platforms such as TensorFlow and PyTorch. This allows for optimal resource allocation and improved task efficiency.

[0703] Furthermore, the server monitors the project's progress in real time and immediately notifies users if any anomalies are detected. Prometheus and Zabbix are used as the monitoring system.

[0704] The device utilizes speech recognition technology to collect user voice data. Speech analysis software such as Google Cloud Speech-to-Text is used to extract emotional states from the user's utterances and transmit the data to a server via emotion recognition.

[0705] Through this system, users can receive timely and necessary information and make business decisions. A specific example of its use is when, in the event of insufficient project progress, the system adjusts notifications in a way that reduces user stress.

[0706] An example of a prompt would be, "Please tell me how to collect departmental data and build a notification system that takes user sentiment into consideration." Based on such prompts, the system uses a generative AI model to provide an appropriate solution.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] The server collects data from each department. The input data comes in different formats depending on the department. Specific data collection operations include API calls and file transfers. The server converts this data into a standardized format and outputs it as integrated data. Data conversion tools are used for this process.

[0710] Step 2:

[0711] The server analyzes the integrated data. The input here is the integrated data formatted in Step 1. The server uses machine learning algorithms to analyze the data and determine the priority of the tasks. Specifically, it performs data model training and predictive calculations, and the result is output as a priority level.

[0712] Step 3:

[0713] The server monitors the project's progress in real time. Its input is the current status information of each process running within the system. The server tracks this using monitoring tools and outputs alert notifications if an anomaly occurs. Specific operations include threshold checks and alert settings.

[0714] Step 4:

[0715] The device collects the user's voice data and sends it to the server. The input is the user's voice data. The device uses speech recognition technology to convert this voice data into text format. The output is sent to the server as text data and used for subsequent sentiment analysis. This specific operation involves capturing voice input and converting it to text.

[0716] Step 5:

[0717] The server analyzes the text extracted from the audio data to determine the user's emotional state. The input is the text data generated in step 4. The server analyzes this data using an emotion analysis model and outputs the emotional state. The specific operations include natural language processing and emotion score calculation.

[0718] Step 6:

[0719] The server adjusts the content and tone of notifications based on the user's emotional state and sends them to the user. Inputs include the emotional state obtained in step 5 and the monitoring information from step 3. Based on this, the server generates appropriate notification content and sends it to the user. Specific actions include information filtering and notification message optimization.

[0720] (Application Example 2)

[0721] 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".

[0722] In modern work environments, workers' emotional states significantly impact productivity, but traditional management systems struggle to prioritize and adjust tasks while considering individual emotions. In such cases, there is a need to achieve an efficient and comfortable work environment while harmonizing human interaction with machines.

[0723] 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.

[0724] In this invention, the server includes an information integration means for aggregating and managing data from each area, a task prioritization evaluation means for dynamically calculating priorities based on the integrated data, and an emotion analysis means for analyzing the user's emotional state and adjusting notification content. This enables flexible and efficient work adjustments that take into account the emotional state of workers.

[0725] An "information integration tool" is a function for centralizing multiple data collected from various domains, converting them into a unified format, and managing them.

[0726] A "task prioritization evaluation tool" is a function that dynamically calculates and adjusts task priorities based on integrated data.

[0727] The "monitoring function" is a function that continuously observes the progress of work and promptly notifies the user when an anomaly is detected.

[0728] "Emotional analysis methods" are technologies that analyze a user's voice and behavioral data to determine their emotional state.

[0729] "Task automation functionality" refers to a function that autonomously performs regular tasks according to pre-set rules.

[0730] In the system for realizing this invention, a server plays a central role. The server uses information integration means to collect data from various domains, converts it into a unified format, and manages it. This eliminates data inconsistencies and enables consistent data analysis. Based on this integrated data, the server uses work prioritization evaluation means to dynamically calculate priorities.

[0731] Furthermore, the server operates sentiment analysis tools and evaluates the user's voice and behavioral data in real time using analysis tools such as Google Cloud's Natural Language API. Based on these evaluation results, it automatically adjusts the content of work notifications and schedules to ensure the user can work in the most optimal state.

[0732] A concrete example is when a server sends commands to robots operating on a factory production line. It analyzes the emotional state of the workers and notifies them to prioritize less burdensome tasks. This reduces worker stress and enables more efficient production.

[0733] As an example of a prompt, the system might input, "Analyze the emotional state of this audio and determine whether the worker is experiencing stress." Based on this prompt, the server performs an emotional analysis and optimizes the task according to the results obtained.

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The server collects data from various domains and converts it into a unified format. The input is raw data obtained from different data sources, and the output is a dataset converted to a unified format. This process extracts and standardizes the data, performing data processing to ensure consistency in subsequent analysis.

[0737] Step 2:

[0738] The server dynamically calculates task priorities using work prioritization evaluation tools based on integrated data. The input is a dataset converted to a unified format, and the output is a prioritized task list. AI algorithms are used to analyze the data, assess the importance and urgency of tasks, and optimize resource allocation based on the results.

[0739] Step 3:

[0740] The server analyzes the user's emotional state by applying emotion analysis to user voice and behavioral data. Voice data from the user is provided as input, and analysis results indicating the emotional state are generated as output. Here, Google Cloud's Natural Language API is used to identify emotional components in the voice data and measure states such as stress and fatigue.

[0741] Step 4:

[0742] The server adjusts the task list content and notifications based on the sentiment analysis results. The input is the sentiment analysis results and a prioritized task list; the output is the adjusted task list. Based on these analysis results, the server flexibly changes the tone and content of notifications to the worker to create an optimal work environment.

[0743] Step 5:

[0744] Ultimately, the server sends the adjusted task list and notifications to the terminal. The input is the adjusted task list and notification settings, and the output is the optimized information displayed on the terminal. The information displayed on the terminal is designed to reduce user stress and support efficient work performance.

[0745] 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.

[0746] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] A data integration method that collects information from each department and manages it centrally,

[0769] A task prioritization method that automatically evaluates and adjusts priorities using integrated information,

[0770] A monitoring system that monitors the project's progress in real time and notifies when an anomaly is detected,

[0771] A real-time report generation means that generates the latest status as a report in response to user requests,

[0772] Task automation methods for automatically performing regular tasks,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, wherein the data integration means converts information of different forms into a standardized format and analyzes it.

[0776] (Claim 3)

[0777] The system according to claim 1, wherein the task prioritization means uses an artificial intelligence algorithm to analyze and dynamically adjust schedules and resources.

[0778] "Example 1"

[0779] (Claim 1)

[0780] An information integration system that collects information from each department and manages it centrally,

[0781] A task prioritization method that automatically evaluates and adjusts priorities using integrated information,

[0782] A monitoring system that monitors the status of ongoing tasks in real time and notifies the relevant personnel when an anomaly is detected,

[0783] An instant report generation means that generates the latest information as a report in response to user requests,

[0784] A means of automating tasks to perform regular tasks automatically,

[0785] An analytical means that performs analysis based on a generative AI model,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, wherein the information integration means converts information of different forms into a standardized format and makes it analyzable.

[0789] (Claim 3)

[0790] The system according to claim 1, wherein the means for prioritizing tasks uses an artificial intelligence algorithm to analyze and dynamically adjust plans and resources.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A data aggregation method that collects information from each department and manages it centrally,

[0794] An activity prioritization method that automatically evaluates and adjusts priorities using aggregated information,

[0795] A monitoring system that monitors the progress of the project in real time and notifies when an anomaly is detected,

[0796] A real-time report generation method that generates the latest status as a report according to the user's request,

[0797] A means of automating tasks to perform regular tasks automatically,

[0798] An optimization method that aggregates operational information of manufacturing equipment and optimizes maintenance timing and operation through analysis,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the data aggregation means converts information in different formats into a standardized format and analyzes it.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the activity prioritization means analyzes using a machine learning algorithm and dynamically adjusts schedules and resources.

[0804] "Example 2 of combining an emotion engine"

[0805] (Claim 1)

[0806] An information integration system that collects information from each department and manages it centrally,

[0807] A task prioritization method that automatically evaluates and adjusts priorities using integrated information,

[0808] A status monitoring system that monitors the project's progress in real time and notifies when an anomaly is detected,

[0809] An emotion recognition means that identifies the user's emotional state and adjusts the content and tone of notifications based on this,

[0810] Interaction optimization means that help users receive information without feeling stressed,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the information integration means converts information of different forms into a standardized format and analyzes it.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the means for prioritizing tasks uses a machine learning algorithm to analyze and dynamically adjust schedules and resources.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] An information integration means that aggregates and manages data from each domain,

[0819] A task prioritization evaluation method that dynamically calculates priority based on integrated data,

[0820] It has a monitoring function that continuously observes the progress of the work and notifies in case of abnormalities,

[0821] An emotion analysis means that analyzes the user's emotional state and adjusts the content of notifications,

[0822] Task automation function that autonomously performs scheduled tasks,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the information integration means converts different types of information into a unified format and analyzes it.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the work prioritization evaluation means uses a machine learning algorithm to analyze and dynamically adjust time management and resource allocation. [Explanation of Symbols]

[0828] 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 data integration method that collects information from each department and manages it centrally, A task prioritization method that automatically evaluates and adjusts priorities using integrated information, A monitoring system that monitors the project's progress in real time and notifies when an anomaly is detected, A real-time report generation means that generates the latest status as a report in response to user requests, Task automation methods for automatically performing regular tasks, A system that includes this.

2. The system according to claim 1, wherein the data integration means converts information of different formats into a standardized format and analyzes it.

3. The system according to claim 1, wherein the task prioritization means uses an artificial intelligence algorithm to analyze and dynamically adjust schedules and resources.

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