system

Abstract

We provide the system. [Solution] A means for receiving data related to the work environment from an information gathering device, A means for generating a virtual environment based on the aforementioned data, A means for performing business process analysis using artificial intelligence within the aforementioned virtual environment, A means for delivering the aforementioned analysis results to a terminal, The terminal includes means for displaying the analysis results on a user interface, 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is difficult to grasp the customer's business environment and usage situation in detail, and in particular, with the spread of remote work, there is a need to provide quick and appropriate business support. Also, the current technology has a problem in that there is a lack of means to identify the causes of inefficiencies in business and immediately present specific improvement proposals. Against this background, there is a need to provide a system that can efficiently and immediately analyze business problems and derive improvement measures.

Means for Solving the Problems

[0005] This invention provides a system that generates a virtual environment based on business environment data received from an information collection device and analyzes business processes using artificial intelligence within that virtual environment. The AI ​​performs anomaly detection and predictive analysis, generates improvement suggestions, and delivers the results to a terminal and displays them on a user interface, enabling customers to quickly implement business improvements. Furthermore, by enabling real-time information updates and user operations, it supports immediate and effective decision-making. This makes it possible to improve customer business efficiency and satisfaction even in remote environments.

[0006] An "information gathering device" is a device that has the function of sensing various data in the work environment, aggregating it, and transmitting it externally.

[0007] "Work environment" refers to the totality of the places and conditions under which a company or organization conducts its activities, and includes both physical and virtual elements.

[0008] "Data" includes various forms of information, such as measured values ​​and observation results obtained from the work environment.

[0009] A "virtual environment" is a digital space that simulates and reproduces an actual work environment on a computer.

[0010] "Artificial intelligence" refers to the technology of intelligent information processing, such as learning, reasoning, and pattern recognition, which is realized by computer systems.

[0011] Anomaly detection is an analytical process for identifying events or actions that deviate from normal business processes.

[0012] "Predictive analytics" refers to statistical or machine learning methods used to predict future trends and events based on past data.

[0013] An "improvement suggestion" is information that outlines specific proposed changes aimed at streamlining business processes or solving problems.

[0014] "Result delivery" is the process of notifying or presenting to the user the analysis and proposals made by the artificial intelligence agent.

[0015] "User interface" refers to the screen and operation parts for the system and the user to exchange information, including visual and operational elements.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of the data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of the data processing device and the robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is an embodiment of a system that reproduces a customer's work environment in a virtual space, analyzes problems in business processes using artificial intelligence, and proposes improvements. The processing of the program of this system is described below.

[0038] Server processing:

[0039] The server first receives data from information gathering devices placed within the customer's business environment. This data includes information from sensors and IoT devices, and contains various measurements related to the physical environment of the business. Next, the server processes the received data, removes noise, and imputes missing values ​​as needed. This prepares the data, which is then used to generate the virtual environment.

[0040] The organized data is then reproduced as a virtual environment on the server. This virtual environment faithfully replicates the customer's actual physical work environment in digital space. The server then uses artificial intelligence (AI) within this virtual environment to analyze the business processes. The AI ​​performs anomaly detection and predictive analytics to identify which parts of the business are problematic and how to improve them.

[0041] Terminal processing:

[0042] The terminal receives analysis results and suggestions from the server, generated by artificial intelligence. The received information is visually presented to the user through a user interface. This user interface allows for the highlighting of important information in a dashboard format and detailed display of analysis results. It is also designed for intuitive user operation.

[0043] User actions:

[0044] Users can use their own devices to view information provided by the server. For example, if the AI ​​alerts a manufacturing plant that the efficiency of its production line is decreasing, the user can view the details on a dashboard and learn specifically which machine is causing the problem and how to address it.

[0045] Users make changes to their business processes according to the suggested improvements. For example, they might revise machine maintenance schedules or modify work procedures to address problems. By returning feedback to the server, the AI ​​can use this information to improve the accuracy of future analyses and suggestions.

[0046] In this way, this system monitors the work environment in real time and supports quick and accurate problem solving. This enables customers to improve their operational efficiency and the working environment of their employees.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server receives various data in real time from information gathering devices installed within the work environment. This includes measurements from temperature sensors, vibration sensors, and object motion detection sensors.

[0050] Step 2:

[0051] The server preprocesses the received data. Specifically, it performs noise reduction and imputation if there are missing values ​​to improve data quality.

[0052] Step 3:

[0053] The server generates a virtual environment based on pre-processed data. This environment is a digital model that reflects actual business processes and faithfully reproduces a variety of business scenarios.

[0054] Step 4:

[0055] The server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify business problems and potential risks.

[0056] Step 5:

[0057] The server sends the results of its AI-generated analysis to the terminal. These results include specific improvement suggestions and alert information.

[0058] Step 6:

[0059] The terminal displays the results received from the server on the user interface. Important information is presented visually and clearly on the interface, allowing the user to operate it intuitively.

[0060] Step 7:

[0061] Users review the AI ​​analysis results using an interface on their device. Based on the information presented, users identify the source of the problem and decide which corrective measures should be taken.

[0062] Step 8:

[0063] The user implements the selected improvement measures into the business process. For example, they might adjust the operating schedule of a specific machine or change the assignment of workers.

[0064] Step 9:

[0065] Users evaluate the effectiveness of the improvements they implement and provide feedback to the server. The server then incorporates this information back into the dataset and uses it to improve the accuracy of future analyses.

[0066] (Example 1)

[0067] 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."

[0068] Conventional business process improvement systems have difficulty analyzing the work environment and proposing improvements in real time, and there is a need for quick and accurate responses to improve efficiency. In particular, there have been problems with properly performing advanced data analysis such as anomaly detection and predictive analytics, and with a lack of feedback functions to improve the accuracy of suggestions to users.

[0069] 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.

[0070] In this invention, the server includes means for receiving information related to the work space from a data generation mechanism, means for constructing a virtual space based on the information, and means for analyzing work procedures using computer-based intelligence within the virtual space. This enables real-time anomaly detection and business improvement suggestions using advanced data analysis.

[0071] A "data generation mechanism" refers to a device or system that collects information related to the work environment and sends that information to a server for processing.

[0072] "Information" refers to data collected from the work environment, and typically includes physical or environmental data such as temperature, vibration, and operating conditions.

[0073] A "virtual space" refers to a digital space that uses digital technology to mimic and recreate a physical work environment.

[0074] "Computer-based intelligence" refers to artificial intelligence technology, and means algorithms or systems that perform complex data analysis processing such as anomaly detection, predictive analytics, and generation of improvement suggestions.

[0075] Anomaly detection is the process of identifying behaviors or situations that deviate from normal patterns in data within a work environment.

[0076] "Data processing means" refers to functions that remove noise and impute missing values ​​from received information to create a clean dataset suitable for analysis.

[0077] Time series analysis is an analytical method that analyzes data patterns over time to predict future trends.

[0078] An "improvement suggestion" refers to presenting specific methods or action plans to improve the efficiency and quality of business processes.

[0079] A "user interface" refers to an interface that visually presents analysis results and suggestions to the user, enabling the user to perform actions and make decisions based on the data.

[0080] "Feedback" refers to the process of returning responses and results to the server in response to user suggestions for improvement, providing information that will help improve the accuracy of future analyses.

[0081] This invention is a system that recreates the work environment in a virtual space, analyzes business processes using artificial intelligence within that space, and provides improvement suggestions. Specific embodiments of this system are described below.

[0082] The server first receives information related to the work environment from the data generation mechanism. This information includes data from sensors and internet-connected devices. For example, it could include information from temperature sensors and operation sensors. To process this information efficiently, the server performs data cleansing using the Python Pandas library. It also digitally recreates the work environment as a virtual space using 3D modeling technologies such as Unity or Unreal Engine.

[0083] Within this virtual space, the server utilizes computer-based intelligence and performs business process analysis using machine learning frameworks such as TENSORFLOW®. During this process, it employs anomaly detection algorithms and time-series analysis to identify business problems in real time and predict future trends. Based on the data obtained from this analysis, it generates concrete improvement proposals.

[0084] The terminal receives analysis results and suggestions sent from the server. The user interface visually displays the results using tools such as Power BI and Tableau. This interface is designed for intuitive user interaction and presents important information clearly. Furthermore, user feedback can be sent back to the server to improve the accuracy of the analysis.

[0085] Based on the analysis results, users can develop specific business improvement measures. For example, in a manufacturing setting, this could involve restructuring maintenance schedules and improving work procedures to enhance line efficiency. One example is inputting a prompt such as, "Generate specific suggestions to improve the efficiency of the production line next month," into the generating AI model.

[0086] This invention enables real-time monitoring and analysis of the work environment, supporting users in improving their work efficiency and the working environment.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The server receives information from data generation mechanisms located in the work environment. This input information includes data such as temperature, vibration, and operating status. After receiving the information, the server stores the data in a database. Specifically, it retrieves data via a REST API and stores it in a data lake for initial analysis.

[0090] Step 2:

[0091] The server performs data cleansing on the received data. It takes raw data as input and outputs a clean dataset with noise removed and missing values ​​imputed. Specifically, it uses the Python Pandas library to detect and remove outliers using statistical methods and imputate missing data using linear interpolation.

[0092] Step 3:

[0093] The server generates a virtual space using cleansed data. The input is the prepared data, and the output is a virtual model of the work environment reproduced digitally. Specifically, it uses Unity or Unreal Engine to build a digital twin using 3D modeling technology.

[0094] Step 4:

[0095] The server uses computer-based intelligence within a virtual space to analyze business processes. Input data consists of model simulation results within the virtual space. Output includes detailed reports of anomalies and processes requiring improvement. Time-series analysis and anomaly detection algorithms using TensorFlow are employed to identify problems and predict future trends.

[0096] Step 5:

[0097] The terminal receives analysis results and improvement suggestions from the server. Inputs are analysis reports and suggested information, and output is a visual display of the results in the user interface. Specifically, it uses Power BI or Tableau to highlight important information in a dashboard format. The design allows for intuitive operation, enabling users to directly view the analysis results.

[0098] Step 6:

[0099] Users review the analysis results on their devices and take specific improvement actions. User input is feedback provided on the dashboard, and the effects of the improvements are returned to the server as output. Specifically, by reviewing the processes and procedures that need improvement and feeding the results back into the system, the accuracy of the AI ​​analysis improves.

[0100] (Application Example 1)

[0101] 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."

[0102] In on-site operations, malfunctions and inefficiencies in machinery and equipment occur frequently, leading to decreased productivity and increased maintenance costs. Rapid detection and response to these anomalies are crucial, but current systems often struggle to provide real-time situational awareness. Furthermore, there is a lack of simple interfaces that allow workers without specialized knowledge to quickly understand and address problems.

[0103] 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.

[0104] In this invention, the server includes means for receiving data related to business activities from information gathering equipment, means for constructing a virtual environment based on the data, and means for performing analysis of business procedures using artificial intelligence within the virtual environment. This enables the detection of abnormalities in machinery and equipment on-site and rapid response. Workers can obtain information immediately through a visual display, making it easy to understand problems and implement countermeasures even without specialized knowledge.

[0105] "Information gathering equipment" refers to hardware devices that collect data related to business activities and transmit it to a server.

[0106] "Data related to business activities" is a general term for information obtained from sensors and IoT devices that relates to the physical environment of the business and the status of machinery and equipment.

[0107] A "virtual environment" is a digital space created on a server to reproduce and analyze the actual physical work environment within a digital space.

[0108] "Artificial intelligence" is a computer program that uses machine learning algorithms and data analysis techniques to analyze business procedures, detect anomalies, and suggest improvements.

[0109] A "terminal with a display screen" is an electronic device that provides users with information in a visualized form, serving as an interface for displaying analysis results delivered from a server.

[0110] "Information display means" refers to interface technology that displays analysis results on a user interface, allowing users to optimize their work based on that information.

[0111] A "visual display" is a visual presentation device that allows users to instantly check the status and abnormal information of equipment in the real world.

[0112] The system for carrying out this invention consists of an information gathering device, a server, a terminal with a display screen, and a visual display.

[0113] The server first receives data related to business activities from information gathering devices. This data is acquired from sensors and IoT devices and includes information about the physical environment of the business and the status of mechanical equipment. Next, the server builds a virtual environment based on this data. The virtual environment is a digital reproduction of the actual physical business environment.

[0114] The server uses artificial intelligence to analyze business procedures within the virtual environment. This analysis utilizes machine learning algorithms and data analysis techniques. Based on this analysis, the server generates anomaly detection and business improvement suggestions, which are then distributed to terminals with display screens.

[0115] Terminals with display screens visualize analysis results transmitted from the server through information display means. The user interface displays the analysis results in an easy-to-understand manner, enabling users to optimize their work based on them. Users can instantly check the status and abnormal information of equipment in the real world using the visual display. For example, if a particular machine shows an abnormality, users can quickly take appropriate action based on the displayed information.

[0116] A concrete example of a prompt message would be, "Please suggest ways to improve the overall operational efficiency of this factory. In particular, ask the AI ​​for specific countermeasures to take when an anomaly is detected." This allows users to receive specific instructions for operation, enabling them to perform their tasks more efficiently.

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

[0118] Step 1:

[0119] The server receives data related to business activities from information gathering equipment. This data includes information about the physical environment and the state of mechanical equipment acquired by sensors and IoT devices. Based on this input data, data processing such as noise reduction and missing value imputation is performed to prepare it for the next processing stage.

[0120] Step 2:

[0121] The server uses the prepared data to build a virtual environment. This virtual environment is designed to reproduce the actual business environment in digital space. It receives processed data as input and converts it into a virtual model that simulates the business process, which is then output. This model generation creates a foundation that faithfully replicates the real environment.

[0122] Step 3:

[0123] The server analyzes business procedures within the virtual environment using a generated AI model. Here, machine learning algorithms are used to perform anomaly detection and predictive analysis, extracting points of concern and improvement measures. The input is the aforementioned virtual environment model, and the output generates results of anomaly detection and specific improvement suggestions.

[0124] Step 4:

[0125] The server distributes information to terminals with display screens to visually present the analysis results. It uses the analysis results and suggestions created in the previous step as input, converting them into a format suitable for the terminal before outputting them.

[0126] Step 5:

[0127] The terminal visualizes data received from the server through an information display mechanism. Specifically, it organizes the data on the user interface and presents important information in a way that users can immediately understand. The output is an intuitive display that supports business optimization.

[0128] Step 6:

[0129] Users can check the status and anomaly information of equipment in the real world through a visual display. Based on the information presented, they can take specific actions such as adjusting the work line or performing equipment maintenance. Rapid response on-site is achieved based on user input.

[0130] 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.

[0131] This invention is an embodiment that combines a system that virtually reproduces a work environment and uses artificial intelligence to analyze business processes based on that environment with an emotion engine that recognizes the emotional state of the user. This system is characterized in that it takes user emotional feedback into consideration in order to improve customer work efficiency and satisfaction.

[0132] Server processing:

[0133] The server first receives data about the work environment from information gathering devices. This data includes information from sensors and IoT devices and reflects the actual work environment. The received data undergoes preprocessing such as noise reduction and imputation of missing values, and then a virtual environment is generated. This virtual environment faithfully replicates the customer's actual work environment in digital space.

[0134] Next, the server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify problems and areas for improvement. At this time, the emotion engine recognizes the user's emotional state and generates new suggestions that take the user's emotional data into account based on the analysis results.

[0135] Terminal processing:

[0136] The terminal receives analysis results and suggestions from the emotion engine sent from the server. This information is displayed visually in the user interface. Users can receive not only AI analysis results but also personalized suggestions based on their own emotional state. For example, if a user is feeling stressed, the system can suggest adjusting their workload. The user interface is interactive and provides users with information that is updated in real time.

[0137] User actions:

[0138] Through the terminal interface, users can review improvement suggestions while receiving interactive feedback based on the analysis results. For example, if they receive a suggestion regarding the efficiency of a production line, and they feel uneasy about it, they can receive the suggestion as a revised version based on their emotional feedback.

[0139] Users apply the suggested improvements to their actual work and evaluate their effectiveness. The evaluation results and the user's emotional state are sent back to the server as feedback. The server can use this information to improve the accuracy of future analyses, including the emotion engine.

[0140] This system incorporates emotional feedback into traditional business process improvements, enabling user-centered improvement measures that significantly enhance both operational efficiency and user satisfaction.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server receives data from sensors and IoT devices installed within the work environment. This data includes temperature, humidity, vibration, and equipment operating status.

[0144] Step 2:

[0145] The server preprocesses the received raw data. Specifically, it performs noise reduction and removes outliers to improve the quality of the dataset.

[0146] Step 3:

[0147] The server uses pre-processed data to generate a virtual environment. This virtual environment mimics the actual business environment, visualizing business processes in digital space.

[0148] Step 4:

[0149] The server runs artificial intelligence within a virtual environment to perform anomaly detection and predictive analysis of business processes. This identifies potential problems and areas for improvement in business operations.

[0150] Step 5:

[0151] The server uses an emotion engine to analyze the user's emotional state. It infers emotions such as stress and fatigue from the user's past activity history and current activity patterns.

[0152] Step 6:

[0153] The server combines business analysis results with user sentiment data to generate optimal improvement suggestions and deliver them to the terminals. These suggestions may take the form of changes to work schedules or recommendations for breaks.

[0154] Step 7:

[0155] The terminal displays analysis results and suggestions sent from the server on its user interface. The user interface is updated in real time, providing users with important information immediately.

[0156] Step 8:

[0157] Users review suggestions displayed through their terminals and adjust their business processes. For example, they might rearrange production schedules or revise work procedures.

[0158] Step 9:

[0159] Users evaluate whether the adjusted tasks were efficient and satisfactory, and send the results, along with individual emotional feedback, back to the server.

[0160] Step 10:

[0161] The server stores feedback information and uses it to improve the quality of future analyses and suggestions. The integration of emotional data and operational data enables the provision of more accurate improvement suggestions.

[0162] (Example 2)

[0163] 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".

[0164] Conventional business process improvement systems only provide analysis results based on actual business operations, and do not offer suggestions that take into account the user's emotional state, thus limiting their ability to improve business efficiency and satisfaction. Furthermore, they lacked real-time interactive feedback and had poor user interface usability.

[0165] 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.

[0166] In this invention, the server includes means for receiving information about the work environment from an information gathering device, means for generating a virtual environment based on the information, and means for analyzing work procedures using a computing device within the virtual environment. This enables the provision of specific improvement suggestions that take into account the user's emotional state and interactive feedback that is updated in real time.

[0167] "Information gathering equipment" refers to devices used to acquire data from the work environment, and includes sensors, IoT devices, and other similar equipment.

[0168] A "virtual environment" is a simulation environment that imitates the actual work environment in digital space, visualizing or reproducing business workflows.

[0169] A "computational device" is a digital platform that uses artificial intelligence technology and machine learning algorithms to analyze data and evaluate and optimize business procedures.

[0170] An "output device" is a device used to present analysis results and improvement suggestions to the user, and includes computer displays and mobile device screens.

[0171] The "display unit" is a screen area that visually shows the analysis results through the user interface, and is a component that enables interactive operation.

[0172] An "emotion engine" is a technology that recognizes a user's emotional state and generates data based on it, evaluating emotions through facial recognition and voice analysis.

[0173] This invention is a system aimed at improving the efficiency of business processes and enhancing user satisfaction. This system utilizes information gathering equipment, a virtual environment, a computing device, an emotion engine, and an interactive display unit.

[0174] Server processing:

[0175] The server generates a virtual work environment based on data received from information gathering devices. This virtual environment is created using software such as Unity or Unreal Engine. The server then uses computing devices to analyze business procedures within the virtual environment. This analysis includes anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. During this process, an emotion engine analyzes the user's emotions and generates data accordingly.

[0176] Terminal processing:

[0177] The terminal receives analysis results and emotion-based data from the server and displays them on an output device. The display is updated in real time and can be interactively manipulated by the user.

[0178] User actions:

[0179] Users review the analysis results and emotionally sensitive suggestions displayed on their devices and incorporate them into their work. The user's feedback is then sent back to the server and used for future analyses.

[0180] As a concrete example, this system can be used to improve workplace communication processes. For instance, it can analyze the flow of messages among employees to identify which communication patterns are more efficient. It can also suggest regular breaks to employees who are experiencing stress.

[0181] An example of a prompt is, "Conduct a process analysis to streamline workplace communication and create improvement suggestions based on employee emotional feedback."

[0182] In this way, the system can suggest appropriate improvement measures that take user emotions into consideration, thereby improving not only operational efficiency but also user satisfaction.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The server receives data about the work environment from information gathering equipment. Inputs include raw data acquired from sensors and IoT devices. This data may include information such as work time, operating status, and work location. The server performs preprocessing, such as noise reduction and missing value imputation, to generate a reliable dataset as output.

[0186] Step 2:

[0187] The server generates a virtual work environment based on pre-processed data. The input is the reliable dataset created in Step 1. The server utilizes software such as Unity or Unreal Engine to output a virtual environment that faithfully replicates the real-world work environment in digital space. This virtual environment visually reproduces the business workflow.

[0188] Step 3:

[0189] The server analyzes the business processes within the virtual environment using a computing device. The virtual environment created in step 2 is used as input. The computing device performs anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, analysis results are generated that show anomalies and areas for improvement in the business processes.

[0190] Step 4:

[0191] The server uses an emotion engine to recognize the user's emotional state. Input data includes the user's facial expressions and voice. The emotion engine analyzes this data and outputs emotional analysis data indicating whether the user is stressed or relaxed. This allows for specific improvement suggestions tailored to the user's emotions.

[0192] Step 5:

[0193] The terminal receives analysis results and sentiment analysis data sent from the server. Inputs include the data generated in steps 3 and 4. The terminal visually displays this information in the user interface and provides interactive feedback as output. Users can see the problems identified by the AI ​​and suggestions for improvement in real time.

[0194] Step 6:

[0195] The user reviews the displayed analysis results and suggested improvements through the terminal interface. The input is the information displayed in step 5. The user applies the suggested improvements to their work and provides feedback on their effectiveness to the terminal. The output is the user's feedback sent to the server, where the data is accumulated for the next analysis.

[0196] (Application Example 2)

[0197] 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".

[0198] Traditional business process improvement systems focus on improving process efficiency, but they fail to consider the emotional state of users in their analysis and suggestions, leading to problems such as a decline in user experience and satisfaction. Furthermore, suggestions based on user emotions often lack the flexibility to address individual needs. Therefore, there is a need to provide more effective and personalized business support for users.

[0199] 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.

[0200] In this invention, the server includes means for receiving data relating to the physical spatial environment from an information gathering device, means for generating a virtual spatial environment based on the data, means for performing analysis of business procedures using an intelligent machine within the virtual environment, emotion analysis means for recognizing the user's emotional state and generating suggestions based thereon, means for distributing the analysis results and suggestions to a terminal, and visual interface means for displaying the analysis results and suggestions on the terminal. This enables individually optimized suggestions that take into account the user's emotional state and efficient improvement of business processes.

[0201] An "information gathering device" is a device that acquires data about the physical environment and transmits it to a server.

[0202] A "virtual space environment" is an environment recreated in digital space based on data acquired from an information gathering device.

[0203] An "intelligent machine" is an artificial intelligence system that can process business procedures using advanced analytical techniques.

[0204] "Emotion analysis tools" are means for recognizing a user's emotional state in real time and using that data to generate suggestions.

[0205] A "terminal" is a device that receives analysis results and suggestions delivered from a server and provides the user with information visually.

[0206] A "visual interface" is a display system that allows users to intuitively understand and interact with analysis results and suggestions on their device.

[0207] The system for realizing this application includes an advanced program that receives, analyzes, and provides suggestions based on information. This program has the following functions:

[0208] First, the server receives data about the physical environment through information gathering devices. This data is acquired from sensors and IoT devices and reflects the detailed state of the work environment. Next, the server generates a virtual environment based on the received data and uses intelligent machines to analyze work procedures.

[0209] This analysis process utilizes emotion analysis tools that identify the user's emotional state in real time. These tools determine emotions from facial expressions, voice tone, and other factors, and generate personalized improvement suggestions based on these findings. These suggestions, along with the analysis results, are delivered to the device, which has the functionality to display this information on a visual interface. This allows users to receive customized feedback tailored to their own emotions.

[0210] The hardware utilizes small computers such as the Raspberry Pi, while the software employs OpenCV and TensorFlow. This enables real-time face recognition and sentiment analysis. Furthermore, the Scikit-learn library is used to denoise the data and classify sentiment, improving the accuracy of the suggestions.

[0211] As a concrete example, consider scenarios where a robot assists users in their homes or offices, such as: "It seems you're feeling stressed from too much housework. Shall I play some meditation music for 10 minutes to help you refresh?" By interacting with the user through prompts like this, it's possible to improve both work efficiency and satisfaction simultaneously. An example of a prompt might be: "What methods can the robot suggest to help a user who is tired from their daily work refresh themselves?"

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server receives data about the physical environment from information gathering devices. This data includes information from sensors and IoT devices, and is used to reflect the actual state of the work environment. The input is raw sensor data, and the output is denoised environmental data. This allows for data cleansing and preprocessing.

[0215] Step 2:

[0216] The server generates a virtual environment based on the received data. Data processing involves using 3D modeling technology to transform the received data into a virtual environment, digitally recreating the user's work environment. The output is a virtual environment model.

[0217] Step 3:

[0218] The server uses intelligent machines in a virtual environment to analyze business procedures. The input is data from the virtual environment, and the AI ​​analyzes business performance based on this data. It performs anomaly detection and predictive analysis, and generates reports of areas for improvement and problem identification as output.

[0219] Step 4:

[0220] The emotion analysis system recognizes the user's emotional state in real time. Inputs include user facial expression and voice data collected from a camera and microphone, which are analyzed to infer the user's emotional state. The output is the analyzed emotion data. During this process, emotion identification is performed using tools such as OpenCV and TensorFlow.

[0221] Step 5:

[0222] The server integrates the results of business analysis and sentiment analysis data, and generates individual improvement suggestions based on this. In this step, a generative AI model is used to output example prompt sentences in an executable format. The input is the business analysis results and sentiment data, and the output is the improvement suggestion sentence.

[0223] Step 6:

[0224] The terminal receives analysis results and suggestions delivered from the server and displays them in a visual interface. This allows users to visually review feedback on their work. The input is the analysis results data, and the output is a visual display in a user-friendly format.

[0225] Step 7:

[0226] The user chooses whether to accept the suggested improvements through an interface on their device. The selection is sent to the server as feedback and used for subsequent analyses. The input is the user's selection data, and the output is the system's evaluation feedback.

[0227] 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.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] This invention is an embodiment of a system that reproduces a customer's work environment in a virtual space, analyzes problems in business processes using artificial intelligence, and proposes improvements. The processing of the program of this system is described below.

[0244] Server processing:

[0245] The server first receives data from information gathering devices placed within the customer's business environment. This data includes information from sensors and IoT devices, and contains various measurements related to the physical environment of the business. Next, the server processes the received data, removes noise, and imputes missing values ​​as needed. This prepares the data, which is then used to generate the virtual environment.

[0246] The organized data is then reproduced as a virtual environment on the server. This virtual environment faithfully replicates the customer's actual physical work environment in digital space. The server then uses artificial intelligence (AI) within this virtual environment to analyze the business processes. The AI ​​performs anomaly detection and predictive analytics to identify which parts of the business are problematic and how to improve them.

[0247] Terminal processing:

[0248] The terminal receives analysis results and suggestions from the server, generated by artificial intelligence. The received information is visually presented to the user through a user interface. This user interface allows for the highlighting of important information in a dashboard format and detailed display of analysis results. It is also designed for intuitive user operation.

[0249] User actions:

[0250] Users can use their own devices to view information provided by the server. For example, if the AI ​​alerts a manufacturing plant that the efficiency of its production line is decreasing, the user can view the details on a dashboard and learn specifically which machine is causing the problem and how to address it.

[0251] Users make changes to their business processes according to the suggested improvements. For example, they might revise machine maintenance schedules or modify work procedures to address problems. By returning feedback to the server, the AI ​​can use this information to improve the accuracy of future analyses and suggestions.

[0252] In this way, this system monitors the work environment in real time and supports quick and accurate problem solving. This enables customers to improve their operational efficiency and the working environment of their employees.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The server receives various data in real time from information gathering devices installed within the work environment. This includes measurements from temperature sensors, vibration sensors, and object motion detection sensors.

[0256] Step 2:

[0257] The server preprocesses the received data. Specifically, it performs noise reduction and imputation if there are missing values ​​to improve data quality.

[0258] Step 3:

[0259] The server generates a virtual environment based on pre-processed data. This environment is a digital model that reflects actual business processes and faithfully reproduces a variety of business scenarios.

[0260] Step 4:

[0261] The server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify business problems and potential risks.

[0262] Step 5:

[0263] The server sends the results of its AI-generated analysis to the terminal. These results include specific improvement suggestions and alert information.

[0264] Step 6:

[0265] The terminal displays the results received from the server on the user interface. Important information is presented visually and clearly on the interface, allowing the user to operate it intuitively.

[0266] Step 7:

[0267] Users review the AI ​​analysis results using an interface on their device. Based on the information presented, users identify the source of the problem and decide which corrective measures should be taken.

[0268] Step 8:

[0269] The user implements the selected improvement measures into the business process. For example, they might adjust the operating schedule of a specific machine or change the assignment of workers.

[0270] Step 9:

[0271] Users evaluate the effectiveness of the improvements they implement and provide feedback to the server. The server then incorporates this information back into the dataset and uses it to improve the accuracy of future analyses.

[0272] (Example 1)

[0273] 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."

[0274] Conventional business process improvement systems have difficulty analyzing the work environment and proposing improvements in real time, and there is a need for quick and accurate responses to improve efficiency. In particular, there have been problems with properly performing advanced data analysis such as anomaly detection and predictive analytics, and with a lack of feedback functions to improve the accuracy of suggestions to users.

[0275] 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.

[0276] In this invention, the server includes means for receiving information related to the work space from a data generation mechanism, means for constructing a virtual space based on the information, and means for analyzing work procedures using computer-based intelligence within the virtual space. This enables real-time anomaly detection and business improvement suggestions using advanced data analysis.

[0277] A "data generation mechanism" refers to a device or system that collects information related to the work environment and sends that information to a server for processing.

[0278] "Information" refers to data collected from the work environment, and typically includes physical or environmental data such as temperature, vibration, and operating conditions.

[0279] A "virtual space" refers to a digital space that uses digital technology to mimic and recreate a physical work environment.

[0280] "Computer-based intelligence" refers to artificial intelligence technology, and means algorithms or systems that perform complex data analysis processing such as anomaly detection, predictive analytics, and generation of improvement suggestions.

[0281] Anomaly detection is the process of identifying behaviors or situations that deviate from normal patterns in data within a work environment.

[0282] "Data processing means" refers to a function for removing noise and complementing missing values from the received information to create a clean dataset suitable for analysis.

[0283] "Time series analysis" is an analytical method for analyzing data patterns over time and predicting future trends.

[0284] "Improvement proposal" refers to presenting specific methods and action plans for improving the efficiency and quality of business processes.

[0285] "User interface" means an interface for visually presenting analysis results and proposals to users and enabling users to perform operations and make decisions based on the data.

[0286] "Feedback" is a process of returning responses and results to the server for improvement proposals from users, and refers to providing information useful for improving the accuracy of the next analysis.

[0287] This invention is a system that reproduces a business environment in a virtual space, analyzes business processes using artificial intelligence within that space, and makes improvement proposals. Specific embodiments of this system are shown below.

[0288] The server first receives information related to the business space from the data generation mechanism. This information includes data from sensors and devices connected to the Internet. For example, information from temperature sensors and operation sensors can be considered. To process this information efficiently, the server performs data cleansing using the Pandas library in Python. Also, using 3D modeling technologies such as Unity and Unreal Engine, the server digitally reproduces the business environment as a virtual space.

[0289] Within this virtual space, the server leverages computer-based intelligence and performs business process analysis using machine learning frameworks such as TensorFlow. During this process, it utilizes anomaly detection algorithms and time-series analysis to identify business problems in real time and predict future trends. Based on the data obtained from this analysis, it generates concrete improvement suggestions.

[0290] The terminal receives analysis results and suggestions sent from the server. The user interface visually displays the results using tools such as Power BI and Tableau. This interface is designed for intuitive user interaction and presents important information clearly. Furthermore, user feedback can be sent back to the server to improve the accuracy of the analysis.

[0291] Based on the analysis results, users can develop specific business improvement measures. For example, in a manufacturing setting, this could involve restructuring maintenance schedules and improving work procedures to enhance line efficiency. One example is inputting a prompt such as, "Generate specific suggestions to improve the efficiency of the production line next month," into the generating AI model.

[0292] This invention enables real-time monitoring and analysis of the work environment, supporting users in improving their work efficiency and the working environment.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The server receives information from data generation mechanisms located in the work environment. This input information includes data such as temperature, vibration, and operating status. After receiving the information, the server stores the data in a database. Specifically, it retrieves data via a REST API and stores it in a data lake for initial analysis.

[0296] Step 2:

[0297] The server performs data cleansing on the received data. It takes raw data as input and outputs a clean dataset with noise removed and missing values ​​imputed. Specifically, it uses the Python Pandas library to detect and remove outliers using statistical methods and imputate missing data using linear interpolation.

[0298] Step 3:

[0299] The server generates a virtual space using cleansed data. The input is the prepared data, and the output is a virtual model of the work environment reproduced digitally. Specifically, it uses Unity or Unreal Engine to build a digital twin using 3D modeling technology.

[0300] Step 4:

[0301] The server uses computer-based intelligence within a virtual space to analyze business processes. Input data consists of model simulation results within the virtual space. Output includes detailed reports of anomalies and processes requiring improvement. Time-series analysis and anomaly detection algorithms using TensorFlow are employed to identify problems and predict future trends.

[0302] Step 5:

[0303] The terminal receives analysis results and improvement suggestions from the server. Inputs are analysis reports and suggested information, and output is a visual display of the results in the user interface. Specifically, it uses Power BI or Tableau to highlight important information in a dashboard format. The design allows for intuitive operation, enabling users to directly view the analysis results.

[0304] Step 6:

[0305] The user checks the analysis results on the terminal and executes specific improvement actions. The user's input is feedback given on the dashboard, and the effect of the implemented improvement is returned to the server as output. Specifically, by reviewing the processes and procedures to be improved and feeding the results back to the system, the analysis accuracy of the AI is improved.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In on-site business activities, malfunctions and efficiency degradation of mechanical equipment frequently occur, resulting in decreased productivity and increased maintenance costs, which pose problems. Quick detection and response to abnormalities on-site are required, but in the current system, it is often difficult to grasp the situation in real time. In addition, there is a lack of a simple interface that allows workers without specialized knowledge to immediately understand and respond to problems.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes means for receiving data related to business activities from information collection devices, means for constructing a virtual environment based on the data, and means for executing analysis of business procedures using artificial intelligence within the virtual environment. This enables detection of abnormalities in mechanical equipment on-site and rapid response. Workers can immediately obtain information through a visual display and can easily grasp problem points and execute countermeasures even without specialized knowledge.

[0311] The "information collection device" is a hardware device that has the role of collecting data related to business activities and transmitting it to the server.

[0312] "Data related to business activities" is a general term for information obtained from sensors and IoT devices that relates to the physical environment of the business and the status of machinery and equipment.

[0313] A "virtual environment" is a digital space created on a server to reproduce and analyze the actual physical work environment within a digital space.

[0314] "Artificial intelligence" is a computer program that uses machine learning algorithms and data analysis techniques to analyze business procedures, detect anomalies, and suggest improvements.

[0315] A "terminal with a display screen" is an electronic device that provides users with information in a visualized form, serving as an interface for displaying analysis results delivered from a server.

[0316] "Information display means" refers to interface technology that displays analysis results on a user interface, allowing users to optimize their work based on that information.

[0317] A "visual display" is a visual presentation device that allows users to instantly check the status and abnormal information of equipment in the real world.

[0318] The system for carrying out this invention consists of an information gathering device, a server, a terminal with a display screen, and a visual display.

[0319] The server first receives data related to business activities from information gathering devices. This data is acquired from sensors and IoT devices and includes information about the physical environment of the business and the status of mechanical equipment. Next, the server builds a virtual environment based on this data. The virtual environment is a digital reproduction of the actual physical business environment.

[0320] The server uses artificial intelligence to analyze business procedures within the virtual environment. This analysis utilizes machine learning algorithms and data analysis techniques. Based on this analysis, the server generates anomaly detection and business improvement suggestions, which are then distributed to terminals with display screens.

[0321] Terminals with display screens visualize analysis results transmitted from the server through information display means. The user interface displays the analysis results in an easy-to-understand manner, enabling users to optimize their work based on them. Users can instantly check the status and abnormal information of equipment in the real world using the visual display. For example, if a particular machine shows an abnormality, users can quickly take appropriate action based on the displayed information.

[0322] A concrete example of a prompt message would be, "Please suggest ways to improve the overall operational efficiency of this factory. In particular, ask the AI ​​for specific countermeasures to take when an anomaly is detected." This allows users to receive specific instructions for operation, enabling them to perform their tasks more efficiently.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server receives data related to business activities from information gathering equipment. This data includes information about the physical environment and the state of mechanical equipment acquired by sensors and IoT devices. Based on this input data, data processing such as noise reduction and missing value imputation is performed to prepare it for the next processing stage.

[0326] Step 2:

[0327] The server uses the prepared data to build a virtual environment. This virtual environment is designed to reproduce the actual business environment in digital space. It receives processed data as input and converts it into a virtual model that simulates the business process, which is then output. This model generation creates a foundation that faithfully replicates the real environment.

[0328] Step 3:

[0329] The server analyzes business procedures within the virtual environment using a generated AI model. Here, machine learning algorithms are used to perform anomaly detection and predictive analysis, extracting points of concern and improvement measures. The input is the aforementioned virtual environment model, and the output generates results of anomaly detection and specific improvement suggestions.

[0330] Step 4:

[0331] The server distributes information to terminals with display screens to visually present the analysis results. It uses the analysis results and suggestions created in the previous step as input, converting them into a format suitable for the terminal before outputting them.

[0332] Step 5:

[0333] The terminal visualizes data received from the server through an information display mechanism. Specifically, it organizes the data on the user interface and presents important information in a way that users can immediately understand. The output is an intuitive display that supports business optimization.

[0334] Step 6:

[0335] Users can check the status and anomaly information of equipment in the real world through a visual display. Based on the information presented, they can take specific actions such as adjusting the work line or performing equipment maintenance. Rapid response on-site is achieved based on user input.

[0336] 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.

[0337] This invention is an embodiment that combines a system that virtually reproduces a work environment and uses artificial intelligence to analyze business processes based on that environment with an emotion engine that recognizes the emotional state of the user. This system is characterized in that it takes user emotional feedback into consideration in order to improve customer work efficiency and satisfaction.

[0338] Server processing:

[0339] The server first receives data about the work environment from information gathering devices. This data includes information from sensors and IoT devices and reflects the actual work environment. The received data undergoes preprocessing such as noise reduction and imputation of missing values, and then a virtual environment is generated. This virtual environment faithfully replicates the customer's actual work environment in digital space.

[0340] Next, the server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify problems and areas for improvement. At this time, the emotion engine recognizes the user's emotional state and generates new suggestions that take the user's emotional data into account based on the analysis results.

[0341] Terminal processing:

[0342] The terminal receives analysis results and suggestions from the emotion engine sent from the server. This information is displayed visually in the user interface. Users can receive not only AI analysis results but also personalized suggestions based on their own emotional state. For example, if a user is feeling stressed, the system can suggest adjusting their workload. The user interface is interactive and provides users with information that is updated in real time.

[0343] User actions:

[0344] Through the terminal interface, users can review improvement suggestions while receiving interactive feedback based on the analysis results. For example, if they receive a suggestion regarding the efficiency of a production line, and they feel uneasy about it, they can receive the suggestion as a revised version based on their emotional feedback.

[0345] Users apply the suggested improvements to their actual work and evaluate their effectiveness. The evaluation results and the user's emotional state are sent back to the server as feedback. The server can use this information to improve the accuracy of future analyses, including the emotion engine.

[0346] This system incorporates emotional feedback into traditional business process improvements, enabling user-centered improvement measures that significantly enhance both operational efficiency and user satisfaction.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] The server receives data from sensors and IoT devices installed within the work environment. This data includes temperature, humidity, vibration, and equipment operating status.

[0350] Step 2:

[0351] The server preprocesses the received raw data. Specifically, it performs noise reduction and removes outliers to improve the quality of the dataset.

[0352] Step 3:

[0353] The server uses pre-processed data to generate a virtual environment. This virtual environment mimics the actual business environment, visualizing business processes in digital space.

[0354] Step 4:

[0355] The server runs artificial intelligence within a virtual environment to perform anomaly detection and predictive analysis of business processes. This identifies potential problems and areas for improvement in business operations.

[0356] Step 5:

[0357] The server uses an emotion engine to analyze the user's emotional state. It infers emotions such as stress and fatigue from the user's past activity history and current activity patterns.

[0358] Step 6:

[0359] The server combines business analysis results with user sentiment data to generate optimal improvement suggestions and deliver them to the terminals. These suggestions may take the form of changes to work schedules or recommendations for breaks.

[0360] Step 7:

[0361] The terminal displays analysis results and suggestions sent from the server on its user interface. The user interface is updated in real time, providing users with important information immediately.

[0362] Step 8:

[0363] Users review suggestions displayed through their terminals and adjust their business processes. For example, they might rearrange production schedules or revise work procedures.

[0364] Step 9:

[0365] Users evaluate whether the adjusted tasks were efficient and satisfactory, and send the results, along with individual emotional feedback, back to the server.

[0366] Step 10:

[0367] The server stores feedback information and uses it to improve the quality of future analyses and suggestions. The integration of emotional data and operational data enables the provision of more accurate improvement suggestions.

[0368] (Example 2)

[0369] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0370] Conventional business process improvement systems only provide analysis results based on actual business operations, and do not offer suggestions that take into account the user's emotional state, thus limiting their ability to improve business efficiency and satisfaction. Furthermore, they lacked real-time interactive feedback and had poor user interface usability.

[0371] 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.

[0372] In this invention, the server includes means for receiving information about the work environment from an information gathering device, means for generating a virtual environment based on the information, and means for analyzing work procedures using a computing device within the virtual environment. This enables the provision of specific improvement suggestions that take into account the user's emotional state and interactive feedback that is updated in real time.

[0373] "Information gathering equipment" refers to devices used to acquire data from the work environment, and includes sensors, IoT devices, and other similar equipment.

[0374] A "virtual environment" is a simulation environment that imitates the actual work environment in digital space, visualizing or reproducing business workflows.

[0375] A "computational device" is a digital platform that uses artificial intelligence technology and machine learning algorithms to analyze data and evaluate and optimize business procedures.

[0376] An "output device" is a device used to present analysis results and improvement suggestions to the user, and includes computer displays and mobile device screens.

[0377] The "display unit" is a screen area that visually shows the analysis results through the user interface, and is a component that enables interactive operation.

[0378] An "emotion engine" is a technology that recognizes a user's emotional state and generates data based on it, evaluating emotions through facial recognition and voice analysis.

[0379] This invention is a system aimed at improving the efficiency of business processes and enhancing user satisfaction. This system utilizes information gathering equipment, a virtual environment, a computing device, an emotion engine, and an interactive display unit.

[0380] Server processing:

[0381] The server generates a virtual work environment based on data received from information gathering devices. This virtual environment is created using software such as Unity or Unreal Engine. The server then uses computing devices to analyze business procedures within the virtual environment. This analysis includes anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. During this process, an emotion engine analyzes the user's emotions and generates data accordingly.

[0382] Terminal processing:

[0383] The terminal receives analysis results and emotion-based data from the server and displays them on an output device. The display is updated in real time and can be interactively manipulated by the user.

[0384] User actions:

[0385] Users review the analysis results and emotionally sensitive suggestions displayed on their devices and incorporate them into their work. The user's feedback is then sent back to the server and used for future analyses.

[0386] As a concrete example, this system can be used to improve workplace communication processes. For instance, it can analyze the flow of messages among employees to identify which communication patterns are more efficient. It can also suggest regular breaks to employees who are experiencing stress.

[0387] An example of a prompt is, "Conduct a process analysis to streamline workplace communication and create improvement suggestions based on employee emotional feedback."

[0388] In this way, the system can suggest appropriate improvement measures that take user emotions into consideration, thereby improving not only operational efficiency but also user satisfaction.

[0389] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0390] Step 1:

[0391] The server receives data about the work environment from information gathering equipment. Inputs include raw data acquired from sensors and IoT devices. This data may include information such as work time, operating status, and work location. The server performs preprocessing, such as noise reduction and missing value imputation, to generate a reliable dataset as output.

[0392] Step 2:

[0393] The server generates a virtual work environment based on pre-processed data. The input is the reliable dataset created in Step 1. The server utilizes software such as Unity or Unreal Engine to output a virtual environment that faithfully replicates the real-world work environment in digital space. This virtual environment visually reproduces the business workflow.

[0394] Step 3:

[0395] The server analyzes the business processes within the virtual environment using a computing device. The virtual environment created in step 2 is used as input. The computing device performs anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, analysis results are generated that show anomalies and areas for improvement in the business processes.

[0396] Step 4:

[0397] The server uses an emotion engine to recognize the user's emotional state. Input data includes the user's facial expressions and voice. The emotion engine analyzes this data and outputs emotional analysis data indicating whether the user is stressed or relaxed. This allows for specific improvement suggestions tailored to the user's emotions.

[0398] Step 5:

[0399] The terminal receives analysis results and sentiment analysis data sent from the server. Inputs include the data generated in steps 3 and 4. The terminal visually displays this information in the user interface and provides interactive feedback as output. Users can see the problems identified by the AI ​​and suggestions for improvement in real time.

[0400] Step 6:

[0401] The user reviews the displayed analysis results and suggested improvements through the terminal interface. The input is the information displayed in step 5. The user applies the suggested improvements to their work and provides feedback on their effectiveness to the terminal. The output is the user's feedback sent to the server, where the data is accumulated for the next analysis.

[0402] (Application Example 2)

[0403] 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 as the "terminal".

[0404] Traditional business process improvement systems focus on improving process efficiency, but they fail to consider the emotional state of users in their analysis and suggestions, leading to problems such as a decline in user experience and satisfaction. Furthermore, suggestions based on user emotions often lack the flexibility to address individual needs. Therefore, there is a need to provide more effective and personalized business support for users.

[0405] 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.

[0406] In this invention, the server includes means for receiving data relating to the physical spatial environment from an information gathering device, means for generating a virtual spatial environment based on the data, means for performing analysis of business procedures using an intelligent machine within the virtual environment, emotion analysis means for recognizing the user's emotional state and generating suggestions based thereon, means for distributing the analysis results and suggestions to a terminal, and visual interface means for displaying the analysis results and suggestions on the terminal. This enables individually optimized suggestions that take into account the user's emotional state and efficient improvement of business processes.

[0407] An "information gathering device" is a device that acquires data about the physical environment and transmits it to a server.

[0408] A "virtual space environment" is an environment recreated in digital space based on data acquired from an information gathering device.

[0409] An "intelligent machine" is an artificial intelligence system that can process business procedures using advanced analytical techniques.

[0410] "Emotion analysis tools" are means for recognizing a user's emotional state in real time and using that data to generate suggestions.

[0411] A "terminal" is a device that receives analysis results and suggestions delivered from a server and provides the user with information visually.

[0412] A "visual interface" is a display system that allows users to intuitively understand and interact with analysis results and suggestions on their device.

[0413] The system for realizing this application includes an advanced program that receives, analyzes, and provides suggestions based on information. This program has the following functions:

[0414] First, the server receives data about the physical environment through information gathering devices. This data is acquired from sensors and IoT devices and reflects the detailed state of the work environment. Next, the server generates a virtual environment based on the received data and uses intelligent machines to analyze work procedures.

[0415] This analysis process utilizes emotion analysis tools that identify the user's emotional state in real time. These tools determine emotions from facial expressions, voice tone, and other factors, and generate personalized improvement suggestions based on these findings. These suggestions, along with the analysis results, are delivered to the device, which has the functionality to display this information on a visual interface. This allows users to receive customized feedback tailored to their own emotions.

[0416] The hardware utilizes small computers such as the Raspberry Pi, while the software employs OpenCV and TensorFlow. This enables real-time face recognition and sentiment analysis. Furthermore, the Scikit-learn library is used to denoise the data and classify sentiment, improving the accuracy of the suggestions.

[0417] As a concrete example, consider scenarios where a robot assists users in their homes or offices, such as: "It seems you're feeling stressed from too much housework. Shall I play some meditation music for 10 minutes to help you refresh?" By interacting with the user through prompts like this, it's possible to improve both work efficiency and satisfaction simultaneously. An example of a prompt might be: "What methods can the robot suggest to help a user who is tired from their daily work refresh themselves?"

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The server receives data about the physical environment from information gathering devices. This data includes information from sensors and IoT devices, and is used to reflect the actual state of the work environment. The input is raw sensor data, and the output is denoised environmental data. This allows for data cleansing and preprocessing.

[0421] Step 2:

[0422] The server generates a virtual environment based on the received data. Data processing involves using 3D modeling technology to transform the received data into a virtual environment, digitally recreating the user's work environment. The output is a virtual environment model.

[0423] Step 3:

[0424] The server uses intelligent machines in a virtual environment to analyze business procedures. The input is data from the virtual environment, and the AI ​​analyzes business performance based on this data. It performs anomaly detection and predictive analysis, and generates reports of areas for improvement and problem identification as output.

[0425] Step 4:

[0426] The emotion analysis system recognizes the user's emotional state in real time. Inputs include user facial expression and voice data collected from a camera and microphone, which are analyzed to infer the user's emotional state. The output is the analyzed emotion data. During this process, emotion identification is performed using tools such as OpenCV and TensorFlow.

[0427] Step 5:

[0428] The server integrates the results of business analysis and sentiment analysis data, and generates individual improvement suggestions based on this. In this step, a generative AI model is used to output example prompt sentences in an executable format. The input is the business analysis results and sentiment data, and the output is the improvement suggestion sentence.

[0429] Step 6:

[0430] The terminal receives analysis results and suggestions delivered from the server and displays them in a visual interface. This allows users to visually review feedback on their work. The input is the analysis results data, and the output is a visual display in a user-friendly format.

[0431] Step 7:

[0432] The user chooses whether to accept the suggested improvements through an interface on their device. The selection is sent to the server as feedback and used for subsequent analyses. The input is the user's selection data, and the output is the system's evaluation feedback.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention is an embodiment of a system that reproduces a customer's work environment in a virtual space, analyzes problems in business processes using artificial intelligence, and proposes improvements. The processing of the program of this system is described below.

[0450] Server processing:

[0451] The server first receives data from information gathering devices placed within the customer's business environment. This data includes information from sensors and IoT devices, and contains various measurements related to the physical environment of the business. Next, the server processes the received data, removes noise, and imputes missing values ​​as needed. This prepares the data, which is then used to generate the virtual environment.

[0452] The organized data is then reproduced as a virtual environment on the server. This virtual environment faithfully replicates the customer's actual physical work environment in digital space. The server then uses artificial intelligence (AI) within this virtual environment to analyze the business processes. The AI ​​performs anomaly detection and predictive analytics to identify which parts of the business are problematic and how to improve them.

[0453] Terminal processing:

[0454] The terminal receives analysis results and suggestions from the server, generated by artificial intelligence. The received information is visually presented to the user through a user interface. This user interface allows for the highlighting of important information in a dashboard format and detailed display of analysis results. It is also designed for intuitive user operation.

[0455] User actions:

[0456] Users can use their own devices to view information provided by the server. For example, if the AI ​​alerts a manufacturing plant that the efficiency of its production line is decreasing, the user can view the details on a dashboard and learn specifically which machine is causing the problem and how to address it.

[0457] Users make changes to their business processes according to the suggested improvements. For example, they might revise machine maintenance schedules or modify work procedures to address problems. By returning feedback to the server, the AI ​​can use this information to improve the accuracy of future analyses and suggestions.

[0458] In this way, this system monitors the work environment in real time and supports quick and accurate problem solving. This enables customers to improve their operational efficiency and the working environment of their employees.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The server receives various data in real time from information gathering devices installed within the work environment. This includes measurements from temperature sensors, vibration sensors, and object motion detection sensors.

[0462] Step 2:

[0463] The server preprocesses the received data. Specifically, it performs noise reduction and imputation if there are missing values ​​to improve data quality.

[0464] Step 3:

[0465] The server generates a virtual environment based on pre-processed data. This environment is a digital model that reflects actual business processes and faithfully reproduces a variety of business scenarios.

[0466] Step 4:

[0467] The server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify business problems and potential risks.

[0468] Step 5:

[0469] The server sends the results of its AI-generated analysis to the terminal. These results include specific improvement suggestions and alert information.

[0470] Step 6:

[0471] The terminal displays the results received from the server on the user interface. Important information is presented visually and clearly on the interface, allowing the user to operate it intuitively.

[0472] Step 7:

[0473] Users review the AI ​​analysis results using an interface on their device. Based on the information presented, users identify the source of the problem and decide which corrective measures should be taken.

[0474] Step 8:

[0475] The user implements the selected improvement measures into the business process. For example, they might adjust the operating schedule of a specific machine or change the assignment of workers.

[0476] Step 9:

[0477] Users evaluate the effectiveness of the improvements they implement and provide feedback to the server. The server then incorporates this information back into the dataset and uses it to improve the accuracy of future analyses.

[0478] (Example 1)

[0479] 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."

[0480] Conventional business process improvement systems have difficulty analyzing the work environment and proposing improvements in real time, and there is a need for quick and accurate responses to improve efficiency. In particular, there have been problems with properly performing advanced data analysis such as anomaly detection and predictive analytics, and with a lack of feedback functions to improve the accuracy of suggestions to users.

[0481] 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.

[0482] In this invention, the server includes means for receiving information related to the work space from a data generation mechanism, means for constructing a virtual space based on the information, and means for analyzing work procedures using computer-based intelligence within the virtual space. This enables real-time anomaly detection and business improvement suggestions using advanced data analysis.

[0483] A "data generation mechanism" refers to a device or system that collects information related to the work environment and sends that information to a server for processing.

[0484] "Information" refers to data collected from the work environment, and typically includes physical or environmental data such as temperature, vibration, and operating conditions.

[0485] A "virtual space" refers to a digital space that uses digital technology to mimic and recreate a physical work environment.

[0486] "Computer-based intelligence" refers to artificial intelligence technology, and means algorithms or systems that perform complex data analysis processing such as anomaly detection, predictive analytics, and generation of improvement suggestions.

[0487] Anomaly detection is the process of identifying behaviors or situations that deviate from normal patterns in data within a work environment.

[0488] "Data processing means" refers to functions that remove noise and impute missing values ​​from received information to create a clean dataset suitable for analysis.

[0489] Time series analysis is an analytical method that analyzes data patterns over time to predict future trends.

[0490] An "improvement suggestion" refers to presenting specific methods or action plans to improve the efficiency and quality of business processes.

[0491] A "user interface" refers to an interface that visually presents analysis results and suggestions to the user, enabling the user to perform actions and make decisions based on the data.

[0492] "Feedback" refers to the process of returning responses and results to the server in response to user suggestions for improvement, providing information that will help improve the accuracy of future analyses.

[0493] This invention is a system that recreates the work environment in a virtual space, analyzes business processes using artificial intelligence within that space, and provides improvement suggestions. Specific embodiments of this system are described below.

[0494] The server first receives information related to the work environment from the data generation mechanism. This information includes data from sensors and internet-connected devices. For example, it could include information from temperature sensors and operation sensors. To process this information efficiently, the server performs data cleansing using the Python Pandas library. It also digitally recreates the work environment as a virtual space using 3D modeling technologies such as Unity or Unreal Engine.

[0495] Within this virtual space, the server leverages computer-based intelligence and performs business process analysis using machine learning frameworks such as TensorFlow. During this process, it utilizes anomaly detection algorithms and time-series analysis to identify business problems in real time and predict future trends. Based on the data obtained from this analysis, it generates concrete improvement suggestions.

[0496] The terminal receives analysis results and suggestions sent from the server. The user interface visually displays the results using tools such as Power BI and Tableau. This interface is designed for intuitive user interaction and presents important information clearly. Furthermore, user feedback can be sent back to the server to improve the accuracy of the analysis.

[0497] Based on the analysis results, users can develop specific business improvement measures. For example, in a manufacturing setting, this could involve restructuring maintenance schedules and improving work procedures to enhance line efficiency. One example is inputting a prompt such as, "Generate specific suggestions to improve the efficiency of the production line next month," into the generating AI model.

[0498] This invention enables real-time monitoring and analysis of the work environment, supporting users in improving their work efficiency and the working environment.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The server receives information from data generation mechanisms located in the work environment. This input information includes data such as temperature, vibration, and operating status. After receiving the information, the server stores the data in a database. Specifically, it retrieves data via a REST API and stores it in a data lake for initial analysis.

[0502] Step 2:

[0503] The server performs data cleansing on the received data. It takes raw data as input and outputs a clean dataset with noise removed and missing values ​​imputed. Specifically, it uses the Python Pandas library to detect and remove outliers using statistical methods and imputate missing data using linear interpolation.

[0504] Step 3:

[0505] The server generates a virtual space using cleansed data. The input is the prepared data, and the output is a virtual model of the work environment reproduced digitally. Specifically, it uses Unity or Unreal Engine to build a digital twin using 3D modeling technology.

[0506] Step 4:

[0507] The server uses computer-based intelligence within a virtual space to analyze business processes. Input data consists of model simulation results within the virtual space. Output includes detailed reports of anomalies and processes requiring improvement. Time-series analysis and anomaly detection algorithms using TensorFlow are employed to identify problems and predict future trends.

[0508] Step 5:

[0509] The terminal receives analysis results and improvement suggestions from the server. Inputs are analysis reports and suggested information, and output is a visual display of the results in the user interface. Specifically, it uses Power BI or Tableau to highlight important information in a dashboard format. The design allows for intuitive operation, enabling users to directly view the analysis results.

[0510] Step 6:

[0511] Users review the analysis results on their devices and take specific improvement actions. User input is feedback provided on the dashboard, and the effects of the improvements are returned to the server as output. Specifically, by reviewing the processes and procedures that need improvement and feeding the results back into the system, the accuracy of the AI ​​analysis improves.

[0512] (Application Example 1)

[0513] 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."

[0514] In on-site operations, malfunctions and inefficiencies in machinery and equipment occur frequently, leading to decreased productivity and increased maintenance costs. Rapid detection and response to these anomalies are crucial, but current systems often struggle to provide real-time situational awareness. Furthermore, there is a lack of simple interfaces that allow workers without specialized knowledge to quickly understand and address problems.

[0515] 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.

[0516] In this invention, the server includes means for receiving data related to business activities from information gathering equipment, means for constructing a virtual environment based on the data, and means for performing analysis of business procedures using artificial intelligence within the virtual environment. This enables the detection of abnormalities in machinery and equipment on-site and rapid response. Workers can obtain information immediately through a visual display, making it easy to understand problems and implement countermeasures even without specialized knowledge.

[0517] "Information gathering equipment" refers to hardware devices that collect data related to business activities and transmit it to a server.

[0518] "Data related to business activities" is a general term for information obtained from sensors and IoT devices that relates to the physical environment of the business and the status of machinery and equipment.

[0519] A "virtual environment" is a digital space created on a server to reproduce and analyze the actual physical work environment within a digital space.

[0520] "Artificial intelligence" is a computer program that uses machine learning algorithms and data analysis techniques to analyze business procedures, detect anomalies, and suggest improvements.

[0521] A "terminal with a display screen" is an electronic device that provides users with information in a visualized form, serving as an interface for displaying analysis results delivered from a server.

[0522] "Information display means" refers to interface technology that displays analysis results on a user interface, allowing users to optimize their work based on that information.

[0523] A "visual display" is a visual presentation device that allows users to instantly check the status and abnormal information of equipment in the real world.

[0524] The system for carrying out this invention consists of an information gathering device, a server, a terminal with a display screen, and a visual display.

[0525] The server first receives data related to business activities from information gathering devices. This data is acquired from sensors and IoT devices and includes information about the physical environment of the business and the status of mechanical equipment. Next, the server builds a virtual environment based on this data. The virtual environment is a digital reproduction of the actual physical business environment.

[0526] The server uses artificial intelligence to analyze business procedures within the virtual environment. This analysis utilizes machine learning algorithms and data analysis techniques. Based on this analysis, the server generates anomaly detection and business improvement suggestions, which are then distributed to terminals with display screens.

[0527] Terminals with display screens visualize analysis results transmitted from the server through information display means. The user interface displays the analysis results in an easy-to-understand manner, enabling users to optimize their work based on them. Users can instantly check the status and abnormal information of equipment in the real world using the visual display. For example, if a particular machine shows an abnormality, users can quickly take appropriate action based on the displayed information.

[0528] A concrete example of a prompt message would be, "Please suggest ways to improve the overall operational efficiency of this factory. In particular, ask the AI ​​for specific countermeasures to take when an anomaly is detected." This allows users to receive specific instructions for operation, enabling them to perform their tasks more efficiently.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The server receives data related to business activities from information gathering equipment. This data includes information about the physical environment and the state of mechanical equipment acquired by sensors and IoT devices. Based on this input data, data processing such as noise reduction and missing value imputation is performed to prepare it for the next processing stage.

[0532] Step 2:

[0533] The server uses the prepared data to build a virtual environment. This virtual environment is designed to reproduce the actual business environment in digital space. It receives processed data as input and converts it into a virtual model that simulates the business process, which is then output. This model generation creates a foundation that faithfully replicates the real environment.

[0534] Step 3:

[0535] The server analyzes business procedures within the virtual environment using a generated AI model. Here, machine learning algorithms are used to perform anomaly detection and predictive analysis, extracting points of concern and improvement measures. The input is the aforementioned virtual environment model, and the output generates results of anomaly detection and specific improvement suggestions.

[0536] Step 4:

[0537] The server distributes information to terminals with display screens to visually present the analysis results. It uses the analysis results and suggestions created in the previous step as input, converting them into a format suitable for the terminal before outputting them.

[0538] Step 5:

[0539] The terminal visualizes data received from the server through an information display mechanism. Specifically, it organizes the data on the user interface and presents important information in a way that users can immediately understand. The output is an intuitive display that supports business optimization.

[0540] Step 6:

[0541] Users can check the status and anomaly information of equipment in the real world through a visual display. Based on the information presented, they can take specific actions such as adjusting the work line or performing equipment maintenance. Rapid response on-site is achieved based on user input.

[0542] 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.

[0543] This invention is an embodiment that combines a system that virtually reproduces a work environment and uses artificial intelligence to analyze business processes based on that environment with an emotion engine that recognizes the emotional state of the user. This system is characterized in that it takes user emotional feedback into consideration in order to improve customer work efficiency and satisfaction.

[0544] Server processing:

[0545] The server first receives data about the work environment from information gathering devices. This data includes information from sensors and IoT devices and reflects the actual work environment. The received data undergoes preprocessing such as noise reduction and imputation of missing values, and then a virtual environment is generated. This virtual environment faithfully replicates the customer's actual work environment in digital space.

[0546] Next, the server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify problems and areas for improvement. At this time, the emotion engine recognizes the user's emotional state and generates new suggestions that take the user's emotional data into account based on the analysis results.

[0547] Terminal processing:

[0548] The terminal receives analysis results and suggestions from the emotion engine sent from the server. This information is displayed visually in the user interface. Users can receive not only AI analysis results but also personalized suggestions based on their own emotional state. For example, if a user is feeling stressed, the system can suggest adjusting their workload. The user interface is interactive and provides users with information that is updated in real time.

[0549] User actions:

[0550] Through the terminal interface, users can review improvement suggestions while receiving interactive feedback based on the analysis results. For example, if they receive a suggestion regarding the efficiency of a production line, and they feel uneasy about it, they can receive the suggestion as a revised version based on their emotional feedback.

[0551] Users apply the suggested improvements to their actual work and evaluate their effectiveness. The evaluation results and the user's emotional state are sent back to the server as feedback. The server can use this information to improve the accuracy of future analyses, including the emotion engine.

[0552] This system incorporates emotional feedback into traditional business process improvements, enabling user-centered improvement measures that significantly enhance both operational efficiency and user satisfaction.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The server receives data from sensors and IoT devices installed within the work environment. This data includes temperature, humidity, vibration, and equipment operating status.

[0556] Step 2:

[0557] The server preprocesses the received raw data. Specifically, it performs noise reduction and removes outliers to improve the quality of the dataset.

[0558] Step 3:

[0559] The server uses pre-processed data to generate a virtual environment. This virtual environment mimics the actual business environment, visualizing business processes in digital space.

[0560] Step 4:

[0561] The server runs artificial intelligence within a virtual environment to perform anomaly detection and predictive analysis of business processes. This identifies potential problems and areas for improvement in business operations.

[0562] Step 5:

[0563] The server uses an emotion engine to analyze the user's emotional state. It infers emotions such as stress and fatigue from the user's past activity history and current activity patterns.

[0564] Step 6:

[0565] The server combines business analysis results with user sentiment data to generate optimal improvement suggestions and deliver them to the terminals. These suggestions may take the form of changes to work schedules or recommendations for breaks.

[0566] Step 7:

[0567] The terminal displays analysis results and suggestions sent from the server on its user interface. The user interface is updated in real time, providing users with important information immediately.

[0568] Step 8:

[0569] Users review suggestions displayed through their terminals and adjust their business processes. For example, they might rearrange production schedules or revise work procedures.

[0570] Step 9:

[0571] Users evaluate whether the adjusted tasks were efficient and satisfactory, and send the results, along with individual emotional feedback, back to the server.

[0572] Step 10:

[0573] The server stores feedback information and uses it to improve the quality of future analyses and suggestions. The integration of emotional data and operational data enables the provision of more accurate improvement suggestions.

[0574] (Example 2)

[0575] 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."

[0576] Conventional business process improvement systems only provide analysis results based on actual business operations, and do not offer suggestions that take into account the user's emotional state, thus limiting their ability to improve business efficiency and satisfaction. Furthermore, they lacked real-time interactive feedback and had poor user interface usability.

[0577] 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.

[0578] In this invention, the server includes means for receiving information about the work environment from an information gathering device, means for generating a virtual environment based on the information, and means for analyzing work procedures using a computing device within the virtual environment. This enables the provision of specific improvement suggestions that take into account the user's emotional state and interactive feedback that is updated in real time.

[0579] "Information gathering equipment" refers to devices used to acquire data from the work environment, and includes sensors, IoT devices, and other similar equipment.

[0580] A "virtual environment" is a simulation environment that imitates the actual work environment in digital space, visualizing or reproducing business workflows.

[0581] A "computational device" is a digital platform that uses artificial intelligence technology and machine learning algorithms to analyze data and evaluate and optimize business procedures.

[0582] An "output device" is a device used to present analysis results and improvement suggestions to the user, and includes computer displays and mobile device screens.

[0583] The "display unit" is a screen area that visually shows the analysis results through the user interface, and is a component that enables interactive operation.

[0584] An "emotion engine" is a technology that recognizes a user's emotional state and generates data based on it, evaluating emotions through facial recognition and voice analysis.

[0585] This invention is a system aimed at improving the efficiency of business processes and enhancing user satisfaction. This system utilizes information gathering equipment, a virtual environment, a computing device, an emotion engine, and an interactive display unit.

[0586] Server processing:

[0587] The server generates a virtual work environment based on data received from information gathering devices. This virtual environment is created using software such as Unity or Unreal Engine. The server then uses computing devices to analyze business procedures within the virtual environment. This analysis includes anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. During this process, an emotion engine analyzes the user's emotions and generates data accordingly.

[0588] Terminal processing:

[0589] The terminal receives analysis results and emotion-based data from the server and displays them on an output device. The display is updated in real time and can be interactively manipulated by the user.

[0590] User actions:

[0591] Users review the analysis results and emotionally sensitive suggestions displayed on their devices and incorporate them into their work. The user's feedback is then sent back to the server and used for future analyses.

[0592] As a concrete example, this system can be used to improve workplace communication processes. For instance, it can analyze the flow of messages among employees to identify which communication patterns are more efficient. It can also suggest regular breaks to employees who are experiencing stress.

[0593] An example of a prompt is, "Conduct a process analysis to streamline workplace communication and create improvement suggestions based on employee emotional feedback."

[0594] In this way, the system can suggest appropriate improvement measures that take user emotions into consideration, thereby improving not only operational efficiency but also user satisfaction.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] The server receives data about the work environment from information gathering equipment. Inputs include raw data acquired from sensors and IoT devices. This data may include information such as work time, operating status, and work location. The server performs preprocessing, such as noise reduction and missing value imputation, to generate a reliable dataset as output.

[0598] Step 2:

[0599] The server generates a virtual work environment based on pre-processed data. The input is the reliable dataset created in Step 1. The server utilizes software such as Unity or Unreal Engine to output a virtual environment that faithfully replicates the real-world work environment in digital space. This virtual environment visually reproduces the business workflow.

[0600] Step 3:

[0601] The server analyzes the business processes within the virtual environment using a computing device. The virtual environment created in step 2 is used as input. The computing device performs anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, analysis results are generated that show anomalies and areas for improvement in the business processes.

[0602] Step 4:

[0603] The server uses an emotion engine to recognize the user's emotional state. Input data includes the user's facial expressions and voice. The emotion engine analyzes this data and outputs emotional analysis data indicating whether the user is stressed or relaxed. This allows for specific improvement suggestions tailored to the user's emotions.

[0604] Step 5:

[0605] The terminal receives analysis results and sentiment analysis data sent from the server. Inputs include the data generated in steps 3 and 4. The terminal visually displays this information in the user interface and provides interactive feedback as output. Users can see the problems identified by the AI ​​and suggestions for improvement in real time.

[0606] Step 6:

[0607] The user reviews the displayed analysis results and suggested improvements through the terminal interface. The input is the information displayed in step 5. The user applies the suggested improvements to their work and provides feedback on their effectiveness to the terminal. The output is the user's feedback sent to the server, where the data is accumulated for the next analysis.

[0608] (Application Example 2)

[0609] 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."

[0610] Traditional business process improvement systems focus on improving process efficiency, but they fail to consider the emotional state of users in their analysis and suggestions, leading to problems such as a decline in user experience and satisfaction. Furthermore, suggestions based on user emotions often lack the flexibility to address individual needs. Therefore, there is a need to provide more effective and personalized business support for users.

[0611] 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.

[0612] In this invention, the server includes means for receiving data relating to the physical spatial environment from an information gathering device, means for generating a virtual spatial environment based on the data, means for performing analysis of business procedures using an intelligent machine within the virtual environment, emotion analysis means for recognizing the user's emotional state and generating suggestions based thereon, means for distributing the analysis results and suggestions to a terminal, and visual interface means for displaying the analysis results and suggestions on the terminal. This enables individually optimized suggestions that take into account the user's emotional state and efficient improvement of business processes.

[0613] An "information gathering device" is a device that acquires data about the physical environment and transmits it to a server.

[0614] A "virtual space environment" is an environment recreated in digital space based on data acquired from an information gathering device.

[0615] An "intelligent machine" is an artificial intelligence system that can process business procedures using advanced analytical techniques.

[0616] "Emotion analysis tools" are means for recognizing a user's emotional state in real time and using that data to generate suggestions.

[0617] A "terminal" is a device that receives analysis results and suggestions delivered from a server and provides the user with information visually.

[0618] A "visual interface" is a display system that allows users to intuitively understand and interact with analysis results and suggestions on their device.

[0619] The system for realizing this application includes an advanced program that receives, analyzes, and provides suggestions based on information. This program has the following functions:

[0620] First, the server receives data about the physical environment through information gathering devices. This data is acquired from sensors and IoT devices and reflects the detailed state of the work environment. Next, the server generates a virtual environment based on the received data and uses intelligent machines to analyze work procedures.

[0621] This analysis process utilizes emotion analysis tools that identify the user's emotional state in real time. These tools determine emotions from facial expressions, voice tone, and other factors, and generate personalized improvement suggestions based on these findings. These suggestions, along with the analysis results, are delivered to the device, which has the functionality to display this information on a visual interface. This allows users to receive customized feedback tailored to their own emotions.

[0622] The hardware utilizes small computers such as the Raspberry Pi, while the software employs OpenCV and TensorFlow. This enables real-time face recognition and sentiment analysis. Furthermore, the Scikit-learn library is used to denoise the data and classify sentiment, improving the accuracy of the suggestions.

[0623] As a concrete example, consider scenarios where a robot assists users in their homes or offices, such as: "It seems you're feeling stressed from too much housework. Shall I play some meditation music for 10 minutes to help you refresh?" By interacting with the user through prompts like this, it's possible to improve both work efficiency and satisfaction simultaneously. An example of a prompt might be: "What methods can the robot suggest to help a user who is tired from their daily work refresh themselves?"

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The server receives data about the physical environment from information gathering devices. This data includes information from sensors and IoT devices, and is used to reflect the actual state of the work environment. The input is raw sensor data, and the output is denoised environmental data. This allows for data cleansing and preprocessing.

[0627] Step 2:

[0628] The server generates a virtual environment based on the received data. Data processing involves using 3D modeling technology to transform the received data into a virtual environment, digitally recreating the user's work environment. The output is a virtual environment model.

[0629] Step 3:

[0630] The server uses intelligent machines in a virtual environment to analyze business procedures. The input is data from the virtual environment, and the AI ​​analyzes business performance based on this data. It performs anomaly detection and predictive analysis, and generates reports of areas for improvement and problem identification as output.

[0631] Step 4:

[0632] The emotion analysis system recognizes the user's emotional state in real time. Inputs include user facial expression and voice data collected from a camera and microphone, which are analyzed to infer the user's emotional state. The output is the analyzed emotion data. During this process, emotion identification is performed using tools such as OpenCV and TensorFlow.

[0633] Step 5:

[0634] The server integrates the results of business analysis and sentiment analysis data, and generates individual improvement suggestions based on this. In this step, a generative AI model is used to output example prompt sentences in an executable format. The input is the business analysis results and sentiment data, and the output is the improvement suggestion sentence.

[0635] Step 6:

[0636] The terminal receives analysis results and suggestions delivered from the server and displays them in a visual interface. This allows users to visually review feedback on their work. The input is the analysis results data, and the output is a visual display in a user-friendly format.

[0637] Step 7:

[0638] The user chooses whether to accept the suggested improvements through an interface on their device. The selection is sent to the server as feedback and used for subsequent analyses. The input is the user's selection data, and the output is the system's evaluation feedback.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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".

[0656] This invention is an embodiment of a system that reproduces a customer's work environment in a virtual space, analyzes problems in business processes using artificial intelligence, and proposes improvements. The processing of the program of this system is described below.

[0657] Server processing:

[0658] The server first receives data from information gathering devices placed within the customer's business environment. This data includes information from sensors and IoT devices, and contains various measurements related to the physical environment of the business. Next, the server processes the received data, removes noise, and imputes missing values ​​as needed. This prepares the data, which is then used to generate the virtual environment.

[0659] The organized data is then reproduced as a virtual environment on the server. This virtual environment faithfully replicates the customer's actual physical work environment in digital space. The server then uses artificial intelligence (AI) within this virtual environment to analyze the business processes. The AI ​​performs anomaly detection and predictive analytics to identify which parts of the business are problematic and how to improve them.

[0660] Terminal processing:

[0661] The terminal receives analysis results and suggestions from the server, generated by artificial intelligence. The received information is visually presented to the user through a user interface. This user interface allows for the highlighting of important information in a dashboard format and detailed display of analysis results. It is also designed for intuitive user operation.

[0662] User actions:

[0663] Users can use their own devices to view information provided by the server. For example, if the AI ​​alerts a manufacturing plant that the efficiency of its production line is decreasing, the user can view the details on a dashboard and learn specifically which machine is causing the problem and how to address it.

[0664] Users make changes to their business processes according to the suggested improvements. For example, they might revise machine maintenance schedules or modify work procedures to address problems. By returning feedback to the server, the AI ​​can use this information to improve the accuracy of future analyses and suggestions.

[0665] In this way, this system monitors the work environment in real time and supports quick and accurate problem solving. This enables customers to improve their operational efficiency and the working environment of their employees.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The server receives various data in real time from information gathering devices installed within the work environment. This includes measurements from temperature sensors, vibration sensors, and object motion detection sensors.

[0669] Step 2:

[0670] The server preprocesses the received data. Specifically, it performs noise reduction and imputation if there are missing values ​​to improve data quality.

[0671] Step 3:

[0672] The server generates a virtual environment based on pre-processed data. This environment is a digital model that reflects actual business processes and faithfully reproduces a variety of business scenarios.

[0673] Step 4:

[0674] The server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify business problems and potential risks.

[0675] Step 5:

[0676] The server sends the results of its AI-generated analysis to the terminal. These results include specific improvement suggestions and alert information.

[0677] Step 6:

[0678] The terminal displays the results received from the server on the user interface. Important information is presented visually and clearly on the interface, allowing the user to operate it intuitively.

[0679] Step 7:

[0680] Users review the AI ​​analysis results using an interface on their device. Based on the information presented, users identify the source of the problem and decide which corrective measures should be taken.

[0681] Step 8:

[0682] The user implements the selected improvement measures into the business process. For example, they might adjust the operating schedule of a specific machine or change the assignment of workers.

[0683] Step 9:

[0684] Users evaluate the effectiveness of the improvements they implement and provide feedback to the server. The server then incorporates this information back into the dataset and uses it to improve the accuracy of future analyses.

[0685] (Example 1)

[0686] 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".

[0687] Conventional business process improvement systems have difficulty analyzing the work environment and proposing improvements in real time, and there is a need for quick and accurate responses to improve efficiency. In particular, there have been problems with properly performing advanced data analysis such as anomaly detection and predictive analytics, and with a lack of feedback functions to improve the accuracy of suggestions to users.

[0688] 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.

[0689] In this invention, the server includes means for receiving information related to the work space from a data generation mechanism, means for constructing a virtual space based on the information, and means for analyzing work procedures using computer-based intelligence within the virtual space. This enables real-time anomaly detection and business improvement suggestions using advanced data analysis.

[0690] A "data generation mechanism" refers to a device or system that collects information related to the work environment and sends that information to a server for processing.

[0691] "Information" refers to data collected from the work environment, and typically includes physical or environmental data such as temperature, vibration, and operating conditions.

[0692] A "virtual space" refers to a digital space that uses digital technology to mimic and recreate a physical work environment.

[0693] "Computer-based intelligence" refers to artificial intelligence technology, and means algorithms or systems that perform complex data analysis processing such as anomaly detection, predictive analytics, and generation of improvement suggestions.

[0694] Anomaly detection is the process of identifying behaviors or situations that deviate from normal patterns in data within a work environment.

[0695] "Data processing means" refers to functions that remove noise and impute missing values ​​from received information to create a clean dataset suitable for analysis.

[0696] Time series analysis is an analytical method that analyzes data patterns over time to predict future trends.

[0697] An "improvement suggestion" refers to presenting specific methods or action plans to improve the efficiency and quality of business processes.

[0698] A "user interface" refers to an interface that visually presents analysis results and suggestions to the user, enabling the user to perform actions and make decisions based on the data.

[0699] "Feedback" refers to the process of returning responses and results to the server in response to user suggestions for improvement, providing information that will help improve the accuracy of future analyses.

[0700] This invention is a system that recreates the work environment in a virtual space, analyzes business processes using artificial intelligence within that space, and provides improvement suggestions. Specific embodiments of this system are described below.

[0701] The server first receives information related to the work environment from the data generation mechanism. This information includes data from sensors and internet-connected devices. For example, it could include information from temperature sensors and operation sensors. To process this information efficiently, the server performs data cleansing using the Python Pandas library. It also digitally recreates the work environment as a virtual space using 3D modeling technologies such as Unity or Unreal Engine.

[0702] Within this virtual space, the server leverages computer-based intelligence and performs business process analysis using machine learning frameworks such as TensorFlow. During this process, it utilizes anomaly detection algorithms and time-series analysis to identify business problems in real time and predict future trends. Based on the data obtained from this analysis, it generates concrete improvement suggestions.

[0703] The terminal receives analysis results and suggestions sent from the server. The user interface visually displays the results using tools such as Power BI and Tableau. This interface is designed for intuitive user interaction and presents important information clearly. Furthermore, user feedback can be sent back to the server to improve the accuracy of the analysis.

[0704] Based on the analysis results, users can develop specific business improvement measures. For example, in a manufacturing setting, this could involve restructuring maintenance schedules and improving work procedures to enhance line efficiency. One example is inputting a prompt such as, "Generate specific suggestions to improve the efficiency of the production line next month," into the generating AI model.

[0705] This invention enables real-time monitoring and analysis of the work environment, supporting users in improving their work efficiency and the working environment.

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] The server receives information from data generation mechanisms located in the work environment. This input information includes data such as temperature, vibration, and operating status. After receiving the information, the server stores the data in a database. Specifically, it retrieves data via a REST API and stores it in a data lake for initial analysis.

[0709] Step 2:

[0710] The server performs data cleansing on the received data. It takes raw data as input and outputs a clean dataset with noise removed and missing values ​​imputed. Specifically, it uses the Python Pandas library to detect and remove outliers using statistical methods and imputate missing data using linear interpolation.

[0711] Step 3:

[0712] The server generates a virtual space using cleansed data. The input is the prepared data, and the output is a virtual model of the work environment reproduced digitally. Specifically, it uses Unity or Unreal Engine to build a digital twin using 3D modeling technology.

[0713] Step 4:

[0714] The server uses computer-based intelligence within a virtual space to analyze business processes. Input data consists of model simulation results within the virtual space. Output includes detailed reports of anomalies and processes requiring improvement. Time-series analysis and anomaly detection algorithms using TensorFlow are employed to identify problems and predict future trends.

[0715] Step 5:

[0716] The terminal receives analysis results and improvement suggestions from the server. Inputs are analysis reports and suggested information, and output is a visual display of the results in the user interface. Specifically, it uses Power BI or Tableau to highlight important information in a dashboard format. The design allows for intuitive operation, enabling users to directly view the analysis results.

[0717] Step 6:

[0718] Users review the analysis results on their devices and take specific improvement actions. User input is feedback provided on the dashboard, and the effects of the improvements are returned to the server as output. Specifically, by reviewing the processes and procedures that need improvement and feeding the results back into the system, the accuracy of the AI ​​analysis improves.

[0719] (Application Example 1)

[0720] 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".

[0721] In on-site operations, malfunctions and inefficiencies in machinery and equipment occur frequently, leading to decreased productivity and increased maintenance costs. Rapid detection and response to these anomalies are crucial, but current systems often struggle to provide real-time situational awareness. Furthermore, there is a lack of simple interfaces that allow workers without specialized knowledge to quickly understand and address problems.

[0722] 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.

[0723] In this invention, the server includes means for receiving data related to business activities from information gathering equipment, means for constructing a virtual environment based on the data, and means for performing analysis of business procedures using artificial intelligence within the virtual environment. This enables the detection of abnormalities in machinery and equipment on-site and rapid response. Workers can obtain information immediately through a visual display, making it easy to understand problems and implement countermeasures even without specialized knowledge.

[0724] "Information gathering equipment" refers to hardware devices that collect data related to business activities and transmit it to a server.

[0725] "Data related to business activities" is a general term for information obtained from sensors and IoT devices that relates to the physical environment of the business and the status of machinery and equipment.

[0726] A "virtual environment" is a digital space created on a server to reproduce and analyze the actual physical work environment within a digital space.

[0727] "Artificial intelligence" is a computer program that uses machine learning algorithms and data analysis techniques to analyze business procedures, detect anomalies, and suggest improvements.

[0728] A "terminal with a display screen" is an electronic device that provides users with information in a visualized form, serving as an interface for displaying analysis results delivered from a server.

[0729] "Information display means" refers to interface technology that displays analysis results on a user interface, allowing users to optimize their work based on that information.

[0730] A "visual display" is a visual presentation device that allows users to instantly check the status and abnormal information of equipment in the real world.

[0731] The system for carrying out this invention consists of an information gathering device, a server, a terminal with a display screen, and a visual display.

[0732] The server first receives data related to business activities from information gathering devices. This data is acquired from sensors and IoT devices and includes information about the physical environment of the business and the status of mechanical equipment. Next, the server builds a virtual environment based on this data. The virtual environment is a digital reproduction of the actual physical business environment.

[0733] The server uses artificial intelligence to analyze business procedures within the virtual environment. This analysis utilizes machine learning algorithms and data analysis techniques. Based on this analysis, the server generates anomaly detection and business improvement suggestions, which are then distributed to terminals with display screens.

[0734] Terminals with display screens visualize analysis results transmitted from the server through information display means. The user interface displays the analysis results in an easy-to-understand manner, enabling users to optimize their work based on them. Users can instantly check the status and abnormal information of equipment in the real world using the visual display. For example, if a particular machine shows an abnormality, users can quickly take appropriate action based on the displayed information.

[0735] A concrete example of a prompt message would be, "Please suggest ways to improve the overall operational efficiency of this factory. In particular, ask the AI ​​for specific countermeasures to take when an anomaly is detected." This allows users to receive specific instructions for operation, enabling them to perform their tasks more efficiently.

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] The server receives data related to business activities from information gathering equipment. This data includes information about the physical environment and the state of mechanical equipment acquired by sensors and IoT devices. Based on this input data, data processing such as noise reduction and missing value imputation is performed to prepare it for the next processing stage.

[0739] Step 2:

[0740] The server uses the prepared data to build a virtual environment. This virtual environment is designed to reproduce the actual business environment in digital space. It receives processed data as input and converts it into a virtual model that simulates the business process, which is then output. This model generation creates a foundation that faithfully replicates the real environment.

[0741] Step 3:

[0742] The server analyzes business procedures within the virtual environment using a generated AI model. Here, machine learning algorithms are used to perform anomaly detection and predictive analysis, extracting points of concern and improvement measures. The input is the aforementioned virtual environment model, and the output generates results of anomaly detection and specific improvement suggestions.

[0743] Step 4:

[0744] The server distributes information to terminals with display screens to visually present the analysis results. It uses the analysis results and suggestions created in the previous step as input, converting them into a format suitable for the terminal before outputting them.

[0745] Step 5:

[0746] The terminal visualizes data received from the server through an information display mechanism. Specifically, it organizes the data on the user interface and presents important information in a way that users can immediately understand. The output is an intuitive display that supports business optimization.

[0747] Step 6:

[0748] Users can check the status and anomaly information of equipment in the real world through a visual display. Based on the information presented, they can take specific actions such as adjusting the work line or performing equipment maintenance. Rapid response on-site is achieved based on user input.

[0749] 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.

[0750] This invention is an embodiment that combines a system that virtually reproduces a work environment and uses artificial intelligence to analyze business processes based on that environment with an emotion engine that recognizes the emotional state of the user. This system is characterized in that it takes user emotional feedback into consideration in order to improve customer work efficiency and satisfaction.

[0751] Server processing:

[0752] The server first receives data about the work environment from information gathering devices. This data includes information from sensors and IoT devices and reflects the actual work environment. The received data undergoes preprocessing such as noise reduction and imputation of missing values, and then a virtual environment is generated. This virtual environment faithfully replicates the customer's actual work environment in digital space.

[0753] Next, the server analyzes business processes using artificial intelligence within a virtual environment. The AI ​​performs anomaly detection and predictive analysis to identify problems and areas for improvement. At this time, the emotion engine recognizes the user's emotional state and generates new suggestions that take the user's emotional data into account based on the analysis results.

[0754] Terminal processing:

[0755] The terminal receives analysis results and suggestions from the emotion engine sent from the server. This information is displayed visually in the user interface. Users can receive not only AI analysis results but also personalized suggestions based on their own emotional state. For example, if a user is feeling stressed, the system can suggest adjusting their workload. The user interface is interactive and provides users with information that is updated in real time.

[0756] User actions:

[0757] Through the terminal interface, users can review improvement suggestions while receiving interactive feedback based on the analysis results. For example, if they receive a suggestion regarding the efficiency of a production line, and they feel uneasy about it, they can receive the suggestion as a revised version based on their emotional feedback.

[0758] Users apply the suggested improvements to their actual work and evaluate their effectiveness. The evaluation results and the user's emotional state are sent back to the server as feedback. The server can use this information to improve the accuracy of future analyses, including the emotion engine.

[0759] This system incorporates emotional feedback into traditional business process improvements, enabling user-centered improvement measures that significantly enhance both operational efficiency and user satisfaction.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The server receives data from sensors and IoT devices installed within the work environment. This data includes temperature, humidity, vibration, and equipment operating status.

[0763] Step 2:

[0764] The server preprocesses the received raw data. Specifically, it performs noise reduction and removes outliers to improve the quality of the dataset.

[0765] Step 3:

[0766] The server uses pre-processed data to generate a virtual environment. This virtual environment mimics the actual business environment, visualizing business processes in digital space.

[0767] Step 4:

[0768] The server runs artificial intelligence within a virtual environment to perform anomaly detection and predictive analysis of business processes. This identifies potential problems and areas for improvement in business operations.

[0769] Step 5:

[0770] The server uses an emotion engine to analyze the user's emotional state. It infers emotions such as stress and fatigue from the user's past activity history and current activity patterns.

[0771] Step 6:

[0772] The server combines business analysis results with user sentiment data to generate optimal improvement suggestions and deliver them to the terminals. These suggestions may take the form of changes to work schedules or recommendations for breaks.

[0773] Step 7:

[0774] The terminal displays analysis results and suggestions sent from the server on its user interface. The user interface is updated in real time, providing users with important information immediately.

[0775] Step 8:

[0776] Users review suggestions displayed through their terminals and adjust their business processes. For example, they might rearrange production schedules or revise work procedures.

[0777] Step 9:

[0778] Users evaluate whether the adjusted tasks were efficient and satisfactory, and send the results, along with individual emotional feedback, back to the server.

[0779] Step 10:

[0780] The server stores feedback information and uses it to improve the quality of future analyses and suggestions. The integration of emotional data and operational data enables the provision of more accurate improvement suggestions.

[0781] (Example 2)

[0782] 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".

[0783] Conventional business process improvement systems only provide analysis results based on actual business operations, and do not offer suggestions that take into account the user's emotional state, thus limiting their ability to improve business efficiency and satisfaction. Furthermore, they lacked real-time interactive feedback and had poor user interface usability.

[0784] 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.

[0785] In this invention, the server includes means for receiving information about the work environment from an information gathering device, means for generating a virtual environment based on the information, and means for analyzing work procedures using a computing device within the virtual environment. This enables the provision of specific improvement suggestions that take into account the user's emotional state and interactive feedback that is updated in real time.

[0786] "Information gathering equipment" refers to devices used to acquire data from the work environment, and includes sensors, IoT devices, and other similar equipment.

[0787] A "virtual environment" is a simulation environment that imitates the actual work environment in digital space, visualizing or reproducing business workflows.

[0788] A "computational device" is a digital platform that uses artificial intelligence technology and machine learning algorithms to analyze data and evaluate and optimize business procedures.

[0789] An "output device" is a device used to present analysis results and improvement suggestions to the user, and includes computer displays and mobile device screens.

[0790] The "display unit" is a screen area that visually shows the analysis results through the user interface, and is a component that enables interactive operation.

[0791] An "emotion engine" is a technology that recognizes a user's emotional state and generates data based on it, evaluating emotions through facial recognition and voice analysis.

[0792] This invention is a system aimed at improving the efficiency of business processes and enhancing user satisfaction. This system utilizes information gathering equipment, a virtual environment, a computing device, an emotion engine, and an interactive display unit.

[0793] Server processing:

[0794] The server generates a virtual work environment based on data received from information gathering devices. This virtual environment is created using software such as Unity or Unreal Engine. The server then uses computing devices to analyze business procedures within the virtual environment. This analysis includes anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. During this process, an emotion engine analyzes the user's emotions and generates data accordingly.

[0795] Terminal processing:

[0796] The terminal receives analysis results and emotion-based data from the server and displays them on an output device. The display is updated in real time and can be interactively manipulated by the user.

[0797] User actions:

[0798] Users review the analysis results and emotionally sensitive suggestions displayed on their devices and incorporate them into their work. The user's feedback is then sent back to the server and used for future analyses.

[0799] As a concrete example, this system can be used to improve workplace communication processes. For instance, it can analyze the flow of messages among employees to identify which communication patterns are more efficient. It can also suggest regular breaks to employees who are experiencing stress.

[0800] An example of a prompt is, "Conduct a process analysis to streamline workplace communication and create improvement suggestions based on employee emotional feedback."

[0801] In this way, the system can suggest appropriate improvement measures that take user emotions into consideration, thereby improving not only operational efficiency but also user satisfaction.

[0802] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0803] Step 1:

[0804] The server receives data about the work environment from information gathering equipment. Inputs include raw data acquired from sensors and IoT devices. This data may include information such as work time, operating status, and work location. The server performs preprocessing, such as noise reduction and missing value imputation, to generate a reliable dataset as output.

[0805] Step 2:

[0806] The server generates a virtual work environment based on pre-processed data. The input is the reliable dataset created in Step 1. The server utilizes software such as Unity or Unreal Engine to output a virtual environment that faithfully replicates the real-world work environment in digital space. This virtual environment visually reproduces the business workflow.

[0807] Step 3:

[0808] The server analyzes the business processes within the virtual environment using a computing device. The virtual environment created in step 2 is used as input. The computing device performs anomaly detection and predictive analysis using machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, analysis results are generated that show anomalies and areas for improvement in the business processes.

[0809] Step 4:

[0810] The server uses an emotion engine to recognize the user's emotional state. Input data includes the user's facial expressions and voice. The emotion engine analyzes this data and outputs emotional analysis data indicating whether the user is stressed or relaxed. This allows for specific improvement suggestions tailored to the user's emotions.

[0811] Step 5:

[0812] The terminal receives analysis results and sentiment analysis data sent from the server. Inputs include the data generated in steps 3 and 4. The terminal visually displays this information in the user interface and provides interactive feedback as output. Users can see the problems identified by the AI ​​and suggestions for improvement in real time.

[0813] Step 6:

[0814] The user reviews the displayed analysis results and suggested improvements through the terminal interface. The input is the information displayed in step 5. The user applies the suggested improvements to their work and provides feedback on their effectiveness to the terminal. The output is the user's feedback sent to the server, where the data is accumulated for the next analysis.

[0815] (Application Example 2)

[0816] 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".

[0817] Traditional business process improvement systems focus on improving process efficiency, but they fail to consider the emotional state of users in their analysis and suggestions, leading to problems such as a decline in user experience and satisfaction. Furthermore, suggestions based on user emotions often lack the flexibility to address individual needs. Therefore, there is a need to provide more effective and personalized business support for users.

[0818] 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.

[0819] In this invention, the server includes means for receiving data relating to the physical spatial environment from an information gathering device, means for generating a virtual spatial environment based on the data, means for performing analysis of business procedures using an intelligent machine within the virtual environment, emotion analysis means for recognizing the user's emotional state and generating suggestions based thereon, means for distributing the analysis results and suggestions to a terminal, and visual interface means for displaying the analysis results and suggestions on the terminal. This enables individually optimized suggestions that take into account the user's emotional state and efficient improvement of business processes.

[0820] An "information gathering device" is a device that acquires data about the physical environment and transmits it to a server.

[0821] A "virtual space environment" is an environment recreated in digital space based on data acquired from an information gathering device.

[0822] An "intelligent machine" is an artificial intelligence system that can process business procedures using advanced analytical techniques.

[0823] "Emotion analysis tools" are means for recognizing a user's emotional state in real time and using that data to generate suggestions.

[0824] A "terminal" is a device that receives analysis results and suggestions delivered from a server and provides the user with information visually.

[0825] A "visual interface" is a display system that allows users to intuitively understand and interact with analysis results and suggestions on their device.

[0826] The system for realizing this application includes an advanced program that receives, analyzes, and provides suggestions based on information. This program has the following functions:

[0827] First, the server receives data about the physical environment through information gathering devices. This data is acquired from sensors and IoT devices and reflects the detailed state of the work environment. Next, the server generates a virtual environment based on the received data and uses intelligent machines to analyze work procedures.

[0828] This analysis process utilizes emotion analysis tools that identify the user's emotional state in real time. These tools determine emotions from facial expressions, voice tone, and other factors, and generate personalized improvement suggestions based on these findings. These suggestions, along with the analysis results, are delivered to the device, which has the functionality to display this information on a visual interface. This allows users to receive customized feedback tailored to their own emotions.

[0829] The hardware utilizes small computers such as the Raspberry Pi, while the software employs OpenCV and TensorFlow. This enables real-time face recognition and sentiment analysis. Furthermore, the Scikit-learn library is used to denoise the data and classify sentiment, improving the accuracy of the suggestions.

[0830] As a concrete example, consider scenarios where a robot assists users in their homes or offices, such as: "It seems you're feeling stressed from too much housework. Shall I play some meditation music for 10 minutes to help you refresh?" By interacting with the user through prompts like this, it's possible to improve both work efficiency and satisfaction simultaneously. An example of a prompt might be: "What methods can the robot suggest to help a user who is tired from their daily work refresh themselves?"

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1:

[0833] The server receives data about the physical environment from information gathering devices. This data includes information from sensors and IoT devices, and is used to reflect the actual state of the work environment. The input is raw sensor data, and the output is denoised environmental data. This allows for data cleansing and preprocessing.

[0834] Step 2:

[0835] The server generates a virtual environment based on the received data. Data processing involves using 3D modeling technology to transform the received data into a virtual environment, digitally recreating the user's work environment. The output is a virtual environment model.

[0836] Step 3:

[0837] The server uses intelligent machines in a virtual environment to analyze business procedures. The input is data from the virtual environment, and the AI ​​analyzes business performance based on this data. It performs anomaly detection and predictive analysis, and generates reports of areas for improvement and problem identification as output.

[0838] Step 4:

[0839] The emotion analysis system recognizes the user's emotional state in real time. Inputs include user facial expression and voice data collected from a camera and microphone, which are analyzed to infer the user's emotional state. The output is the analyzed emotion data. During this process, emotion identification is performed using tools such as OpenCV and TensorFlow.

[0840] Step 5:

[0841] The server integrates the results of business analysis and sentiment analysis data, and generates individual improvement suggestions based on this. In this step, a generative AI model is used to output example prompt sentences in an executable format. The input is the business analysis results and sentiment data, and the output is the improvement suggestion sentence.

[0842] Step 6:

[0843] The terminal receives analysis results and suggestions delivered from the server and displays them in a visual interface. This allows users to visually review feedback on their work. The input is the analysis results data, and the output is a visual display in a user-friendly format.

[0844] Step 7:

[0845] The user chooses whether to accept the suggested improvements through an interface on their device. The selection is sent to the server as feedback and used for subsequent analyses. The input is the user's selection data, and the output is the system's evaluation feedback.

[0846] 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.

[0847] 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.

[0848] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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."

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] The following is further disclosed regarding the embodiments described above.

[0868] (Claim 1)

[0869] A means for receiving data related to the work environment from an information gathering device,

[0870] A means for generating a virtual environment based on the aforementioned data,

[0871] A means for performing business process analysis using artificial intelligence within the aforementioned virtual environment,

[0872] A means for delivering the aforementioned analysis results to a terminal,

[0873] The terminal includes means for displaying the analysis results on a user interface,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for the artificial intelligence to perform anomaly detection and predictive analysis and generate improvement suggestions.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the user interface has a display means that is updated in real time, enabling the user to perform operations based on the analysis results.

[0879] "Example 1"

[0880] (Claim 1)

[0881] A means for receiving information related to the work space from a data generation mechanism,

[0882] A means of constructing a virtual space based on the aforementioned information,

[0883] A means for performing analysis of business procedures using computer-based intelligence within the aforementioned virtual space,

[0884] Means for transferring the analysis results to a terminal,

[0885] The terminal includes means for providing a user interface for displaying the analysis results,

[0886] Data processing means for removing noise from the aforementioned information and imputing missing values,

[0887] A means of creating a faithful imitation in digital space based on organized data,

[0888] Means for performing advanced data analysis, including time series analysis and anomaly detection,

[0889] A means that has a function to present improvement suggestions to users,

[0890] A means that has a function to receive user feedback and reflect it in the next analysis,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, further comprising means for the computer-based intelligence to use a generative data model to optimize the proposals it generates.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the user interface has a dynamically updated display function, enabling the user to perform operations based on the analysis results.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] A means of receiving data related to business activities from information gathering equipment,

[0899] A means of constructing a virtual environment based on the aforementioned data,

[0900] A means for performing an analysis of business procedures using artificial intelligence within the aforementioned virtual environment,

[0901] A means for distributing the aforementioned analysis results to a terminal with a display screen,

[0902] The terminal includes means for displaying the analysis results using information display means,

[0903] A means of visually presenting the state of equipment in real space,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, further comprising means for artificial intelligence to perform anomaly detection and future prediction analysis and generate improvement suggestions, which are immediately presented to workers on-site.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the information display means has a display function that is updated in real time, enabling the user to perform operations based on the analysis results, and prompting immediate action through presentation using a visual display.

[0909] "Example 2 of combining an emotion engine"

[0910] (Claim 1)

[0911] A means of receiving information about the work environment from information gathering equipment,

[0912] A means for generating a virtual environment based on the aforementioned information,

[0913] A means for analyzing business procedures using a computing device within the aforementioned virtual environment,

[0914] Means for transferring the analysis results to an output device,

[0915] The output device includes means for displaying the analysis results on a display unit,

[0916] A means equipped with an emotion engine that recognizes the user's emotional state,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, further comprising means for the computing device to detect anomalies and perform predictive analysis, generate improvement proposals, and consider user sentiment data.

[0920] (Claim 3)

[0921] The system according to claim 1, wherein the display unit has a presentation means that is updated immediately, enabling the user to perform operations based on the analysis results, and providing an interactive display including emotional feedback.

[0922] "Application example 2 when combining with an emotional engine"

[0923] (Claim 1)

[0924] A means for receiving data about the physical spatial environment from an information gathering device,

[0925] A means for generating a virtual space environment based on the aforementioned data,

[0926] A means for performing an analysis of business procedures using an intelligent machine within the aforementioned virtual environment,

[0927] A sentiment analysis means that recognizes the user's emotional state and generates suggestions based on it,

[0928] A means for delivering the aforementioned analysis results and proposals to a terminal,

[0929] The terminal includes a visual interface means for displaying the analysis results and proposals,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, wherein the intelligent machine has means for detecting anomalies and performing predictive analysis, and for generating improvement suggestions based on emotional states.

[0933] (Claim 3)

[0934] The system according to claim 1, wherein the visual interface has a display means that is updated in real time, enabling the user to perform operations based on the analysis results and suggestions. [Explanation of symbols]

[0935] 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 for receiving data related to the work environment from an information gathering device, A means for generating a virtual environment based on the aforementioned data, A means for performing business process analysis using artificial intelligence within the aforementioned virtual environment, A means for delivering the aforementioned analysis results to a terminal, The terminal includes means for displaying the analysis results on a user interface, A system that includes this.

2. The system according to claim 1, further comprising means for the artificial intelligence to perform anomaly detection and predictive analysis and generate improvement suggestions.

3. The system according to claim 1, wherein the user interface has a display means that is updated in real time, enabling the user to perform operations based on the analysis results.

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