system

Abstract

We provide the system. [Solution] A means of collecting information on a company's business procedures and consolidating it in a central memory device, A means of analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidelines, A means of monitoring business procedures in real time and automatically providing appropriate instructions using a learned computational model, A means to support user decision-making and improve the consistency and efficiency of business procedures, A means for recording the results of work execution and generating record documents for inspection and regulatory compliance, A means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing visual or audible instructions regarding such deviations, A system that includes this.

Description

Technical Field

[0005]

[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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for a company to implement business processes compliant with standard frameworks and regulatory guidelines, a great deal of manual work and time are required. Also, when there is a lack of business consistency, the operation efficiency decreases and the burden of regulatory compliance increases. Further, due to human errors and variations in judgment, the quality of business may become unstable. It is necessary to solve such problems and improve the efficiency and quality of business operations.

Means for Solving the Problems

[0005] The proposed system has a means of aggregating information on a company's business processes and storing it in a central database. This allows it to analyze the company's standard frameworks and regulatory guidelines and learn models based on them. Furthermore, it has a means of using these learned models to monitor business flows in real time and provide appropriate guidance to users. This supports user decision-making and ensures the consistency and efficiency of business processes. In addition, by recording the results of business execution in detail and automatically generating the documents necessary for audits and regulatory compliance, it can reduce the burden of regulatory compliance.

[0006] A "company" is an organization that provides goods or services with a specific purpose and pursues profit.

[0007] A "business process" refers to a series of business activities carried out within a company, and is a set of procedures designed to achieve a specific goal.

[0008] A "central database" is a data collection facility used for centralized information management, and it is a system with unified access.

[0009] A "standard framework" is a collection of generally recognized guidelines and reference models for a particular industry or field, aimed at improving efficiency and consistency in business operations.

[0010] "Regulatory guidelines" are guidelines that outline the laws and industry rules that must be followed when conducting business.

[0011] A "model" is an abstract or mathematical structure created to predict or reproduce specific actions or results based on data or information.

[0012] "Monitoring" is the act of observing and recording a specific object or process in order to keep track of the situation at all times.

[0013] "Guidance" refers to the act of providing instructions or advice when carrying out tasks, and includes providing information to support decision-making.

[0014] "Decision-making" is the process of judging and selecting the most appropriate action or solution from among multiple options.

[0015] "Execution result" refers to the final state or deliverable obtained after a specific process or action has been completed.

[0016] An "audit" is an activity that verifies and evaluates whether business operations and processes are being conducted in accordance with established standards.

[0017] "Regulatory compliance" is a concept that ensures business operations are conducted in accordance with relevant laws and industry standards. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0019] 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.

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

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

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

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

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention is an AI agent system designed to improve efficiency and consistency in a company's business processes. The system begins by collecting information about the company's operations and aggregating it in a central database as needed. The server analyzes key documents such as the company's standard frameworks and regulatory guidelines, and builds a learning model based on this information. This model is then used to monitor business flows in real time, and the AI ​​agent automatically detects deviations from the standard.

[0040] Next, the terminal grasps the user's current work situation and, as needed, provides AI-powered guidance to the user visually or audibly. Based on this guidance, it helps the user make quick and accurate decisions. This standardizes work processes and maintains consistency in operations.

[0041] Furthermore, once a task is performed, the terminal automatically records the results and saves them as audit logs. This data is later analyzed by the server to generate reports for regulatory compliance and necessary improvement guidelines. For example, in the event of a server failure, as soon as the terminal detects the anomaly, the server's AI model refers to past failure data and instructs the user on the optimal solution based on this data. This process enables rapid failure response and leads to long-term improvement in service quality.

[0042] In this way, AI agent systems play a role in promoting standardization and efficiency in corporate business processes and reducing the burden of regulatory compliance that users face.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects information related to business processes from various data sources within the enterprise and integrates it into a central database. This process includes importing and organizing documentation, including standard frameworks and regulatory guidelines.

[0046] Step 2:

[0047] The server analyzes the collected information and trains a generative AI model. This model learns standard patterns in a company's operations and serves as a basis for providing guidance that conforms to those patterns.

[0048] Step 3:

[0049] The terminal monitors the progress of business processes in real time and tracks user input and actions. If a deviation from the normal flow based on the standard framework is detected, the terminal alerts the AI ​​agent.

[0050] Step 4:

[0051] The server's AI agent analyzes the problem that occurred and, while referring to collected historical data, provides the user with the optimal countermeasures and solutions. This information is presented to the user via their device.

[0052] Step 5:

[0053] The user makes business decisions and takes action based on the AI ​​agent's suggestions. The terminal records the results of this action process in detail and sends them to the server as needed.

[0054] Step 6:

[0055] The server receives the results of business operations sent from the terminals and performs analysis. These analysis results are used to improve future business processes and formulate regulatory compliance measures.

[0056] Step 7:

[0057] The server generates audit reports based on the analysis data and prepares them for output when needed. This streamlines the process of understanding regulatory compliance and preparing for audits.

[0058] (Example 1)

[0059] 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."

[0060] Corporate business processes are required to reduce the burden of regulatory compliance while ensuring efficiency and consistency. However, current methods make it difficult to monitor business processes, detect deviations, and provide appropriate guidance in real time, and properly recording and analyzing business results is also cumbersome. To solve this problem, it is necessary to improve operational efficiency using advanced technologies.

[0061] 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.

[0062] In this invention, the server includes means for collecting information related to business processes and aggregating it into information resources; means for analyzing the collected information and training an AI model to generate a learning model that conforms to standard frameworks and regulatory guidelines; and means for monitoring work procedures in real time and detecting deviations from standards using the trained model. This enables efficient and automatic management of business processes, standardized work execution, and a reduction in the burden of regulatory compliance.

[0063] "Information resources" refer to databases and storage systems that centrally manage information related to business processes, enabling analysis and utilization as needed.

[0064] A "standard framework" refers to a set of rules and procedures designed to standardize and efficiently execute a company's business processes.

[0065] "Regulatory guidelines" refer to rules and guidelines for conducting business that are formulated by external organizations such as industry associations or governments.

[0066] A "generative AI model" refers to an artificial intelligence learning model built to analyze large amounts of data and make predictions and decisions tailored to specific purposes.

[0067] "Detecting deviations" refers to identifying actions or results that deviate from established standards or criteria in business processes, and prompting necessary corrective actions.

[0068] "Work procedure" refers to a set of predetermined, sequential steps required to complete a specific task or operation.

[0069] This invention is an AI agent system for improving the efficiency and consistency of business processes. The system is configured as follows:

[0070] The server first collects information related to business processes from both inside and outside the company and aggregates it into information resources. The server uses general database software and cloud-based data storage services to analyze this information. Based on the analyzed information, the server uses a generative AI model to build a learning model that conforms to standard frameworks and regulatory guidelines. This learning model has the capability to monitor business flows in real time and detect deviations from the established standards.

[0071] As a concrete example, the server retrieves data from project management systems and customer relationship management systems and aggregates it in cloud storage. The generative AI model built by the server analyzes this data and constructs an efficient business model using prompts such as, "Learn standard processes to improve the efficiency of the company's operations."

[0072] The terminal provides visual or audio guidance based on information retrieved from the server to offer the user the most optimal form of guidance. The terminal is equipped with a user interface for interaction with the user, and tablet devices and voice assistant devices are often used. For example, if a deviation from a procedure is detected while the user is performing a task, the terminal will immediately display a warning and provide the standard procedure.

[0073] Users make quick and efficient decisions by referring to guidance provided on the terminal during the execution of each task. As a result, business processes are standardized and ensured to be performed efficiently. By performing tasks according to the guidance presented on the terminal, users maintain consistency in their work and reduce the burden of regulatory compliance.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects information related to business processes from internal and external data sources. Inputs include data from the company's project management system and customer relationship management system. This data is aggregated in cloud storage and prepared for analysis. Specifically, it automatically retrieves data from each system using APIs and stores it in a centralized database.

[0077] Step 2:

[0078] The server performs data analysis using a generative AI model based on the collected data. The input consists of aggregated information stored in cloud storage. This analysis builds a learning model that conforms to standard frameworks and regulatory guidelines. Specifically, the dataset is input into the AI ​​model, and prompts such as "Please learn standard processes to improve the operational efficiency of companies" are used. Based on these prompts, the model learns and optimizes business processes.

[0079] Step 3:

[0080] The server uses a trained model to monitor business processes in real time. The input consists of business data acquired in real time from the company's daily operations. This data is analyzed to detect deviations from the established criteria. Specifically, this involves continuously tracking business data using monitoring tools and comparing it to the criteria predicted by the trained model.

[0081] Step 4:

[0082] The terminal provides visual or audio guidance to the user based on monitoring results. Input is the monitoring results of the business flow sent from the server. Output includes instructions and warnings displayed to the user. Specifically, pop-up messages or voice assistant guidance appear on the user's terminal screen to prompt appropriate action.

[0083] Step 5:

[0084] The terminal automatically records user actions and saves them as audit logs. Input includes operational data obtained via the user interface. This data is stored in storage, providing a foundation for later analysis. Specifically, this involves recording operational data in a database with timestamps and organizing it in a format suitable for later auditing.

[0085] Step 6:

[0086] The server analyzes recorded log data to create reports for regulatory compliance and generate guidelines for business improvement. The input is accumulated operation log data. The output includes reports on regulatory compliance and suggestions for business improvement. Specifically, this involves analyzing log data, using AI models to identify areas for efficiency improvements in current processes, and generating documents to report to management.

[0087] (Application Example 1)

[0088] 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."

[0089] Companies demand improved efficiency and consistency in their business procedures and manufacturing processes. However, in reality, compliance with regulations and the application of standard frameworks are complex, leading to numerous challenges. Furthermore, deviations in productivity and quality in manufacturing sites may not be addressed immediately, potentially leading to a decline in productivity and quality.

[0090] 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.

[0091] In this invention, the server includes means for collecting information on a company's business procedures and aggregating it in a central memory; means for analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidance; and means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing instructions for such deviations visually or audibly. This enables improved operational efficiency for companies and immediate response in the manufacturing process.

[0092] A "company" is a legal entity or organization that engages in profit-making activities and is an organization that carries out various business and production activities.

[0093] "Business procedures" refer to a series of processes or methods that systematically organize the various activities carried out by a company.

[0094] A "central memory device" is a centrally managed data storage system used for managing and storing information.

[0095] A "standard framework" is a guideline that sets out the standards and norms for conducting business and manufacturing activities.

[0096] "Regulatory guidance" refers to guidelines and instructions based on laws and regulations related to business operations and manufacturing processes.

[0097] A "computational model" is a mathematical or statistical model constructed to solve a problem based on data and information.

[0098] "Industrial products" is a concept that refers to products or goods produced in factories or manufacturing facilities.

[0099] A "production process" refers to a series of tasks and processes carried out when manufacturing industrial products.

[0100] "Deviation" refers to actions or states that deviate from standards or frameworks, and can potentially affect quality or efficiency.

[0101] "To provide visually or audibly" means to convey information or instructions to a user by showing them visually or making them hear them audibly.

[0102] This invention is a system that utilizes an AI agent to improve efficiency and consistency in the production of industrial products. The server collects information on a company's business procedures using data input devices such as sensors and cameras, and aggregates it in a central memory using cloud services such as AWS®. After aggregation, the server learns a computational model based on standard frameworks and regulatory guidance, performs data analysis on machine learning platforms such as Azure® ML and Google® Cloud AI, and builds the model. Using this learned computational model, the server monitors the production process of industrial products in real time and detects deviations in productivity and quality.

[0103] The terminal receives this information and enables immediate response by providing workers with appropriate instructions visually or audibly through smart glasses or other visual information transmission devices. For example, if the position of a part found to be out of the standard framework on a production line, the terminal visually displays and audibly guides the worker with instructions such as "Adjust the position of the part." This process allows workers to make quick and accurate corrections, thus maintaining manufacturing quality.

[0104] One concrete example used here is to input a prompt message to the generative AI model saying, "If the placement of parts is incorrect, please instruct me on how to correct it." This allows the generative AI model to provide appropriate instructions.

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

[0106] Step 1:

[0107] The server collects operational data from sensors and cameras within the factory and aggregates it into a central storage device via a cloud platform such as AWS. In this step, real-time data from each work station on the production line is used as input, and a processed dataset is output.

[0108] Step 2:

[0109] The server uses aggregated data and leverages Azure ML and Google Cloud AI to generate computational models that comply with standard frameworks and regulatory guidelines. The input is the dataset obtained in the previous step, and by analyzing and learning from this, it outputs a highly accurate computational model.

[0110] Step 3:

[0111] The server uses the generated computational model to monitor the production process of industrial products in real time. In this process, the current production status is compared with the model, and deviations are detected. The input is real-time data newly obtained from sensors, and this data is used to determine whether or not there is a deviation, and the detection information is output.

[0112] Step 4:

[0113] The terminal receives information about deviations and provides visual or audible instructions to the worker using smart glasses or other visual information devices. The input is deviation information from the server, and based on this, it outputs specific instructions. The worker adjusts their work based on these instructions.

[0114] Step 5:

[0115] The user follows the instructions provided by the terminal and makes the necessary corrections. The input in this step is the instruction itself to the user, and the output is the maintenance of production quality, achieved when the user performs the correct correction operations correctly.

[0116] 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.

[0117] This invention is a system that efficiently manages a company's business processes and improves the quality of work by recognizing user emotions. Basically, it collects business information from the company, aggregates it in a central database, performs analysis, and learns a model that conforms to a standard framework. The server uses this model to monitor in real time whether business processes are progressing according to standards and provides appropriate guidance to users.

[0118] A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses the emotion engine to analyze the user's emotional state based on the user's voice, input data, or information obtained from sensors. Based on this analysis, the server can adjust the guidance provided to the user to provide more effective support. For example, if the user is feeling stressed, the system can adjust the workload or reduce the frequency of notifications.

[0119] One possible scenario is when the emotional engine detects a high stress level while a user is handling an urgent task. In this case, the terminal immediately sends the data to the server, which then adjusts the guidance based on that information to reduce the user's mental burden. This user emotional data is recorded along with the results of the task and used to improve future operations and enhance support measures.

[0120] In this way, by standardizing corporate workflows and combining them with user support powered by an emotional engine, higher quality business operations become possible. This system contributes to automating regulatory compliance and improving user satisfaction.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The server collects data related to the company's business processes from various data sources and imports it into a central database. This includes standard frameworks, regulatory guidelines, and historical business execution data.

[0124] Step 2:

[0125] The server analyzes the aggregated data and trains a generative AI model. This model is then used to learn standard patterns in business operations and prepare to provide appropriate guidance.

[0126] Step 3:

[0127] The terminal monitors the user's actions and input data in real time during work. It also uses a built-in emotion engine to analyze the user's emotional state based on their voice tone, facial expressions, and input speed.

[0128] Step 4:

[0129] The server receives analysis data from the emotion engine and determines guidance content based on the user's psychological state. For example, if the server detects that the user is stressed, it generates instructions to adjust the priority and method of tasks.

[0130] Step 5:

[0131] The device presents the user with determined guidance. This includes notifications via a visual interface and audio feedback. The user can then make decisions based on this information.

[0132] Step 6:

[0133] The user follows the guidance and executes the business process. The terminal records the actions performed and their results.

[0134] Step 7:

[0135] The server analyzes the work execution results and sentiment data sent from the terminal to evaluate work efficiency and the effectiveness of user support. This data will be used to plan future process improvements and regulatory compliance measures.

[0136] Step 8:

[0137] The server generates audit reports based on these analysis results, which are used as needed for operational guidelines and regulatory compliance. This process improves the quality and efficiency of operations.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] In corporate business operations, there is a need to provide accurate business support that takes into account the emotional state of users while maintaining efficiency and consistency in operations. However, there is no system that combines real-time monitoring of work progress with flexible responses based on users' emotions, making it difficult to simultaneously achieve improved work quality and user satisfaction.

[0141] 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.

[0142] In this invention, the server includes means for collecting information related to business activities and aggregating it in an information management device, means for analyzing the user's emotional state through voice information and biosensors, and means for optimizing the workload based on the user's emotional state. This enables flexible work support that responds to the user's emotions while maintaining standardization of operations in real time.

[0143] "Business activities" refer to a series of tasks and processes that a company or organization undertakes to achieve its objectives.

[0144] An "information management device" refers to a computer system used to collect, record, and analyze data related to business activities.

[0145] "Standard structure" refers to the criteria and frameworks established to ensure that business activities maintain a certain level of quality and efficiency.

[0146] "Regulatory guidelines" refer to specific instructions and standards to ensure that business operations comply with relevant laws and industry standards.

[0147] A "model" refers to a mathematical or computer science structure built on collected data to help improve the progress and efficiency of business activities.

[0148] "Users" refers to individuals who carry out business activities or operate systems.

[0149] "Voice information" refers to data obtained through the user's voice and is used for sentiment analysis in business activities.

[0150] A "biosensor" is a device that acquires biometric information such as a user's heart rate and facial expressions, and is used for emotion analysis.

[0151] "Emotional state" refers to data about the user's current psychological reactions and emotions.

[0152] "Workload" refers to the quantity and complexity of tasks required of users.

[0153] This invention is a system that efficiently manages a company's business activities and provides effective support based on the emotional state of users. The server first collects information on business activities from each business department within the company and aggregates it in an information management device. This aggregated information includes data such as the progress, schedule, and achievement level of business activities.

[0154] Next, the server uses this data to train a model that conforms to standard structures and regulatory guidelines. Generative AI models are used to train the model, building a structure that maximizes operational efficiency.

[0155] Meanwhile, the user's device analyzes the user's emotional state through voice information and biosensors. Voice recognition software and sensors (e.g., microphone, camera, heart rate sensor) are used. The emotional analysis data provided by the device is sent to a server and used to tailor individual work guidance to the user.

[0156] One concrete example of this system's application is in a support center. When a user is handling customer complaints, the terminal detects high stress levels through facial recognition and voice analysis. The server can then immediately use this information to adjust the workload, provide relaxation techniques, and issue other instructions to reduce the user's stress.

[0157] As an example of a prompt to the generating AI model, by inputting "Generate a specific plan on how to adjust the workload if the user is experiencing significant stress," the AI ​​model can propose an appropriate workload adjustment plan.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server collects information about business activities from various departments within the company and aggregates it into an information management system. Specifically, this includes progress reports, schedules, and performance data entered by employees. This input data is aggregated into a database and converted into a format used in subsequent analysis processes.

[0161] Step 2:

[0162] The server analyzes aggregated business data and uses a generated AI model to train a model aimed at improving business efficiency. Specifically, it identifies data patterns based on standard structures and regulatory guidelines and processes the data accordingly. This analyzed data is reflected in the trained model and used to optimize business processes.

[0163] Step 3:

[0164] The server uses a model to monitor business activities in real time and provides appropriate guidance to users as needed. Inputs include continuously updated business data and the model's analysis results. Outputs are specific feedback regarding improvements to business processes and workload adjustments.

[0165] Step 4:

[0166] The device analyzes the user's emotional state using voice information and data obtained from biosensors. Specifically, voice recognition software analyzes the user's voice and feeds it into an emotion engine to identify the emotional state. The input is the user's real-time voice data and sensor data, and the output is the emotional evaluation information resulting from the analysis.

[0167] Step 5:

[0168] The server adjusts work instructions for users and optimizes workload based on the sentiment analysis results. Based on the sentiment evaluation information, the AI ​​model dynamically modifies the workload and instruction content, and presents users with concrete suggestions for stress reduction and increased efficiency.

[0169] Step 6:

[0170] The server records all business execution results and user sentiment data, and generates documentation to support future audits and regulatory compliance. This process involves analyzing and documenting information stored in the database. Specifically, the generated documentation can also be used for future business improvements.

[0171] (Application Example 2)

[0172] 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".

[0173] In modern business operations, maintaining efficient work processes and user mental health are crucial challenges. However, excessive pursuit of efficiency in workflows can increase the mental burden on users, ultimately leading to a decline in work quality. Therefore, there is a need for a system that can recognize and appropriately adjust user emotions, along with real-time management of business processes.

[0174] 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.

[0175] In this invention, the server includes means for aggregating information related to the higher-level structure of a company and compiling it into a central information group; means for utilizing artificial intelligence to recognize the user's emotional state and adjust guidance based on that state to provide highly efficient work support; and means for reducing the mental burden by adjusting the amount of tasks when the user feels stressed. This enables efficient management of business processes as well as flexible work adjustments based on the user's emotional state.

[0176] "Corporate higher-order structure" refers to the entire collection of strategic and operational processes within an organization and their associated data.

[0177] A "central information set" refers to the main datasets required in business activities, collected for the purpose of efficiently managing each business process.

[0178] Artificial intelligence is an information processing system that can mimic human intellectual activity and make various decisions while learning independently.

[0179] "Emotional state" refers to the psychological situation or mood expressed by the user, and recognizing this allows for adjustments to work efficiency.

[0180] "Guidance" or "directions" refers to instructions or advice provided to a user to effectively carry out a business process.

[0181] "Highly efficient work support" refers to a series of support measures aimed at enabling tasks to be performed quickly and effectively.

[0182] "Mental load" refers to the degree of mental and cognitive burden and stress that users experience when performing their work.

[0183] A description of embodiments for carrying out this invention will be given.

[0184] This system achieves integrated process management that combines efficient management of business activities with user emotion recognition. The server aggregates information related to the higher-level structure of the company, forming a central data set. This information includes business data, performance metrics, and user feedback. A Python program processes this data using the Google Speech-to-Text API, converting user speech data into text data.

[0185] The artificial intelligence operates on TENSORFLOW® and uses an emotion recognition model to determine the user's emotional state. To achieve this, it integrates not only voice data but also sensor data to analyze the emotional state from multiple perspectives. Based on the user's emotional state, the server dynamically generates guidance and adjusts the workflow. For example, if the user says "I'm tired," the system evaluates stress indicators and changes the guidance content to reduce the priority of tasks.

[0186] The guidance provided to the user will be implemented visually or audibly to enhance the user experience. For example, if a user expresses stress upon returning home, the robot might announce, "I will play music to help you relax," and adjust the ambient lighting.

[0187] An example of a prompt using a generative AI model is, "Consider the user's emotional state and generate suggestions for adjusting the workflow." This prompt allows the system to optimize the work structure in accordance with the user's emotions.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The device uses a microphone and sensors to collect user voice and contextual data. The collected voice data is converted into text data using the Google Speech-to-Text API. In this process, the input is voice data and the output is text data.

[0191] Step 2:

[0192] The terminal centralizes text data and environmental data from sensors and sends it to the server. The server prepares this data as input for an emotion recognition model. It processes the data through integration and transformation to prepare it in a format that the model can process. The input is a combination of text and environmental data, and the output is in a model-compatible data format.

[0193] Step 3:

[0194] The server uses an emotion recognition model built on TensorFlow to analyze the user's emotional state from the input data. The analysis results are output as an index indicating whether the user is in a different emotional state, such as anger, joy, or stress. The input for this step is formatted data, and the output is an evaluation of the emotional state.

[0195] Step 4:

[0196] The server generates appropriate guidance for the user based on the results of the emotional state assessment. The specific guidance content is automatically generated using a generation AI model based on the prompt text. The input is the emotional state assessment result, and the output is the generated guidance message.

[0197] Step 5:

[0198] The terminal presents the generated guidance message to the user visually or audibly. Through the user interface, the guidance is presented as action instructions, facilitating adjustments to the user's work environment and tasks. The input is the guidance message, and the output is a specific action suggestion for the user.

[0199] 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.

[0200] Data generation model 58 is a type of 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] This invention is an AI agent system designed to improve efficiency and consistency in a company's business processes. The system begins by collecting information about the company's operations and aggregating it in a central database as needed. The server analyzes key documents such as the company's standard frameworks and regulatory guidelines, and builds a learning model based on this information. This model is then used to monitor business flows in real time, and the AI ​​agent automatically detects deviations from the standard.

[0216] Next, the terminal grasps the user's current work situation and, as needed, provides AI-powered guidance to the user visually or audibly. Based on this guidance, it helps the user make quick and accurate decisions. This standardizes work processes and maintains consistency in operations.

[0217] Furthermore, once a task is performed, the terminal automatically records the results and saves them as audit logs. This data is later analyzed by the server to generate reports for regulatory compliance and necessary improvement guidelines. For example, in the event of a server failure, as soon as the terminal detects the anomaly, the server's AI model refers to past failure data and instructs the user on the optimal solution based on this data. This process enables rapid failure response and leads to long-term improvement in service quality.

[0218] In this way, AI agent systems play a role in promoting standardization and efficiency in corporate business processes and reducing the burden of regulatory compliance that users face.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server collects information related to business processes from various data sources within the enterprise and integrates it into a central database. This process includes importing and organizing documentation, including standard frameworks and regulatory guidelines.

[0222] Step 2:

[0223] The server analyzes the collected information and trains a generative AI model. This model learns standard patterns in a company's operations and serves as a basis for providing guidance that conforms to those patterns.

[0224] Step 3:

[0225] The terminal monitors the progress of business processes in real time and tracks user input and actions. If a deviation from the normal flow based on the standard framework is detected, the terminal alerts the AI ​​agent.

[0226] Step 4:

[0227] The server's AI agent analyzes the problem that occurred and, while referring to collected historical data, provides the user with the optimal countermeasures and solutions. This information is presented to the user via their device.

[0228] Step 5:

[0229] The user makes business decisions and takes action based on the AI ​​agent's suggestions. The terminal records the results of this action process in detail and sends them to the server as needed.

[0230] Step 6:

[0231] The server receives the results of business operations sent from the terminals and performs analysis. These analysis results are used to improve future business processes and formulate regulatory compliance measures.

[0232] Step 7:

[0233] The server generates audit reports based on the analysis data and prepares them for output when needed. This streamlines the process of understanding regulatory compliance and preparing for audits.

[0234] (Example 1)

[0235] 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."

[0236] Corporate business processes are required to reduce the burden of regulatory compliance while ensuring efficiency and consistency. However, current methods make it difficult to monitor business processes, detect deviations, and provide appropriate guidance in real time, and properly recording and analyzing business results is also cumbersome. To solve this problem, it is necessary to improve operational efficiency using advanced technologies.

[0237] 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.

[0238] In this invention, the server includes means for collecting information related to business processes and aggregating it into information resources; means for analyzing the collected information and training an AI model to generate a learning model that conforms to standard frameworks and regulatory guidelines; and means for monitoring work procedures in real time and detecting deviations from standards using the trained model. This enables efficient and automatic management of business processes, standardized work execution, and a reduction in the burden of regulatory compliance.

[0239] "Information resources" refer to databases and storage systems that centrally manage information related to business processes, enabling analysis and utilization as needed.

[0240] A "standard framework" refers to a set of rules and procedures designed to standardize and efficiently execute a company's business processes.

[0241] "Regulatory guidelines" refer to rules and guidelines for conducting business that are formulated by external organizations such as industry associations or governments.

[0242] A "generative AI model" refers to an artificial intelligence learning model built to analyze large amounts of data and make predictions and decisions tailored to specific purposes.

[0243] "Detecting deviations" refers to identifying actions or results that deviate from established standards or criteria in business processes, and prompting necessary corrective actions.

[0244] "Work procedure" refers to a set of predetermined, sequential steps required to complete a specific task or operation.

[0245] This invention is an AI agent system for improving the efficiency and consistency of business processes. The system is configured as follows:

[0246] The server first collects information related to business processes from both inside and outside the company and aggregates it into information resources. The server uses general database software and cloud-based data storage services to analyze this information. Based on the analyzed information, the server uses a generative AI model to build a learning model that conforms to standard frameworks and regulatory guidelines. This learning model has the capability to monitor business flows in real time and detect deviations from the established standards.

[0247] As a concrete example, the server retrieves data from project management systems and customer relationship management systems and aggregates it in cloud storage. The generative AI model built by the server analyzes this data and constructs an efficient business model using prompts such as, "Learn standard processes to improve the efficiency of the company's operations."

[0248] The terminal provides visual or audio guidance based on information retrieved from the server to offer the user the most optimal form of guidance. The terminal is equipped with a user interface for interaction with the user, and tablet devices and voice assistant devices are often used. For example, if a deviation from a procedure is detected while the user is performing a task, the terminal will immediately display a warning and provide the standard procedure.

[0249] Users make quick and efficient decisions by referring to guidance provided on the terminal during the execution of each task. As a result, business processes are standardized and ensured to be performed efficiently. By performing tasks according to the guidance presented on the terminal, users maintain consistency in their work and reduce the burden of regulatory compliance.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server collects information related to business processes from internal and external data sources. Inputs include data from the company's project management system and customer relationship management system. This data is aggregated in cloud storage and prepared for analysis. Specifically, it automatically retrieves data from each system using APIs and stores it in a centralized database.

[0253] Step 2:

[0254] The server performs data analysis using a generative AI model based on the collected data. The input consists of aggregated information stored in cloud storage. This analysis builds a learning model that conforms to standard frameworks and regulatory guidelines. Specifically, the dataset is input into the AI ​​model, and prompts such as "Please learn standard processes to improve the operational efficiency of companies" are used. Based on these prompts, the model learns and optimizes business processes.

[0255] Step 3:

[0256] The server uses a trained model to monitor business processes in real time. The input consists of business data acquired in real time from the company's daily operations. This data is analyzed to detect deviations from the established criteria. Specifically, this involves continuously tracking business data using monitoring tools and comparing it to the criteria predicted by the trained model.

[0257] Step 4:

[0258] The terminal provides visual or audio guidance to the user based on monitoring results. Input is the monitoring results of the business flow sent from the server. Output includes instructions and warnings displayed to the user. Specifically, pop-up messages or voice assistant guidance appear on the user's terminal screen to prompt appropriate action.

[0259] Step 5:

[0260] The terminal automatically records user actions and saves them as audit logs. Input includes operational data obtained via the user interface. This data is stored in storage, providing a foundation for later analysis. Specifically, this involves recording operational data in a database with timestamps and organizing it in a format suitable for later auditing.

[0261] Step 6:

[0262] The server analyzes recorded log data to create reports for regulatory compliance and generate guidelines for business improvement. The input is accumulated operation log data. The output includes reports on regulatory compliance and suggestions for business improvement. Specifically, this involves analyzing log data, using AI models to identify areas for efficiency improvements in current processes, and generating documents to report to management.

[0263] (Application Example 1)

[0264] 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."

[0265] Companies demand improved efficiency and consistency in their business procedures and manufacturing processes. However, in reality, compliance with regulations and the application of standard frameworks are complex, leading to numerous challenges. Furthermore, deviations in productivity and quality in manufacturing sites may not be addressed immediately, potentially leading to a decline in productivity and quality.

[0266] 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.

[0267] In this invention, the server includes means for collecting information on a company's business procedures and aggregating it in a central memory; means for analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidance; and means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing instructions for such deviations visually or audibly. This enables improved operational efficiency for companies and immediate response in the manufacturing process.

[0268] A "company" is a legal entity or organization that engages in profit-making activities and is an organization that carries out various business and production activities.

[0269] "Business procedures" refer to a series of processes or methods that systematically organize the various activities carried out by a company.

[0270] A "central memory device" is a centrally managed data storage system used for managing and storing information.

[0271] A "standard framework" is a guideline that sets out the standards and norms for conducting business and manufacturing activities.

[0272] "Regulatory guidance" refers to guidelines and instructions based on laws and regulations related to business operations and manufacturing processes.

[0273] A "computational model" is a mathematical or statistical model constructed to solve a problem based on data and information.

[0274] "Industrial products" is a concept that refers to products or goods produced in factories or manufacturing facilities.

[0275] A "production process" refers to a series of tasks and processes carried out when manufacturing industrial products.

[0276] "Deviation" refers to actions or states that deviate from standards or frameworks, and can potentially affect quality or efficiency.

[0277] "To provide visually or audibly" means to convey information or instructions to a user by showing them visually or making them hear them audibly.

[0278] This invention is a system that utilizes an AI agent to improve efficiency and consistency in the production of industrial products. The server collects information on a company's business procedures using data input devices such as sensors and cameras, and aggregates it in a central memory using cloud services such as AWS. After aggregation, the server learns a computational model based on standard frameworks and regulatory guidance, performs data analysis on machine learning platforms such as Azure ML and Google Cloud AI, and builds the model. Using this learned computational model, the server monitors the production process of industrial products in real time and detects deviations in productivity and quality.

[0279] The terminal receives this information and enables immediate response by providing workers with appropriate instructions visually or audibly through smart glasses or other visual information transmission devices. For example, if the position of a part found to be out of the standard framework on a production line, the terminal visually displays and audibly guides the worker with instructions such as "Adjust the position of the part." This process allows workers to make quick and accurate corrections, thus maintaining manufacturing quality.

[0280] As one of the specific examples used here, there is an example of inputting a prompt sentence "Please indicate how to correct the incorrect part placement." into the generative AI model. This enables the generative AI model to give appropriate instructions.

[0281] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0282] Step 1:

[0283] The server collects operation data from sensors and cameras in the factory and aggregates it in a central storage device through a cloud platform such as AWS. In this step, real-time data from each work station on the production line is used as input, and a processed data set is output.

[0284] Step 2:

[0285] Based on the aggregated data, the server utilizes Azure ML or Google Cloud AI to generate a computational model that complies with the standard framework and regulatory guidelines. The input is the data set obtained in the previous step, and by analyzing and learning this data, a highly accurate computational model is output.

[0286] Step 3:

[0287] The server uses the generated computational model to monitor the production process of industrial products in real time. In this process, the current production status is compared with the model, and deviations are detected. The input is the real-time data newly obtained from the sensors, and using this data, the presence or absence of deviations is determined, and detection information is output.

[0288] Step 4:

[0289] The terminal receives information about deviations and provides visual or audible instructions to the worker using smart glasses or other visual information devices. The input is deviation information from the server, and based on this, it outputs specific instructions. The worker adjusts their work based on these instructions.

[0290] Step 5:

[0291] The user follows the instructions provided by the terminal and makes the necessary corrections. The input in this step is the instruction itself to the user, and the output is the maintenance of production quality, achieved when the user performs the correct correction operations correctly.

[0292] 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.

[0293] This invention is a system that efficiently manages a company's business processes and improves the quality of work by recognizing user emotions. Basically, it collects business information from the company, aggregates it in a central database, performs analysis, and learns a model that conforms to a standard framework. The server uses this model to monitor in real time whether business processes are progressing according to standards and provides appropriate guidance to users.

[0294] A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses the emotion engine to analyze the user's emotional state based on the user's voice, input data, or information obtained from sensors. Based on this analysis, the server can adjust the guidance provided to the user to provide more effective support. For example, if the user is feeling stressed, the system can adjust the workload or reduce the frequency of notifications.

[0295] One possible scenario is when the emotional engine detects a high stress level while a user is handling an urgent task. In this case, the terminal immediately sends the data to the server, which then adjusts the guidance based on that information to reduce the user's mental burden. This user emotional data is recorded along with the results of the task and used to improve future operations and enhance support measures.

[0296] In this way, by standardizing corporate workflows and combining them with user support powered by an emotional engine, higher quality business operations become possible. This system contributes to automating regulatory compliance and improving user satisfaction.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The server collects data related to the company's business processes from various data sources and imports it into a central database. This includes standard frameworks, regulatory guidelines, and historical business execution data.

[0300] Step 2:

[0301] The server analyzes the aggregated data and trains a generative AI model. This model is then used to learn standard patterns in business operations and prepare to provide appropriate guidance.

[0302] Step 3:

[0303] The terminal monitors the user's actions and input data in real time during work. It also uses a built-in emotion engine to analyze the user's emotional state based on their voice tone, facial expressions, and input speed.

[0304] Step 4:

[0305] The server receives the analysis data from the emotion engine and determines the guidance content according to the user's psychological state. For example, when it is detected that the user is in a stressed state, an instruction to adjust the work priority and method is generated.

[0306] Step 5:

[0307] The terminal presents the determined guidance content to the user. This includes notifications via a visual interface and voice feedback. The user can make decisions based on this information.

[0308] Step 6:

[0309] The user follows the guidance and executes the business process. The terminal records the operations performed and their results.

[0310] Step 7:

[0311] The server analyzes the business execution results and emotion data transmitted from the terminal, and evaluates the business efficiency and the effect of user support. This data is utilized for future process improvement and formulation of regulatory countermeasures.

[0312] Step 8:

[0313] The server generates an audit report based on these analysis results and uses it for business guidelines and regulatory compliance as needed. This process realizes the improvement of the quality and efficiency of the business.

[0314] (Example 2)

[0315] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0316] In corporate business operations, there is a need to provide accurate business support that takes into account the emotional state of users while maintaining efficiency and consistency in operations. However, there is no system that combines real-time monitoring of work progress with flexible responses based on users' emotions, making it difficult to simultaneously achieve improved work quality and user satisfaction.

[0317] 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.

[0318] In this invention, the server includes means for collecting information related to business activities and aggregating it in an information management device, means for analyzing the user's emotional state through voice information and biosensors, and means for optimizing the workload based on the user's emotional state. This enables flexible work support that responds to the user's emotions while maintaining standardization of operations in real time.

[0319] "Business activities" refer to a series of tasks and processes that a company or organization undertakes to achieve its objectives.

[0320] An "information management device" refers to a computer system used to collect, record, and analyze data related to business activities.

[0321] "Standard structure" refers to the criteria and frameworks established to ensure that business activities maintain a certain level of quality and efficiency.

[0322] "Regulatory guidelines" refer to specific instructions and standards to ensure that business operations comply with relevant laws and industry standards.

[0323] A "model" refers to a mathematical or computer science structure built on collected data to help improve the progress and efficiency of business activities.

[0324] "Users" refers to individuals who carry out business activities or operate systems.

[0325] "Voice information" refers to data obtained through the user's voice and is used for sentiment analysis in business activities.

[0326] A "biosensor" is a device that acquires biometric information such as a user's heart rate and facial expressions, and is used for emotion analysis.

[0327] "Emotional state" refers to data about the user's current psychological reactions and emotions.

[0328] "Workload" refers to the quantity and complexity of tasks required of users.

[0329] This invention is a system that efficiently manages a company's business activities and provides effective support based on the emotional state of users. The server first collects information on business activities from each business department within the company and aggregates it in an information management device. This aggregated information includes data such as the progress, schedule, and achievement level of business activities.

[0330] Next, the server uses this data to train a model that conforms to standard structures and regulatory guidelines. Generative AI models are used to train the model, building a structure that maximizes operational efficiency.

[0331] Meanwhile, the user's device analyzes the user's emotional state through voice information and biosensors. Voice recognition software and sensors (e.g., microphone, camera, heart rate sensor) are used. The emotional analysis data provided by the device is sent to a server and used to tailor individual work guidance to the user.

[0332] One concrete example of this system's application is in a support center. When a user is handling customer complaints, the terminal detects high stress levels through facial recognition and voice analysis. The server can then immediately use this information to adjust the workload, provide relaxation techniques, and issue other instructions to reduce the user's stress.

[0333] As an example of a prompt to the generating AI model, by inputting "Generate a specific plan on how to adjust the workload if the user is experiencing significant stress," the AI ​​model can propose an appropriate workload adjustment plan.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] The server collects information about business activities from various departments within the company and aggregates it into an information management system. Specifically, this includes progress reports, schedules, and performance data entered by employees. This input data is aggregated into a database and converted into a format used in subsequent analysis processes.

[0337] Step 2:

[0338] The server analyzes aggregated business data and uses a generated AI model to train a model aimed at improving business efficiency. Specifically, it identifies data patterns based on standard structures and regulatory guidelines and processes the data accordingly. This analyzed data is reflected in the trained model and used to optimize business processes.

[0339] Step 3:

[0340] The server uses a model to monitor business activities in real time and provides appropriate guidance to users as needed. Inputs include continuously updated business data and the model's analysis results. Outputs are specific feedback regarding improvements to business processes and workload adjustments.

[0341] Step 4:

[0342] The device analyzes the user's emotional state using voice information and data obtained from biosensors. Specifically, voice recognition software analyzes the user's voice and feeds it into an emotion engine to identify the emotional state. The input is the user's real-time voice data and sensor data, and the output is the emotional evaluation information resulting from the analysis.

[0343] Step 5:

[0344] The server adjusts work instructions for users and optimizes workload based on the sentiment analysis results. Based on the sentiment evaluation information, the AI ​​model dynamically modifies the workload and instruction content, and presents users with concrete suggestions for stress reduction and increased efficiency.

[0345] Step 6:

[0346] The server records all business execution results and user sentiment data, and generates documentation to support future audits and regulatory compliance. This process involves analyzing and documenting information stored in the database. Specifically, the generated documentation can also be used for future business improvements.

[0347] (Application Example 2)

[0348] 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."

[0349] In modern business operations, maintaining efficient work processes and user mental health are crucial challenges. However, excessive pursuit of efficiency in workflows can increase the mental burden on users, ultimately leading to a decline in work quality. Therefore, there is a need for a system that can recognize and appropriately adjust user emotions, along with real-time management of business processes.

[0350] 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.

[0351] In this invention, the server includes means for aggregating information related to the higher-level structure of a company and compiling it into a central information group; means for utilizing artificial intelligence to recognize the user's emotional state and adjust guidance based on that state to provide highly efficient work support; and means for reducing the mental burden by adjusting the amount of tasks when the user feels stressed. This enables efficient management of business processes as well as flexible work adjustments based on the user's emotional state.

[0352] "Corporate higher-order structure" refers to the entire collection of strategic and operational processes within an organization and their associated data.

[0353] A "central information set" refers to the main datasets required in business activities, collected for the purpose of efficiently managing each business process.

[0354] Artificial intelligence is an information processing system that can mimic human intellectual activity and make various decisions while learning independently.

[0355] "Emotional state" refers to the psychological situation or mood expressed by the user, and recognizing this allows for adjustments to work efficiency.

[0356] "Guidance" or "directions" refers to instructions or advice provided to a user to effectively carry out a business process.

[0357] "Highly efficient work support" refers to a series of support measures aimed at enabling tasks to be performed quickly and effectively.

[0358] "Mental load" refers to the degree of mental and cognitive burden and stress that users experience when performing their work.

[0359] A description of embodiments for carrying out this invention will be given.

[0360] This system achieves integrated process management that combines efficient management of business activities with user emotion recognition. The server aggregates information related to the higher-level structure of the company, forming a central data set. This information includes business data, performance metrics, and user feedback. A Python program processes this data using the Google Speech-to-Text API, converting user speech data into text data.

[0361] The artificial intelligence runs on TensorFlow and uses an emotion recognition model to determine the user's emotional state. To achieve this, it integrates not only voice data but also sensor data to analyze the emotional state from multiple perspectives. Based on the user's emotional state, the server dynamically generates guidance and adjusts the workflow. For example, if the user says "I'm tired," the system evaluates stress indicators and modifies the guidance to reduce the priority of tasks.

[0362] The guidance provided to the user will be implemented visually or audibly to enhance the user experience. For example, if a user expresses stress upon returning home, the robot might announce, "I will play music to help you relax," and adjust the ambient lighting.

[0363] An example of a prompt using a generative AI model is, "Consider the user's emotional state and generate suggestions for adjusting the workflow." This prompt allows the system to optimize the work structure in accordance with the user's emotions.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The device uses a microphone and sensors to collect user voice and contextual data. The collected voice data is converted into text data using the Google Speech-to-Text API. In this process, the input is voice data and the output is text data.

[0367] Step 2:

[0368] The terminal centralizes text data and environmental data from sensors and sends it to the server. The server prepares this data as input for an emotion recognition model. It processes the data through integration and transformation to prepare it in a format that the model can process. The input is a combination of text and environmental data, and the output is in a model-compatible data format.

[0369] Step 3:

[0370] The server uses an emotion recognition model built on TensorFlow to analyze the user's emotional state from the input data. The analysis results are output as an index indicating whether the user is in a different emotional state, such as anger, joy, or stress. The input for this step is formatted data, and the output is an evaluation of the emotional state.

[0371] Step 4:

[0372] The server generates appropriate guidance for the user based on the results of the emotional state assessment. The specific guidance content is automatically generated using a generation AI model based on the prompt text. The input is the emotional state assessment result, and the output is the generated guidance message.

[0373] Step 5:

[0374] The terminal presents the generated guidance message to the user visually or audibly. Through the user interface, the guidance is presented as action instructions, facilitating adjustments to the user's work environment and tasks. The input is the guidance message, and the output is a specific action suggestion for the user.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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".

[0391] This invention is an AI agent system designed to improve efficiency and consistency in a company's business processes. The system begins by collecting information about the company's operations and aggregating it in a central database as needed. The server analyzes key documents such as the company's standard frameworks and regulatory guidelines, and builds a learning model based on this information. This model is then used to monitor business flows in real time, and the AI ​​agent automatically detects deviations from the standard.

[0392] Next, the terminal grasps the user's current work situation and, as needed, provides AI-powered guidance to the user visually or audibly. Based on this guidance, it helps the user make quick and accurate decisions. This standardizes work processes and maintains consistency in operations.

[0393] Furthermore, once a task is performed, the terminal automatically records the results and saves them as audit logs. This data is later analyzed by the server to generate reports for regulatory compliance and necessary improvement guidelines. For example, in the event of a server failure, as soon as the terminal detects the anomaly, the server's AI model refers to past failure data and instructs the user on the optimal solution based on this data. This process enables rapid failure response and leads to long-term improvement in service quality.

[0394] In this way, AI agent systems play a role in promoting standardization and efficiency in corporate business processes and reducing the burden of regulatory compliance that users face.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The server collects information related to business processes from various data sources within the enterprise and integrates it into a central database. This process includes importing and organizing documentation, including standard frameworks and regulatory guidelines.

[0398] Step 2:

[0399] The server analyzes the collected information and trains a generative AI model. This model learns standard patterns in a company's operations and serves as a basis for providing guidance that conforms to those patterns.

[0400] Step 3:

[0401] The terminal monitors the progress of business processes in real time and tracks user input and actions. If a deviation from the normal flow based on the standard framework is detected, the terminal alerts the AI ​​agent.

[0402] Step 4:

[0403] The server's AI agent analyzes the problem that occurred and, while referring to collected historical data, provides the user with the optimal countermeasures and solutions. This information is presented to the user via their device.

[0404] Step 5:

[0405] The user makes business decisions and takes action based on the AI ​​agent's suggestions. The terminal records the results of this action process in detail and sends them to the server as needed.

[0406] Step 6:

[0407] The server receives the results of business operations sent from the terminals and performs analysis. These analysis results are used to improve future business processes and formulate regulatory compliance measures.

[0408] Step 7:

[0409] The server generates audit reports based on the analysis data and prepares them for output when needed. This streamlines the process of understanding regulatory compliance and preparing for audits.

[0410] (Example 1)

[0411] 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."

[0412] Corporate business processes are required to reduce the burden of regulatory compliance while ensuring efficiency and consistency. However, current methods make it difficult to monitor business processes, detect deviations, and provide appropriate guidance in real time, and properly recording and analyzing business results is also cumbersome. To solve this problem, it is necessary to improve operational efficiency using advanced technologies.

[0413] 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.

[0414] In this invention, the server includes means for collecting information related to business processes and aggregating it into information resources; means for analyzing the collected information and training an AI model to generate a learning model that conforms to standard frameworks and regulatory guidelines; and means for monitoring work procedures in real time and detecting deviations from standards using the trained model. This enables efficient and automatic management of business processes, standardized work execution, and a reduction in the burden of regulatory compliance.

[0415] "Information resources" refer to databases and storage systems that centrally manage information related to business processes, enabling analysis and utilization as needed.

[0416] A "standard framework" refers to a set of rules and procedures designed to standardize and efficiently execute a company's business processes.

[0417] "Regulatory guidelines" refer to rules and guidelines for conducting business that are formulated by external organizations such as industry associations or governments.

[0418] A "generative AI model" refers to an artificial intelligence learning model built to analyze large amounts of data and make predictions and decisions tailored to specific purposes.

[0419] "Detecting deviations" refers to identifying actions or results that deviate from established standards or criteria in business processes, and prompting necessary corrective actions.

[0420] "Work procedure" refers to a set of predetermined, sequential steps required to complete a specific task or operation.

[0421] This invention is an AI agent system for improving the efficiency and consistency of business processes. The system is configured as follows:

[0422] The server first collects information related to business processes from both inside and outside the company and aggregates it into information resources. The server uses general database software and cloud-based data storage services to analyze this information. Based on the analyzed information, the server uses a generative AI model to build a learning model that conforms to standard frameworks and regulatory guidelines. This learning model has the capability to monitor business flows in real time and detect deviations from the established standards.

[0423] As a concrete example, the server retrieves data from project management systems and customer relationship management systems and aggregates it in cloud storage. The generative AI model built by the server analyzes this data and constructs an efficient business model using prompts such as, "Learn standard processes to improve the efficiency of the company's operations."

[0424] The terminal provides visual or audio guidance based on information retrieved from the server to offer the user the most optimal form of guidance. The terminal is equipped with a user interface for interaction with the user, and tablet devices and voice assistant devices are often used. For example, if a deviation from a procedure is detected while the user is performing a task, the terminal will immediately display a warning and provide the standard procedure.

[0425] Users make quick and efficient decisions by referring to guidance provided on the terminal during the execution of each task. As a result, business processes are standardized and ensured to be performed efficiently. By performing tasks according to the guidance presented on the terminal, users maintain consistency in their work and reduce the burden of regulatory compliance.

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The server collects information related to business processes from internal and external data sources. Inputs include data from the company's project management system and customer relationship management system. This data is aggregated in cloud storage and prepared for analysis. Specifically, it automatically retrieves data from each system using APIs and stores it in a centralized database.

[0429] Step 2:

[0430] The server performs data analysis using a generative AI model based on the collected data. The input consists of aggregated information stored in cloud storage. This analysis builds a learning model that conforms to standard frameworks and regulatory guidelines. Specifically, the dataset is input into the AI ​​model, and prompts such as "Please learn standard processes to improve the operational efficiency of companies" are used. Based on these prompts, the model learns and optimizes business processes.

[0431] Step 3:

[0432] The server uses a trained model to monitor business processes in real time. The input consists of business data acquired in real time from the company's daily operations. This data is analyzed to detect deviations from the established criteria. Specifically, this involves continuously tracking business data using monitoring tools and comparing it to the criteria predicted by the trained model.

[0433] Step 4:

[0434] The terminal provides visual or audio guidance to the user based on monitoring results. Input is the monitoring results of the business flow sent from the server. Output includes instructions and warnings displayed to the user. Specifically, pop-up messages or voice assistant guidance appear on the user's terminal screen to prompt appropriate action.

[0435] Step 5:

[0436] The terminal automatically records user actions and saves them as audit logs. Input includes operational data obtained via the user interface. This data is stored in storage, providing a foundation for later analysis. Specifically, this involves recording operational data in a database with timestamps and organizing it in a format suitable for later auditing.

[0437] Step 6:

[0438] The server analyzes recorded log data to create reports for regulatory compliance and generate guidelines for business improvement. The input is accumulated operation log data. The output includes reports on regulatory compliance and suggestions for business improvement. Specifically, this involves analyzing log data, using AI models to identify areas for efficiency improvements in current processes, and generating documents to report to management.

[0439] (Application Example 1)

[0440] 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."

[0441] Companies demand improved efficiency and consistency in their business procedures and manufacturing processes. However, in reality, compliance with regulations and the application of standard frameworks are complex, leading to numerous challenges. Furthermore, deviations in productivity and quality in manufacturing sites may not be addressed immediately, potentially leading to a decline in productivity and quality.

[0442] 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.

[0443] In this invention, the server includes means for collecting information on a company's business procedures and aggregating it in a central memory; means for analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidance; and means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing instructions for such deviations visually or audibly. This enables improved operational efficiency for companies and immediate response in the manufacturing process.

[0444] A "company" is a legal entity or organization that engages in profit-making activities and is an organization that carries out various business and production activities.

[0445] "Business procedures" refer to a series of processes or methods that systematically organize the various activities carried out by a company.

[0446] A "central memory device" is a centrally managed data storage system used for managing and storing information.

[0447] A "standard framework" is a guideline that sets out the standards and norms for conducting business and manufacturing activities.

[0448] "Regulatory guidance" refers to guidelines and instructions based on laws and regulations related to business operations and manufacturing processes.

[0449] A "computational model" is a mathematical or statistical model constructed to solve a problem based on data and information.

[0450] "Industrial products" is a concept that refers to products or goods produced in factories or manufacturing facilities.

[0451] A "production process" refers to a series of tasks and processes carried out when manufacturing industrial products.

[0452] "Deviation" refers to actions or states that deviate from standards or frameworks, and can potentially affect quality or efficiency.

[0453] "To provide visually or audibly" means to convey information or instructions to a user by showing them visually or making them hear them audibly.

[0454] This invention is a system that utilizes an AI agent to improve efficiency and consistency in the production of industrial products. The server collects information on a company's business procedures using data input devices such as sensors and cameras, and aggregates it in a central memory using cloud services such as AWS. After aggregation, the server learns a computational model based on standard frameworks and regulatory guidance, performs data analysis on machine learning platforms such as Azure ML and Google Cloud AI, and builds the model. Using this learned computational model, the server monitors the production process of industrial products in real time and detects deviations in productivity and quality.

[0455] The terminal receives this information and enables immediate response by providing workers with appropriate instructions visually or audibly through smart glasses or other visual information transmission devices. For example, if the position of a part found to be out of the standard framework on a production line, the terminal visually displays and audibly guides the worker with instructions such as "Adjust the position of the part." This process allows workers to make quick and accurate corrections, thus maintaining manufacturing quality.

[0456] One concrete example used here is to input a prompt message to the generative AI model saying, "If the placement of parts is incorrect, please instruct me on how to correct it." This allows the generative AI model to provide appropriate instructions.

[0457] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0458] Step 1:

[0459] The server collects operational data from sensors and cameras within the factory and aggregates it into a central storage device via a cloud platform such as AWS. In this step, real-time data from each work station on the production line is used as input, and a processed dataset is output.

[0460] Step 2:

[0461] The server uses aggregated data and leverages Azure ML and Google Cloud AI to generate computational models that comply with standard frameworks and regulatory guidelines. The input is the dataset obtained in the previous step, and by analyzing and learning from this, it outputs a highly accurate computational model.

[0462] Step 3:

[0463] The server uses the generated computational model to monitor the production process of industrial products in real time. In this process, the current production status is compared with the model, and deviations are detected. The input is real-time data newly obtained from sensors, and this data is used to determine whether or not there is a deviation, and the detection information is output.

[0464] Step 4:

[0465] The terminal receives information about deviations and provides visual or audible instructions to the worker using smart glasses or other visual information devices. The input is deviation information from the server, and based on this, it outputs specific instructions. The worker adjusts their work based on these instructions.

[0466] Step 5:

[0467] The user follows the instructions provided by the terminal and makes the necessary corrections. The input in this step is the instruction itself to the user, and the output is the maintenance of production quality, achieved when the user performs the correct correction operations correctly.

[0468] 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.

[0469] This invention is a system that efficiently manages a company's business processes and improves the quality of work by recognizing user emotions. Basically, it collects business information from the company, aggregates it in a central database, performs analysis, and learns a model that conforms to a standard framework. The server uses this model to monitor in real time whether business processes are progressing according to standards and provides appropriate guidance to users.

[0470] A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses the emotion engine to analyze the user's emotional state based on the user's voice, input data, or information obtained from sensors. Based on this analysis, the server can adjust the guidance provided to the user to provide more effective support. For example, if the user is feeling stressed, the system can adjust the workload or reduce the frequency of notifications.

[0471] One possible scenario is when the emotional engine detects a high stress level while a user is handling an urgent task. In this case, the terminal immediately sends the data to the server, which then adjusts the guidance based on that information to reduce the user's mental burden. This user emotional data is recorded along with the results of the task and used to improve future operations and enhance support measures.

[0472] In this way, by standardizing corporate workflows and combining them with user support powered by an emotional engine, higher quality business operations become possible. This system contributes to automating regulatory compliance and improving user satisfaction.

[0473] The following describes the processing flow.

[0474] Step 1:

[0475] The server collects data related to the company's business processes from various data sources and imports it into a central database. This includes standard frameworks, regulatory guidelines, and historical business execution data.

[0476] Step 2:

[0477] The server analyzes the aggregated data and trains a generative AI model. This model is then used to learn standard patterns in business operations and prepare to provide appropriate guidance.

[0478] Step 3:

[0479] The terminal monitors the user's actions and input data in real time during work. It also uses a built-in emotion engine to analyze the user's emotional state based on their voice tone, facial expressions, and input speed.

[0480] Step 4:

[0481] The server receives analysis data from the emotion engine and determines guidance content based on the user's psychological state. For example, if the server detects that the user is stressed, it generates instructions to adjust the priority and method of tasks.

[0482] Step 5:

[0483] The device presents the user with determined guidance. This includes notifications via a visual interface and audio feedback. The user can then make decisions based on this information.

[0484] Step 6:

[0485] The user follows the guidance and executes the business process. The terminal records the actions performed and their results.

[0486] Step 7:

[0487] The server analyzes the work execution results and sentiment data sent from the terminal to evaluate work efficiency and the effectiveness of user support. This data will be used to plan future process improvements and regulatory compliance measures.

[0488] Step 8:

[0489] The server generates audit reports based on these analysis results, which are used as needed for operational guidelines and regulatory compliance. This process improves the quality and efficiency of operations.

[0490] (Example 2)

[0491] 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."

[0492] In corporate business operations, there is a need to provide accurate business support that takes into account the emotional state of users while maintaining efficiency and consistency in operations. However, there is no system that combines real-time monitoring of work progress with flexible responses based on users' emotions, making it difficult to simultaneously achieve improved work quality and user satisfaction.

[0493] 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.

[0494] In this invention, the server includes means for collecting information related to business activities and aggregating it in an information management device, means for analyzing the user's emotional state through voice information and biosensors, and means for optimizing the workload based on the user's emotional state. This enables flexible work support that responds to the user's emotions while maintaining standardization of operations in real time.

[0495] "Business activities" refer to a series of tasks and processes that a company or organization undertakes to achieve its objectives.

[0496] An "information management device" refers to a computer system used to collect, record, and analyze data related to business activities.

[0497] "Standard structure" refers to the criteria and frameworks established to ensure that business activities maintain a certain level of quality and efficiency.

[0498] "Regulatory guidelines" refer to specific instructions and standards to ensure that business operations comply with relevant laws and industry standards.

[0499] A "model" refers to a mathematical or computer science structure built on collected data to help improve the progress and efficiency of business activities.

[0500] "Users" refers to individuals who carry out business activities or operate systems.

[0501] "Voice information" refers to data obtained through the user's voice and is used for sentiment analysis in business activities.

[0502] A "biosensor" is a device that acquires biometric information such as a user's heart rate and facial expressions, and is used for emotion analysis.

[0503] "Emotional state" refers to data about the user's current psychological reactions and emotions.

[0504] "Workload" refers to the quantity and complexity of tasks required of users.

[0505] This invention is a system that efficiently manages a company's business activities and provides effective support based on the emotional state of users. The server first collects information on business activities from each business department within the company and aggregates it in an information management device. This aggregated information includes data such as the progress, schedule, and achievement level of business activities.

[0506] Next, the server uses this data to train a model that conforms to standard structures and regulatory guidelines. Generative AI models are used to train the model, building a structure that maximizes operational efficiency.

[0507] Meanwhile, the user's device analyzes the user's emotional state through voice information and biosensors. Voice recognition software and sensors (e.g., microphone, camera, heart rate sensor) are used. The emotional analysis data provided by the device is sent to a server and used to tailor individual work guidance to the user.

[0508] One concrete example of this system's application is in a support center. When a user is handling customer complaints, the terminal detects high stress levels through facial recognition and voice analysis. The server can then immediately use this information to adjust the workload, provide relaxation techniques, and issue other instructions to reduce the user's stress.

[0509] As an example of a prompt to the generating AI model, by inputting "Generate a specific plan on how to adjust the workload if the user is experiencing significant stress," the AI ​​model can propose an appropriate workload adjustment plan.

[0510] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0511] Step 1:

[0512] The server collects information about business activities from various departments within the company and aggregates it into an information management system. Specifically, this includes progress reports, schedules, and performance data entered by employees. This input data is aggregated into a database and converted into a format used in subsequent analysis processes.

[0513] Step 2:

[0514] The server analyzes aggregated business data and uses a generated AI model to train a model aimed at improving business efficiency. Specifically, it identifies data patterns based on standard structures and regulatory guidelines and processes the data accordingly. This analyzed data is reflected in the trained model and used to optimize business processes.

[0515] Step 3:

[0516] The server uses a model to monitor business activities in real time and provides appropriate guidance to users as needed. Inputs include continuously updated business data and the model's analysis results. Outputs are specific feedback regarding improvements to business processes and workload adjustments.

[0517] Step 4:

[0518] The device analyzes the user's emotional state using voice information and data obtained from biosensors. Specifically, voice recognition software analyzes the user's voice and feeds it into an emotion engine to identify the emotional state. The input is the user's real-time voice data and sensor data, and the output is the emotional evaluation information resulting from the analysis.

[0519] Step 5:

[0520] The server adjusts work instructions for users and optimizes workload based on the sentiment analysis results. Based on the sentiment evaluation information, the AI ​​model dynamically modifies the workload and instruction content, and presents users with concrete suggestions for stress reduction and increased efficiency.

[0521] Step 6:

[0522] The server records all business execution results and user sentiment data, and generates documentation to support future audits and regulatory compliance. This process involves analyzing and documenting information stored in the database. Specifically, the generated documentation can also be used for future business improvements.

[0523] (Application Example 2)

[0524] 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."

[0525] In modern business operations, maintaining efficient work processes and user mental health are crucial challenges. However, excessive pursuit of efficiency in workflows can increase the mental burden on users, ultimately leading to a decline in work quality. Therefore, there is a need for a system that can recognize and appropriately adjust user emotions, along with real-time management of business processes.

[0526] 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.

[0527] In this invention, the server includes means for aggregating information related to the higher-level structure of a company and compiling it into a central information group; means for utilizing artificial intelligence to recognize the user's emotional state and adjust guidance based on that state to provide highly efficient work support; and means for reducing the mental burden by adjusting the amount of tasks when the user feels stressed. This enables efficient management of business processes as well as flexible work adjustments based on the user's emotional state.

[0528] "Corporate higher-order structure" refers to the entire collection of strategic and operational processes within an organization and their associated data.

[0529] A "central information set" refers to the main datasets required in business activities, collected for the purpose of efficiently managing each business process.

[0530] Artificial intelligence is an information processing system that can mimic human intellectual activity and make various decisions while learning independently.

[0531] "Emotional state" refers to the psychological situation or mood expressed by the user, and recognizing this allows for adjustments to work efficiency.

[0532] "Guidance" or "directions" refers to instructions or advice provided to a user to effectively carry out a business process.

[0533] "Highly efficient work support" refers to a series of support measures aimed at enabling tasks to be performed quickly and effectively.

[0534] "Mental load" refers to the degree of mental and cognitive burden and stress that users experience when performing their work.

[0535] A description of embodiments for carrying out this invention will be given.

[0536] This system achieves integrated process management that combines efficient management of business activities with user emotion recognition. The server aggregates information related to the higher-level structure of the company, forming a central data set. This information includes business data, performance metrics, and user feedback. A Python program processes this data using the Google Speech-to-Text API, converting user speech data into text data.

[0537] The artificial intelligence runs on TensorFlow and uses an emotion recognition model to determine the user's emotional state. To achieve this, it integrates not only voice data but also sensor data to analyze the emotional state from multiple perspectives. Based on the user's emotional state, the server dynamically generates guidance and adjusts the workflow. For example, if the user says "I'm tired," the system evaluates stress indicators and modifies the guidance to reduce the priority of tasks.

[0538] The guidance provided to the user will be implemented visually or audibly to enhance the user experience. For example, if a user expresses stress upon returning home, the robot might announce, "I will play music to help you relax," and adjust the ambient lighting.

[0539] An example of a prompt using a generative AI model is, "Consider the user's emotional state and generate suggestions for adjusting the workflow." This prompt allows the system to optimize the work structure in accordance with the user's emotions.

[0540] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0541] Step 1:

[0542] The device uses a microphone and sensors to collect user voice and contextual data. The collected voice data is converted into text data using the Google Speech-to-Text API. In this process, the input is voice data and the output is text data.

[0543] Step 2:

[0544] The terminal centralizes text data and environmental data from sensors and sends it to the server. The server prepares this data as input for an emotion recognition model. It processes the data through integration and transformation to prepare it in a format that the model can process. The input is a combination of text and environmental data, and the output is in a model-compatible data format.

[0545] Step 3:

[0546] The server uses an emotion recognition model built on TensorFlow to analyze the user's emotional state from the input data. The analysis results are output as an index indicating whether the user is in a different emotional state, such as anger, joy, or stress. The input for this step is formatted data, and the output is an evaluation of the emotional state.

[0547] Step 4:

[0548] The server generates appropriate guidance for the user based on the results of the emotional state assessment. The specific guidance content is automatically generated using a generation AI model based on the prompt text. The input is the emotional state assessment result, and the output is the generated guidance message.

[0549] Step 5:

[0550] The terminal presents the generated guidance message to the user visually or audibly. Through the user interface, the guidance is presented as action instructions, facilitating adjustments to the user's work environment and tasks. The input is the guidance message, and the output is a specific action suggestion for the user.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] [Fourth Embodiment]

[0555] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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".

[0568] This invention is an AI agent system designed to improve efficiency and consistency in a company's business processes. The system begins by collecting information about the company's operations and aggregating it in a central database as needed. The server analyzes key documents such as the company's standard frameworks and regulatory guidelines, and builds a learning model based on this information. This model is then used to monitor business flows in real time, and the AI ​​agent automatically detects deviations from the standard.

[0569] Next, the terminal grasps the user's current work situation and, as needed, provides AI-powered guidance to the user visually or audibly. Based on this guidance, it helps the user make quick and accurate decisions. This standardizes work processes and maintains consistency in operations.

[0570] Furthermore, once a task is performed, the terminal automatically records the results and saves them as audit logs. This data is later analyzed by the server to generate reports for regulatory compliance and necessary improvement guidelines. For example, in the event of a server failure, as soon as the terminal detects the anomaly, the server's AI model refers to past failure data and instructs the user on the optimal solution based on this data. This process enables rapid failure response and leads to long-term improvement in service quality.

[0571] In this way, AI agent systems play a role in promoting standardization and efficiency in corporate business processes and reducing the burden of regulatory compliance that users face.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The server collects information related to business processes from various data sources within the enterprise and integrates it into a central database. This process includes importing and organizing documentation, including standard frameworks and regulatory guidelines.

[0575] Step 2:

[0576] The server analyzes the collected information and trains a generative AI model. This model learns standard patterns in a company's operations and serves as a basis for providing guidance that conforms to those patterns.

[0577] Step 3:

[0578] The terminal monitors the progress of business processes in real time and tracks user input and actions. If a deviation from the normal flow based on the standard framework is detected, the terminal alerts the AI ​​agent.

[0579] Step 4:

[0580] The server's AI agent analyzes the problem that occurred and, while referring to collected historical data, provides the user with the optimal countermeasures and solutions. This information is presented to the user via their device.

[0581] Step 5:

[0582] The user makes business decisions and takes action based on the AI ​​agent's suggestions. The terminal records the results of this action process in detail and sends them to the server as needed.

[0583] Step 6:

[0584] The server receives the results of business operations sent from the terminals and performs analysis. These analysis results are used to improve future business processes and formulate regulatory compliance measures.

[0585] Step 7:

[0586] The server generates audit reports based on the analysis data and prepares them for output when needed. This streamlines the process of understanding regulatory compliance and preparing for audits.

[0587] (Example 1)

[0588] 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".

[0589] Corporate business processes are required to reduce the burden of regulatory compliance while ensuring efficiency and consistency. However, current methods make it difficult to monitor business processes, detect deviations, and provide appropriate guidance in real time, and properly recording and analyzing business results is also cumbersome. To solve this problem, it is necessary to improve operational efficiency using advanced technologies.

[0590] 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.

[0591] In this invention, the server includes means for collecting information related to business processes and aggregating it into information resources; means for analyzing the collected information and training an AI model to generate a learning model that conforms to standard frameworks and regulatory guidelines; and means for monitoring work procedures in real time and detecting deviations from standards using the trained model. This enables efficient and automatic management of business processes, standardized work execution, and a reduction in the burden of regulatory compliance.

[0592] "Information resources" refer to databases and storage systems that centrally manage information related to business processes, enabling analysis and utilization as needed.

[0593] A "standard framework" refers to a set of rules and procedures designed to standardize and efficiently execute a company's business processes.

[0594] "Regulatory guidelines" refer to rules and guidelines for conducting business that are formulated by external organizations such as industry associations or governments.

[0595] A "generative AI model" refers to an artificial intelligence learning model built to analyze large amounts of data and make predictions and decisions tailored to specific purposes.

[0596] "Detecting deviations" refers to identifying actions or results that deviate from established standards or criteria in business processes, and prompting necessary corrective actions.

[0597] "Work procedure" refers to a set of predetermined, sequential steps required to complete a specific task or operation.

[0598] This invention is an AI agent system for improving the efficiency and consistency of business processes. The system is configured as follows:

[0599] The server first collects information related to business processes from both inside and outside the company and aggregates it into information resources. The server uses general database software and cloud-based data storage services to analyze this information. Based on the analyzed information, the server uses a generative AI model to build a learning model that conforms to standard frameworks and regulatory guidelines. This learning model has the capability to monitor business flows in real time and detect deviations from the established standards.

[0600] As a concrete example, the server retrieves data from project management systems and customer relationship management systems and aggregates it in cloud storage. The generative AI model built by the server analyzes this data and constructs an efficient business model using prompts such as, "Learn standard processes to improve the efficiency of the company's operations."

[0601] The terminal provides visual or audio guidance based on information retrieved from the server to offer the user the most optimal form of guidance. The terminal is equipped with a user interface for interaction with the user, and tablet devices and voice assistant devices are often used. For example, if a deviation from a procedure is detected while the user is performing a task, the terminal will immediately display a warning and provide the standard procedure.

[0602] Users make quick and efficient decisions by referring to guidance provided on the terminal during the execution of each task. As a result, business processes are standardized and ensured to be performed efficiently. By performing tasks according to the guidance presented on the terminal, users maintain consistency in their work and reduce the burden of regulatory compliance.

[0603] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0604] Step 1:

[0605] The server collects information related to business processes from internal and external data sources. Inputs include data from the company's project management system and customer relationship management system. This data is aggregated in cloud storage and prepared for analysis. Specifically, it automatically retrieves data from each system using APIs and stores it in a centralized database.

[0606] Step 2:

[0607] The server performs data analysis using a generative AI model based on the collected data. The input consists of aggregated information stored in cloud storage. This analysis builds a learning model that conforms to standard frameworks and regulatory guidelines. Specifically, the dataset is input into the AI ​​model, and prompts such as "Please learn standard processes to improve the operational efficiency of companies" are used. Based on these prompts, the model learns and optimizes business processes.

[0608] Step 3:

[0609] The server uses a trained model to monitor business processes in real time. The input consists of business data acquired in real time from the company's daily operations. This data is analyzed to detect deviations from the established criteria. Specifically, this involves continuously tracking business data using monitoring tools and comparing it to the criteria predicted by the trained model.

[0610] Step 4:

[0611] The terminal provides visual or audio guidance to the user based on monitoring results. Input is the monitoring results of the business flow sent from the server. Output includes instructions and warnings displayed to the user. Specifically, pop-up messages or voice assistant guidance appear on the user's terminal screen to prompt appropriate action.

[0612] Step 5:

[0613] The terminal automatically records user actions and saves them as audit logs. Input includes operational data obtained via the user interface. This data is stored in storage, providing a foundation for later analysis. Specifically, this involves recording operational data in a database with timestamps and organizing it in a format suitable for later auditing.

[0614] Step 6:

[0615] The server analyzes recorded log data to create reports for regulatory compliance and generate guidelines for business improvement. The input is accumulated operation log data. The output includes reports on regulatory compliance and suggestions for business improvement. Specifically, this involves analyzing log data, using AI models to identify areas for efficiency improvements in current processes, and generating documents to report to management.

[0616] (Application Example 1)

[0617] 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".

[0618] Companies demand improved efficiency and consistency in their business procedures and manufacturing processes. However, in reality, compliance with regulations and the application of standard frameworks are complex, leading to numerous challenges. Furthermore, deviations in productivity and quality in manufacturing sites may not be addressed immediately, potentially leading to a decline in productivity and quality.

[0619] 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.

[0620] In this invention, the server includes means for collecting information on a company's business procedures and aggregating it in a central memory; means for analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidance; and means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing instructions for such deviations visually or audibly. This enables improved operational efficiency for companies and immediate response in the manufacturing process.

[0621] A "company" is a legal entity or organization that engages in profit-making activities and is an organization that carries out various business and production activities.

[0622] "Business procedures" refer to a series of processes or methods that systematically organize the various activities carried out by a company.

[0623] A "central memory device" is a centrally managed data storage system used for managing and storing information.

[0624] A "standard framework" is a guideline that sets out the standards and norms for conducting business and manufacturing activities.

[0625] "Regulatory guidance" refers to guidelines and instructions based on laws and regulations related to business operations and manufacturing processes.

[0626] A "computational model" is a mathematical or statistical model constructed to solve a problem based on data and information.

[0627] "Industrial products" is a concept that refers to products or goods produced in factories or manufacturing facilities.

[0628] A "production process" refers to a series of tasks and processes carried out when manufacturing industrial products.

[0629] "Deviation" refers to actions or states that deviate from standards or frameworks, and can potentially affect quality or efficiency.

[0630] "To provide visually or audibly" means to convey information or instructions to a user by showing them visually or making them hear them audibly.

[0631] This invention is a system that utilizes an AI agent to improve efficiency and consistency in the production of industrial products. The server collects information on a company's business procedures using data input devices such as sensors and cameras, and aggregates it in a central memory using cloud services such as AWS. After aggregation, the server learns a computational model based on standard frameworks and regulatory guidance, performs data analysis on machine learning platforms such as Azure ML and Google Cloud AI, and builds the model. Using this learned computational model, the server monitors the production process of industrial products in real time and detects deviations in productivity and quality.

[0632] The terminal receives this information and enables immediate response by providing workers with appropriate instructions visually or audibly through smart glasses or other visual information transmission devices. For example, if the position of a part found to be out of the standard framework on a production line, the terminal visually displays and audibly guides the worker with instructions such as "Adjust the position of the part." This process allows workers to make quick and accurate corrections, thus maintaining manufacturing quality.

[0633] One concrete example used here is to input a prompt message to the generative AI model saying, "If the placement of parts is incorrect, please instruct me on how to correct it." This allows the generative AI model to provide appropriate instructions.

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The server collects operational data from sensors and cameras within the factory and aggregates it into a central storage device via a cloud platform such as AWS. In this step, real-time data from each work station on the production line is used as input, and a processed dataset is output.

[0637] Step 2:

[0638] The server uses aggregated data and leverages Azure ML and Google Cloud AI to generate computational models that comply with standard frameworks and regulatory guidelines. The input is the dataset obtained in the previous step, and by analyzing and learning from this, it outputs a highly accurate computational model.

[0639] Step 3:

[0640] The server uses the generated computational model to monitor the production process of industrial products in real time. In this process, the current production status is compared with the model, and deviations are detected. The input is real-time data newly obtained from sensors, and this data is used to determine whether or not there is a deviation, and the detection information is output.

[0641] Step 4:

[0642] The terminal receives information about deviations and provides visual or audible instructions to the worker using smart glasses or other visual information devices. The input is deviation information from the server, and based on this, it outputs specific instructions. The worker adjusts their work based on these instructions.

[0643] Step 5:

[0644] The user follows the instructions provided by the terminal and makes the necessary corrections. The input in this step is the instruction itself to the user, and the output is the maintenance of production quality, achieved when the user performs the correct correction operations correctly.

[0645] 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.

[0646] This invention is a system that efficiently manages a company's business processes and improves the quality of work by recognizing user emotions. Basically, it collects business information from the company, aggregates it in a central database, performs analysis, and learns a model that conforms to a standard framework. The server uses this model to monitor in real time whether business processes are progressing according to standards and provides appropriate guidance to users.

[0647] A distinctive feature of this invention is the incorporation of an emotion engine. The terminal uses the emotion engine to analyze the user's emotional state based on the user's voice, input data, or information obtained from sensors. Based on this analysis, the server can adjust the guidance provided to the user to provide more effective support. For example, if the user is feeling stressed, the system can adjust the workload or reduce the frequency of notifications.

[0648] One possible scenario is when the emotional engine detects a high stress level while a user is handling an urgent task. In this case, the terminal immediately sends the data to the server, which then adjusts the guidance based on that information to reduce the user's mental burden. This user emotional data is recorded along with the results of the task and used to improve future operations and enhance support measures.

[0649] In this way, by standardizing corporate workflows and combining them with user support powered by an emotional engine, higher quality business operations become possible. This system contributes to automating regulatory compliance and improving user satisfaction.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The server collects data related to the company's business processes from various data sources and imports it into a central database. This includes standard frameworks, regulatory guidelines, and historical business execution data.

[0653] Step 2:

[0654] The server analyzes the aggregated data and trains a generative AI model. This model is then used to learn standard patterns in business operations and prepare to provide appropriate guidance.

[0655] Step 3:

[0656] The terminal monitors the user's actions and input data in real time during work. It also uses a built-in emotion engine to analyze the user's emotional state based on their voice tone, facial expressions, and input speed.

[0657] Step 4:

[0658] The server receives analysis data from the emotion engine and determines guidance content based on the user's psychological state. For example, if the server detects that the user is stressed, it generates instructions to adjust the priority and method of tasks.

[0659] Step 5:

[0660] The device presents the user with determined guidance. This includes notifications via a visual interface and audio feedback. The user can then make decisions based on this information.

[0661] Step 6:

[0662] The user follows the guidance and executes the business process. The terminal records the actions performed and their results.

[0663] Step 7:

[0664] The server analyzes the work execution results and sentiment data sent from the terminal to evaluate work efficiency and the effectiveness of user support. This data will be used to plan future process improvements and regulatory compliance measures.

[0665] Step 8:

[0666] The server generates audit reports based on these analysis results, which are used as needed for operational guidelines and regulatory compliance. This process improves the quality and efficiency of operations.

[0667] (Example 2)

[0668] 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".

[0669] In corporate business operations, there is a need to provide accurate business support that takes into account the emotional state of users while maintaining efficiency and consistency in operations. However, there is no system that combines real-time monitoring of work progress with flexible responses based on users' emotions, making it difficult to simultaneously achieve improved work quality and user satisfaction.

[0670] 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.

[0671] In this invention, the server includes means for collecting information related to business activities and aggregating it in an information management device, means for analyzing the user's emotional state through voice information and biosensors, and means for optimizing the workload based on the user's emotional state. This enables flexible work support that responds to the user's emotions while maintaining standardization of operations in real time.

[0672] "Business activities" refer to a series of tasks and processes that a company or organization undertakes to achieve its objectives.

[0673] An "information management device" refers to a computer system used to collect, record, and analyze data related to business activities.

[0674] "Standard structure" refers to the criteria and frameworks established to ensure that business activities maintain a certain level of quality and efficiency.

[0675] "Regulatory guidelines" refer to specific instructions and standards to ensure that business operations comply with relevant laws and industry standards.

[0676] A "model" refers to a mathematical or computer science structure built on collected data to help improve the progress and efficiency of business activities.

[0677] "Users" refers to individuals who carry out business activities or operate systems.

[0678] "Voice information" refers to data obtained through the user's voice and is used for sentiment analysis in business activities.

[0679] A "biosensor" is a device that acquires biometric information such as a user's heart rate and facial expressions, and is used for emotion analysis.

[0680] "Emotional state" refers to data about the user's current psychological reactions and emotions.

[0681] "Workload" refers to the quantity and complexity of tasks required of users.

[0682] This invention is a system that efficiently manages a company's business activities and provides effective support based on the emotional state of users. The server first collects information on business activities from each business department within the company and aggregates it in an information management device. This aggregated information includes data such as the progress, schedule, and achievement level of business activities.

[0683] Next, the server uses this data to train a model that conforms to standard structures and regulatory guidelines. Generative AI models are used to train the model, building a structure that maximizes operational efficiency.

[0684] Meanwhile, the user's device analyzes the user's emotional state through voice information and biosensors. Voice recognition software and sensors (e.g., microphone, camera, heart rate sensor) are used. The emotional analysis data provided by the device is sent to a server and used to tailor individual work guidance to the user.

[0685] One concrete example of this system's application is in a support center. When a user is handling customer complaints, the terminal detects high stress levels through facial recognition and voice analysis. The server can then immediately use this information to adjust the workload, provide relaxation techniques, and issue other instructions to reduce the user's stress.

[0686] As an example of a prompt to the generating AI model, by inputting "Generate a specific plan on how to adjust the workload if the user is experiencing significant stress," the AI ​​model can propose an appropriate workload adjustment plan.

[0687] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0688] Step 1:

[0689] The server collects information about business activities from various departments within the company and aggregates it into an information management system. Specifically, this includes progress reports, schedules, and performance data entered by employees. This input data is aggregated into a database and converted into a format used in subsequent analysis processes.

[0690] Step 2:

[0691] The server analyzes aggregated business data and uses a generated AI model to train a model aimed at improving business efficiency. Specifically, it identifies data patterns based on standard structures and regulatory guidelines and processes the data accordingly. This analyzed data is reflected in the trained model and used to optimize business processes.

[0692] Step 3:

[0693] The server uses a model to monitor business activities in real time and provides appropriate guidance to users as needed. Inputs include continuously updated business data and the model's analysis results. Outputs are specific feedback regarding improvements to business processes and workload adjustments.

[0694] Step 4:

[0695] The device analyzes the user's emotional state using voice information and data obtained from biosensors. Specifically, voice recognition software analyzes the user's voice and feeds it into an emotion engine to identify the emotional state. The input is the user's real-time voice data and sensor data, and the output is the emotional evaluation information resulting from the analysis.

[0696] Step 5:

[0697] The server adjusts work instructions for users and optimizes workload based on the sentiment analysis results. Based on the sentiment evaluation information, the AI ​​model dynamically modifies the workload and instruction content, and presents users with concrete suggestions for stress reduction and increased efficiency.

[0698] Step 6:

[0699] The server records all business execution results and user sentiment data, and generates documentation to support future audits and regulatory compliance. This process involves analyzing and documenting information stored in the database. Specifically, the generated documentation can also be used for future business improvements.

[0700] (Application Example 2)

[0701] 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".

[0702] In modern business operations, maintaining efficient work processes and user mental health are crucial challenges. However, excessive pursuit of efficiency in workflows can increase the mental burden on users, ultimately leading to a decline in work quality. Therefore, there is a need for a system that can recognize and appropriately adjust user emotions, along with real-time management of business processes.

[0703] 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.

[0704] In this invention, the server includes means for aggregating information related to the higher-level structure of a company and compiling it into a central information group; means for utilizing artificial intelligence to recognize the user's emotional state and adjust guidance based on that state to provide highly efficient work support; and means for reducing the mental burden by adjusting the amount of tasks when the user feels stressed. This enables efficient management of business processes as well as flexible work adjustments based on the user's emotional state.

[0705] "Corporate higher-order structure" refers to the entire collection of strategic and operational processes within an organization and their associated data.

[0706] A "central information set" refers to the main datasets required in business activities, collected for the purpose of efficiently managing each business process.

[0707] Artificial intelligence is an information processing system that can mimic human intellectual activity and make various decisions while learning independently.

[0708] "Emotional state" refers to the psychological situation or mood expressed by the user, and recognizing this allows for adjustments to work efficiency.

[0709] "Guidance" or "directions" refers to instructions or advice provided to a user to effectively carry out a business process.

[0710] "Highly efficient work support" refers to a series of support measures aimed at enabling tasks to be performed quickly and effectively.

[0711] "Mental load" refers to the degree of mental and cognitive burden and stress that users experience when performing their work.

[0712] A description of embodiments for carrying out this invention will be given.

[0713] This system achieves integrated process management that combines efficient management of business activities with user emotion recognition. The server aggregates information related to the higher-level structure of the company, forming a central data set. This information includes business data, performance metrics, and user feedback. A Python program processes this data using the Google Speech-to-Text API, converting user speech data into text data.

[0714] The artificial intelligence runs on TensorFlow and uses an emotion recognition model to determine the user's emotional state. To achieve this, it integrates not only voice data but also sensor data to analyze the emotional state from multiple perspectives. Based on the user's emotional state, the server dynamically generates guidance and adjusts the workflow. For example, if the user says "I'm tired," the system evaluates stress indicators and modifies the guidance to reduce the priority of tasks.

[0715] The guidance provided to the user will be implemented visually or audibly to enhance the user experience. For example, if a user expresses stress upon returning home, the robot might announce, "I will play music to help you relax," and adjust the ambient lighting.

[0716] An example of a prompt using a generative AI model is, "Consider the user's emotional state and generate suggestions for adjusting the workflow." This prompt allows the system to optimize the work structure in accordance with the user's emotions.

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The device uses a microphone and sensors to collect user voice and contextual data. The collected voice data is converted into text data using the Google Speech-to-Text API. In this process, the input is voice data and the output is text data.

[0720] Step 2:

[0721] The terminal centralizes text data and environmental data from sensors and sends it to the server. The server prepares this data as input for an emotion recognition model. It processes the data through integration and transformation to prepare it in a format that the model can process. The input is a combination of text and environmental data, and the output is in a model-compatible data format.

[0722] Step 3:

[0723] The server uses an emotion recognition model built on TensorFlow to analyze the user's emotional state from the input data. The analysis results are output as an index indicating whether the user is in a different emotional state, such as anger, joy, or stress. The input for this step is formatted data, and the output is an evaluation of the emotional state.

[0724] Step 4:

[0725] The server generates appropriate guidance for the user based on the results of the emotional state assessment. The specific guidance content is automatically generated using a generation AI model based on the prompt text. The input is the emotional state assessment result, and the output is the generated guidance message.

[0726] Step 5:

[0727] The terminal presents the generated guidance message to the user visually or audibly. Through the user interface, the guidance is presented as action instructions, facilitating adjustments to the user's work environment and tasks. The input is the guidance message, and the output is a specific action suggestion for the user.

[0728] 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.

[0729] 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.

[0730] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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."

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] The following is further disclosed regarding the embodiments described above.

[0750] (Claim 1)

[0751] A means of collecting information on a company's business processes and aggregating it in a central database,

[0752] A means of analyzing the collected information and training a model that conforms to standard frameworks and regulatory guidelines,

[0753] A means of monitoring business processes in real time using a trained model and providing appropriate guidance,

[0754] A means to support user decision-making and improve the consistency and efficiency of business processes,

[0755] A means of recording the results of business operations and generating documents to respond to audits and regulatory compliance,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, comprising means for improving processes and updating guidelines based on the results of data analysis and model learning.

[0759] (Claim 3)

[0760] The system according to claim 1, comprising means for providing visual or auditory guidance to the user.

[0761] "Example 1"

[0762] (Claim 1)

[0763] A means of collecting information related to business processes and consolidating it into information resources,

[0764] A means of analyzing collected information and generating a learning model that conforms to standard frameworks and regulatory guidelines using an AI model,

[0765] A means for monitoring work procedures in real time using a trained model and detecting deviations from standards,

[0766] A means of understanding the user's operation status and providing appropriate visual or audible guidance as needed,

[0767] A means of recording the results of work execution and automatically generating audit logs,

[0768] A means of analyzing recorded information and generating regulatory compliance reports and guidelines for work improvement,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising means for improving processes and updating guidelines based on the results of data analysis and model learning.

[0772] (Claim 3)

[0773] The system according to claim 1, comprising means for providing visual or auditory guidance to the user to support rapid decision-making.

[0774] "Application Example 1"

[0775] (Claim 1)

[0776] A means of collecting information on a company's business procedures and consolidating it in a central memory device,

[0777] A means of analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidelines,

[0778] A means of monitoring business procedures in real time and automatically providing appropriate instructions using a learned computational model,

[0779] A means to support user decision-making and improve the consistency and efficiency of business procedures,

[0780] A means for recording the results of work execution and generating record documents for inspection and regulatory compliance,

[0781] A means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing visual or audible instructions regarding such deviations,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, comprising means for improving the process and updating guidance based on the results of data analysis and computational model learning.

[0785] (Claim 3)

[0786] The system according to claim 1, comprising means for providing visual or auditory instructions to a user.

[0787] "Example 2 of combining an emotion engine"

[0788] (Claim 1)

[0789] A means of collecting information related to business activities and consolidating it in an information management device,

[0790] A means of analyzing the collected information and learning a model that conforms to standard structures and regulatory guidelines,

[0791] A means of using trained models to monitor work activities in real time and provide appropriate guidance,

[0792] A means to support user decision-making and improve the consistency and efficiency of business activities,

[0793] A means of analyzing the user's emotional state through voice information and biosensors,

[0794] A means of adjusting instruction and optimizing workload based on the emotional state of the user,

[0795] A means of recording business execution results and user sentiment data, and generating materials for audits and regulatory compliance,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, comprising means for improving activities and updating guidelines based on the results of data analysis and model learning.

[0799] (Claim 3)

[0800] The system according to claim 1, comprising means for providing visual or auditory instruction to a user.

[0801] "Application example 2 when combining with an emotional engine"

[0802] (Claim 1)

[0803] A means of aggregating information on the higher-level structure of a company and compiling it into a central information set,

[0804] A means of analyzing aggregated information and learning a model that conforms to standard frameworks and regulatory guidelines,

[0805] A means of using a trained model to continuously monitor the workflow and provide appropriate guidance,

[0806] A means of providing highly efficient work support by utilizing artificial intelligence to recognize the emotional state of the user, adjust guidance based on that state, and

[0807] When a user experiences stress, measures are taken to reduce their mental burden, such as adjusting the amount of tasks they have to complete.

[0808] A means of recording the results of business operations and generating documents to respond to audits and regulatory compliance,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, comprising means for improving processes and updating guidance based on the results of data analysis and model learning.

[0812] (Claim 3)

[0813] The system according to claim 1, comprising means for providing visual or auditory guidance to the user, and means for adjusting environmental settings and audio output to match the user's psychological state when a stressful state is detected. [Explanation of Symbols]

[0814] 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 information on a company's business procedures and consolidating it in a central memory device, A means of analyzing the collected information and learning a computational model that conforms to standard frameworks and regulatory guidelines, A means of monitoring business procedures in real time and automatically providing appropriate instructions using a learned computational model, A means to support user decision-making and improve the consistency and efficiency of business procedures, A means for recording the results of work execution and generating record documents for inspection and regulatory compliance, A means for monitoring the production process of industrial products, automatically detecting deviations in productivity and quality, and providing visual or audible instructions regarding such deviations, A system that includes this.

2. The system according to claim 1, comprising means for improving the process and updating guidance based on the results of data analysis and computational model learning.

3. The system according to claim 1, comprising means for providing instructions to a user visually or audibly.

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