system

Abstract

We provide the system. [Solution] A means of collecting operational data from multiple measuring devices, An analysis method using artificial intelligence to analyze the aforementioned business data, A means for automatically optimizing the production process based on the results of the analysis means, A means of sending notifications to the operator and receiving instructions from the operator, Means for performing tasks based on instructions from the operator or the results of the analysis means, Means for adjusting the operation plan of the production equipment, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As a major factor hindering the efficiency of business processes in enterprises, there is a problem that overall optimization is difficult because the management of individual tasks is performed manually each time. In addition, delays caused by unsmooth information transmission and the inability to smoothly handle irregular events are reducing productivity. The present invention aims to eliminate such inefficiencies in business management and improve the productivity of the entire business.

Means for Solving the Problems

[0005] This invention provides a system that includes means for collecting business data from multiple data sources, means for analyzing business data using artificial intelligence, and means for automatically optimizing business flows based on the analysis results. This system appropriately monitors the progress of each task and identifies business bottlenecks. Furthermore, it supports rapid decision-making by providing means for sending notifications to users and receiving instructions as needed. This enables overall business efficiency and productivity improvement.

[0006] "Data sources" refer to multiple sources from which business-related information is obtained.

[0007] "Business data" refers to information necessary to understand the progress and performance of business operations.

[0008] "Artificial intelligence" refers to the technology that allows computer systems to mimic human intellectual activity.

[0009] "Analysis methods" refer to methods that utilize artificial intelligence to evaluate business data and extract insights to achieve specific objectives.

[0010] "Optimization" refers to the process of improving business workflows and resource allocation to maximize efficiency and productivity.

[0011] "Notification" refers to a means of transmitting information from a system to a user, and its role is to inform them of the current status of their work and the actions that need to be taken.

[0012] "Instructions" refer to commands or feedback given by the user to the system, and are information that affects the system's operation. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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.

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

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention realizes an AI agent system for optimizing and streamlining business workflows within a company. Servers, terminals, and users each play their respective roles, and the entire system supports the automation and efficiency of business processes.

[0035] The server plays a central role, collecting business data from various data sources both inside and outside the company. This includes project management systems, mail servers, and other digital databases. The collected data is then provided to AI agents, who analyze the progress of business operations based on that data.

[0036] The terminal functions as an interface with the user. When the AI ​​agent makes optimization suggestions, assigns tasks, or makes changes based on data analysis, it notifies the user. The terminal also serves as an input channel to the system by receiving user feedback and instructions.

[0037] Users receive notifications from the AI ​​agent via their device and review their content as needed. Users can then approve or modify the optimization suggestions proposed by the AI ​​agent. For example, by changing the priority of a specific task, users can adjust the workflow in real time.

[0038] As a concrete example, if a specific task in Project A is delayed, the server communicates the delay to the AI, which analyzes the cause of the delay. Next, the terminal notifies the user of the analysis results, and the user makes the necessary decisions. Subsequently, the server, following the user's instructions, performs tasks reassignment and resource reallocation. Through this series of processes, the overall efficiency of the work is improved.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects business data from various data sources both inside and outside the company. This includes retrieving daily progress and related information from tasks management systems, mail servers, and other sources.

[0042] Step 2:

[0043] The server passes the collected business data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the various data, perform a current business analysis, and identify problems.

[0044] Step 3:

[0045] Based on the AI ​​agent's analysis results, the server generates optimization proposals and improvement measures for the workflow. This includes reprioritizing tasks that are behind schedule and reallocating resources.

[0046] Step 4:

[0047] The device receives notifications from the server and presents the user with optimization suggestions and progress reports. The information is displayed in a way that is easy for the user to understand.

[0048] Step 5:

[0049] The user reviews the information displayed on the device and approves, modifies, or gives other instructions regarding the optimization suggestions made by the AI ​​agent. This allows the user to adjust the workflow.

[0050] Step 6:

[0051] The server then adjusts the workflow based on user instructions. This means taking specific actions such as changing system settings, assigning new tasks, or replanning existing tasks.

[0052] Step 7:

[0053] The server monitors the effects of the actions performed and feeds the results back to the AI ​​agent. This allows the AI ​​to update its predictive model and use it for the next analysis.

[0054] (Example 1)

[0055] 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."

[0056] Business processes within companies are becoming increasingly complex, and the inefficiency of information gathering, analysis, and optimization processes is a major challenge. Furthermore, the lack of sufficient AI-powered efficient progress management and optimization suggestions raises concerns about a decline in overall business productivity.

[0057] 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.

[0058] In this invention, the server includes an information gathering means, an analysis means using artificial intelligence, and an optimization means. This makes it possible to efficiently collect information accumulated in business processes and automatically construct optimal business procedures based on the analysis results.

[0059] An "information gathering method" is a system for automatically obtaining necessary information from multiple sources.

[0060] "Analysis methods" refer to techniques that use artificial intelligence to analyze information acquired and evaluate the progress and efficiency of work.

[0061] An "optimization method" is a process for automatically improving business procedures based on the results of analysis.

[0062] An "interface means" is a means of communication that allows users to access a system and send and receive instructions.

[0063] "Execution means" refers to control means for carrying out tasks based on user instructions or analysis results.

[0064] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate natural language.

[0065] "Communication methods" refer to means of exchanging information and collecting feedback electronically.

[0066] A "user interface" is an interface used to exchange information between a user and a system via a terminal.

[0067] This system utilizes a combination of components to efficiently manage business processes within an organization. The details are described below.

[0068] The server plays a central role as a means of information gathering. It retrieves information from common project management software and email systems via APIs to collect data from multiple sources. For example, it integrates with project management tools and email systems to collect progress information and communication logs for each project.

[0069] Next, the server uses an artificial intelligence model as an analysis tool. This analysis utilizes a generative AI model to implement natural language processing technology. This allows the server to analyze the progress of tasks and identify potential bottlenecks, generating easy-to-understand reports in natural language. The results of the analysis are presented as concrete documents detailing the status of tasks and suggesting improvements.

[0070] The terminal serves as an interface, allowing users to access it directly. Here, it provides users with AI-driven optimization suggestions and notifications of business updates. The terminal presents this information using common, everyday communication software.

[0071] Ultimately, the user makes informed decisions about their work. Through the terminal, the user can respond to suggestions from the server with approval or requests for modifications. As an example of a specific prompt, the user could ask the system, "Tell me the highest priority tasks for the next week."

[0072] This series of processes makes it possible to improve overall business productivity. Business workflows are continuously optimized, maintaining an optimal state based on the latest business data.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server retrieves data from multiple sources using information gathering methods. It uses connection information for project management tools and email servers via APIs as input. The server collects project progress, the number of unread emails, and other data from these sources and stores it in a database. This process aggregates the most up-to-date data necessary for business operations.

[0076] Step 2:

[0077] The server executes an analysis using artificial intelligence. It uses the data collected in Step 1 as input. A generative AI model is used to analyze the data and identify business progress and potential problems. The analysis results are output as an easy-to-understand report using natural language processing technology. This process provides actionable, data-driven insights.

[0078] Step 3:

[0079] The terminal notifies the user of the analysis results via an interface. It receives output from the server as input and displays it through communication software the user uses daily. Specifically, it presents the user with high-priority tasks and improvement suggestions. This allows the user to quickly receive information and make decisions on how to respond.

[0080] Step 4:

[0081] The user decides how to respond to information received through the device. The input is the content of the notification from the device. Based on this information, the user gives specific instructions and sends feedback via the device if changes are needed. For example, it is possible to instruct a change in the priority of a specific task. This allows for flexible adjustment of the workflow.

[0082] Step 5:

[0083] The server coordinates tasks using execution methods based on user instructions. It receives user feedback as input. The server uses APIs to reallocate tasks and adjust resources to project management tools. This operation optimizes operational efficiency and enables real-time problem resolution.

[0084] (Application Example 1)

[0085] 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."

[0086] Identifying bottlenecks in each process of the production line and improving their efficiency is a crucial challenge for many manufacturers. However, a lack of consistent data collection and analysis, as well as harmonious cooperation between humans and machines, is hindering sufficient efficiency improvements.

[0087] 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.

[0088] In this invention, the server includes means for collecting operational data from measuring devices, means for analyzing the data using artificial intelligence, and means for automatically optimizing the production process. This enables real-time optimization of the production flow and efficient execution of operations.

[0089] A "measuring device" is a device used to collect various types of data in the production process in real time.

[0090] "Business data" refers to information about the production process within a factory, as well as numerical values ​​and indicators that show its progress.

[0091] "Artificial intelligence" refers to a computer program or system used to analyze large amounts of data and derive the optimal solution.

[0092] An "analysis tool" is a processing device that uses collected business data to provide information for making decisions regarding process optimization and efficiency.

[0093] A "production process" refers to a series of work steps and processes that are followed when manufacturing a product.

[0094] An "operator" is a person responsible for overseeing the production process and approving or adjusting suggestions from the system as needed.

[0095] A "notification" is information or a suggestion sent from a system to the user.

[0096] An "operation plan" is a schedule that shows the tasks and timings that various production machines should perform.

[0097] In order to implement this invention, it is necessary to introduce measuring devices, servers, artificial intelligence, terminals, and production equipment into the production management system within the factory.

[0098] The measuring devices are placed on the production line and are responsible for acquiring progress information and environmental data for each process in real time. This data is then transmitted to a server via the network for later analysis.

[0099] The server stores the collected data and performs analysis using artificial intelligence. This analysis can utilize environments with powerful computing capabilities, such as AWS® AI services. The server generates detailed reports that include bottlenecks and suggestions for efficiency improvements.

[0100] Artificial intelligence can offer various suggestions to improve productivity through data analysis. This could involve the use of deep learning and other machine learning algorithms.

[0101] The terminal functions as a user interface for the operator and receives notifications sent from the server. The operator then reviews the proposed optimizations and makes modifications or approvals as needed.

[0102] Production equipment can autonomously adjust its movements based on a new motion plan received via a terminal. This is made possible by robot control software such as ROS (Robot Operating System).

[0103] For example, if a time delay is detected in process B of product A, the server analyzes the data, and artificial intelligence generates efficiency improvement measures. The proposed measures are sent to the operator via a terminal, and after the operator approves them, the production equipment starts operating according to the new plan.

[0104] An example of a prompt for a generative AI model would be: "Analyze the bottleneck in process B of product A's production and suggest methods for optimizing it."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The measuring device collects the data.

[0108] Measurement devices collect process progress and environmental information, which is then transmitted to a server via the network. This data is sent to the server in digital format by sensor devices that detect the product's position, temperature, speed, etc.

[0109] Step 2:

[0110] The server receives the data and begins analysis.

[0111] The server temporarily stores the received data in a storage system. Then, it applies an analysis algorithm utilizing a generative AI model to perform data analysis, identifying bottlenecks and trends. This reveals the efficiency and problems of each process, and the analysis results are output.

[0112] Step 3:

[0113] The server generates the analysis results.

[0114] Based on the analysis performed on the server, specific optimization plans and improvement suggestions are generated. At this stage, the potential for improvement for each process is listed based on the data analysis results. The generated results are output in report format and are ready to be notified to the responsible operator.

[0115] Step 4:

[0116] The terminal notifies the operator of the analysis results.

[0117] The terminal receives reports sent from the server and displays their contents to the operator. Analysis results and suggestions are presented on the user interface, allowing the operator to review them. Specifically, the operation involves displaying information on the terminal's screen.

[0118] Step 5:

[0119] The user reviews the optimization suggestions and submits instructions.

[0120] Users review the reports on their devices in detail and approve or modify optimization suggestions as needed. This is done using touchscreens or input devices, and if there are any suggested modifications, feedback is sent to the server via the device.

[0121] Step 6:

[0122] The server adjusts its operation plan based on instructions from the user.

[0123] The server receives user feedback and creates a new operation plan. Based on the revised operation plan, it prepares to automatically adjust the operation of the production equipment. The output is generated as the adjusted operation plan.

[0124] Step 7:

[0125] The production equipment starts operating according to the new operation plan.

[0126] The production equipment begins autonomous movement based on a new operation plan received from the server. This means that it executes the set operations precisely according to the programmed schedule. During operation, the equipment collects data again through the measuring device, and the process repeats from step 1.

[0127] 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.

[0128] This invention is an AI agent system that realizes efficiency and optimization of business operations within a company, and further incorporates an emotion engine that recognizes user emotions and influences business processes. Each element, including the server, terminal, and emotion engine, works in coordination.

[0129] The server collects business data from various sources within the company. This business data includes information handled in digital format, such as project management systems, emails, and even video conference recordings. This allows the server to understand the details of the entire business flow and provide AI agents with the foundational information for analysis.

[0130] The AI ​​agent analyzes business data provided by the server using machine learning models. This allows it to evaluate the progress and efficiency of tasks and generate instructions to automatically optimize the workflow as needed. For example, if a particular task is delayed, it can identify the cause and suggest a reallocation of resources.

[0131] The device functions as an interface with the user. Here, the emotion engine recognizes the user's emotions and adjusts the presentation and priority of notifications based on those emotions. For example, if the emotion engine detects that the user is stressed, the device will display only high-priority notifications to reduce the burden on the user.

[0132] The user reviews the information presented on the device and adjusts the workflow based on the AI ​​agent's suggestions. The emotion engine infers the user's emotional state from their feedback and facial expressions, and provides feedback to the agent accordingly. This allows the server to dynamically adjust tasks while taking the user's emotional state into account.

[0133] For example, if a user is feeling fatigued during a project meeting, the emotion engine can detect this and, via the terminal, suggest a plan to the server to support the meeting's progress. The organic collaboration of these elements not only improves work efficiency but also enhances the overall user work environment.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The server collects business data from various sources within the company. This includes obtaining progress data from project management tools and communication history from email servers.

[0137] Step 2:

[0138] The server provides the collected operational data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the current operational status and identify progress and potential problems.

[0139] Step 3:

[0140] Based on the analyzed data, the server generates optimization suggestions for the business workflow. This includes prioritizing backlogged tasks and suggesting changes to resource allocation.

[0141] Step 4:

[0142] The terminal receives notifications from the server and displays optimization suggestions and work progress to the user. This information is formatted in a way that is useful to the user. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0143] Step 5:

[0144] Users review the display on their devices and provide feedback on their workflow by approving, modifying, or rejecting optimization suggestions proposed by the AI ​​agent as needed.

[0145] Step 6:

[0146] The server uses user feedback and sentiment data from the sentiment engine to make final adjustments to the workflow. This includes taking the user's state into consideration, such as highlighting only high-priority tasks.

[0147] Step 7:

[0148] The server monitors the results of the adjustments made and evaluates operational efficiency and user satisfaction. This evaluation is fed back to the AI ​​agent and used to improve the model.

[0149] (Example 2)

[0150] 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 will be referred to as the "terminal."

[0151] Conventional business management systems, while focusing on streamlining and optimizing operations, lacked the flexibility to make adjustments based on users' emotions and mental states. Furthermore, they failed to respond quickly to unforeseen circumstances arising during work, leading to decreased work efficiency and accumulated user stress. This invention aims to provide a more comfortable and efficient work environment by optimizing the workflow while considering user emotions.

[0152] 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.

[0153] In this invention, the server includes means for collecting information from multiple information sources, means for analyzing the information using intelligence, means for automatically optimizing work procedures based on the results of the analysis, and means for recognizing the user's emotions and adjusting the expression and priority of notifications based on those emotions. This enables dynamic work adjustments that take into account the user's emotions, thereby improving work efficiency and the user's work environment.

[0154] "Information source" refers to the source from which business data and related information are collected within a company.

[0155] "Intelligence" refers to artificial intelligence technologies and machine learning models used to analyze business data.

[0156] "Analysis methods" refer to techniques and processes used to evaluate and optimize the efficiency and progress of operations based on collected information.

[0157] "Optimizing work procedures" refers to making adjustments to business processes and resource allocations in the most efficient way, based on the results of analysis.

[0158] "Emotion" refers to the user's emotional state or mental condition, including their reactions to the work environment and tasks.

[0159] "Adjusting notification wording and priority" refers to the process of changing notification content and urgency based on the user's emotions to deliver information in the most optimal way for the user.

[0160] This invention is an AI agent system that realizes the efficiency and optimization of internal business operations. The system functions through the organic cooperation of its components: server, terminal, and user.

[0161] The server plays a role in collecting business data from a wide variety of sources within the company. This includes information provided in digital formats such as project management systems, emails, and video conference recordings. The server aggregates this data and provides it as foundational data for analysis by AI agents. The server uses database management systems and other tools to enable efficient data collection.

[0162] The AI ​​agent utilizes machine learning models to analyze business data provided by the server. This analysis allows for real-time evaluation of business progress and efficiency, and optimization of automated workflows. Specifically, it can identify the cause of delays in specific tasks and propose the reallocation of necessary resources. In this analysis, the AI ​​agent uses programming languages ​​such as Python and R, and leverages libraries such as TENSORFLOW® and PyTorch.

[0163] The device functions as an interface with the user. It is equipped with an emotion engine that recognizes the user's emotions. This recognition is based on the user's facial expressions, voice tone, and past activity data, and adjusts the way notifications are presented and their priority. For example, if the user is stressed, the device will only display important notifications, working to reduce the user's burden. The device uses hardware such as a camera and microphone, and software such as OpenCV and Mediapipe for emotion recognition technology.

[0164] Users can review the information displayed on their device and adjust their workflow based on the AI ​​agent's suggestions. The emotion engine continuously monitors the user's reactions and provides information to the AI ​​agent based on that feedback. This allows users to perform their tasks efficiently and with less stress.

[0165] For example, if a user feels fatigued during a project meeting, the emotion engine can detect this and make suggestions to the server via the terminal to ensure the meeting runs smoothly. In this way, the coordination of each element not only improves work efficiency but also enhances the user's work environment.

[0166] An example of a prompt to be input into the generated AI model is: "Please describe in detail how the AI ​​agent optimizes the workflow by considering the user's emotions. Also, please show how the emotion engine detects user fatigue and provides efficient work support."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects business data from multiple sources within the company. Inputs include project management system APIs, email server queries, and video conferencing system logs. It retrieves data from these sources, converts it to a digital format, and stores it in a database. Specifically, the server automates the process of periodically accessing each system and querying for new data.

[0170] Step 2:

[0171] The AI ​​agent processes business data provided by the server. It uses information from various data sources obtained from the server as input. Using a machine learning model, it analyzes this data and generates reports on business progress and efficiency. Data processing includes normalization of different data formats, scaling of numerical data, and natural language processing of text data. Outputs include task delay status and resource optimization suggestions.

[0172] Step 3:

[0173] The device functions as an interface with the user. At this stage, the user's facial expressions and voice information are used as input. The emotion engine analyzes this in real time and extracts emotional data. Specifically, it uses the camera and microphone to detect the user's emotional state and adjusts the presentation and priority of notifications based on the results. The output includes an optimized list of notifications displayed to the user.

[0174] Step 4:

[0175] The user reviews the information displayed on the terminal and adjusts the workflow based on suggestions from the AI ​​agent. They receive notifications and suggestions from the terminal as input. Based on this, they input necessary instructions into the system and manage the progress of their work. Specifically, the user selects options displayed on the screen, and the feedback is reprocessed by the AI ​​agent. As a result, the work environment is optimized in real time.

[0176] Step 5:

[0177] The server dynamically updates business data based on user instructions and feedback, preparing for the next cycle. It receives user instructions and emotional state feedback as input. This data is then re-analyzed to optimize the next business process. Specifically, the updated data is provided back to the AI ​​agent to generate new business improvement suggestions. The output is updated business flow information.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] While there is a demand for increased productivity in manufacturing, the impact of workers' workload and emotional state on production efficiency remains a challenge. In particular, worker stress and fatigue are problematic as they reduce productivity, and a system is needed to address these issues while efficiently optimizing operations.

[0181] 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.

[0182] In this invention, the server includes means for collecting business data from multiple information sources, analysis means using artificial intelligence to analyze the business data, means for automatically optimizing the workflow based on the results of the analysis means, and emotion recognition means. This enables business optimization that takes into account the emotional state of the worker and adjustment of notification priorities.

[0183] "Information sources" refer to the various data providers used to acquire business data.

[0184] "Business data" refers to all data that includes information about a company's business processes.

[0185] "Artificial intelligence" is a technology that enables computer systems to perform processing that mimics human intelligence.

[0186] "Analysis methods" is a general term for methods and tools used to analyze acquired business data and extract useful information.

[0187] "Means for automatically optimizing business processes" refer to processes and technologies for efficiently adjusting business procedures based on analysis results.

[0188] "Emotion recognition means" refers to a system of technologies that collects and analyzes data in order to identify a user's emotional state.

[0189] "Notification priority adjustment" is a process that changes the order and method of information delivery according to the user's emotional state and the importance of the task.

[0190] In implementing this invention, the system primarily uses a server, a terminal, and a device for emotion recognition. This system utilizes both AI technology and emotion recognition to improve work efficiency in the workplace.

[0191] The server collects business data from multiple sources. Specifically, this includes a wide range of digital information, such as work progress data from production management systems and worker sentiment data from sensor devices. This data is analyzed using artificial intelligence analysis tools to optimize business workflows. The analysis utilizes Python-based machine learning models and deep learning frameworks such as TensorFlow and PyTorch.

[0192] The terminal provides the user interface and adjusts the priority of notifications received by the user. Emotion recognition means recognize emotions from the user's facial expressions and voice tone using hardware such as a camera and microphone. For this purpose, technologies such as OpenCV and Google's (registered trademark) speech recognition API are used.

[0193] Users can receive notifications from the system via their devices and take actions in line with their work progress. The system takes into account the user's emotional state and suggests work adjustments to reduce stress. For example, if a worker is feeling fatigued, the system can suggest when to take a break or temporarily slow down the pace of work.

[0194] The generative AI model generates suggestions based on work progress and emotion recognition data. These suggestions support optimal work adjustments for the user. An example of a prompt is: "When field workers are experiencing stress, suggest how to optimize break times and work schedules."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server collects operational data from multiple sources. Inputs include progress data from the production management system and emotion data from sensor devices. This data is integrated and stored on the server as foundational information for data analysis.

[0198] Step 2:

[0199] The server performs analysis on the collected data using machine learning models. For data processing during the analysis, TensorFlow and PyTorch are used for data preprocessing and feature extraction, and the progress of the business is evaluated. As output, metrics necessary for business optimization are generated.

[0200] Step 3:

[0201] The server automatically proposes optimization plans for business workflows based on the generated metrics. The input is the business efficiency metrics obtained through analysis, and the output is a proposed adjustment to the specific work schedule. The generating AI model creates the proposal content using prompt messages.

[0202] Step 4:

[0203] The terminal notifies the user of optimization suggestions via a user interface. The input is the business optimization suggestions from the server, and the output is a notification message displayed to the user. The notifications are prioritized based on sentiment recognition, and important messages are highlighted.

[0204] Step 5:

[0205] Users review suggestions from the server via their terminals and respond with instructions. Input is the notification content from the terminal, and output is the user's feedback. User instructions are recorded within the system and used for further data analysis to adjust operations.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention realizes an AI agent system for optimizing and streamlining business workflows within a company. Servers, terminals, and users each play their respective roles, and the entire system supports the automation and efficiency of business processes.

[0223] The server plays a central role, collecting business data from various data sources both inside and outside the company. This includes project management systems, mail servers, and other digital databases. The collected data is then provided to AI agents, who analyze the progress of business operations based on that data.

[0224] The terminal functions as an interface with the user. When the AI ​​agent makes optimization suggestions, assigns tasks, or makes changes based on data analysis, it notifies the user. The terminal also serves as an input channel to the system by receiving user feedback and instructions.

[0225] Users receive notifications from the AI ​​agent via their device and review their content as needed. Users can then approve or modify the optimization suggestions proposed by the AI ​​agent. For example, by changing the priority of a specific task, users can adjust the workflow in real time.

[0226] As a concrete example, if a specific task in Project A is delayed, the server communicates the delay to the AI, which analyzes the cause of the delay. Next, the terminal notifies the user of the analysis results, and the user makes the necessary decisions. Subsequently, the server, following the user's instructions, performs tasks reassignment and resource reallocation. Through this series of processes, the overall efficiency of the work is improved.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server collects business data from various data sources both inside and outside the company. This includes retrieving daily progress and related information from tasks management systems, mail servers, and other sources.

[0230] Step 2:

[0231] The server passes the collected business data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the various data, perform a current business analysis, and identify problems.

[0232] Step 3:

[0233] Based on the AI ​​agent's analysis results, the server generates optimization proposals and improvement measures for the workflow. This includes reprioritizing tasks that are behind schedule and reallocating resources.

[0234] Step 4:

[0235] The device receives notifications from the server and presents the user with optimization suggestions and progress reports. The information is displayed in a way that is easy for the user to understand.

[0236] Step 5:

[0237] The user reviews the information displayed on the device and approves, modifies, or gives other instructions regarding the optimization suggestions made by the AI ​​agent. This allows the user to adjust the workflow.

[0238] Step 6:

[0239] The server then adjusts the workflow based on user instructions. This means taking specific actions such as changing system settings, assigning new tasks, or replanning existing tasks.

[0240] Step 7:

[0241] The server monitors the effects of the actions performed and feeds the results back to the AI ​​agent. This allows the AI ​​to update its predictive model and use it for the next analysis.

[0242] (Example 1)

[0243] 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."

[0244] Business processes within companies are becoming increasingly complex, and the inefficiency of information gathering, analysis, and optimization processes is a major challenge. Furthermore, the lack of sufficient AI-powered efficient progress management and optimization suggestions raises concerns about a decline in overall business productivity.

[0245] 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.

[0246] In this invention, the server includes an information gathering means, an analysis means using artificial intelligence, and an optimization means. This makes it possible to efficiently collect information accumulated in business processes and automatically construct optimal business procedures based on the analysis results.

[0247] An "information gathering method" is a system for automatically obtaining necessary information from multiple sources.

[0248] "Analysis methods" refer to techniques that use artificial intelligence to analyze information acquired and evaluate the progress and efficiency of work.

[0249] An "optimization method" is a process for automatically improving business procedures based on the results of analysis.

[0250] An "interface means" is a means of communication that allows users to access a system and send and receive instructions.

[0251] "Execution means" refers to control means for carrying out tasks based on user instructions or analysis results.

[0252] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate natural language.

[0253] "Communication methods" refer to means of exchanging information and collecting feedback electronically.

[0254] A "user interface" is an interface used to exchange information between a user and a system via a terminal.

[0255] This system utilizes a combination of components to efficiently manage business processes within an organization. The details are described below.

[0256] The server plays a central role as a means of information gathering. It retrieves information from common project management software and email systems via APIs to collect data from multiple sources. For example, it integrates with project management tools and email systems to collect progress information and communication logs for each project.

[0257] Next, the server uses an artificial intelligence model as an analysis tool. This analysis utilizes a generative AI model to implement natural language processing technology. This allows the server to analyze the progress of tasks and identify potential bottlenecks, generating easy-to-understand reports in natural language. The results of the analysis are presented as concrete documents detailing the status of tasks and suggesting improvements.

[0258] The terminal serves as an interface, allowing users to access it directly. Here, it provides users with AI-driven optimization suggestions and notifications of business updates. The terminal presents this information using common, everyday communication software.

[0259] Ultimately, the user makes informed decisions about their work. Through the terminal, the user can respond to suggestions from the server with approval or requests for modifications. As an example of a specific prompt, the user could ask the system, "Tell me the highest priority tasks for the next week."

[0260] This series of processes makes it possible to improve overall business productivity. Business workflows are continuously optimized, maintaining an optimal state based on the latest business data.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server retrieves data from multiple sources using information gathering methods. It uses connection information for project management tools and email servers via APIs as input. The server collects project progress, the number of unread emails, and other data from these sources and stores it in a database. This process aggregates the most up-to-date data necessary for business operations.

[0264] Step 2:

[0265] The server executes an analysis using artificial intelligence. It uses the data collected in Step 1 as input. A generative AI model is used to analyze the data and identify business progress and potential problems. The analysis results are output as an easy-to-understand report using natural language processing technology. This process provides actionable, data-driven insights.

[0266] Step 3:

[0267] The terminal notifies the user of the analysis results via an interface. It receives output from the server as input and displays it through communication software the user uses daily. Specifically, it presents the user with high-priority tasks and improvement suggestions. This allows the user to quickly receive information and make decisions on how to respond.

[0268] Step 4:

[0269] The user decides how to respond to information received through the device. The input is the content of the notification from the device. Based on this information, the user gives specific instructions and sends feedback via the device if changes are needed. For example, it is possible to instruct a change in the priority of a specific task. This allows for flexible adjustment of the workflow.

[0270] Step 5:

[0271] The server coordinates tasks using execution methods based on user instructions. It receives user feedback as input. The server uses APIs to reallocate tasks and adjust resources to project management tools. This operation optimizes operational efficiency and enables real-time problem resolution.

[0272] (Application Example 1)

[0273] 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."

[0274] Identifying bottlenecks in each process of the production line and improving their efficiency is a crucial challenge for many manufacturers. However, a lack of consistent data collection and analysis, as well as harmonious cooperation between humans and machines, is hindering sufficient efficiency improvements.

[0275] 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.

[0276] In this invention, the server includes means for collecting operational data from measuring devices, means for analyzing the data using artificial intelligence, and means for automatically optimizing the production process. This enables real-time optimization of the production flow and efficient execution of operations.

[0277] A "measurement device" is a device for collecting various data in real time in a production process.

[0278] "Business data" refers to information related to the production process in a factory and numerical values or indicators indicating its progress.

[0279] "Artificial intelligence" is a computer program or system used to analyze a large amount of data and derive an optimal solution.

[0280] "Analysis means" is a processing device that provides judgment materials for optimizing and improving the efficiency of the process based on the collected business data.

[0281] "Production process" refers to a series of work steps or processes that a product goes through when being manufactured.

[0282] "Operator" refers to a person who supervises the production process and has the role of approving or adjusting proposals from the system as necessary.

[0283] "Notification" refers to information and proposal content sent from the system to the operator.

[0284] "Operation plan" is a schedule indicating the work content and timing that various production devices should perform.

[0285] To implement this invention, it is necessary to introduce a measurement device, a server, artificial intelligence, a terminal, and a production device into the production management system in the factory.

[0286] The measurement device is arranged on the production line and is responsible for acquiring in real time the progress information and environmental data of each process. This data is transmitted to the server via the network for later analysis.

[0287] The server stores the collected data and performs analysis using artificial intelligence. This analysis can utilize environments with powerful computing capabilities, such as AWS AI services. The server generates detailed reports that include bottlenecks and suggestions for efficiency improvements.

[0288] Artificial intelligence can offer various suggestions to improve productivity through data analysis. This could involve the use of deep learning and other machine learning algorithms.

[0289] The terminal functions as a user interface for the operator and receives notifications sent from the server. The operator then reviews the proposed optimizations and makes modifications or approvals as needed.

[0290] Production equipment can autonomously adjust its movements based on a new motion plan received via a terminal. This is made possible by robot control software such as ROS (Robot Operating System).

[0291] For example, if a time delay is detected in process B of product A, the server analyzes the data, and artificial intelligence generates efficiency improvement measures. The proposed measures are sent to the operator via a terminal, and after the operator approves them, the production equipment starts operating according to the new plan.

[0292] An example of a prompt for a generative AI model would be: "Analyze the bottleneck in process B of product A's production and suggest methods for optimizing it."

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The measuring device collects the data.

[0296] Process progress and environmental information are collected from the measurement device and sent to the server via the network. This data is detected by the sensor device for the position, temperature, speed, etc. of the product and sent to the server in digital format.

[0297] Step 2:

[0298] The server receives the data and starts analysis.

[0299] The server temporarily stores the received data in the storage system. Then, it applies an analysis algorithm using the generated AI model to perform data analysis for identifying bottlenecks and trends. As a result, the efficiency and problems of each process become clear, and the analysis results are output.

[0300] Step 3:

[0301] The server generates analysis results.

[0302] Based on the analysis performed on the server, specific optimization plans and improvement proposals are generated. At this stage, based on the results of the data analysis, the improvement possibilities for each process are listed. The generated results are output in report format and are ready to be notified to the responsible operator.

[0303] Step 4:

[0304] The terminal notifies the operator of the analysis results.

[0305] The terminal receives the report sent from the server and displays its content to the operator. The analysis results and proposals are presented on the user interface, and the operator can confirm them. The specific operation is to display information on the terminal's display.

[0306] Step 5:

[0307] The user checks the optimization plan and sends an instruction.

[0308] Users review the reports on their devices in detail and approve or modify optimization suggestions as needed. This is done using touchscreens or input devices, and if there are any suggested modifications, feedback is sent to the server via the device.

[0309] Step 6:

[0310] The server adjusts its operation plan based on instructions from the user.

[0311] The server receives user feedback and creates a new operation plan. Based on the revised operation plan, it prepares to automatically adjust the operation of the production equipment. The output is generated as the adjusted operation plan.

[0312] Step 7:

[0313] The production equipment starts operating according to the new operation plan.

[0314] The production equipment begins autonomous movement based on a new operation plan received from the server. This means that it executes the set operations precisely according to the programmed schedule. During operation, the equipment collects data again through the measuring device, and the process repeats from step 1.

[0315] 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.

[0316] This invention is an AI agent system that realizes efficiency and optimization of business operations within a company, and further incorporates an emotion engine that recognizes user emotions and influences business processes. Each element, including the server, terminal, and emotion engine, works in coordination.

[0317] The server collects business data from various sources within the company. This business data includes information handled in digital format, such as project management systems, emails, and even video conference recordings. This allows the server to understand the details of the entire business flow and provide AI agents with the foundational information for analysis.

[0318] The AI ​​agent analyzes business data provided by the server using machine learning models. This allows it to evaluate the progress and efficiency of tasks and generate instructions to automatically optimize the workflow as needed. For example, if a particular task is delayed, it can identify the cause and suggest a reallocation of resources.

[0319] The device functions as an interface with the user. Here, the emotion engine recognizes the user's emotions and adjusts the presentation and priority of notifications based on those emotions. For example, if the emotion engine detects that the user is stressed, the device will display only high-priority notifications to reduce the burden on the user.

[0320] The user reviews the information presented on the device and adjusts the workflow based on the AI ​​agent's suggestions. The emotion engine infers the user's emotional state from their feedback and facial expressions, and provides feedback to the agent accordingly. This allows the server to dynamically adjust tasks while taking the user's emotional state into account.

[0321] For example, if a user is feeling fatigued during a project meeting, the emotion engine can detect this and, via the terminal, suggest a plan to the server to support the meeting's progress. The organic collaboration of these elements not only improves work efficiency but also enhances the overall user work environment.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server collects business data from various sources within the company. This includes obtaining progress data from project management tools and communication history from email servers.

[0325] Step 2:

[0326] The server provides the collected operational data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the current operational status and identify progress and potential problems.

[0327] Step 3:

[0328] Based on the analyzed data, the server generates optimization suggestions for the business workflow. This includes prioritizing backlogged tasks and suggesting changes to resource allocation.

[0329] Step 4:

[0330] The terminal receives notifications from the server and displays optimization suggestions and work progress to the user. This information is formatted in a way that is useful to the user. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0331] Step 5:

[0332] Users review the display on their devices and provide feedback on their workflow by approving, modifying, or rejecting optimization suggestions proposed by the AI ​​agent as needed.

[0333] Step 6:

[0334] The server uses user feedback and sentiment data from the sentiment engine to make final adjustments to the workflow. This includes taking the user's state into consideration, such as highlighting only high-priority tasks.

[0335] Step 7:

[0336] The server monitors the results of the adjustments made and evaluates operational efficiency and user satisfaction. This evaluation is fed back to the AI ​​agent and used to improve the model.

[0337] (Example 2)

[0338] 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".

[0339] Conventional business management systems, while focusing on streamlining and optimizing operations, lacked the flexibility to make adjustments based on users' emotions and mental states. Furthermore, they failed to respond quickly to unforeseen circumstances arising during work, leading to decreased work efficiency and accumulated user stress. This invention aims to provide a more comfortable and efficient work environment by optimizing the workflow while considering user emotions.

[0340] 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.

[0341] In this invention, the server includes means for collecting information from multiple information sources, means for analyzing the information using intelligence, means for automatically optimizing work procedures based on the results of the analysis, and means for recognizing the user's emotions and adjusting the expression and priority of notifications based on those emotions. This enables dynamic work adjustments that take into account the user's emotions, thereby improving work efficiency and the user's work environment.

[0342] "Information source" refers to the source from which business data and related information are collected within a company.

[0343] "Intelligence" refers to artificial intelligence technologies and machine learning models used to analyze business data.

[0344] "Analysis methods" refer to techniques and processes used to evaluate and optimize the efficiency and progress of operations based on collected information.

[0345] "Optimizing work procedures" refers to making adjustments to business processes and resource allocations in the most efficient way, based on the results of analysis.

[0346] "Emotion" refers to the user's emotional state or mental condition, including their reactions to the work environment and tasks.

[0347] "Adjusting notification wording and priority" refers to the process of changing notification content and urgency based on the user's emotions to deliver information in the most optimal way for the user.

[0348] This invention is an AI agent system that realizes the efficiency and optimization of internal business operations. The system functions through the organic cooperation of its components: server, terminal, and user.

[0349] The server plays a role in collecting business data from a wide variety of sources within the company. This includes information provided in digital formats such as project management systems, emails, and video conference recordings. The server aggregates this data and provides it as foundational data for analysis by AI agents. The server uses database management systems and other tools to enable efficient data collection.

[0350] The AI ​​agent utilizes machine learning models to analyze business data provided by the server. This analysis allows for real-time evaluation of business progress and efficiency, and optimization of automated workflows. Specifically, it can identify the cause of delays in specific tasks and propose the reallocation of necessary resources. In this analysis, the AI ​​agent uses programming languages ​​such as Python and R, and leverages libraries such as TensorFlow and PyTorch.

[0351] The device functions as an interface with the user. It is equipped with an emotion engine that recognizes the user's emotions. This recognition is based on the user's facial expressions, voice tone, and past activity data, and adjusts the way notifications are presented and their priority. For example, if the user is stressed, the device will only display important notifications, working to reduce the user's burden. The device uses hardware such as a camera and microphone, and software such as OpenCV and Mediapipe for emotion recognition technology.

[0352] Users can review the information displayed on their device and adjust their workflow based on the AI ​​agent's suggestions. The emotion engine continuously monitors the user's reactions and provides information to the AI ​​agent based on that feedback. This allows users to perform their tasks efficiently and with less stress.

[0353] For example, if a user feels fatigued during a project meeting, the emotion engine can detect this and make suggestions to the server via the terminal to ensure the meeting runs smoothly. In this way, the coordination of each element not only improves work efficiency but also enhances the user's work environment.

[0354] An example of a prompt to be input into the generated AI model is: "Please describe in detail how the AI ​​agent optimizes the workflow by considering the user's emotions. Also, please show how the emotion engine detects user fatigue and provides efficient work support."

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The server collects business data from multiple sources within the company. Inputs include project management system APIs, email server queries, and video conferencing system logs. It retrieves data from these sources, converts it to a digital format, and stores it in a database. Specifically, the server automates the process of periodically accessing each system and querying for new data.

[0358] Step 2:

[0359] The AI ​​agent processes business data provided by the server. It uses information from various data sources obtained from the server as input. Using a machine learning model, it analyzes this data and generates reports on business progress and efficiency. Data processing includes normalization of different data formats, scaling of numerical data, and natural language processing of text data. Outputs include task delay status and resource optimization suggestions.

[0360] Step 3:

[0361] The device functions as an interface with the user. At this stage, the user's facial expressions and voice information are used as input. The emotion engine analyzes this in real time and extracts emotional data. Specifically, it uses the camera and microphone to detect the user's emotional state and adjusts the presentation and priority of notifications based on the results. The output includes an optimized list of notifications displayed to the user.

[0362] Step 4:

[0363] The user reviews the information displayed on the terminal and adjusts the workflow based on suggestions from the AI ​​agent. They receive notifications and suggestions from the terminal as input. Based on this, they input necessary instructions into the system and manage the progress of their work. Specifically, the user selects options displayed on the screen, and the feedback is reprocessed by the AI ​​agent. As a result, the work environment is optimized in real time.

[0364] Step 5:

[0365] The server dynamically updates business data based on user instructions and feedback, preparing for the next cycle. It receives user instructions and emotional state feedback as input. This data is then re-analyzed to optimize the next business process. Specifically, the updated data is provided back to the AI ​​agent to generate new business improvement suggestions. The output is updated business flow information.

[0366] (Application Example 2)

[0367] 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."

[0368] While there is a demand for increased productivity in manufacturing, the impact of workers' workload and emotional state on production efficiency remains a challenge. In particular, worker stress and fatigue are problematic as they reduce productivity, and a system is needed to address these issues while efficiently optimizing operations.

[0369] 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.

[0370] In this invention, the server includes means for collecting business data from multiple information sources, analysis means using artificial intelligence to analyze the business data, means for automatically optimizing the workflow based on the results of the analysis means, and emotion recognition means. This enables business optimization that takes into account the emotional state of the worker and adjustment of notification priorities.

[0371] "Information sources" refer to the various data providers used to acquire business data.

[0372] "Business data" refers to all data that includes information about a company's business processes.

[0373] "Artificial intelligence" is a technology that enables computer systems to perform processing that mimics human intelligence.

[0374] "Analysis methods" is a general term for methods and tools used to analyze acquired business data and extract useful information.

[0375] "Means for automatically optimizing business processes" refer to processes and technologies for efficiently adjusting business procedures based on analysis results.

[0376] "Emotion recognition means" refers to a system of technologies that collects and analyzes data in order to identify a user's emotional state.

[0377] "Notification priority adjustment" is a process that changes the order and method of information delivery according to the user's emotional state and the importance of the task.

[0378] In implementing this invention, the system primarily uses a server, a terminal, and a device for emotion recognition. This system utilizes both AI technology and emotion recognition to improve work efficiency in the workplace.

[0379] The server collects business data from multiple sources. Specifically, this includes a wide range of digital information, such as work progress data from production management systems and worker sentiment data from sensor devices. This data is analyzed using artificial intelligence analysis tools to optimize business workflows. The analysis utilizes Python-based machine learning models and deep learning frameworks such as TensorFlow and PyTorch.

[0380] The device provides the user interface and adjusts the priority of notifications received by the user. Emotion recognition means recognize emotions from the user's facial expressions and voice tone using hardware such as a camera and microphone. For this purpose, technologies such as OpenCV and Google's speech recognition API are utilized.

[0381] Users can receive notifications from the system via their devices and take actions in line with their work progress. The system takes into account the user's emotional state and suggests work adjustments to reduce stress. For example, if a worker is feeling fatigued, the system can suggest when to take a break or temporarily slow down the pace of work.

[0382] The generative AI model generates suggestions based on work progress and emotion recognition data. These suggestions support optimal work adjustments for the user. An example of a prompt is: "When field workers are experiencing stress, suggest how to optimize break times and work schedules."

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The server collects operational data from multiple sources. Inputs include progress data from the production management system and emotion data from sensor devices. This data is integrated and stored on the server as foundational information for data analysis.

[0386] Step 2:

[0387] The server performs analysis on the collected data using machine learning models. For data processing during the analysis, TensorFlow and PyTorch are used for data preprocessing and feature extraction, and the progress of the business is evaluated. As output, metrics necessary for business optimization are generated.

[0388] Step 3:

[0389] The server automatically proposes optimization plans for business workflows based on the generated metrics. The input is the business efficiency metrics obtained through analysis, and the output is a proposed adjustment to the specific work schedule. The generating AI model creates the proposal content using prompt messages.

[0390] Step 4:

[0391] The terminal notifies the user of optimization suggestions via a user interface. The input is the business optimization suggestions from the server, and the output is a notification message displayed to the user. The notifications are prioritized based on sentiment recognition, and important messages are highlighted.

[0392] Step 5:

[0393] Users review suggestions from the server via their terminals and respond with instructions. Input is the notification content from the terminal, and output is the user's feedback. User instructions are recorded within the system and used for further data analysis to adjust operations.

[0394] 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.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] This invention realizes an AI agent system for optimizing and streamlining business workflows within a company. Servers, terminals, and users each play their respective roles, and the entire system supports the automation and efficiency of business processes.

[0411] The server plays a central role, collecting business data from various data sources both inside and outside the company. This includes project management systems, mail servers, and other digital databases. The collected data is then provided to AI agents, who analyze the progress of business operations based on that data.

[0412] The terminal functions as an interface with the user. When the AI ​​agent makes optimization suggestions, assigns tasks, or makes changes based on data analysis, it notifies the user. The terminal also serves as an input channel to the system by receiving user feedback and instructions.

[0413] Users receive notifications from the AI ​​agent via their device and review their content as needed. Users can then approve or modify the optimization suggestions proposed by the AI ​​agent. For example, by changing the priority of a specific task, users can adjust the workflow in real time.

[0414] As a concrete example, if a specific task in Project A is delayed, the server communicates the delay to the AI, which analyzes the cause of the delay. Next, the terminal notifies the user of the analysis results, and the user makes the necessary decisions. Subsequently, the server, following the user's instructions, performs tasks reassignment and resource reallocation. Through this series of processes, the overall efficiency of the work is improved.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The server collects business data from various data sources both inside and outside the company. This includes retrieving daily progress and related information from tasks management systems, mail servers, and other sources.

[0418] Step 2:

[0419] The server passes the collected business data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the various data, perform a current business analysis, and identify problems.

[0420] Step 3:

[0421] Based on the AI ​​agent's analysis results, the server generates optimization proposals and improvement measures for the workflow. This includes reprioritizing tasks that are behind schedule and reallocating resources.

[0422] Step 4:

[0423] The device receives notifications from the server and presents the user with optimization suggestions and progress reports. The information is displayed in a way that is easy for the user to understand.

[0424] Step 5:

[0425] The user reviews the information displayed on the device and approves, modifies, or gives other instructions regarding the optimization suggestions made by the AI ​​agent. This allows the user to adjust the workflow.

[0426] Step 6:

[0427] The server then adjusts the workflow based on user instructions. This means taking specific actions such as changing system settings, assigning new tasks, or replanning existing tasks.

[0428] Step 7:

[0429] The server monitors the effects of the actions performed and feeds the results back to the AI ​​agent. This allows the AI ​​to update its predictive model and use it for the next analysis.

[0430] (Example 1)

[0431] 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."

[0432] Business processes within companies are becoming increasingly complex, and the inefficiency of information gathering, analysis, and optimization processes is a major challenge. Furthermore, the lack of sufficient AI-powered efficient progress management and optimization suggestions raises concerns about a decline in overall business productivity.

[0433] 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.

[0434] In this invention, the server includes an information gathering means, an analysis means using artificial intelligence, and an optimization means. This makes it possible to efficiently collect information accumulated in business processes and automatically construct optimal business procedures based on the analysis results.

[0435] An "information gathering method" is a system for automatically obtaining necessary information from multiple sources.

[0436] "Analysis methods" refer to techniques that use artificial intelligence to analyze information acquired and evaluate the progress and efficiency of work.

[0437] An "optimization method" is a process for automatically improving business procedures based on the results of analysis.

[0438] An "interface means" is a means of communication that allows users to access a system and send and receive instructions.

[0439] "Execution means" refers to control means for carrying out tasks based on user instructions or analysis results.

[0440] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate natural language.

[0441] "Communication methods" refer to means of exchanging information and collecting feedback electronically.

[0442] A "user interface" is an interface used to exchange information between a user and a system via a terminal.

[0443] This system utilizes a combination of components to efficiently manage business processes within an organization. The details are described below.

[0444] The server plays a central role as a means of information gathering. It retrieves information from common project management software and email systems via APIs to collect data from multiple sources. For example, it integrates with project management tools and email systems to collect progress information and communication logs for each project.

[0445] Next, the server uses an artificial intelligence model as an analysis tool. This analysis utilizes a generative AI model to implement natural language processing technology. This allows the server to analyze the progress of tasks and identify potential bottlenecks, generating easy-to-understand reports in natural language. The results of the analysis are presented as concrete documents detailing the status of tasks and suggesting improvements.

[0446] The terminal serves as an interface, allowing users to access it directly. Here, it provides users with AI-driven optimization suggestions and notifications of business updates. The terminal presents this information using common, everyday communication software.

[0447] Ultimately, the user makes informed decisions about their work. Through the terminal, the user can respond to suggestions from the server with approval or requests for modifications. As an example of a specific prompt, the user could ask the system, "Tell me the highest priority tasks for the next week."

[0448] This series of processes makes it possible to improve overall business productivity. Business workflows are continuously optimized, maintaining an optimal state based on the latest business data.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The server retrieves data from multiple sources using information gathering methods. It uses connection information for project management tools and email servers via APIs as input. The server collects project progress, the number of unread emails, and other data from these sources and stores it in a database. This process aggregates the most up-to-date data necessary for business operations.

[0452] Step 2:

[0453] The server executes an analysis using artificial intelligence. It uses the data collected in Step 1 as input. A generative AI model is used to analyze the data and identify business progress and potential problems. The analysis results are output as an easy-to-understand report using natural language processing technology. This process provides actionable, data-driven insights.

[0454] Step 3:

[0455] The terminal notifies the user of the analysis results via an interface. It receives output from the server as input and displays it through communication software the user uses daily. Specifically, it presents the user with high-priority tasks and improvement suggestions. This allows the user to quickly receive information and make decisions on how to respond.

[0456] Step 4:

[0457] The user decides how to respond to information received through the device. The input is the content of the notification from the device. Based on this information, the user gives specific instructions and sends feedback via the device if changes are needed. For example, it is possible to instruct a change in the priority of a specific task. This allows for flexible adjustment of the workflow.

[0458] Step 5:

[0459] The server coordinates tasks using execution methods based on user instructions. It receives user feedback as input. The server uses APIs to reallocate tasks and adjust resources to project management tools. This operation optimizes operational efficiency and enables real-time problem resolution.

[0460] (Application Example 1)

[0461] 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."

[0462] Identifying bottlenecks in each process of the production line and improving their efficiency is a crucial challenge for many manufacturers. However, a lack of consistent data collection and analysis, as well as harmonious cooperation between humans and machines, is hindering sufficient efficiency improvements.

[0463] 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.

[0464] In this invention, the server includes means for collecting operational data from measuring devices, means for analyzing the data using artificial intelligence, and means for automatically optimizing the production process. This enables real-time optimization of the production flow and efficient execution of operations.

[0465] A "measuring device" is a device used to collect various types of data in the production process in real time.

[0466] "Business data" refers to information about the production process within a factory, as well as numerical values ​​and indicators that show its progress.

[0467] "Artificial intelligence" refers to a computer program or system used to analyze large amounts of data and derive the optimal solution.

[0468] An "analysis tool" is a processing device that uses collected business data to provide information for making decisions regarding process optimization and efficiency.

[0469] A "production process" refers to a series of work steps and processes that are followed when manufacturing a product.

[0470] An "operator" is a person responsible for overseeing the production process and approving or adjusting suggestions from the system as needed.

[0471] A "notification" is information or a suggestion sent from a system to the user.

[0472] An "operation plan" is a schedule that shows the tasks and timings that various production machines should perform.

[0473] In order to implement this invention, it is necessary to introduce measuring devices, servers, artificial intelligence, terminals, and production equipment into the production management system within the factory.

[0474] The measuring devices are placed on the production line and are responsible for acquiring progress information and environmental data for each process in real time. This data is then transmitted to a server via the network for later analysis.

[0475] The server stores the collected data and performs analysis using artificial intelligence. This analysis can utilize environments with powerful computing capabilities, such as AWS AI services. The server generates detailed reports that include bottlenecks and suggestions for efficiency improvements.

[0476] Artificial intelligence can offer various suggestions to improve productivity through data analysis. This could involve the use of deep learning and other machine learning algorithms.

[0477] The terminal functions as a user interface for the operator and receives notifications sent from the server. The operator then reviews the proposed optimizations and makes modifications or approvals as needed.

[0478] Production equipment can autonomously adjust its movements based on a new motion plan received via a terminal. This is made possible by robot control software such as ROS (Robot Operating System).

[0479] For example, if a time delay is detected in process B of product A, the server analyzes the data, and artificial intelligence generates efficiency improvement measures. The proposed measures are sent to the operator via a terminal, and after the operator approves them, the production equipment starts operating according to the new plan.

[0480] An example of a prompt for a generative AI model would be: "Analyze the bottleneck in process B of product A's production and suggest methods for optimizing it."

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The measuring device collects the data.

[0484] Measurement devices collect process progress and environmental information, which is then transmitted to a server via the network. This data is sent to the server in digital format by sensor devices that detect the product's position, temperature, speed, etc.

[0485] Step 2:

[0486] The server receives the data and begins analysis.

[0487] The server temporarily stores the received data in a storage system. Then, it applies an analysis algorithm utilizing a generative AI model to perform data analysis, identifying bottlenecks and trends. This reveals the efficiency and problems of each process, and the analysis results are output.

[0488] Step 3:

[0489] The server generates the analysis results.

[0490] Based on the analysis performed on the server, specific optimization plans and improvement suggestions are generated. At this stage, the potential for improvement for each process is listed based on the data analysis results. The generated results are output in report format and are ready to be notified to the responsible operator.

[0491] Step 4:

[0492] The terminal notifies the operator of the analysis results.

[0493] The terminal receives reports sent from the server and displays their contents to the operator. Analysis results and suggestions are presented on the user interface, allowing the operator to review them. Specifically, the operation involves displaying information on the terminal's screen.

[0494] Step 5:

[0495] The user reviews the optimization suggestions and submits instructions.

[0496] Users review the reports on their devices in detail and approve or modify optimization suggestions as needed. This is done using touchscreens or input devices, and if there are any suggested modifications, feedback is sent to the server via the device.

[0497] Step 6:

[0498] The server adjusts its operation plan based on instructions from the user.

[0499] The server receives user feedback and creates a new operation plan. Based on the revised operation plan, it prepares to automatically adjust the operation of the production equipment. The output is generated as the adjusted operation plan.

[0500] Step 7:

[0501] The production equipment starts operating according to the new operation plan.

[0502] The production equipment begins autonomous movement based on a new operation plan received from the server. This means that it executes the set operations precisely according to the programmed schedule. During operation, the equipment collects data again through the measuring device, and the process repeats from step 1.

[0503] 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.

[0504] This invention is an AI agent system that realizes efficiency and optimization of business operations within a company, and further incorporates an emotion engine that recognizes user emotions and influences business processes. Each element, including the server, terminal, and emotion engine, works in coordination.

[0505] The server collects business data from various sources within the company. This business data includes information handled in digital format, such as project management systems, emails, and even video conference recordings. This allows the server to understand the details of the entire business flow and provide AI agents with the foundational information for analysis.

[0506] The AI ​​agent analyzes business data provided by the server using machine learning models. This allows it to evaluate the progress and efficiency of tasks and generate instructions to automatically optimize the workflow as needed. For example, if a particular task is delayed, it can identify the cause and suggest a reallocation of resources.

[0507] The device functions as an interface with the user. Here, the emotion engine recognizes the user's emotions and adjusts the presentation and priority of notifications based on those emotions. For example, if the emotion engine detects that the user is stressed, the device will display only high-priority notifications to reduce the burden on the user.

[0508] The user reviews the information presented on the device and adjusts the workflow based on the AI ​​agent's suggestions. The emotion engine infers the user's emotional state from their feedback and facial expressions, and provides feedback to the agent accordingly. This allows the server to dynamically adjust tasks while taking the user's emotional state into account.

[0509] For example, if a user is feeling fatigued during a project meeting, the emotion engine can detect this and, via the terminal, suggest a plan to the server to support the meeting's progress. The organic collaboration of these elements not only improves work efficiency but also enhances the overall user work environment.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The server collects business data from various sources within the company. This includes obtaining progress data from project management tools and communication history from email servers.

[0513] Step 2:

[0514] The server provides the collected operational data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the current operational status and identify progress and potential problems.

[0515] Step 3:

[0516] Based on the analyzed data, the server generates optimization suggestions for the business workflow. This includes prioritizing backlogged tasks and suggesting changes to resource allocation.

[0517] Step 4:

[0518] The terminal receives notifications from the server and displays optimization suggestions and work progress to the user. This information is formatted in a way that is useful to the user. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0519] Step 5:

[0520] Users review the display on their devices and provide feedback on their workflow by approving, modifying, or rejecting optimization suggestions proposed by the AI ​​agent as needed.

[0521] Step 6:

[0522] The server uses user feedback and sentiment data from the sentiment engine to make final adjustments to the workflow. This includes taking the user's state into consideration, such as highlighting only high-priority tasks.

[0523] Step 7:

[0524] The server monitors the results of the adjustments made and evaluates operational efficiency and user satisfaction. This evaluation is fed back to the AI ​​agent and used to improve the model.

[0525] (Example 2)

[0526] 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."

[0527] Conventional business management systems, while focusing on streamlining and optimizing operations, lacked the flexibility to make adjustments based on users' emotions and mental states. Furthermore, they failed to respond quickly to unforeseen circumstances arising during work, leading to decreased work efficiency and accumulated user stress. This invention aims to provide a more comfortable and efficient work environment by optimizing the workflow while considering user emotions.

[0528] 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.

[0529] In this invention, the server includes means for collecting information from multiple information sources, means for analyzing the information using intelligence, means for automatically optimizing work procedures based on the results of the analysis, and means for recognizing the user's emotions and adjusting the expression and priority of notifications based on those emotions. This enables dynamic work adjustments that take into account the user's emotions, thereby improving work efficiency and the user's work environment.

[0530] "Information source" refers to the source from which business data and related information are collected within a company.

[0531] "Intelligence" refers to artificial intelligence technologies and machine learning models used to analyze business data.

[0532] "Analysis methods" refer to techniques and processes used to evaluate and optimize the efficiency and progress of operations based on collected information.

[0533] "Optimizing work procedures" refers to making adjustments to business processes and resource allocations in the most efficient way, based on the results of analysis.

[0534] "Emotion" refers to the user's emotional state or mental condition, including their reactions to the work environment and tasks.

[0535] "Adjusting notification wording and priority" refers to the process of changing notification content and urgency based on the user's emotions to deliver information in the most optimal way for the user.

[0536] This invention is an AI agent system that realizes the efficiency and optimization of internal business operations. The system functions through the organic cooperation of its components: server, terminal, and user.

[0537] The server plays a role in collecting business data from a wide variety of sources within the company. This includes information provided in digital formats such as project management systems, emails, and video conference recordings. The server aggregates this data and provides it as foundational data for analysis by AI agents. The server uses database management systems and other tools to enable efficient data collection.

[0538] The AI ​​agent utilizes machine learning models to analyze business data provided by the server. This analysis allows for real-time evaluation of business progress and efficiency, and optimization of automated workflows. Specifically, it can identify the cause of delays in specific tasks and propose the reallocation of necessary resources. In this analysis, the AI ​​agent uses programming languages ​​such as Python and R, and leverages libraries such as TensorFlow and PyTorch.

[0539] The device functions as an interface with the user. It is equipped with an emotion engine that recognizes the user's emotions. This recognition is based on the user's facial expressions, voice tone, and past activity data, and adjusts the way notifications are presented and their priority. For example, if the user is stressed, the device will only display important notifications, working to reduce the user's burden. The device uses hardware such as a camera and microphone, and software such as OpenCV and Mediapipe for emotion recognition technology.

[0540] Users can review the information displayed on their device and adjust their workflow based on the AI ​​agent's suggestions. The emotion engine continuously monitors the user's reactions and provides information to the AI ​​agent based on that feedback. This allows users to perform their tasks efficiently and with less stress.

[0541] For example, if a user feels fatigued during a project meeting, the emotion engine can detect this and make suggestions to the server via the terminal to ensure the meeting runs smoothly. In this way, the coordination of each element not only improves work efficiency but also enhances the user's work environment.

[0542] An example of a prompt to be input into the generated AI model is: "Please describe in detail how the AI ​​agent optimizes the workflow by considering the user's emotions. Also, please show how the emotion engine detects user fatigue and provides efficient work support."

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The server collects business data from multiple sources within the company. Inputs include project management system APIs, email server queries, and video conferencing system logs. It retrieves data from these sources, converts it to a digital format, and stores it in a database. Specifically, the server automates the process of periodically accessing each system and querying for new data.

[0546] Step 2:

[0547] The AI ​​agent processes business data provided by the server. It uses information from various data sources obtained from the server as input. Using a machine learning model, it analyzes this data and generates reports on business progress and efficiency. Data processing includes normalization of different data formats, scaling of numerical data, and natural language processing of text data. Outputs include task delay status and resource optimization suggestions.

[0548] Step 3:

[0549] The device functions as an interface with the user. At this stage, the user's facial expressions and voice information are used as input. The emotion engine analyzes this in real time and extracts emotional data. Specifically, it uses the camera and microphone to detect the user's emotional state and adjusts the presentation and priority of notifications based on the results. The output includes an optimized list of notifications displayed to the user.

[0550] Step 4:

[0551] The user reviews the information displayed on the terminal and adjusts the workflow based on suggestions from the AI ​​agent. They receive notifications and suggestions from the terminal as input. Based on this, they input necessary instructions into the system and manage the progress of their work. Specifically, the user selects options displayed on the screen, and the feedback is reprocessed by the AI ​​agent. As a result, the work environment is optimized in real time.

[0552] Step 5:

[0553] The server dynamically updates business data based on user instructions and feedback, preparing for the next cycle. It receives user instructions and emotional state feedback as input. This data is then re-analyzed to optimize the next business process. Specifically, the updated data is provided back to the AI ​​agent to generate new business improvement suggestions. The output is updated business flow information.

[0554] (Application Example 2)

[0555] 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."

[0556] While there is a demand for increased productivity in manufacturing, the impact of workers' workload and emotional state on production efficiency remains a challenge. In particular, worker stress and fatigue are problematic as they reduce productivity, and a system is needed to address these issues while efficiently optimizing operations.

[0557] 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.

[0558] In this invention, the server includes means for collecting business data from multiple information sources, analysis means using artificial intelligence to analyze the business data, means for automatically optimizing the workflow based on the results of the analysis means, and emotion recognition means. This enables business optimization that takes into account the emotional state of the worker and adjustment of notification priorities.

[0559] "Information sources" refer to the various data providers used to acquire business data.

[0560] "Business data" refers to all data that includes information about a company's business processes.

[0561] "Artificial intelligence" is a technology that enables computer systems to perform processing that mimics human intelligence.

[0562] "Analysis methods" is a general term for methods and tools used to analyze acquired business data and extract useful information.

[0563] "Means for automatically optimizing business processes" refer to processes and technologies for efficiently adjusting business procedures based on analysis results.

[0564] "Emotion recognition means" refers to a system of technologies that collects and analyzes data in order to identify a user's emotional state.

[0565] "Notification priority adjustment" is a process that changes the order and method of information delivery according to the user's emotional state and the importance of the task.

[0566] In implementing this invention, the system primarily uses a server, a terminal, and a device for emotion recognition. This system utilizes both AI technology and emotion recognition to improve work efficiency in the workplace.

[0567] The server collects business data from multiple sources. Specifically, this includes a wide range of digital information, such as work progress data from production management systems and worker sentiment data from sensor devices. This data is analyzed using artificial intelligence analysis tools to optimize business workflows. The analysis utilizes Python-based machine learning models and deep learning frameworks such as TensorFlow and PyTorch.

[0568] The device provides the user interface and adjusts the priority of notifications received by the user. Emotion recognition means recognize emotions from the user's facial expressions and voice tone using hardware such as a camera and microphone. For this purpose, technologies such as OpenCV and Google's speech recognition API are utilized.

[0569] Users can receive notifications from the system via their devices and take actions in line with their work progress. The system takes into account the user's emotional state and suggests work adjustments to reduce stress. For example, if a worker is feeling fatigued, the system can suggest when to take a break or temporarily slow down the pace of work.

[0570] The generative AI model generates suggestions based on work progress and emotion recognition data. These suggestions support optimal work adjustments for the user. An example of a prompt is: "When field workers are experiencing stress, suggest how to optimize break times and work schedules."

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The server collects operational data from multiple sources. Inputs include progress data from the production management system and emotion data from sensor devices. This data is integrated and stored on the server as foundational information for data analysis.

[0574] Step 2:

[0575] The server performs analysis on the collected data using machine learning models. For data processing during the analysis, TensorFlow and PyTorch are used for data preprocessing and feature extraction, and the progress of the business is evaluated. As output, metrics necessary for business optimization are generated.

[0576] Step 3:

[0577] The server automatically proposes optimization plans for business workflows based on the generated metrics. The input is the business efficiency metrics obtained through analysis, and the output is a proposed adjustment to the specific work schedule. The generating AI model creates the proposal content using prompt messages.

[0578] Step 4:

[0579] The terminal notifies the user of optimization suggestions via a user interface. The input is the business optimization suggestions from the server, and the output is a notification message displayed to the user. The notifications are prioritized based on sentiment recognition, and important messages are highlighted.

[0580] Step 5:

[0581] Users review suggestions from the server via their terminals and respond with instructions. Input is the notification content from the terminal, and output is the user's feedback. User instructions are recorded within the system and used for further data analysis to adjust operations.

[0582] 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.

[0583] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] This invention realizes an AI agent system for optimizing and streamlining business workflows within a company. Servers, terminals, and users each play their respective roles, and the entire system supports the automation and efficiency of business processes.

[0600] The server plays a central role, collecting business data from various data sources both inside and outside the company. This includes project management systems, mail servers, and other digital databases. The collected data is then provided to AI agents, who analyze the progress of business operations based on that data.

[0601] The terminal functions as an interface with the user. When the AI ​​agent makes optimization suggestions, assigns tasks, or makes changes based on data analysis, it notifies the user. The terminal also serves as an input channel to the system by receiving user feedback and instructions.

[0602] Users receive notifications from the AI ​​agent via their device and review their content as needed. Users can then approve or modify the optimization suggestions proposed by the AI ​​agent. For example, by changing the priority of a specific task, users can adjust the workflow in real time.

[0603] As a concrete example, if a specific task in Project A is delayed, the server communicates the delay to the AI, which analyzes the cause of the delay. Next, the terminal notifies the user of the analysis results, and the user makes the necessary decisions. Subsequently, the server, following the user's instructions, performs tasks reassignment and resource reallocation. Through this series of processes, the overall efficiency of the work is improved.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The server collects business data from various data sources both inside and outside the company. This includes retrieving daily progress and related information from tasks management systems, mail servers, and other sources.

[0607] Step 2:

[0608] The server passes the collected business data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the various data, perform a current business analysis, and identify problems.

[0609] Step 3:

[0610] Based on the AI ​​agent's analysis results, the server generates optimization proposals and improvement measures for the workflow. This includes reprioritizing tasks that are behind schedule and reallocating resources.

[0611] Step 4:

[0612] The device receives notifications from the server and presents the user with optimization suggestions and progress reports. The information is displayed in a way that is easy for the user to understand.

[0613] Step 5:

[0614] The user reviews the information displayed on the device and approves, modifies, or gives other instructions regarding the optimization suggestions made by the AI ​​agent. This allows the user to adjust the workflow.

[0615] Step 6:

[0616] The server then adjusts the workflow based on user instructions. This means taking specific actions such as changing system settings, assigning new tasks, or replanning existing tasks.

[0617] Step 7:

[0618] The server monitors the effects of the actions performed and feeds the results back to the AI ​​agent. This allows the AI ​​to update its predictive model and use it for the next analysis.

[0619] (Example 1)

[0620] 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".

[0621] Business processes within companies are becoming increasingly complex, and the inefficiency of information gathering, analysis, and optimization processes is a major challenge. Furthermore, the lack of sufficient AI-powered efficient progress management and optimization suggestions raises concerns about a decline in overall business productivity.

[0622] 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.

[0623] In this invention, the server includes an information gathering means, an analysis means using artificial intelligence, and an optimization means. This makes it possible to efficiently collect information accumulated in business processes and automatically construct optimal business procedures based on the analysis results.

[0624] An "information gathering method" is a system for automatically obtaining necessary information from multiple sources.

[0625] "Analysis methods" refer to techniques that use artificial intelligence to analyze information acquired and evaluate the progress and efficiency of work.

[0626] An "optimization method" is a process for automatically improving business procedures based on the results of analysis.

[0627] An "interface means" is a means of communication that allows users to access a system and send and receive instructions.

[0628] "Execution means" refers to control means for carrying out tasks based on user instructions or analysis results.

[0629] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate natural language.

[0630] "Communication methods" refer to means of exchanging information and collecting feedback electronically.

[0631] A "user interface" is an interface used to exchange information between a user and a system via a terminal.

[0632] This system utilizes a combination of components to efficiently manage business processes within an organization. The details are described below.

[0633] The server plays a central role as a means of information gathering. It retrieves information from common project management software and email systems via APIs to collect data from multiple sources. For example, it integrates with project management tools and email systems to collect progress information and communication logs for each project.

[0634] Next, the server uses an artificial intelligence model as an analysis tool. This analysis utilizes a generative AI model to implement natural language processing technology. This allows the server to analyze the progress of tasks and identify potential bottlenecks, generating easy-to-understand reports in natural language. The results of the analysis are presented as concrete documents detailing the status of tasks and suggesting improvements.

[0635] The terminal serves as an interface, allowing users to access it directly. Here, it provides users with AI-driven optimization suggestions and notifications of business updates. The terminal presents this information using common, everyday communication software.

[0636] Ultimately, the user makes informed decisions about their work. Through the terminal, the user can respond to suggestions from the server with approval or requests for modifications. As an example of a specific prompt, the user could ask the system, "Tell me the highest priority tasks for the next week."

[0637] This series of processes makes it possible to improve overall business productivity. Business workflows are continuously optimized, maintaining an optimal state based on the latest business data.

[0638] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0639] Step 1:

[0640] The server retrieves data from multiple sources using information gathering methods. It uses connection information for project management tools and email servers via APIs as input. The server collects project progress, the number of unread emails, and other data from these sources and stores it in a database. This process aggregates the most up-to-date data necessary for business operations.

[0641] Step 2:

[0642] The server executes an analysis using artificial intelligence. It uses the data collected in Step 1 as input. A generative AI model is used to analyze the data and identify business progress and potential problems. The analysis results are output as an easy-to-understand report using natural language processing technology. This process provides actionable, data-driven insights.

[0643] Step 3:

[0644] The terminal notifies the user of the analysis results via an interface. It receives output from the server as input and displays it through communication software the user uses daily. Specifically, it presents the user with high-priority tasks and improvement suggestions. This allows the user to quickly receive information and make decisions on how to respond.

[0645] Step 4:

[0646] The user decides how to respond to information received through the device. The input is the content of the notification from the device. Based on this information, the user gives specific instructions and sends feedback via the device if changes are needed. For example, it is possible to instruct a change in the priority of a specific task. This allows for flexible adjustment of the workflow.

[0647] Step 5:

[0648] The server coordinates tasks using execution methods based on user instructions. It receives user feedback as input. The server uses APIs to reallocate tasks and adjust resources to project management tools. This operation optimizes operational efficiency and enables real-time problem resolution.

[0649] (Application Example 1)

[0650] 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".

[0651] Identifying bottlenecks in each process of the production line and improving their efficiency is a crucial challenge for many manufacturers. However, a lack of consistent data collection and analysis, as well as harmonious cooperation between humans and machines, is hindering sufficient efficiency improvements.

[0652] 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.

[0653] In this invention, the server includes means for collecting operational data from measuring devices, means for analyzing the data using artificial intelligence, and means for automatically optimizing the production process. This enables real-time optimization of the production flow and efficient execution of operations.

[0654] A "measuring device" is a device used to collect various types of data in the production process in real time.

[0655] "Business data" refers to information about the production process within a factory, as well as numerical values ​​and indicators that show its progress.

[0656] "Artificial intelligence" refers to a computer program or system used to analyze large amounts of data and derive the optimal solution.

[0657] An "analysis tool" is a processing device that uses collected business data to provide information for making decisions regarding process optimization and efficiency.

[0658] A "production process" refers to a series of work steps and processes that are followed when manufacturing a product.

[0659] An "operator" is a person responsible for overseeing the production process and approving or adjusting suggestions from the system as needed.

[0660] A "notification" is information or a suggestion sent from a system to the user.

[0661] An "operation plan" is a schedule that shows the tasks and timings that various production machines should perform.

[0662] In order to implement this invention, it is necessary to introduce measuring devices, servers, artificial intelligence, terminals, and production equipment into the production management system within the factory.

[0663] The measuring devices are placed on the production line and are responsible for acquiring progress information and environmental data for each process in real time. This data is then transmitted to a server via the network for later analysis.

[0664] The server stores the collected data and performs analysis using artificial intelligence. This analysis can utilize environments with powerful computing capabilities, such as AWS AI services. The server generates detailed reports that include bottlenecks and suggestions for efficiency improvements.

[0665] Artificial intelligence can offer various suggestions to improve productivity through data analysis. This could involve the use of deep learning and other machine learning algorithms.

[0666] The terminal functions as a user interface for the operator and receives notifications sent from the server. The operator then reviews the proposed optimizations and makes modifications or approvals as needed.

[0667] Production equipment can autonomously adjust its movements based on a new motion plan received via a terminal. This is made possible by robot control software such as ROS (Robot Operating System).

[0668] For example, if a time delay is detected in process B of product A, the server analyzes the data, and artificial intelligence generates efficiency improvement measures. The proposed measures are sent to the operator via a terminal, and after the operator approves them, the production equipment starts operating according to the new plan.

[0669] An example of a prompt for a generative AI model would be: "Analyze the bottleneck in process B of product A's production and suggest methods for optimizing it."

[0670] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0671] Step 1:

[0672] The measuring device collects the data.

[0673] Measurement devices collect process progress and environmental information, which is then transmitted to a server via the network. This data is sent to the server in digital format by sensor devices that detect the product's position, temperature, speed, etc.

[0674] Step 2:

[0675] The server receives the data and begins analysis.

[0676] The server temporarily stores the received data in a storage system. Then, it applies an analysis algorithm utilizing a generative AI model to perform data analysis, identifying bottlenecks and trends. This reveals the efficiency and problems of each process, and the analysis results are output.

[0677] Step 3:

[0678] The server generates the analysis results.

[0679] Based on the analysis performed on the server, specific optimization plans and improvement suggestions are generated. At this stage, the potential for improvement for each process is listed based on the data analysis results. The generated results are output in report format and are ready to be notified to the responsible operator.

[0680] Step 4:

[0681] The terminal notifies the operator of the analysis results.

[0682] The terminal receives reports sent from the server and displays their contents to the operator. Analysis results and suggestions are presented on the user interface, allowing the operator to review them. Specifically, the operation involves displaying information on the terminal's screen.

[0683] Step 5:

[0684] The user reviews the optimization suggestions and submits instructions.

[0685] Users review the reports on their devices in detail and approve or modify optimization suggestions as needed. This is done using touchscreens or input devices, and if there are any suggested modifications, feedback is sent to the server via the device.

[0686] Step 6:

[0687] The server adjusts its operation plan based on instructions from the user.

[0688] The server receives user feedback and creates a new operation plan. Based on the revised operation plan, it prepares to automatically adjust the operation of the production equipment. The output is generated as the adjusted operation plan.

[0689] Step 7:

[0690] The production equipment starts operating according to the new operation plan.

[0691] The production equipment begins autonomous movement based on a new operation plan received from the server. This means that it executes the set operations precisely according to the programmed schedule. During operation, the equipment collects data again through the measuring device, and the process repeats from step 1.

[0692] 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.

[0693] This invention is an AI agent system that realizes efficiency and optimization of business operations within a company, and further incorporates an emotion engine that recognizes user emotions and influences business processes. Each element, including the server, terminal, and emotion engine, works in coordination.

[0694] The server collects business data from various sources within the company. This business data includes information handled in digital format, such as project management systems, emails, and even video conference recordings. This allows the server to understand the details of the entire business flow and provide AI agents with the foundational information for analysis.

[0695] The AI ​​agent analyzes business data provided by the server using machine learning models. This allows it to evaluate the progress and efficiency of tasks and generate instructions to automatically optimize the workflow as needed. For example, if a particular task is delayed, it can identify the cause and suggest a reallocation of resources.

[0696] The device functions as an interface with the user. Here, the emotion engine recognizes the user's emotions and adjusts the presentation and priority of notifications based on those emotions. For example, if the emotion engine detects that the user is stressed, the device will display only high-priority notifications to reduce the burden on the user.

[0697] The user reviews the information presented on the device and adjusts the workflow based on the AI ​​agent's suggestions. The emotion engine infers the user's emotional state from their feedback and facial expressions, and provides feedback to the agent accordingly. This allows the server to dynamically adjust tasks while taking the user's emotional state into account.

[0698] For example, if a user is feeling fatigued during a project meeting, the emotion engine can detect this and, via the terminal, suggest a plan to the server to support the meeting's progress. The organic collaboration of these elements not only improves work efficiency but also enhances the overall user work environment.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server collects business data from various sources within the company. This includes obtaining progress data from project management tools and communication history from email servers.

[0702] Step 2:

[0703] The server provides the collected operational data to the AI ​​agent. The AI ​​agent uses a machine learning model to analyze the current operational status and identify progress and potential problems.

[0704] Step 3:

[0705] Based on the analyzed data, the server generates optimization suggestions for the business workflow. This includes prioritizing backlogged tasks and suggesting changes to resource allocation.

[0706] Step 4:

[0707] The terminal receives notifications from the server and displays optimization suggestions and work progress to the user. This information is formatted in a way that is useful to the user. At the same time, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0708] Step 5:

[0709] Users review the display on their devices and provide feedback on their workflow by approving, modifying, or rejecting optimization suggestions proposed by the AI ​​agent as needed.

[0710] Step 6:

[0711] The server uses user feedback and sentiment data from the sentiment engine to make final adjustments to the workflow. This includes taking the user's state into consideration, such as highlighting only high-priority tasks.

[0712] Step 7:

[0713] The server monitors the results of the adjustments made and evaluates operational efficiency and user satisfaction. This evaluation is fed back to the AI ​​agent and used to improve the model.

[0714] (Example 2)

[0715] 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".

[0716] Conventional business management systems, while focusing on streamlining and optimizing operations, lacked the flexibility to make adjustments based on users' emotions and mental states. Furthermore, they failed to respond quickly to unforeseen circumstances arising during work, leading to decreased work efficiency and accumulated user stress. This invention aims to provide a more comfortable and efficient work environment by optimizing the workflow while considering user emotions.

[0717] 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.

[0718] In this invention, the server includes means for collecting information from multiple information sources, means for analyzing the information using intelligence, means for automatically optimizing work procedures based on the results of the analysis, and means for recognizing the user's emotions and adjusting the expression and priority of notifications based on those emotions. This enables dynamic work adjustments that take into account the user's emotions, thereby improving work efficiency and the user's work environment.

[0719] "Information source" refers to the source from which business data and related information are collected within a company.

[0720] "Intelligence" refers to artificial intelligence technologies and machine learning models used to analyze business data.

[0721] "Analysis methods" refer to techniques and processes used to evaluate and optimize the efficiency and progress of operations based on collected information.

[0722] "Optimizing work procedures" refers to making adjustments to business processes and resource allocations in the most efficient way, based on the results of analysis.

[0723] "Emotion" refers to the user's emotional state or mental condition, including their reactions to the work environment and tasks.

[0724] "Adjusting notification wording and priority" refers to the process of changing notification content and urgency based on the user's emotions to deliver information in the most optimal way for the user.

[0725] This invention is an AI agent system that realizes the efficiency and optimization of internal business operations. The system functions through the organic cooperation of its components: server, terminal, and user.

[0726] The server plays a role in collecting business data from a wide variety of sources within the company. This includes information provided in digital formats such as project management systems, emails, and video conference recordings. The server aggregates this data and provides it as foundational data for analysis by AI agents. The server uses database management systems and other tools to enable efficient data collection.

[0727] The AI ​​agent utilizes machine learning models to analyze business data provided by the server. This analysis allows for real-time evaluation of business progress and efficiency, and optimization of automated workflows. Specifically, it can identify the cause of delays in specific tasks and propose the reallocation of necessary resources. In this analysis, the AI ​​agent uses programming languages ​​such as Python and R, and leverages libraries such as TensorFlow and PyTorch.

[0728] The device functions as an interface with the user. It is equipped with an emotion engine that recognizes the user's emotions. This recognition is based on the user's facial expressions, voice tone, and past activity data, and adjusts the way notifications are presented and their priority. For example, if the user is stressed, the device will only display important notifications, working to reduce the user's burden. The device uses hardware such as a camera and microphone, and software such as OpenCV and Mediapipe for emotion recognition technology.

[0729] Users can review the information displayed on their device and adjust their workflow based on the AI ​​agent's suggestions. The emotion engine continuously monitors the user's reactions and provides information to the AI ​​agent based on that feedback. This allows users to perform their tasks efficiently and with less stress.

[0730] For example, if a user feels fatigued during a project meeting, the emotion engine can detect this and make suggestions to the server via the terminal to ensure the meeting runs smoothly. In this way, the coordination of each element not only improves work efficiency but also enhances the user's work environment.

[0731] An example of a prompt to be input into the generated AI model is: "Please describe in detail how the AI ​​agent optimizes the workflow by considering the user's emotions. Also, please show how the emotion engine detects user fatigue and provides efficient work support."

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The server collects business data from multiple sources within the company. Inputs include project management system APIs, email server queries, and video conferencing system logs. It retrieves data from these sources, converts it to a digital format, and stores it in a database. Specifically, the server automates the process of periodically accessing each system and querying for new data.

[0735] Step 2:

[0736] The AI ​​agent processes business data provided by the server. It uses information from various data sources obtained from the server as input. Using a machine learning model, it analyzes this data and generates reports on business progress and efficiency. Data processing includes normalization of different data formats, scaling of numerical data, and natural language processing of text data. Outputs include task delay status and resource optimization suggestions.

[0737] Step 3:

[0738] The device functions as an interface with the user. At this stage, the user's facial expressions and voice information are used as input. The emotion engine analyzes this in real time and extracts emotional data. Specifically, it uses the camera and microphone to detect the user's emotional state and adjusts the presentation and priority of notifications based on the results. The output includes an optimized list of notifications displayed to the user.

[0739] Step 4:

[0740] The user reviews the information displayed on the terminal and adjusts the workflow based on suggestions from the AI ​​agent. They receive notifications and suggestions from the terminal as input. Based on this, they input necessary instructions into the system and manage the progress of their work. Specifically, the user selects options displayed on the screen, and the feedback is reprocessed by the AI ​​agent. As a result, the work environment is optimized in real time.

[0741] Step 5:

[0742] The server dynamically updates business data based on user instructions and feedback, preparing for the next cycle. It receives user instructions and emotional state feedback as input. This data is then re-analyzed to optimize the next business process. Specifically, the updated data is provided back to the AI ​​agent to generate new business improvement suggestions. The output is updated business flow information.

[0743] (Application Example 2)

[0744] 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".

[0745] While there is a demand for increased productivity in manufacturing, the impact of workers' workload and emotional state on production efficiency remains a challenge. In particular, worker stress and fatigue are problematic as they reduce productivity, and a system is needed to address these issues while efficiently optimizing operations.

[0746] 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.

[0747] In this invention, the server includes means for collecting business data from multiple information sources, analysis means using artificial intelligence to analyze the business data, means for automatically optimizing the workflow based on the results of the analysis means, and emotion recognition means. This enables business optimization that takes into account the emotional state of the worker and adjustment of notification priorities.

[0748] "Information sources" refer to the various data providers used to acquire business data.

[0749] "Business data" refers to all data that includes information about a company's business processes.

[0750] "Artificial intelligence" is a technology that enables computer systems to perform processing that mimics human intelligence.

[0751] "Analysis methods" is a general term for methods and tools used to analyze acquired business data and extract useful information.

[0752] "Means for automatically optimizing business processes" refer to processes and technologies for efficiently adjusting business procedures based on analysis results.

[0753] "Emotion recognition means" refers to a system of technologies that collects and analyzes data in order to identify a user's emotional state.

[0754] "Notification priority adjustment" is a process that changes the order and method of information delivery according to the user's emotional state and the importance of the task.

[0755] In implementing this invention, the system primarily uses a server, a terminal, and a device for emotion recognition. This system utilizes both AI technology and emotion recognition to improve work efficiency in the workplace.

[0756] The server collects business data from multiple sources. Specifically, this includes a wide range of digital information, such as work progress data from production management systems and worker sentiment data from sensor devices. This data is analyzed using artificial intelligence analysis tools to optimize business workflows. The analysis utilizes Python-based machine learning models and deep learning frameworks such as TensorFlow and PyTorch.

[0757] The device provides the user interface and adjusts the priority of notifications received by the user. Emotion recognition means recognize emotions from the user's facial expressions and voice tone using hardware such as a camera and microphone. For this purpose, technologies such as OpenCV and Google's speech recognition API are utilized.

[0758] Users can receive notifications from the system via their devices and take actions in line with their work progress. The system takes into account the user's emotional state and suggests work adjustments to reduce stress. For example, if a worker is feeling fatigued, the system can suggest when to take a break or temporarily slow down the pace of work.

[0759] The generative AI model generates suggestions based on work progress and emotion recognition data. These suggestions support optimal work adjustments for the user. An example of a prompt is: "When field workers are experiencing stress, suggest how to optimize break times and work schedules."

[0760] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0761] Step 1:

[0762] The server collects operational data from multiple sources. Inputs include progress data from the production management system and emotion data from sensor devices. This data is integrated and stored on the server as foundational information for data analysis.

[0763] Step 2:

[0764] The server performs analysis on the collected data using machine learning models. For data processing during the analysis, TensorFlow and PyTorch are used for data preprocessing and feature extraction, and the progress of the business is evaluated. As output, metrics necessary for business optimization are generated.

[0765] Step 3:

[0766] The server automatically proposes optimization plans for business workflows based on the generated metrics. The input is the business efficiency metrics obtained through analysis, and the output is a proposed adjustment to the specific work schedule. The generating AI model creates the proposal content using prompt messages.

[0767] Step 4:

[0768] The terminal notifies the user of optimization suggestions via a user interface. The input is the business optimization suggestions from the server, and the output is a notification message displayed to the user. The notifications are prioritized based on sentiment recognition, and important messages are highlighted.

[0769] Step 5:

[0770] Users review suggestions from the server via their terminals and respond with instructions. Input is the notification content from the terminal, and output is the user's feedback. User instructions are recorded within the system and used for further data analysis to adjust operations.

[0771] 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.

[0772] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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."

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A means of collecting business data from multiple data sources,

[0795] An analysis method using artificial intelligence to analyze the aforementioned business data,

[0796] A means for automatically optimizing the business flow based on the results of the analysis means,

[0797] A means of sending notifications to users and receiving instructions from users,

[0798] Means for performing tasks based on instructions from the user or the results of the analysis means,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the analysis means uses machine learning to evaluate the progress of the work.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the notification transmission and instruction reception means uses an interface provided via a terminal.

[0804] "Example 1"

[0805] (Claim 1)

[0806] Information gathering means that collect information from multiple sources,

[0807] An analysis means using artificial intelligence to analyze the aforementioned information,

[0808] An optimization means that automatically optimizes the business procedure based on the results of the analysis means,

[0809] An interface means for sending notifications to users and receiving instructions from users,

[0810] An execution means that performs tasks based on instructions from the user or the results of the analysis means,

[0811] An analytical support tool that uses a generative AI model to analyze information in natural language and improve the efficiency of operations,

[0812] A means of communication that provides information and collects feedback via electronic communication,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the analysis means uses machine learning to evaluate progress and generate optimization proposals.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the interface means uses a user interface provided through a terminal.

[0818] "Application Example 1"

[0819] (Claim 1)

[0820] A means of collecting operational data from multiple measuring devices,

[0821] An analysis method using artificial intelligence to analyze the aforementioned business data,

[0822] A means for automatically optimizing the production process based on the results of the analysis means,

[0823] A means of sending notifications to the operator and receiving instructions from the operator,

[0824] Means for performing tasks based on instructions from the operator or the results of the analysis means,

[0825] Means for adjusting the operation plan of the production equipment,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the analysis means uses a learning algorithm to evaluate the progress of the work.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the notification transmission and instruction reception means uses a user interface provided via a terminal.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] Means of gathering information from multiple sources,

[0834] An analytical means using intelligence to analyze the aforementioned information,

[0835] A means for automatically optimizing the work procedure based on the results of the analysis means,

[0836] A means of sending notifications to users and receiving instructions from users,

[0837] Means for performing work based on instructions from the user or the results of the analysis means,

[0838] A means of recognizing the user's emotions and adjusting the expression and priority of notifications based on those emotions,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the analysis means uses machine learning to evaluate the progress of work and identifies the cause if a particular task is delayed.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the notification transmission and instruction reception means uses an interface provided via a terminal equipped with a function to recognize user emotions.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] Means for collecting business data from multiple sources,

[0847] An analysis method using artificial intelligence to analyze the aforementioned business data,

[0848] A means for automatically optimizing the workflow based on the results of the aforementioned analysis means,

[0849] A means of recognizing the user's emotions,

[0850] Means for adjusting the priority of notifications based on the results of the emotion recognition means,

[0851] A means of notifying users of important information and receiving instructions from users,

[0852] Means for performing tasks based on instructions from the user or the results of the analysis means,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the analysis means uses machine learning to evaluate the progress of the work and optimizes the efficiency of the work by combining it with the output of the emotion recognition means.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the notification transmission and instruction reception means uses information exchange means provided via a user terminal. [Explanation of symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting operational data from multiple measuring devices, An analysis method using artificial intelligence to analyze the aforementioned business data, A means for automatically optimizing the production process based on the results of the analysis means, A means of sending notifications to the operator and receiving instructions from the operator, Means for performing tasks based on instructions from the operator or the results of the analysis means, Means for adjusting the operation plan of the production equipment, A system that includes this.

2. The system according to claim 1, wherein the analysis means uses a learning algorithm to evaluate the progress of the work.

3. The system according to claim 1, wherein the notification transmission and instruction reception means uses a user interface provided via a terminal.

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