system

Abstract

We provide the system. [Solution] Data collection means for collecting environmental data, A data preprocessing means for preprocessing collected data and removing outliers, A data analysis means for analyzing pre-processed data and optimizing environmental conditions, An inference and condition generation means for adjusting environmental parameters based on the analysis results, An environment adjustment means for controlling the actual device based on the generated conditions, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the biofuel production process, it is difficult to keep the growth of microorganisms in an optimal state, and the production efficiency and product quality may decrease with changes in environmental conditions. In particular, since a large number of variables are involved, manual management has limitations. In addition, there is no means to quickly obtain the information necessary for process optimization, so the decrease in efficiency due to trial and error has become a problem. In view of such a situation, there is a need for a technology that optimizes the culture environment of microorganisms in real time and improves the efficiency and quality of biofuel production.

Means for Solving the Problems

[0005] The present invention provides data collection means for collecting environmental data, and data preprocessing means for preprocessing the collected data and removing outliers. Furthermore, it includes data analysis means for analyzing the preprocessed data and estimating optimal environmental parameters based on microbial growth conditions and metabolic activity. Based on the analysis results, inference and condition generation means generate specific instructions necessary for adjusting the environmental parameters. To execute the generated instructions, environmental adjustment means control the actual device and maintain appropriate environmental conditions. The invention also includes improvement suggestion generation means for suggesting long-term process improvements based on past data history, and feedback means for providing data and adjustment status to the user in real time via a user interface. This makes it possible to automatically optimize the microbial culture environment and improve the efficiency of biofuel production.

[0006] "Data collection means" refers to the function of acquiring process environmental data in real time using sensors and communication devices.

[0007] "Data preprocessing means" refers to a function that corrects and removes missing or outlier values ​​from collected raw data, processing it into a state that can be analyzed.

[0008] "Data analysis means" refers to a function that analyzes the growth conditions and metabolic activity of microorganisms based on pre-processed data and estimates the optimal environmental conditions.

[0009] "Inference and condition generation means" refers to a function that generates specific instructions for adjusting environmental parameters based on the results of data analysis.

[0010] "Environmental adjustment means" refers to a function that adjusts environmental conditions such as temperature and pH via a control device according to generated instructions.

[0011] The term "improvement suggestion generation method" refers to a function that refers to past data history and makes suggestions for future process improvements.

[0012] "Feedback mechanism" refers to a function that displays environmental data and adjustment status to the user in real time via the user interface. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0016] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0019] 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).

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention provides a system that optimizes the growth environment of microorganisms in the biofuel production process by using sensors and an AI agent in combination. Specific embodiments of the system are described below.

[0035] First, the server collects environmental data such as temperature, pH, and nutrient concentration from various sensors in real time. This data is reliably transmitted and stored via a stable communication protocol.

[0036] Next, the collected data is preprocessed by the server, correcting and removing outliers and missing values. This process ensures that the data is in a reliable state before proceeding to analysis.

[0037] Next, the server uses an AI agent to analyze the growth conditions of microorganisms based on the pre-processed data. The AI ​​agent utilizes machine learning algorithms to estimate the optimal environmental parameters. This clarifies the conditions necessary for efficient microbial growth and biofuel production.

[0038] Subsequently, based on the analysis results, the server generates specific instructions for optimizing the environment and sends them to the control unit. The control unit receives these instructions and operates the corresponding devices (e.g., temperature control devices, pH adjusters, etc.). This adjusts the environmental conditions, ensuring that the microbial growth environment is constantly optimized.

[0039] Furthermore, the terminal provides real-time feedback on the overall operating status to the user through the user interface. This allows the user to check how the system is working and make manual adjustments as needed.

[0040] Furthermore, to support long-term process improvement, the server analyzes historical data and generates candidate new microbial strains and process improvement measures. These suggestions are automatically generated and reported to the user, contributing to improved production efficiency in the future.

[0041] As a concrete example, consider a case where microbial growth is insufficient in a manufacturing facility. The server detects this from real-time data and identifies that the temperature is inappropriate through AI analysis. Based on the analysis results, the control device automatically adjusts the temperature, resulting in improved microbial growth. In this way, the present invention autonomously optimizes environmental conditions, enabling efficient biofuel production.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects real-time data such as temperature, pH, and nutrient concentration from the sensors. This data is transmitted to the server using a communication protocol and stored securely.

[0045] Step 2:

[0046] The server receives the collected raw data and begins data preprocessing. It detects outliers from the collected data and uses statistical methods to impute missing values.

[0047] Step 3:

[0048] The server passes the pre-processed data to the AI ​​agent for data analysis. The AI ​​agent uses machine learning algorithms to evaluate the growth state of microorganisms and estimate the optimal environmental parameters.

[0049] Step 4:

[0050] The server generates specific control instructions using inference and condition generation means based on the estimated environmental parameters obtained from the AI ​​agent. These instructions include specific operations such as temperature adjustment and pH adjustment.

[0051] Step 5:

[0052] The server transmits the generated control instructions to the device control module, which then operates the actual device via environmental adjustment means. This maintains optimal environmental conditions for microbial growth.

[0053] Step 6:

[0054] The terminal displays real-time collected environmental data and current settings to the user via a user interface. The user can review the data and make manual adjustments as needed.

[0055] Step 7:

[0056] The server analyzes historical data for long-term process improvement. Using an improvement suggestion generation method, it automatically generates candidate new microbial strains and measures to improve process efficiency, and reports them to the user.

[0057] (Example 1)

[0058] 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."

[0059] We face the challenge of automatically finding the optimal conditions for efficient microbial growth and appropriately adjusting the environment in response to real-time, fluctuating environmental conditions. Conventional methods often involve manual collection, processing, and adjustment of environmental information, which is time-consuming, labor-intensive, and inefficient. Furthermore, there is often no mechanism for long-term implementation of improvement measures, preventing sufficient process optimization.

[0060] 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.

[0061] In this invention, the server includes an information acquisition means for obtaining environmental information, an information preprocessing means for preprocessing the acquired information and removing anomalies, and a machine learning means for analyzing the preprocessed information and estimating optimal environment variables. This enables the rapid calculation of optimal microbial growth conditions, real-time automation of environmental adjustments, and increased process efficiency.

[0062] An "information acquisition means" is a mechanism for collecting environmental data from sensors and other sources.

[0063] "Information preprocessing means" refers to the process of organizing acquired data and correcting or removing outliers and missing values.

[0064] "Machine learning methods" are techniques that use algorithms to analyze data and estimate optimal conditions.

[0065] The "instruction generation means" is a system for creating instructions for adjusting environmental conditions based on the analysis results.

[0066] "Device control means" refers to a mechanism for operating actual equipment according to generated instructions.

[0067] A "problem-solving method" is a function that automatically generates process improvement proposals using past data.

[0068] A "feedback mechanism" is a means of displaying information in real time through a user interface to inform users of the situation.

[0069] The present invention provides a system for optimizing the growth environment of microorganisms in the biofuel production process. Specific embodiments for carrying out this invention are described below.

[0070] First, the server collects environmental data such as temperature, pH, and nutrient concentration from multiple sensors. Common temperature and pH sensors are used, and the data is transmitted to the server via a stable communication protocol. The server then organizes the collected data using the Python pandas library, correcting or removing outliers and missing values.

[0071] Next, the server uses machine learning techniques to analyze the optimal growth conditions for microorganisms from the pre-processed data. Generative AI models such as TENSORFLOW® and PyTorch are utilized, and appropriate machine learning algorithms (e.g., neural networks and random forests) are used to estimate environment variables.

[0072] Based on the analysis results, the server uses an instruction generation mechanism to generate specific instructions for adjusting the environment. These instructions are sent to the control unit, which then uses a PLC (Programmable Logic Controller) or similar device to operate temperature control devices, pH adjusters, and other devices to optimize the environmental conditions.

[0073] The terminal provides real-time feedback to the user regarding the environmental status and the analysis and adjustment status of the server through the user interface. Based on this information, the user can make fine adjustments manually as needed.

[0074] Furthermore, the server analyzes historical data and automatically generates process improvement strategies. It utilizes Python's scikit-learn library and Jupyter Notebook to perform data analysis, create candidate microbial strains and process improvement strategies, and report them to the user.

[0075] For example, if insufficient microbial growth is detected, the server uses data analysis to identify the cause and sends specific instructions to the control unit to adjust the temperature to an appropriate range. In this way, environmental conditions are optimized in real time, supporting the efficient production of biofuels.

[0076] An example of a prompt message would be, "Based on the environmental data collected from the sensor, please use the AI ​​model to analyze the optimal growth conditions for microorganisms and provide feedback."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects environmental data from sensors. This data includes temperature, pH, and nutrient concentration, and is transmitted to the server in real time. The input is raw data from the sensors, and the output is storing this data in a database in an appropriate format. This step ensures the accuracy and precision of the data.

[0080] Step 2:

[0081] The server preprocesses the collected data. At this stage, it detects outliers and corrects missing values ​​to improve data reliability. The input is raw environmental data, and the output is cleaned and filtered data. The Python pandas library is used for this processing.

[0082] Step 3:

[0083] The server analyzes pre-processed data based on an AI agent. The input is pre-processed data, and the output is the optimal growth conditions for microorganisms. Specifically, a generative AI model (using TensorFlow or PyTorch) executes machine learning algorithms to estimate the growth conditions. This result clarifies which environmental parameters should be adjusted.

[0084] Step 4:

[0085] The server generates instructions for environmental adjustment based on the analysis results. The input is the result of the AI ​​analysis, and the output is specific environmental adjustment instructions. These instructions are sent to the control unit, enabling the operation of the equipment (e.g., adjustment of a temperature control unit). In this step, a PLC is used to control the device.

[0086] Step 5:

[0087] The terminal provides real-time feedback to the user on environmental conditions and adjustment details. Input consists of environmental data and adjustment status from the server, while output is an information screen viewable by the user. The user can use this information to make additional manual adjustments if necessary.

[0088] Step 6:

[0089] The server generates long-term process improvement strategies by analyzing historical data. The input is historical data, and the output is a report of improvement suggestions for the user. Results are generated and reported to the user using Python's scikit-learn library and statistical analysis in Jupyter Notebook. In this way, the overall efficiency of the system improves.

[0090] (Application Example 1)

[0091] 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."

[0092] In microbial production environments, environmental conditions significantly impact microbial growth; therefore, these conditions must be optimized in real time for efficient production. However, conventional methods often involve manual adjustment of environmental conditions, making rapid response difficult. In addition, automated feedback systems for long-term improvements based on data are not yet adequately developed.

[0093] 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.

[0094] In this invention, the server includes information gathering means for acquiring environmental data, information preprocessing means for preprocessing the acquired information and removing outliers, and information analysis means for analyzing the preprocessed information and optimizing environmental conditions. This enables real-time optimization of environmental conditions in the microbial production environment and an efficient production process.

[0095] "Information gathering means" refers to a device or system for acquiring environmental data such as temperature, pH, and nutrient concentration in a microbial production environment in real time.

[0096] "Information preprocessing means" refers to a mechanism that automatically detects and removes abnormal values ​​and missing values ​​in order to prepare acquired production environment data for analysis.

[0097] "Information analysis means" refers to a device or system that estimates the optimal conditions for microbial growth based on pre-processed environmental data and performs analysis to optimize the environment.

[0098] The "inference and condition generation means" is a function that, based on the analysis results, issues commands to environmental devices and control devices and generates conditions for appropriately adjusting various parameters.

[0099] "Environmental adjustment means" refers to a device or system that operates the actual equipment according to the generated conditions and performs control to maintain environmental conditions suitable for the growth of microorganisms.

[0100] The "improvement suggestion generation method" is a function that automatically generates long-term improvement suggestions to improve the efficiency of production processes by analyzing past data history.

[0101] A "feedback mechanism" is a function that visually presents the user with real-time environmental data and its adjustment status via the user interface.

[0102] In order to implement the invention, the following system configuration and operation are necessary.

[0103] The server first uses information gathering devices to acquire data such as temperature, pH, and nutrient concentration from the microbial production environment in real time. This information is then aggregated on the server via a group of sensors.

[0104] Next, the server utilizes data preprocessing tools to prepare the acquired data for analysis. Specifically, it uses a Python program to detect and remove outliers, ensuring data reliability.

[0105] Subsequently, the server uses machine learning libraries such as TensorFlow and PyTorch as information analysis tools to estimate the optimal conditions for microbial growth based on the pre-processed data. Based on the results of this analysis, the inference and condition generation tools calculate appropriate environmental conditions and send instructions to the control device.

[0106] Furthermore, the environmental control system adjusts the environment using temperature control devices, pH adjusters, and other means according to the generated conditions. This ensures that the optimal growth environment for microorganisms is always maintained.

[0107] Furthermore, users can monitor the collected data and the adjustment status of the environment in real time through feedback mechanisms via their devices. The UI uses Flask and Django to provide information in a visually user-friendly format.

[0108] With the aim of long-term process improvement, the server analyzes past data using an improvement suggestion generation mechanism and automatically generates suggestions for efficiency improvements. These suggestions are notified to the user and contribute to future productivity improvements.

[0109] For example, if a sudden temperature fluctuation occurs during microbial cultivation, the server instantly detects the abnormal value, calculates the optimal temperature through AI analysis, and the control device immediately adjusts the set temperature, thereby maintaining growth efficiency.

[0110] Examples of prompts to input into a generative AI model are as follows:

[0111] "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration to estimate the optimal conditions."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server acquires temperature, pH, and nutrient concentration from the environment via information gathering devices. It converts the analog signals received from the sensors into digital data and stores it in a database. This allows for the acquisition of data that reflects the actual conditions of the production environment in real time.

[0115] Step 2:

[0116] The server uses data preprocessing to detect outliers and missing values ​​from the acquired data and performs filtering. Specifically, it uses Python to remove outliers using standard deviation, maintaining the normal range of the data. This ensures that reliable, clean data is output.

[0117] Step 3:

[0118] The server uses generative AI models in TensorFlow or PyTorch as data analysis tools to estimate the optimal conditions for microbial growth based on pre-processed data. Clean data is fed to the model as input, and the analysis process outputs the optimal environmental parameters. This clearly shows the conditions best suited for growth in numerical terms.

[0119] Step 4:

[0120] The server generates specific conditions based on the analysis results using inference and condition generation means. Here, it creates control signals using the generated environmental parameters and outputs instructions to be sent to the control device. These instructions include specific operating methods such as temperature setpoints and pH adjustment amounts.

[0121] Step 5:

[0122] Through the environmental control system, the server sends commands to the control unit, which then operates the temperature control unit and pH regulator. This modifies the environment according to the generated conditions, optimizing the microbial growth environment. Feedback on whether the control was successful is also received as input.

[0123] Step 6:

[0124] The device visually presents the collected data and the adjusted environmental state to the user through feedback mechanisms. A user interface using Flask or Django is created here, enabling real-time data monitoring. The user receives this information as input and can perform actions as needed.

[0125] Step 7:

[0126] The user receives automatically generated suggestions for long-term process improvement through the improvement suggestion generation mechanism. The server outputs improvement suggestions based on historical data analysis and presents specific action plans for efficiency improvement. An example of a prompt message could be, "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration and estimate the optimal conditions."

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention provides a system that recognizes user emotions and enables process optimization in the biofuel production process utilizing microorganisms. By incorporating an emotion engine, this system can improve the user experience in addition to optimizing environmental data.

[0129] First, the server collects environmental data such as temperature, pH, and nutrient concentration through various sensors. After data collection, the server uses data preprocessing to remove outliers and missing values ​​from the raw data, making it suitable for analysis. This allows for detailed data analysis using an AI agent based on highly reliable data.

[0130] Data analysis means that pre-processed data is processed by an AI agent to optimize the growth conditions of microorganisms. The AI ​​utilizes machine learning algorithms to estimate efficient environmental parameters. Based on the analysis results, the server sends control instructions generated using inference and condition generation means to various devices, and operates the actual equipment via environmental adjustment means.

[0131] A distinctive feature of this invention is that the server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice through a camera and microphone to identify their current emotions. This emotion data is fed back and helps to dynamically adjust the system's operability and user interface.

[0132] For example, if a process is delayed and the system determines that the user is experiencing stress, the emotion engine recognizes that emotion. Based on this information, the device improves the user interface display and shows support messages and solutions that clearly explain the situation to the user. This increases the user's sense of security and improves the overall system user experience.

[0133] Furthermore, the server accumulates emotional history and provides emotional data analysis tools to support long-term improvement of user satisfaction. This generates suggestions for improving the user experience and contributes to continuous process optimization.

[0134] Overall, this system not only automatically optimizes the microbial growth environment but also achieves advanced process management that takes the user experience into consideration.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server uses various sensors to collect environmental data, acquiring information such as temperature, pH, and nutrient concentration. This data is collected in real time and stored in a database on the server.

[0138] Step 2:

[0139] The server preprocesses the collected data, detecting and removing outliers and imputing missing values. Statistical methods are used for preprocessing to generate a reliable dataset.

[0140] Step 3:

[0141] The server uses an AI agent to perform data analysis based on pre-processed data. The AI ​​agent applies machine learning algorithms to estimate the optimal environmental conditions for microbial growth. The results of this analysis provide the information necessary for environmental adjustment.

[0142] Step 4:

[0143] The server performs inferences based on the analysis results and generates specific environmental adjustment instructions. This inference and condition generation means formulates signals to, for example, temperature control devices and pH balancers.

[0144] Step 5:

[0145] The server sends control instructions to the device and operates the actual equipment via environmental adjustment means. This ensures that the process environment is maintained in an optimized state.

[0146] Step 6:

[0147] The terminal displays current environmental data and adjustment status to the user in an easy-to-understand manner through its user interface. Through this interface, the user can monitor processes and perform manual adjustments.

[0148] Step 7:

[0149] The server uses an emotion engine to recognize the user's emotions through facial recognition and voice analysis. Based on this emotion information, it adapts the system's operation methods and interface to optimize the user experience.

[0150] Step 8:

[0151] The server records the user's emotional history and uses this to suggest improvements to long-term user satisfaction. These suggestions include custom messages and notifications tailored to system usage.

[0152] Step 9:

[0153] Based on the feedback and suggestions provided, users can take actions to deepen their understanding of the system and improve their confidence in it.

[0154] (Example 2)

[0155] 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".

[0156] While current biofuel production systems optimize environmental conditions, they lack dynamic system adjustments that take into account user emotions and experiences, leading to problems such as user stress and a poor user experience. Furthermore, long-term improvements in user satisfaction and the lack of improvement suggestions based on emotional data were also challenges.

[0157] 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.

[0158] In this invention, the server includes measurement means for collecting environmental data, data processing means for preprocessing the collected information and removing abnormal values, and information analysis means for analyzing the preprocessed information and optimizing the biological environment. This makes it possible to recognize the user's emotional data in real time and dynamically adjust the operation and interface of the device.

[0159] "Measurement means" refers to devices and systems used to accurately acquire environmental data, such as temperature, pH, and nutrient concentration, which are collected in real time through sensors.

[0160] "Data processing means" refers to methods and devices for converting acquired raw data into an analyzable format, including removing outliers and supplementing missing data.

[0161] "Information analysis means" refers to algorithms and processing methods for optimizing the biological environment based on pre-processed data, and is intended to efficiently estimate the growth conditions of microorganisms.

[0162] "Inference and condition generation means" refers to methods and processes for generating specific control instructions to adjust environmental conditions based on the results of information analysis.

[0163] "Environmental manipulation means" refers to mechanisms or systems for physically controlling a device based on generated conditions, and for adjusting environmental parameters.

[0164] "Emotional analysis means" refers to systems and technologies for analyzing and recognizing a user's emotions in real time, identifying emotional states from facial expressions and voice data.

[0165] "Interface adjustment means" refers to technologies and methods for dynamically customizing the user interface based on user sentiment data, thereby improving the user experience.

[0166] "Display means" refers to display systems and technologies that visually present the operating status and progress of a device to the user, thereby enhancing a sense of security through the provision of information.

[0167] This invention provides a system for optimizing a biofuel production process utilizing microorganisms and improving the user experience. The main components are a measurement means for collecting environmental data, a data processing means for preprocessing the collected data, an information analysis means for analyzing the preprocessed data, an inference and condition generation means, an environmental manipulation means, an emotion analysis means, an interface adjustment means, and a display means.

[0168] The server collects environmental data in real time using devices such as temperature sensors, pH sensors, and nutrient concentration sensors. This data is processed to remove outliers and noise, preparing it for analysis. After the data is cleaned, the server uses information analysis tools that implement machine learning algorithms to estimate the optimal environmental conditions for microorganisms. Random forests and neural networks are applied to these algorithms.

[0169] Based on the analysis results, the server generates control instructions for environmental manipulation using inference and condition generation means, and adjusts the device using automated environmental manipulation means. This optimizes the growth conditions for microorganisms.

[0170] Furthermore, the server uses emotion analysis tools to analyze the user's facial expressions and voice characteristics via the camera and microphone, recognizing the user's emotions in real time. This allows for appropriate feedback and interface adjustments depending on whether the user is stressed or happy.

[0171] The device uses sentiment data from the server to dynamically adjust the user interface according to the situation. For example, if the user is feeling anxious, the device displays a supportive message that clearly explains the situation, helping to improve the user experience.

[0172] For example, consider a scenario where a user experiences anxiety due to a delay in a microbial process. In this case, the emotion analysis system recognizes the user's emotion, and the terminal immediately displays an explanatory message. This reduces the user's anxiety.

[0173] Examples of prompts for generative AI models

[0174] Prompt: "Explain what measures can be taken to address user stress while optimizing the microbial biofuel production process."

[0175] This invention enables advanced process management that optimizes environmental conditions while also considering the user experience.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server collects environmental data in real time using temperature sensors, pH sensors, and nutrient concentration sensors. This data is obtained in the form of temperature, pH value, nutrient concentration, etc. It receives sensor signals as input, digitizes them, and stores them in a database.

[0179] Step 2:

[0180] The server preprocesses the collected raw data using data processing tools. The input is the environmental data collected in step 1. Specifically, statistical methods are used to detect outliers, and z-scores are calculated to remove abnormal data points. Missing values ​​are linearly imputed using past data history. The output is the cleaned data.

[0181] Step 3:

[0182] The server passes the cleaning results to the AI ​​agent, which uses information analysis tools to estimate the optimal growth conditions for microorganisms. The input is the pre-processed data from step 2. Specifically, machine learning algorithms such as random forests and neural networks are applied to estimate efficient environmental conditions. The output is the estimated optimal environmental parameters.

[0183] Step 4:

[0184] The server generates control instructions using inference and condition generation means based on the estimated results. The input is the optimal parameters obtained in step 3. Specifically, control instructions are generated, and signals are created to control regulating devices such as valves and heaters. The output is the control signal.

[0185] Step 5:

[0186] The server recognizes the user's emotions in real time using emotion analysis tools. Inputs include user facial and voice data obtained via camera and microphone. Specifically, it uses image recognition and voice analysis technologies to identify the user's emotional state. The output is the identified user's emotion data.

[0187] Step 6:

[0188] The terminal dynamically adjusts the user interface using interface adjustment means based on emotional data. The input is the emotional data obtained in step 5. Specifically, if the user is experiencing stress, adjustments are made such as displaying a situational explanation message or simplifying the operation menu. The output is the adjusted interface for the user.

[0189] Step 7:

[0190] The server uses a display to show the device's operating status and the user's emotional state in real time. The inputs are the control signals in step 4 and the emotional data in step 5. Specifically, the display screen provides graphs and text messages indicating the process's progress, providing the user with a sense of security. The output is real-time visual information.

[0191] (Application Example 2)

[0192] 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".

[0193] In biofuel production processes, it is necessary not only to optimize environmental conditions but also to improve the user experience by considering the emotional state of workers, thereby simultaneously increasing work efficiency and satisfaction. However, conventional systems have struggled to recognize workers' emotional states in real time and provide dynamic environmental adjustments and feedback based on them.

[0194] 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.

[0195] In this invention, the server includes information gathering means for collecting environmental information, information preprocessing means for preprocessing the collected information and removing outliers, information analysis means for analyzing the preprocessed information and optimizing environmental conditions, inference and condition generation means for adjusting environmental parameters based on the analysis results, environment adjustment means for controlling the actual device based on the generated conditions, emotion recognition means for analyzing the user's emotions, and experience adjustment means for dynamically adjusting the user experience based on the recognized emotions. This enables process optimization in accordance with the worker's emotions and improvement of the user experience.

[0196] "Environmental information" refers to data on physical and chemical conditions related to the biofuel production process, such as temperature, pH, and nutrient concentration.

[0197] "Information gathering means" refers to devices or technologies for acquiring environmental information and transmitting it to a server.

[0198] "Information preprocessing means" refers to a processing device or method for removing outliers and missing values ​​from collected information and preparing it in a format useful for analysis.

[0199] "Information analysis means" refers to a device or program that performs analysis to optimize environmental conditions based on pre-processed information.

[0200] "Inference and condition generation means" refers to an apparatus or method that determines the optimal environmental parameters based on the analysis results and creates the conditions for those parameters.

[0201] "Environmental adjustment means" refers to a device or system for controlling an actual device and adjusting the environment based on generated conditions.

[0202] "Emotion recognition means" refers to technology that uses input devices such as cameras and microphones to analyze a user's facial expressions and tone of voice to identify their emotions.

[0203] "Experience adjustment means" refers to a device or method that dynamically adjusts the interface and operating environment based on the user's emotions to improve the user experience.

[0204] The embodiments for carrying out the invention are described below.

[0205] This invention is a system aimed at optimizing the biofuel production process and improving the user experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0206] The server integrates various sensors for collecting environmental information. This allows for the efficient acquisition of various data such as temperature, pH, and nutrient concentration. The collected data is preprocessed by information preprocessing means to remove outliers and impute missing values. Subsequently, based on the preprocessed data, information analysis means analyze the optimal environmental conditions, and inference and condition generation means determine the environmental parameters. Specifically, this process is realized using a generative AI model that utilizes machine learning algorithms.

[0207] The device is equipped with emotion recognition technology to recognize the user's emotions, analyzing their facial expressions and tone of voice using a camera and microphone. The emotional state is grasped in real time, and the user interface is dynamically changed by an experience adjustment mechanism. This allows the user to monitor the process while reducing stress and confusion.

[0208] Users can check environmental information and adjustment status through the interface and receive appropriate feedback as needed. For example, if a delay occurs in process progress, a message such as "The process is currently behind schedule, but the environment settings are optimized. We will suggest measures to resolve this situation." will be displayed, providing reassurance to the user.

[0209] In this way, the efficiency of the biofuel production process and the user experience are optimized through the collaboration of the server, terminal, and user. The program to realize this system uses software such as Python, OpenCV, and TensorFlow, as well as hardware such as smart glasses. An example of a prompt message is, "There is a delay in the process progress, but does the user understand the current situation? Calculate stress indicators from the worker's facial expressions and voice, and create a plan of action."

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server acquires environmental information from various sensors. Inputs are raw data such as temperature, pH, and nutrient concentration, while outputs are aggregated versions of this data. The acquired raw data is collected using various data collection methods.

[0213] Step 2:

[0214] The server preprocesses the collected data using information preprocessing means. The input is the raw data obtained in step 1. Outliers are removed and missing values ​​are imputed, and the output is data in a state suitable for analysis.

[0215] Step 3:

[0216] The server analyzes the preprocessed data using information analysis tools. The input is the preprocessed data obtained in step 2. The environmental conditions are optimized using a generative AI model. The output is the optimal environmental parameters.

[0217] Step 4:

[0218] The server generates control commands based on optimized environmental parameters using inference and condition generation means. The input is the optimal environmental parameters obtained in step 3. The output is the control command, which is sent to the environment adjustment means.

[0219] Step 5:

[0220] The device uses emotion recognition to monitor the user's emotions in real time. Input is data on facial expressions and voice tone acquired through the camera and microphone. Output is the result of the analysis of the user's emotional state.

[0221] Step 6:

[0222] The server uses experience adjustment mechanisms to adjust the user interface in response to the user's emotions. The input is the result of the emotional state analysis obtained in step 5. The user interface is dynamically changed, and the output is the content displayed to the visitor.

[0223] Step 7:

[0224] The user checks support messages and process status in real time through the terminal. The input is the information displayed in the interface adjusted in step 6. The output is the user's understanding of the process and sense of security.

[0225] Step 8:

[0226] The server uses example prompts and, if necessary, provides support from a generating AI model. The input is a prompt appropriate to the situation. An example prompt is, "There is a delay in process progress, but does the user understand the current situation?" The output is the generated countermeasures and additional messages.

[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 provides a system that optimizes the growth environment of microorganisms in the biofuel production process by using sensors and an AI agent in combination. Specific embodiments of the system are described below.

[0244] First, the server collects environmental data such as temperature, pH, and nutrient concentration from various sensors in real time. This data is reliably transmitted and stored via a stable communication protocol.

[0245] Next, the collected data is preprocessed by the server, correcting and removing outliers and missing values. This process ensures that the data is in a reliable state before proceeding to analysis.

[0246] Next, the server uses an AI agent to analyze the growth conditions of microorganisms based on the pre-processed data. The AI ​​agent utilizes machine learning algorithms to estimate the optimal environmental parameters. This clarifies the conditions necessary for efficient microbial growth and biofuel production.

[0247] Subsequently, based on the analysis results, the server generates specific instructions for optimizing the environment and sends them to the control unit. The control unit receives these instructions and operates the corresponding devices (e.g., temperature control devices, pH adjusters, etc.). This adjusts the environmental conditions, ensuring that the microbial growth environment is constantly optimized.

[0248] Furthermore, the terminal provides real-time feedback on the overall operating status to the user through the user interface. This allows the user to check how the system is working and make manual adjustments as needed.

[0249] Furthermore, to support long-term process improvement, the server analyzes historical data and generates candidate new microbial strains and process improvement measures. These suggestions are automatically generated and reported to the user, contributing to improved production efficiency in the future.

[0250] As a concrete example, consider a case where microbial growth is insufficient in a manufacturing facility. The server detects this from real-time data and identifies that the temperature is inappropriate through AI analysis. Based on the analysis results, the control device automatically adjusts the temperature, resulting in improved microbial growth. In this way, the present invention autonomously optimizes environmental conditions, enabling efficient biofuel production.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server collects real-time data such as temperature, pH, and nutrient concentration from the sensors. This data is transmitted to the server using a communication protocol and stored securely.

[0254] Step 2:

[0255] The server receives the collected raw data and begins data preprocessing. It detects outliers from the collected data and uses statistical methods to impute missing values.

[0256] Step 3:

[0257] The server passes the pre-processed data to the AI ​​agent for data analysis. The AI ​​agent uses machine learning algorithms to evaluate the growth state of microorganisms and estimate the optimal environmental parameters.

[0258] Step 4:

[0259] The server generates specific control instructions using inference and condition generation means based on the estimated environmental parameters obtained from the AI ​​agent. These instructions include specific operations such as temperature adjustment and pH adjustment.

[0260] Step 5:

[0261] The server transmits the generated control instructions to the device control module, which then operates the actual device via environmental adjustment means. This maintains optimal environmental conditions for microbial growth.

[0262] Step 6:

[0263] The terminal displays real-time collected environmental data and current settings to the user via a user interface. The user can review the data and make manual adjustments as needed.

[0264] Step 7:

[0265] The server analyzes historical data for long-term process improvement. Using an improvement suggestion generation method, it automatically generates candidate new microbial strains and measures to improve process efficiency, and reports them to the user.

[0266] (Example 1)

[0267] 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."

[0268] We face the challenge of automatically finding the optimal conditions for efficient microbial growth and appropriately adjusting the environment in response to real-time, fluctuating environmental conditions. Conventional methods often involve manual collection, processing, and adjustment of environmental information, which is time-consuming, labor-intensive, and inefficient. Furthermore, there is often no mechanism for long-term implementation of improvement measures, preventing sufficient process optimization.

[0269] 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.

[0270] In this invention, the server includes an information acquisition means for obtaining environmental information, an information preprocessing means for preprocessing the acquired information and removing anomalies, and a machine learning means for analyzing the preprocessed information and estimating optimal environment variables. This enables the rapid calculation of optimal microbial growth conditions, real-time automation of environmental adjustments, and increased process efficiency.

[0271] An "information acquisition means" is a mechanism for collecting environmental data from sensors and other sources.

[0272] "Information preprocessing means" refers to the process of organizing acquired data and correcting or removing outliers and missing values.

[0273] "Machine learning methods" are techniques that use algorithms to analyze data and estimate optimal conditions.

[0274] The "instruction generation means" is a system for creating instructions for adjusting environmental conditions based on the analysis results.

[0275] "Device control means" refers to a mechanism for operating actual equipment according to generated instructions.

[0276] A "problem-solving method" is a function that automatically generates process improvement proposals using past data.

[0277] A "feedback mechanism" is a means of displaying information in real time through a user interface to inform users of the situation.

[0278] The present invention provides a system for optimizing the growth environment of microorganisms in the biofuel production process. Specific embodiments for carrying out this invention are described below.

[0279] First, the server collects environmental data such as temperature, pH, and nutrient concentration from multiple sensors. Common temperature and pH sensors are used, and the data is transmitted to the server via a stable communication protocol. The server then organizes the collected data using the Python pandas library, correcting or removing outliers and missing values.

[0280] Next, the server uses machine learning techniques to analyze the optimal growth conditions for microorganisms from the pre-processed data. Generative AI models such as TensorFlow and PyTorch are utilized, and appropriate machine learning algorithms (e.g., neural networks and random forests) are used to estimate environment variables.

[0281] Based on the analysis results, the server uses the instruction generation means to generate specific instructions for adjusting the environment. These instructions are sent to the control device, which uses a PLC (Programmable Logic Controller) or the like to operate the temperature control device and the pH adjustment device to optimize the environmental conditions.

[0282] The terminal provides real-time feedback to the user on the environmental situation and the server's analysis and adjustment status through the user interface. Based on this information, the user can manually make fine adjustments as needed.

[0283] Furthermore, the server analyzes past data and automatically generates process improvement measures. Utilizing Python's scikit-learn library and Jupyter Notebook, it conducts data analysis, creates candidates for new microbial strains and process improvement measures, and reports them to the user.

[0284] As a specific example, when it is detected that the growth of microorganisms is insufficient, the server uses data analysis to identify the cause and sends specific instructions to the control device to adjust the temperature to the appropriate range. In this way, the environmental conditions are optimized in real time to support the efficient production of biofuels.

[0285] As an example of a prompt sentence, an instruction is input to the AI in the form of "Please analyze and provide feedback on the optimal growth conditions of microorganisms using an AI model based on the environmental data collected from the sensors."

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The server collects environmental data from sensors. This data includes temperature, pH, and nutrient concentration, and is transmitted to the server in real time. The input is raw data from the sensors, and the output is storing this data in a database in an appropriate format. This step ensures the accuracy and precision of the data.

[0289] Step 2:

[0290] The server preprocesses the collected data. At this stage, it detects outliers and corrects missing values ​​to improve data reliability. The input is raw environmental data, and the output is cleaned and filtered data. The Python pandas library is used for this processing.

[0291] Step 3:

[0292] The server analyzes pre-processed data based on an AI agent. The input is pre-processed data, and the output is the optimal growth conditions for microorganisms. Specifically, a generative AI model (using TensorFlow or PyTorch) executes machine learning algorithms to estimate the growth conditions. This result clarifies which environmental parameters should be adjusted.

[0293] Step 4:

[0294] The server generates instructions for environmental adjustment based on the analysis results. The input is the result of the AI ​​analysis, and the output is specific environmental adjustment instructions. These instructions are sent to the control unit, enabling the operation of the equipment (e.g., adjustment of a temperature control unit). In this step, a PLC is used to control the device.

[0295] Step 5:

[0296] The terminal provides real-time feedback to the user on environmental conditions and adjustment details. Input consists of environmental data and adjustment status from the server, while output is an information screen viewable by the user. The user can use this information to make additional manual adjustments if necessary.

[0297] Step 6:

[0298] The server generates long-term process improvement strategies by analyzing historical data. The input is historical data, and the output is a report of improvement suggestions for the user. Results are generated and reported to the user using Python's scikit-learn library and statistical analysis in Jupyter Notebook. In this way, the overall efficiency of the system improves.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] In microbial production environments, environmental conditions significantly impact microbial growth; therefore, these conditions must be optimized in real time for efficient production. However, conventional methods often involve manual adjustment of environmental conditions, making rapid response difficult. In addition, automated feedback systems for long-term improvements based on data are not yet adequately developed.

[0302] 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.

[0303] In this invention, the server includes information gathering means for acquiring environmental data, information preprocessing means for preprocessing the acquired information and removing outliers, and information analysis means for analyzing the preprocessed information and optimizing environmental conditions. This enables real-time optimization of environmental conditions in the microbial production environment and an efficient production process.

[0304] The "information collection means" is a device or system for acquiring environmental data such as temperature, pH, nutrient concentration, etc. in the microbial production environment in real time.

[0305] The "information preprocessing means" is a mechanism that performs a process of automatically detecting and removing outliers and missing values in order to arrange the acquired production environment data in a form suitable for analysis.

[0306] The "information analysis means" is a device or system that estimates the optimal conditions for the growth of microorganisms based on the preprocessed environmental data and performs analysis for optimizing the environment.

[0307] The "inference and condition generation means" is a function that issues commands to environmental devices and control devices based on the analysis results and generates conditions for appropriately adjusting various parameters.

[0308] The "environment adjustment means" is a device or system that operates the actual device according to the generated conditions and performs control to maintain environmental conditions suitable for the growth of microorganisms.

[0309] The "improvement proposal generation means" is a function that automatically generates long-term improvement proposals for improving the efficiency of the production process by analyzing past data histories.

[0310] The "feedback means" is a function that visually presents environmental data acquired in real time and its adjustment status to the user via a user interface.

[0311] In order to implement the invention, the following system configuration and operations are required.

[0312] First, the server uses the information collection means to acquire data such as temperature, pH, nutrient concentration, etc. from the microbial production environment in real time. This information is aggregated to the server via a group of sensors.

[0313] Next, the server utilizes data preprocessing tools to prepare the acquired data for analysis. Specifically, it uses a Python program to detect and remove outliers, ensuring data reliability.

[0314] Subsequently, the server uses machine learning libraries such as TensorFlow and PyTorch as information analysis tools to estimate the optimal conditions for microbial growth based on the pre-processed data. Based on the results of this analysis, the inference and condition generation tools calculate appropriate environmental conditions and send instructions to the control device.

[0315] Furthermore, the environmental control system adjusts the environment using temperature control devices, pH adjusters, and other means according to the generated conditions. This ensures that the optimal growth environment for microorganisms is always maintained.

[0316] Furthermore, users can monitor the collected data and the adjustment status of the environment in real time through feedback mechanisms via their devices. The UI uses Flask and Django to provide information in a visually user-friendly format.

[0317] With the aim of long-term process improvement, the server analyzes past data using an improvement suggestion generation mechanism and automatically generates suggestions for efficiency improvements. These suggestions are notified to the user and contribute to future productivity improvements.

[0318] For example, if a sudden temperature fluctuation occurs during microbial cultivation, the server instantly detects the abnormal value, calculates the optimal temperature through AI analysis, and the control device immediately adjusts the set temperature, thereby maintaining growth efficiency.

[0319] Examples of prompts to input into a generative AI model are as follows:

[0320] "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration to estimate the optimal conditions."

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The server acquires temperature, pH, and nutrient concentration from the environment via information gathering devices. It converts the analog signals received from the sensors into digital data and stores it in a database. This allows for the acquisition of data that reflects the actual conditions of the production environment in real time.

[0324] Step 2:

[0325] The server uses data preprocessing to detect outliers and missing values ​​from the acquired data and performs filtering. Specifically, it uses Python to remove outliers using standard deviation, maintaining the normal range of the data. This ensures that reliable, clean data is output.

[0326] Step 3:

[0327] The server uses generative AI models in TensorFlow or PyTorch as data analysis tools to estimate the optimal conditions for microbial growth based on pre-processed data. Clean data is fed to the model as input, and the analysis process outputs the optimal environmental parameters. This clearly shows the conditions best suited for growth in numerical terms.

[0328] Step 4:

[0329] The server generates specific conditions based on the analysis results using inference and condition generation means. Here, it creates control signals using the generated environmental parameters and outputs instructions to be sent to the control device. These instructions include specific operating methods such as temperature setpoints and pH adjustment amounts.

[0330] Step 5:

[0331] Through the environmental control system, the server sends commands to the control unit, which then operates the temperature control unit and pH regulator. This modifies the environment according to the generated conditions, optimizing the microbial growth environment. Feedback on whether the control was successful is also received as input.

[0332] Step 6:

[0333] The device visually presents the collected data and the adjusted environmental state to the user through feedback mechanisms. A user interface using Flask or Django is created here, enabling real-time data monitoring. The user receives this information as input and can perform actions as needed.

[0334] Step 7:

[0335] The user receives automatically generated suggestions for long-term process improvement through the improvement suggestion generation mechanism. The server outputs improvement suggestions based on historical data analysis and presents specific action plans for efficiency improvement. An example of a prompt message could be, "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration and estimate the optimal conditions."

[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 provides a system that recognizes user emotions and enables process optimization in the biofuel production process utilizing microorganisms. By incorporating an emotion engine, this system can improve the user experience in addition to optimizing environmental data.

[0338] First, the server collects environmental data such as temperature, pH, and nutrient concentration through various sensors. After data collection, the server uses data preprocessing to remove outliers and missing values ​​from the raw data, making it suitable for analysis. This allows for detailed data analysis using an AI agent based on highly reliable data.

[0339] Data analysis means that pre-processed data is processed by an AI agent to optimize the growth conditions of microorganisms. The AI ​​utilizes machine learning algorithms to estimate efficient environmental parameters. Based on the analysis results, the server sends control instructions generated using inference and condition generation means to various devices, and operates the actual equipment via environmental adjustment means.

[0340] A distinctive feature of this invention is that the server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice through a camera and microphone to identify their current emotions. This emotion data is fed back and helps to dynamically adjust the system's operability and user interface.

[0341] For example, if a process is delayed and the system determines that the user is experiencing stress, the emotion engine recognizes that emotion. Based on this information, the device improves the user interface display and shows support messages and solutions that clearly explain the situation to the user. This increases the user's sense of security and improves the overall system user experience.

[0342] Furthermore, the server accumulates emotional history and provides emotional data analysis tools to support long-term improvement of user satisfaction. This generates suggestions for improving the user experience and contributes to continuous process optimization.

[0343] Overall, this system not only automatically optimizes the microbial growth environment but also achieves advanced process management that takes the user experience into consideration.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] The server uses various sensors to collect environmental data, acquiring information such as temperature, pH, and nutrient concentration. This data is collected in real time and stored in a database on the server.

[0347] Step 2:

[0348] The server preprocesses the collected data, detecting and removing outliers and imputing missing values. Statistical methods are used for preprocessing to generate a reliable dataset.

[0349] Step 3:

[0350] The server uses an AI agent to perform data analysis based on pre-processed data. The AI ​​agent applies machine learning algorithms to estimate the optimal environmental conditions for microbial growth. The results of this analysis provide the information necessary for environmental adjustment.

[0351] Step 4:

[0352] The server performs inferences based on the analysis results and generates specific environmental adjustment instructions. This inference and condition generation means formulates signals to, for example, temperature control devices and pH balancers.

[0353] Step 5:

[0354] The server sends control instructions to the device and operates the actual equipment via environmental adjustment means. This ensures that the process environment is maintained in an optimized state.

[0355] Step 6:

[0356] The terminal displays current environmental data and adjustment status to the user in an easy-to-understand manner through its user interface. Through this interface, the user can monitor processes and perform manual adjustments.

[0357] Step 7:

[0358] The server uses an emotion engine to recognize the user's emotions through facial recognition and voice analysis. Based on this emotion information, it adapts the system's operation methods and interface to optimize the user experience.

[0359] Step 8:

[0360] The server records the user's emotional history and uses this to suggest improvements to long-term user satisfaction. These suggestions include custom messages and notifications tailored to system usage.

[0361] Step 9:

[0362] Based on the feedback and suggestions provided, users can take actions to deepen their understanding of the system and improve their confidence in it.

[0363] (Example 2)

[0364] 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".

[0365] While current biofuel production systems optimize environmental conditions, they lack dynamic system adjustments that take into account user emotions and experiences, leading to problems such as user stress and a poor user experience. Furthermore, long-term improvements in user satisfaction and the lack of improvement suggestions based on emotional data were also challenges.

[0366] 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.

[0367] In this invention, the server includes measurement means for collecting environmental data, data processing means for preprocessing the collected information and removing abnormal values, and information analysis means for analyzing the preprocessed information and optimizing the biological environment. This makes it possible to recognize the user's emotional data in real time and dynamically adjust the operation and interface of the device.

[0368] "Measurement means" refers to devices and systems used to accurately acquire environmental data, such as temperature, pH, and nutrient concentration, which are collected in real time through sensors.

[0369] "Data processing means" refers to methods and devices for converting acquired raw data into an analyzable format, including removing outliers and supplementing missing data.

[0370] "Information analysis means" refers to algorithms and processing methods for optimizing the biological environment based on pre-processed data, and is intended to efficiently estimate the growth conditions of microorganisms.

[0371] "Inference and condition generation means" refers to methods and processes for generating specific control instructions to adjust environmental conditions based on the results of information analysis.

[0372] "Environmental manipulation means" refers to mechanisms or systems for physically controlling a device based on generated conditions, and for adjusting environmental parameters.

[0373] "Emotional analysis means" refers to systems and technologies for analyzing and recognizing a user's emotions in real time, identifying emotional states from facial expressions and voice data.

[0374] "Interface adjustment means" refers to technologies and methods for dynamically customizing the user interface based on user sentiment data, thereby improving the user experience.

[0375] "Display means" refers to display systems and technologies that visually present the operating status and progress of a device to the user, thereby enhancing a sense of security through the provision of information.

[0376] This invention provides a system for optimizing a biofuel production process utilizing microorganisms and improving the user experience. The main components are a measurement means for collecting environmental data, a data processing means for preprocessing the collected data, an information analysis means for analyzing the preprocessed data, an inference and condition generation means, an environmental manipulation means, an emotion analysis means, an interface adjustment means, and a display means.

[0377] The server collects environmental data in real time using devices such as temperature sensors, pH sensors, and nutrient concentration sensors. This data is processed to remove outliers and noise, preparing it for analysis. After the data is cleaned, the server uses information analysis tools that implement machine learning algorithms to estimate the optimal environmental conditions for microorganisms. Random forests and neural networks are applied to these algorithms.

[0378] Based on the analysis results, the server generates control instructions for environmental manipulation using inference and condition generation means, and adjusts the device using automated environmental manipulation means. This optimizes the growth conditions for microorganisms.

[0379] Furthermore, the server uses emotion analysis tools to analyze the user's facial expressions and voice characteristics via the camera and microphone, recognizing the user's emotions in real time. This allows for appropriate feedback and interface adjustments depending on whether the user is stressed or happy.

[0380] The device uses sentiment data from the server to dynamically adjust the user interface according to the situation. For example, if the user is feeling anxious, the device displays a supportive message that clearly explains the situation, helping to improve the user experience.

[0381] For example, consider a scenario where a user experiences anxiety due to a delay in a microbial process. In this case, the emotion analysis system recognizes the user's emotion, and the terminal immediately displays an explanatory message. This reduces the user's anxiety.

[0382] Examples of prompts for generative AI models

[0383] Prompt: "Explain what measures can be taken to address user stress while optimizing the microbial biofuel production process."

[0384] This invention enables advanced process management that optimizes environmental conditions while also considering the user experience.

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] The server collects environmental data in real time using temperature sensors, pH sensors, and nutrient concentration sensors. This data is obtained in the form of temperature, pH value, nutrient concentration, etc. It receives sensor signals as input, digitizes them, and stores them in a database.

[0388] Step 2:

[0389] The server preprocesses the collected raw data using data processing tools. The input is the environmental data collected in step 1. Specifically, statistical methods are used to detect outliers, and z-scores are calculated to remove abnormal data points. Missing values ​​are linearly imputed using past data history. The output is the cleaned data.

[0390] Step 3:

[0391] The server passes the cleaning results to the AI ​​agent, which uses information analysis tools to estimate the optimal growth conditions for microorganisms. The input is the pre-processed data from step 2. Specifically, machine learning algorithms such as random forests and neural networks are applied to estimate efficient environmental conditions. The output is the estimated optimal environmental parameters.

[0392] Step 4:

[0393] The server generates control instructions using inference and condition generation means based on the estimated results. The input is the optimal parameters obtained in step 3. Specifically, control instructions are generated, and signals are created to control regulating devices such as valves and heaters. The output is the control signal.

[0394] Step 5:

[0395] The server recognizes the user's emotions in real time using emotion analysis tools. Inputs include user facial and voice data obtained via camera and microphone. Specifically, it uses image recognition and voice analysis technologies to identify the user's emotional state. The output is the identified user's emotion data.

[0396] Step 6:

[0397] The terminal dynamically adjusts the user interface using interface adjustment means based on emotional data. The input is the emotional data obtained in step 5. Specifically, if the user is experiencing stress, adjustments are made such as displaying a situational explanation message or simplifying the operation menu. The output is the adjusted interface for the user.

[0398] Step 7:

[0399] The server uses a display to show the device's operating status and the user's emotional state in real time. The inputs are the control signals in step 4 and the emotional data in step 5. Specifically, the display screen provides graphs and text messages indicating the process's progress, providing the user with a sense of security. The output is real-time visual information.

[0400] (Application Example 2)

[0401] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0402] In biofuel production processes, it is necessary not only to optimize environmental conditions but also to improve the user experience by considering the emotional state of workers, thereby simultaneously increasing work efficiency and satisfaction. However, conventional systems have struggled to recognize workers' emotional states in real time and provide dynamic environmental adjustments and feedback based on them.

[0403] 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.

[0404] In this invention, the server includes information gathering means for collecting environmental information, information preprocessing means for preprocessing the collected information and removing outliers, information analysis means for analyzing the preprocessed information and optimizing environmental conditions, inference and condition generation means for adjusting environmental parameters based on the analysis results, environment adjustment means for controlling the actual device based on the generated conditions, emotion recognition means for analyzing the user's emotions, and experience adjustment means for dynamically adjusting the user experience based on the recognized emotions. This enables process optimization in accordance with the worker's emotions and improvement of the user experience.

[0405] "Environmental information" refers to data on physical and chemical conditions related to the biofuel production process, such as temperature, pH, and nutrient concentration.

[0406] "Information gathering means" refers to devices or technologies for acquiring environmental information and transmitting it to a server.

[0407] "Information preprocessing means" refers to a processing device or method for removing outliers and missing values ​​from collected information and preparing it in a format useful for analysis.

[0408] "Information analysis means" refers to a device or program that performs analysis to optimize environmental conditions based on pre-processed information.

[0409] "Inference and condition generation means" refers to an apparatus or method that determines the optimal environmental parameters based on the analysis results and creates the conditions for those parameters.

[0410] "Environmental adjustment means" refers to a device or system for controlling an actual device and adjusting the environment based on generated conditions.

[0411] "Emotion recognition means" refers to technology that uses input devices such as cameras and microphones to analyze a user's facial expressions and tone of voice to identify their emotions.

[0412] "Experience adjustment means" refers to a device or method that dynamically adjusts the interface and operating environment based on the user's emotions to improve the user experience.

[0413] The embodiments for carrying out the invention are described below.

[0414] This invention is a system aimed at optimizing the biofuel production process and improving the user experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0415] The server integrates various sensors for collecting environmental information. This allows for the efficient acquisition of various data such as temperature, pH, and nutrient concentration. The collected data is preprocessed by information preprocessing means to remove outliers and impute missing values. Subsequently, based on the preprocessed data, information analysis means analyze the optimal environmental conditions, and inference and condition generation means determine the environmental parameters. Specifically, this process is realized using a generative AI model that utilizes machine learning algorithms.

[0416] The device is equipped with emotion recognition technology to recognize the user's emotions, analyzing their facial expressions and tone of voice using a camera and microphone. The emotional state is grasped in real time, and the user interface is dynamically changed by an experience adjustment mechanism. This allows the user to monitor the process while reducing stress and confusion.

[0417] Users can check environmental information and adjustment status through the interface and receive appropriate feedback as needed. For example, if a delay occurs in process progress, a message such as "The process is currently behind schedule, but the environment settings are optimized. We will suggest measures to resolve this situation." will be displayed, providing reassurance to the user.

[0418] In this way, the efficiency of the biofuel production process and the user experience are optimized through the collaboration of the server, terminal, and user. The program to realize this system uses software such as Python, OpenCV, and TensorFlow, as well as hardware such as smart glasses. An example of a prompt message is, "There is a delay in the process progress, but does the user understand the current situation? Calculate stress indicators from the worker's facial expressions and voice, and create a plan of action."

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] The server acquires environmental information from various sensors. Inputs are raw data such as temperature, pH, and nutrient concentration, while outputs are aggregated versions of this data. The acquired raw data is collected using various data collection methods.

[0422] Step 2:

[0423] The server preprocesses the collected data using information preprocessing means. The input is the raw data obtained in step 1. Outliers are removed and missing values ​​are imputed, and the output is data in a state suitable for analysis.

[0424] Step 3:

[0425] The server analyzes the preprocessed data using information analysis tools. The input is the preprocessed data obtained in step 2. The environmental conditions are optimized using a generative AI model. The output is the optimal environmental parameters.

[0426] Step 4:

[0427] The server generates control commands based on optimized environmental parameters using inference and condition generation means. The input is the optimal environmental parameters obtained in step 3. The output is the control command, which is sent to the environment adjustment means.

[0428] Step 5:

[0429] The device uses emotion recognition to monitor the user's emotions in real time. Input is data on facial expressions and voice tone acquired through the camera and microphone. Output is the result of the analysis of the user's emotional state.

[0430] Step 6:

[0431] The server uses experience adjustment mechanisms to adjust the user interface in response to the user's emotions. The input is the result of the emotional state analysis obtained in step 5. The user interface is dynamically changed, and the output is the content displayed to the visitor.

[0432] Step 7:

[0433] The user checks support messages and process status in real time through the terminal. The input is the information displayed in the interface adjusted in step 6. The output is the user's understanding of the process and sense of security.

[0434] Step 8:

[0435] The server uses example prompts and, if necessary, provides support from a generating AI model. The input is a prompt appropriate to the situation. An example prompt is, "There is a delay in process progress, but does the user understand the current situation?" The output is the generated countermeasures and additional messages.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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".

[0452] This invention provides a system that optimizes the growth environment of microorganisms in the biofuel production process by using sensors and an AI agent in combination. Specific embodiments of the system are described below.

[0453] First, the server collects environmental data such as temperature, pH, and nutrient concentration from various sensors in real time. This data is reliably transmitted and stored via a stable communication protocol.

[0454] Next, the collected data is preprocessed by the server, correcting and removing outliers and missing values. This process ensures that the data is in a reliable state before proceeding to analysis.

[0455] Next, the server uses an AI agent to analyze the growth conditions of microorganisms based on the pre-processed data. The AI ​​agent utilizes machine learning algorithms to estimate the optimal environmental parameters. This clarifies the conditions necessary for efficient microbial growth and biofuel production.

[0456] Subsequently, based on the analysis results, the server generates specific instructions for optimizing the environment and sends them to the control unit. The control unit receives these instructions and operates the corresponding devices (e.g., temperature control devices, pH adjusters, etc.). This adjusts the environmental conditions, ensuring that the microbial growth environment is constantly optimized.

[0457] Furthermore, the terminal provides real-time feedback on the overall operating status to the user through the user interface. This allows the user to check how the system is working and make manual adjustments as needed.

[0458] Furthermore, to support long-term process improvement, the server analyzes historical data and generates candidate new microbial strains and process improvement measures. These suggestions are automatically generated and reported to the user, contributing to improved production efficiency in the future.

[0459] As a concrete example, consider a case where microbial growth is insufficient in a manufacturing facility. The server detects this from real-time data and identifies that the temperature is inappropriate through AI analysis. Based on the analysis results, the control device automatically adjusts the temperature, resulting in improved microbial growth. In this way, the present invention autonomously optimizes environmental conditions, enabling efficient biofuel production.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The server collects real-time data such as temperature, pH, and nutrient concentration from the sensors. This data is transmitted to the server using a communication protocol and stored securely.

[0463] Step 2:

[0464] The server receives the collected raw data and begins data preprocessing. It detects outliers from the collected data and uses statistical methods to impute missing values.

[0465] Step 3:

[0466] The server passes the pre-processed data to the AI ​​agent for data analysis. The AI ​​agent uses machine learning algorithms to evaluate the growth state of microorganisms and estimate the optimal environmental parameters.

[0467] Step 4:

[0468] The server generates specific control instructions using inference and condition generation means based on the estimated environmental parameters obtained from the AI ​​agent. These instructions include specific operations such as temperature adjustment and pH adjustment.

[0469] Step 5:

[0470] The server transmits the generated control instructions to the device control module, which then operates the actual device via environmental adjustment means. This maintains optimal environmental conditions for microbial growth.

[0471] Step 6:

[0472] The terminal displays real-time collected environmental data and current settings to the user via a user interface. The user can review the data and make manual adjustments as needed.

[0473] Step 7:

[0474] The server analyzes historical data for long-term process improvement. Using an improvement suggestion generation method, it automatically generates candidate new microbial strains and measures to improve process efficiency, and reports them to the user.

[0475] (Example 1)

[0476] 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."

[0477] We face the challenge of automatically finding the optimal conditions for efficient microbial growth and appropriately adjusting the environment in response to real-time, fluctuating environmental conditions. Conventional methods often involve manual collection, processing, and adjustment of environmental information, which is time-consuming, labor-intensive, and inefficient. Furthermore, there is often no mechanism for long-term implementation of improvement measures, preventing sufficient process optimization.

[0478] 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.

[0479] In this invention, the server includes an information acquisition means for obtaining environmental information, an information preprocessing means for preprocessing the acquired information and removing anomalies, and a machine learning means for analyzing the preprocessed information and estimating optimal environment variables. This enables the rapid calculation of optimal microbial growth conditions, real-time automation of environmental adjustments, and increased process efficiency.

[0480] An "information acquisition means" is a mechanism for collecting environmental data from sensors and other sources.

[0481] "Information preprocessing means" refers to the process of organizing acquired data and correcting or removing outliers and missing values.

[0482] "Machine learning methods" are techniques that use algorithms to analyze data and estimate optimal conditions.

[0483] The "instruction generation means" is a system for creating instructions for adjusting environmental conditions based on the analysis results.

[0484] "Device control means" refers to a mechanism for operating actual equipment according to generated instructions.

[0485] A "problem-solving method" is a function that automatically generates process improvement proposals using past data.

[0486] A "feedback mechanism" is a means of displaying information in real time through a user interface to inform users of the situation.

[0487] The present invention provides a system for optimizing the growth environment of microorganisms in the biofuel production process. Specific embodiments for carrying out this invention are described below.

[0488] First, the server collects environmental data such as temperature, pH, and nutrient concentration from multiple sensors. Common temperature and pH sensors are used, and the data is transmitted to the server via a stable communication protocol. The server then organizes the collected data using the Python pandas library, correcting or removing outliers and missing values.

[0489] Next, the server uses machine learning techniques to analyze the optimal growth conditions for microorganisms from the pre-processed data. Generative AI models such as TensorFlow and PyTorch are utilized, and appropriate machine learning algorithms (e.g., neural networks and random forests) are used to estimate environment variables.

[0490] Based on the analysis results, the server uses an instruction generation mechanism to generate specific instructions for adjusting the environment. These instructions are sent to the control unit, which then uses a PLC (Programmable Logic Controller) or similar device to operate temperature control devices, pH adjusters, and other devices to optimize the environmental conditions.

[0491] The terminal provides real-time feedback to the user regarding the environmental status and the analysis and adjustment status of the server through the user interface. Based on this information, the user can make fine adjustments manually as needed.

[0492] Furthermore, the server analyzes historical data and automatically generates process improvement strategies. It utilizes Python's scikit-learn library and Jupyter Notebook to perform data analysis, create candidate microbial strains and process improvement strategies, and report them to the user.

[0493] For example, if insufficient microbial growth is detected, the server uses data analysis to identify the cause and sends specific instructions to the control unit to adjust the temperature to an appropriate range. In this way, environmental conditions are optimized in real time, supporting the efficient production of biofuels.

[0494] An example of a prompt message would be, "Based on the environmental data collected from the sensor, please use the AI ​​model to analyze the optimal growth conditions for microorganisms and provide feedback."

[0495] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0496] Step 1:

[0497] The server collects environmental data from sensors. This data includes temperature, pH, and nutrient concentration, and is transmitted to the server in real time. The input is raw data from the sensors, and the output is storing this data in a database in an appropriate format. This step ensures the accuracy and precision of the data.

[0498] Step 2:

[0499] The server preprocesses the collected data. At this stage, it detects outliers and corrects missing values ​​to improve data reliability. The input is raw environmental data, and the output is cleaned and filtered data. The Python pandas library is used for this processing.

[0500] Step 3:

[0501] The server analyzes pre-processed data based on an AI agent. The input is pre-processed data, and the output is the optimal growth conditions for microorganisms. Specifically, a generative AI model (using TensorFlow or PyTorch) executes machine learning algorithms to estimate the growth conditions. This result clarifies which environmental parameters should be adjusted.

[0502] Step 4:

[0503] The server generates instructions for environmental adjustment based on the analysis results. The input is the result of the AI ​​analysis, and the output is specific environmental adjustment instructions. These instructions are sent to the control unit, enabling the operation of the equipment (e.g., adjustment of a temperature control unit). In this step, a PLC is used to control the device.

[0504] Step 5:

[0505] The terminal provides real-time feedback to the user on environmental conditions and adjustment details. Input consists of environmental data and adjustment status from the server, while output is an information screen viewable by the user. The user can use this information to make additional manual adjustments if necessary.

[0506] Step 6:

[0507] The server generates long-term process improvement strategies by analyzing historical data. The input is historical data, and the output is a report of improvement suggestions for the user. Results are generated and reported to the user using Python's scikit-learn library and statistical analysis in Jupyter Notebook. In this way, the overall efficiency of the system improves.

[0508] (Application Example 1)

[0509] 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."

[0510] In microbial production environments, environmental conditions significantly impact microbial growth; therefore, these conditions must be optimized in real time for efficient production. However, conventional methods often involve manual adjustment of environmental conditions, making rapid response difficult. In addition, automated feedback systems for long-term improvements based on data are not yet adequately developed.

[0511] 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.

[0512] In this invention, the server includes information gathering means for acquiring environmental data, information preprocessing means for preprocessing the acquired information and removing outliers, and information analysis means for analyzing the preprocessed information and optimizing environmental conditions. This enables real-time optimization of environmental conditions in the microbial production environment and an efficient production process.

[0513] "Information gathering means" refers to a device or system for acquiring environmental data such as temperature, pH, and nutrient concentration in a microbial production environment in real time.

[0514] "Information preprocessing means" refers to a mechanism that automatically detects and removes abnormal values ​​and missing values ​​in order to prepare acquired production environment data for analysis.

[0515] "Information analysis means" refers to a device or system that estimates the optimal conditions for microbial growth based on pre-processed environmental data and performs analysis to optimize the environment.

[0516] The "inference and condition generation means" is a function that, based on the analysis results, issues commands to environmental devices and control devices and generates conditions for appropriately adjusting various parameters.

[0517] "Environmental adjustment means" refers to a device or system that operates the actual equipment according to the generated conditions and performs control to maintain environmental conditions suitable for the growth of microorganisms.

[0518] The "improvement suggestion generation method" is a function that automatically generates long-term improvement suggestions to improve the efficiency of production processes by analyzing past data history.

[0519] A "feedback mechanism" is a function that visually presents the user with real-time environmental data and its adjustment status via the user interface.

[0520] In order to implement the invention, the following system configuration and operation are necessary.

[0521] The server first uses information gathering devices to acquire data such as temperature, pH, and nutrient concentration from the microbial production environment in real time. This information is then aggregated on the server via a group of sensors.

[0522] Next, the server utilizes data preprocessing tools to prepare the acquired data for analysis. Specifically, it uses a Python program to detect and remove outliers, ensuring data reliability.

[0523] Subsequently, the server uses machine learning libraries such as TensorFlow and PyTorch as information analysis tools to estimate the optimal conditions for microbial growth based on the pre-processed data. Based on the results of this analysis, the inference and condition generation tools calculate appropriate environmental conditions and send instructions to the control device.

[0524] Furthermore, the environmental control system adjusts the environment using temperature control devices, pH adjusters, and other means according to the generated conditions. This ensures that the optimal growth environment for microorganisms is always maintained.

[0525] Furthermore, users can monitor the collected data and the adjustment status of the environment in real time through feedback mechanisms via their devices. The UI uses Flask and Django to provide information in a visually user-friendly format.

[0526] With the aim of long-term process improvement, the server analyzes past data using an improvement suggestion generation mechanism and automatically generates suggestions for efficiency improvements. These suggestions are notified to the user and contribute to future productivity improvements.

[0527] For example, if a sudden temperature fluctuation occurs during microbial cultivation, the server instantly detects the abnormal value, calculates the optimal temperature through AI analysis, and the control device immediately adjusts the set temperature, thereby maintaining growth efficiency.

[0528] Examples of prompts to input into a generative AI model are as follows:

[0529] "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration to estimate the optimal conditions."

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The server acquires temperature, pH, and nutrient concentration from the environment via information gathering devices. It converts the analog signals received from the sensors into digital data and stores it in a database. This allows for the acquisition of data that reflects the actual conditions of the production environment in real time.

[0533] Step 2:

[0534] The server uses data preprocessing to detect outliers and missing values ​​from the acquired data and performs filtering. Specifically, it uses Python to remove outliers using standard deviation, maintaining the normal range of the data. This ensures that reliable, clean data is output.

[0535] Step 3:

[0536] The server uses generative AI models in TensorFlow or PyTorch as data analysis tools to estimate the optimal conditions for microbial growth based on pre-processed data. Clean data is fed to the model as input, and the analysis process outputs the optimal environmental parameters. This clearly shows the conditions best suited for growth in numerical terms.

[0537] Step 4:

[0538] The server generates specific conditions based on the analysis results using inference and condition generation means. Here, it creates control signals using the generated environmental parameters and outputs instructions to be sent to the control device. These instructions include specific operating methods such as temperature setpoints and pH adjustment amounts.

[0539] Step 5:

[0540] Through the environmental control system, the server sends commands to the control unit, which then operates the temperature control unit and pH regulator. This modifies the environment according to the generated conditions, optimizing the microbial growth environment. Feedback on whether the control was successful is also received as input.

[0541] Step 6:

[0542] The device visually presents the collected data and the adjusted environmental state to the user through feedback mechanisms. A user interface using Flask or Django is created here, enabling real-time data monitoring. The user receives this information as input and can perform actions as needed.

[0543] Step 7:

[0544] The user receives automatically generated suggestions for long-term process improvement through the improvement suggestion generation mechanism. The server outputs improvement suggestions based on historical data analysis and presents specific action plans for efficiency improvement. An example of a prompt message could be, "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration and estimate the optimal conditions."

[0545] 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.

[0546] This invention provides a system that recognizes user emotions and enables process optimization in the biofuel production process utilizing microorganisms. By incorporating an emotion engine, this system can improve the user experience in addition to optimizing environmental data.

[0547] First, the server collects environmental data such as temperature, pH, and nutrient concentration through various sensors. After data collection, the server uses data preprocessing to remove outliers and missing values ​​from the raw data, making it suitable for analysis. This allows for detailed data analysis using an AI agent based on highly reliable data.

[0548] Data analysis means that pre-processed data is processed by an AI agent to optimize the growth conditions of microorganisms. The AI ​​utilizes machine learning algorithms to estimate efficient environmental parameters. Based on the analysis results, the server sends control instructions generated using inference and condition generation means to various devices, and operates the actual equipment via environmental adjustment means.

[0549] A distinctive feature of this invention is that the server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice through a camera and microphone to identify their current emotions. This emotion data is fed back and helps to dynamically adjust the system's operability and user interface.

[0550] For example, if a process is delayed and the system determines that the user is experiencing stress, the emotion engine recognizes that emotion. Based on this information, the device improves the user interface display and shows support messages and solutions that clearly explain the situation to the user. This increases the user's sense of security and improves the overall system user experience.

[0551] Furthermore, the server accumulates emotional history and provides emotional data analysis tools to support long-term improvement of user satisfaction. This generates suggestions for improving the user experience and contributes to continuous process optimization.

[0552] Overall, this system not only automatically optimizes the microbial growth environment but also achieves advanced process management that takes the user experience into consideration.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The server uses various sensors to collect environmental data, acquiring information such as temperature, pH, and nutrient concentration. This data is collected in real time and stored in a database on the server.

[0556] Step 2:

[0557] The server preprocesses the collected data, detecting and removing outliers and imputing missing values. Statistical methods are used for preprocessing to generate a reliable dataset.

[0558] Step 3:

[0559] The server uses an AI agent to perform data analysis based on pre-processed data. The AI ​​agent applies machine learning algorithms to estimate the optimal environmental conditions for microbial growth. The results of this analysis provide the information necessary for environmental adjustment.

[0560] Step 4:

[0561] The server performs inferences based on the analysis results and generates specific environmental adjustment instructions. This inference and condition generation means formulates signals to, for example, temperature control devices and pH balancers.

[0562] Step 5:

[0563] The server sends control instructions to the device and operates the actual equipment via environmental adjustment means. This ensures that the process environment is maintained in an optimized state.

[0564] Step 6:

[0565] The terminal displays current environmental data and adjustment status to the user in an easy-to-understand manner through its user interface. Through this interface, the user can monitor processes and perform manual adjustments.

[0566] Step 7:

[0567] The server uses an emotion engine to recognize the user's emotions through facial recognition and voice analysis. Based on this emotion information, it adapts the system's operation methods and interface to optimize the user experience.

[0568] Step 8:

[0569] The server records the user's emotional history and uses this to suggest improvements to long-term user satisfaction. These suggestions include custom messages and notifications tailored to system usage.

[0570] Step 9:

[0571] Based on the feedback and suggestions provided, users can take actions to deepen their understanding of the system and improve their confidence in it.

[0572] (Example 2)

[0573] 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."

[0574] While current biofuel production systems optimize environmental conditions, they lack dynamic system adjustments that take into account user emotions and experiences, leading to problems such as user stress and a poor user experience. Furthermore, long-term improvements in user satisfaction and the lack of improvement suggestions based on emotional data were also challenges.

[0575] 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.

[0576] In this invention, the server includes measurement means for collecting environmental data, data processing means for preprocessing the collected information and removing abnormal values, and information analysis means for analyzing the preprocessed information and optimizing the biological environment. This makes it possible to recognize the user's emotional data in real time and dynamically adjust the operation and interface of the device.

[0577] "Measurement means" refers to devices and systems used to accurately acquire environmental data, such as temperature, pH, and nutrient concentration, which are collected in real time through sensors.

[0578] "Data processing means" refers to methods and devices for converting acquired raw data into an analyzable format, including removing outliers and supplementing missing data.

[0579] "Information analysis means" refers to algorithms and processing methods for optimizing the biological environment based on pre-processed data, and is intended to efficiently estimate the growth conditions of microorganisms.

[0580] "Inference and condition generation means" refers to methods and processes for generating specific control instructions to adjust environmental conditions based on the results of information analysis.

[0581] "Environmental manipulation means" refers to mechanisms or systems for physically controlling a device based on generated conditions, and for adjusting environmental parameters.

[0582] "Emotional analysis means" refers to systems and technologies for analyzing and recognizing a user's emotions in real time, identifying emotional states from facial expressions and voice data.

[0583] "Interface adjustment means" refers to technologies and methods for dynamically customizing the user interface based on user sentiment data, thereby improving the user experience.

[0584] "Display means" refers to display systems and technologies that visually present the operating status and progress of a device to the user, thereby enhancing a sense of security through the provision of information.

[0585] This invention provides a system for optimizing a biofuel production process utilizing microorganisms and improving the user experience. The main components are a measurement means for collecting environmental data, a data processing means for preprocessing the collected data, an information analysis means for analyzing the preprocessed data, an inference and condition generation means, an environmental manipulation means, an emotion analysis means, an interface adjustment means, and a display means.

[0586] The server collects environmental data in real time using devices such as temperature sensors, pH sensors, and nutrient concentration sensors. This data is processed to remove outliers and noise, preparing it for analysis. After the data is cleaned, the server uses information analysis tools that implement machine learning algorithms to estimate the optimal environmental conditions for microorganisms. Random forests and neural networks are applied to these algorithms.

[0587] Based on the analysis results, the server generates control instructions for environmental manipulation using inference and condition generation means, and adjusts the device using automated environmental manipulation means. This optimizes the growth conditions for microorganisms.

[0588] Furthermore, the server uses emotion analysis tools to analyze the user's facial expressions and voice characteristics via the camera and microphone, recognizing the user's emotions in real time. This allows for appropriate feedback and interface adjustments depending on whether the user is stressed or happy.

[0589] The device uses sentiment data from the server to dynamically adjust the user interface according to the situation. For example, if the user is feeling anxious, the device displays a supportive message that clearly explains the situation, helping to improve the user experience.

[0590] For example, consider a scenario where a user experiences anxiety due to a delay in a microbial process. In this case, the emotion analysis system recognizes the user's emotion, and the terminal immediately displays an explanatory message. This reduces the user's anxiety.

[0591] Examples of prompts for generative AI models

[0592] Prompt: "Explain what measures can be taken to address user stress while optimizing the microbial biofuel production process."

[0593] This invention enables advanced process management that optimizes environmental conditions while also considering the user experience.

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] The server collects environmental data in real time using temperature sensors, pH sensors, and nutrient concentration sensors. This data is obtained in the form of temperature, pH value, nutrient concentration, etc. It receives sensor signals as input, digitizes them, and stores them in a database.

[0597] Step 2:

[0598] The server preprocesses the collected raw data using data processing tools. The input is the environmental data collected in step 1. Specifically, statistical methods are used to detect outliers, and z-scores are calculated to remove abnormal data points. Missing values ​​are linearly imputed using past data history. The output is the cleaned data.

[0599] Step 3:

[0600] The server passes the cleaning results to the AI ​​agent, which uses information analysis tools to estimate the optimal growth conditions for microorganisms. The input is the pre-processed data from step 2. Specifically, machine learning algorithms such as random forests and neural networks are applied to estimate efficient environmental conditions. The output is the estimated optimal environmental parameters.

[0601] Step 4:

[0602] The server generates control instructions using inference and condition generation means based on the estimated results. The input is the optimal parameters obtained in step 3. Specifically, control instructions are generated, and signals are created to control regulating devices such as valves and heaters. The output is the control signal.

[0603] Step 5:

[0604] The server recognizes the user's emotions in real time using emotion analysis tools. Inputs include user facial and voice data obtained via camera and microphone. Specifically, it uses image recognition and voice analysis technologies to identify the user's emotional state. The output is the identified user's emotion data.

[0605] Step 6:

[0606] The terminal dynamically adjusts the user interface using interface adjustment means based on emotional data. The input is the emotional data obtained in step 5. Specifically, if the user is experiencing stress, adjustments are made such as displaying a situational explanation message or simplifying the operation menu. The output is the adjusted interface for the user.

[0607] Step 7:

[0608] The server uses a display to show the device's operating status and the user's emotional state in real time. The inputs are the control signals in step 4 and the emotional data in step 5. Specifically, the display screen provides graphs and text messages indicating the process's progress, providing the user with a sense of security. The output is real-time visual information.

[0609] (Application Example 2)

[0610] 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."

[0611] In biofuel production processes, it is necessary not only to optimize environmental conditions but also to improve the user experience by considering the emotional state of workers, thereby simultaneously increasing work efficiency and satisfaction. However, conventional systems have struggled to recognize workers' emotional states in real time and provide dynamic environmental adjustments and feedback based on them.

[0612] 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.

[0613] In this invention, the server includes information gathering means for collecting environmental information, information preprocessing means for preprocessing the collected information and removing outliers, information analysis means for analyzing the preprocessed information and optimizing environmental conditions, inference and condition generation means for adjusting environmental parameters based on the analysis results, environment adjustment means for controlling the actual device based on the generated conditions, emotion recognition means for analyzing the user's emotions, and experience adjustment means for dynamically adjusting the user experience based on the recognized emotions. This enables process optimization in accordance with the worker's emotions and improvement of the user experience.

[0614] "Environmental information" refers to data on physical and chemical conditions related to the biofuel production process, such as temperature, pH, and nutrient concentration.

[0615] "Information gathering means" refers to devices or technologies for acquiring environmental information and transmitting it to a server.

[0616] "Information preprocessing means" refers to a processing device or method for removing outliers and missing values ​​from collected information and preparing it in a format useful for analysis.

[0617] "Information analysis means" refers to a device or program that performs analysis to optimize environmental conditions based on pre-processed information.

[0618] "Inference and condition generation means" refers to an apparatus or method that determines the optimal environmental parameters based on the analysis results and creates the conditions for those parameters.

[0619] "Environmental adjustment means" refers to a device or system for controlling an actual device and adjusting the environment based on generated conditions.

[0620] "Emotion recognition means" refers to technology that uses input devices such as cameras and microphones to analyze a user's facial expressions and tone of voice to identify their emotions.

[0621] "Experience adjustment means" refers to a device or method that dynamically adjusts the interface and operating environment based on the user's emotions to improve the user experience.

[0622] The embodiments for carrying out the invention are described below.

[0623] This invention is a system aimed at optimizing the biofuel production process and improving the user experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0624] The server integrates various sensors for collecting environmental information. This allows for the efficient acquisition of various data such as temperature, pH, and nutrient concentration. The collected data is preprocessed by information preprocessing means to remove outliers and impute missing values. Subsequently, based on the preprocessed data, information analysis means analyze the optimal environmental conditions, and inference and condition generation means determine the environmental parameters. Specifically, this process is realized using a generative AI model that utilizes machine learning algorithms.

[0625] The device is equipped with emotion recognition technology to recognize the user's emotions, analyzing their facial expressions and tone of voice using a camera and microphone. The emotional state is grasped in real time, and the user interface is dynamically changed by an experience adjustment mechanism. This allows the user to monitor the process while reducing stress and confusion.

[0626] Users can check environmental information and adjustment status through the interface and receive appropriate feedback as needed. For example, if a delay occurs in process progress, a message such as "The process is currently behind schedule, but the environment settings are optimized. We will suggest measures to resolve this situation." will be displayed, providing reassurance to the user.

[0627] In this way, the efficiency of the biofuel production process and the user experience are optimized through the collaboration of the server, terminal, and user. The program to realize this system uses software such as Python, OpenCV, and TensorFlow, as well as hardware such as smart glasses. An example of a prompt message is, "There is a delay in the process progress, but does the user understand the current situation? Calculate stress indicators from the worker's facial expressions and voice, and create a plan of action."

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The server acquires environmental information from various sensors. Inputs are raw data such as temperature, pH, and nutrient concentration, while outputs are aggregated versions of this data. The acquired raw data is collected using various data collection methods.

[0631] Step 2:

[0632] The server preprocesses the collected data using information preprocessing means. The input is the raw data obtained in step 1. Outliers are removed and missing values ​​are imputed, and the output is data in a state suitable for analysis.

[0633] Step 3:

[0634] The server analyzes the preprocessed data using information analysis tools. The input is the preprocessed data obtained in step 2. The environmental conditions are optimized using a generative AI model. The output is the optimal environmental parameters.

[0635] Step 4:

[0636] The server generates control commands based on optimized environmental parameters using inference and condition generation means. The input is the optimal environmental parameters obtained in step 3. The output is the control command, which is sent to the environment adjustment means.

[0637] Step 5:

[0638] The device uses emotion recognition to monitor the user's emotions in real time. Input is data on facial expressions and voice tone acquired through the camera and microphone. Output is the result of the analysis of the user's emotional state.

[0639] Step 6:

[0640] The server uses experience adjustment mechanisms to adjust the user interface in response to the user's emotions. The input is the result of the emotional state analysis obtained in step 5. The user interface is dynamically changed, and the output is the content displayed to the visitor.

[0641] Step 7:

[0642] The user checks support messages and process status in real time through the terminal. The input is the information displayed in the interface adjusted in step 6. The output is the user's understanding of the process and sense of security.

[0643] Step 8:

[0644] The server uses example prompts and, if necessary, provides support from a generating AI model. The input is a prompt appropriate to the situation. An example prompt is, "There is a delay in process progress, but does the user understand the current situation?" The output is the generated countermeasures and additional messages.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] This invention provides a system that optimizes the growth environment of microorganisms in the biofuel production process by using sensors and an AI agent in combination. Specific embodiments of the system are described below.

[0663] First, the server collects environmental data such as temperature, pH, and nutrient concentration from various sensors in real time. This data is reliably transmitted and stored via a stable communication protocol.

[0664] Next, the collected data is preprocessed by the server, correcting and removing outliers and missing values. This process ensures that the data is in a reliable state before proceeding to analysis.

[0665] Next, the server uses an AI agent to analyze the growth conditions of microorganisms based on the pre-processed data. The AI ​​agent utilizes machine learning algorithms to estimate the optimal environmental parameters. This clarifies the conditions necessary for efficient microbial growth and biofuel production.

[0666] Subsequently, based on the analysis results, the server generates specific instructions for optimizing the environment and sends them to the control unit. The control unit receives these instructions and operates the corresponding devices (e.g., temperature control devices, pH adjusters, etc.). This adjusts the environmental conditions, ensuring that the microbial growth environment is constantly optimized.

[0667] Furthermore, the terminal provides real-time feedback on the overall operating status to the user through the user interface. This allows the user to check how the system is working and make manual adjustments as needed.

[0668] Furthermore, to support long-term process improvement, the server analyzes historical data and generates candidate new microbial strains and process improvement measures. These suggestions are automatically generated and reported to the user, contributing to improved production efficiency in the future.

[0669] As a concrete example, consider a case where microbial growth is insufficient in a manufacturing facility. The server detects this from real-time data and identifies that the temperature is inappropriate through AI analysis. Based on the analysis results, the control device automatically adjusts the temperature, resulting in improved microbial growth. In this way, the present invention autonomously optimizes environmental conditions, enabling efficient biofuel production.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The server collects real-time data such as temperature, pH, and nutrient concentration from the sensors. This data is transmitted to the server using a communication protocol and stored securely.

[0673] Step 2:

[0674] The server receives the collected raw data and begins data preprocessing. It detects outliers from the collected data and uses statistical methods to impute missing values.

[0675] Step 3:

[0676] The server passes the pre-processed data to the AI ​​agent for data analysis. The AI ​​agent uses machine learning algorithms to evaluate the growth state of microorganisms and estimate the optimal environmental parameters.

[0677] Step 4:

[0678] The server generates specific control instructions using inference and condition generation means based on the estimated environmental parameters obtained from the AI ​​agent. These instructions include specific operations such as temperature adjustment and pH adjustment.

[0679] Step 5:

[0680] The server transmits the generated control instructions to the device control module, which then operates the actual device via environmental adjustment means. This maintains optimal environmental conditions for microbial growth.

[0681] Step 6:

[0682] The terminal displays real-time collected environmental data and current settings to the user via a user interface. The user can review the data and make manual adjustments as needed.

[0683] Step 7:

[0684] The server analyzes historical data for long-term process improvement. Using an improvement suggestion generation method, it automatically generates candidate new microbial strains and measures to improve process efficiency, and reports them to the user.

[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] We face the challenge of automatically finding the optimal conditions for efficient microbial growth and appropriately adjusting the environment in response to real-time, fluctuating environmental conditions. Conventional methods often involve manual collection, processing, and adjustment of environmental information, which is time-consuming, labor-intensive, and inefficient. Furthermore, there is often no mechanism for long-term implementation of improvement measures, preventing sufficient process optimization.

[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 an information acquisition means for obtaining environmental information, an information preprocessing means for preprocessing the acquired information and removing anomalies, and a machine learning means for analyzing the preprocessed information and estimating optimal environment variables. This enables the rapid calculation of optimal microbial growth conditions, real-time automation of environmental adjustments, and increased process efficiency.

[0690] An "information acquisition means" is a mechanism for collecting environmental data from sensors and other sources.

[0691] "Information preprocessing means" refers to the process of organizing acquired data and correcting or removing outliers and missing values.

[0692] "Machine learning methods" are techniques that use algorithms to analyze data and estimate optimal conditions.

[0693] The "instruction generation means" is a system for creating instructions for adjusting environmental conditions based on the analysis results.

[0694] "Device control means" refers to a mechanism for operating actual equipment according to generated instructions.

[0695] A "problem-solving method" is a function that automatically generates process improvement proposals using past data.

[0696] A "feedback mechanism" is a means of displaying information in real time through a user interface to inform users of the situation.

[0697] The present invention provides a system for optimizing the growth environment of microorganisms in the biofuel production process. Specific embodiments for carrying out this invention are described below.

[0698] First, the server collects environmental data such as temperature, pH, and nutrient concentration from multiple sensors. Common temperature and pH sensors are used, and the data is transmitted to the server via a stable communication protocol. The server then organizes the collected data using the Python pandas library, correcting or removing outliers and missing values.

[0699] Next, the server uses machine learning techniques to analyze the optimal growth conditions for microorganisms from the pre-processed data. Generative AI models such as TensorFlow and PyTorch are utilized, and appropriate machine learning algorithms (e.g., neural networks and random forests) are used to estimate environment variables.

[0700] Based on the analysis results, the server uses an instruction generation mechanism to generate specific instructions for adjusting the environment. These instructions are sent to the control unit, which then uses a PLC (Programmable Logic Controller) or similar device to operate temperature control devices, pH adjusters, and other devices to optimize the environmental conditions.

[0701] The terminal provides real-time feedback to the user regarding the environmental status and the analysis and adjustment status of the server through the user interface. Based on this information, the user can make fine adjustments manually as needed.

[0702] Furthermore, the server analyzes historical data and automatically generates process improvement strategies. It utilizes Python's scikit-learn library and Jupyter Notebook to perform data analysis, create candidate microbial strains and process improvement strategies, and report them to the user.

[0703] For example, if insufficient microbial growth is detected, the server uses data analysis to identify the cause and sends specific instructions to the control unit to adjust the temperature to an appropriate range. In this way, environmental conditions are optimized in real time, supporting the efficient production of biofuels.

[0704] An example of a prompt message would be, "Based on the environmental data collected from the sensor, please use the AI ​​model to analyze the optimal growth conditions for microorganisms and provide feedback."

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] The server collects environmental data from sensors. This data includes temperature, pH, and nutrient concentration, and is transmitted to the server in real time. The input is raw data from the sensors, and the output is storing this data in a database in an appropriate format. This step ensures the accuracy and precision of the data.

[0708] Step 2:

[0709] The server preprocesses the collected data. At this stage, it detects outliers and corrects missing values ​​to improve data reliability. The input is raw environmental data, and the output is cleaned and filtered data. The Python pandas library is used for this processing.

[0710] Step 3:

[0711] The server analyzes pre-processed data based on an AI agent. The input is pre-processed data, and the output is the optimal growth conditions for microorganisms. Specifically, a generative AI model (using TensorFlow or PyTorch) executes machine learning algorithms to estimate the growth conditions. This result clarifies which environmental parameters should be adjusted.

[0712] Step 4:

[0713] The server generates instructions for environmental adjustment based on the analysis results. The input is the result of the AI ​​analysis, and the output is specific environmental adjustment instructions. These instructions are sent to the control unit, enabling the operation of the equipment (e.g., adjustment of a temperature control unit). In this step, a PLC is used to control the device.

[0714] Step 5:

[0715] The terminal provides real-time feedback to the user on environmental conditions and adjustment details. Input consists of environmental data and adjustment status from the server, while output is an information screen viewable by the user. The user can use this information to make additional manual adjustments if necessary.

[0716] Step 6:

[0717] The server generates long-term process improvement strategies by analyzing historical data. The input is historical data, and the output is a report of improvement suggestions for the user. Results are generated and reported to the user using Python's scikit-learn library and statistical analysis in Jupyter Notebook. In this way, the overall efficiency of the system improves.

[0718] (Application Example 1)

[0719] 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".

[0720] In microbial production environments, environmental conditions significantly impact microbial growth; therefore, these conditions must be optimized in real time for efficient production. However, conventional methods often involve manual adjustment of environmental conditions, making rapid response difficult. In addition, automated feedback systems for long-term improvements based on data are not yet adequately developed.

[0721] 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.

[0722] In this invention, the server includes information gathering means for acquiring environmental data, information preprocessing means for preprocessing the acquired information and removing outliers, and information analysis means for analyzing the preprocessed information and optimizing environmental conditions. This enables real-time optimization of environmental conditions in the microbial production environment and an efficient production process.

[0723] "Information gathering means" refers to a device or system for acquiring environmental data such as temperature, pH, and nutrient concentration in a microbial production environment in real time.

[0724] "Information preprocessing means" refers to a mechanism that automatically detects and removes abnormal values ​​and missing values ​​in order to prepare acquired production environment data for analysis.

[0725] "Information analysis means" refers to a device or system that estimates the optimal conditions for microbial growth based on pre-processed environmental data and performs analysis to optimize the environment.

[0726] The "inference and condition generation means" is a function that, based on the analysis results, issues commands to environmental devices and control devices and generates conditions for appropriately adjusting various parameters.

[0727] "Environmental adjustment means" refers to a device or system that operates the actual equipment according to the generated conditions and performs control to maintain environmental conditions suitable for the growth of microorganisms.

[0728] The "improvement suggestion generation method" is a function that automatically generates long-term improvement suggestions to improve the efficiency of production processes by analyzing past data history.

[0729] A "feedback mechanism" is a function that visually presents the user with real-time environmental data and its adjustment status via the user interface.

[0730] In order to implement the invention, the following system configuration and operation are necessary.

[0731] The server first uses information gathering devices to acquire data such as temperature, pH, and nutrient concentration from the microbial production environment in real time. This information is then aggregated on the server via a group of sensors.

[0732] Next, the server utilizes data preprocessing tools to prepare the acquired data for analysis. Specifically, it uses a Python program to detect and remove outliers, ensuring data reliability.

[0733] Subsequently, the server uses machine learning libraries such as TensorFlow and PyTorch as information analysis tools to estimate the optimal conditions for microbial growth based on the pre-processed data. Based on the results of this analysis, the inference and condition generation tools calculate appropriate environmental conditions and send instructions to the control device.

[0734] Furthermore, the environmental control system adjusts the environment using temperature control devices, pH adjusters, and other means according to the generated conditions. This ensures that the optimal growth environment for microorganisms is always maintained.

[0735] Furthermore, users can monitor the collected data and the adjustment status of the environment in real time through feedback mechanisms via their devices. The UI uses Flask and Django to provide information in a visually user-friendly format.

[0736] With the aim of long-term process improvement, the server analyzes past data using an improvement suggestion generation mechanism and automatically generates suggestions for efficiency improvements. These suggestions are notified to the user and contribute to future productivity improvements.

[0737] For example, if a sudden temperature fluctuation occurs during microbial cultivation, the server instantly detects the abnormal value, calculates the optimal temperature through AI analysis, and the control device immediately adjusts the set temperature, thereby maintaining growth efficiency.

[0738] Examples of prompts to input into a generative AI model are as follows:

[0739] "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration to estimate the optimal conditions."

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The server acquires temperature, pH, and nutrient concentration from the environment via information gathering devices. It converts the analog signals received from the sensors into digital data and stores it in a database. This allows for the acquisition of data that reflects the actual conditions of the production environment in real time.

[0743] Step 2:

[0744] The server uses data preprocessing to detect outliers and missing values ​​from the acquired data and performs filtering. Specifically, it uses Python to remove outliers using standard deviation, maintaining the normal range of the data. This ensures that reliable, clean data is output.

[0745] Step 3:

[0746] The server uses generative AI models in TensorFlow or PyTorch as data analysis tools to estimate the optimal conditions for microbial growth based on pre-processed data. Clean data is fed to the model as input, and the analysis process outputs the optimal environmental parameters. This clearly shows the conditions best suited for growth in numerical terms.

[0747] Step 4:

[0748] The server generates specific conditions based on the analysis results using inference and condition generation means. Here, it creates control signals using the generated environmental parameters and outputs instructions to be sent to the control device. These instructions include specific operating methods such as temperature setpoints and pH adjustment amounts.

[0749] Step 5:

[0750] Through the environmental control system, the server sends commands to the control unit, which then operates the temperature control unit and pH regulator. This modifies the environment according to the generated conditions, optimizing the microbial growth environment. Feedback on whether the control was successful is also received as input.

[0751] Step 6:

[0752] The device visually presents the collected data and the adjusted environmental state to the user through feedback mechanisms. A user interface using Flask or Django is created here, enabling real-time data monitoring. The user receives this information as input and can perform actions as needed.

[0753] Step 7:

[0754] The user receives automatically generated suggestions for long-term process improvement through the improvement suggestion generation mechanism. The server outputs improvement suggestions based on historical data analysis and presents specific action plans for efficiency improvement. An example of a prompt message could be, "To optimize the microbial growth environment, analyze the current temperature, pH, and nutrient concentration and estimate the optimal conditions."

[0755] 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.

[0756] This invention provides a system that recognizes user emotions and enables process optimization in the biofuel production process utilizing microorganisms. By incorporating an emotion engine, this system can improve the user experience in addition to optimizing environmental data.

[0757] First, the server collects environmental data such as temperature, pH, and nutrient concentration through various sensors. After data collection, the server uses data preprocessing to remove outliers and missing values ​​from the raw data, making it suitable for analysis. This allows for detailed data analysis using an AI agent based on highly reliable data.

[0758] Data analysis means that pre-processed data is processed by an AI agent to optimize the growth conditions of microorganisms. The AI ​​utilizes machine learning algorithms to estimate efficient environmental parameters. Based on the analysis results, the server sends control instructions generated using inference and condition generation means to various devices, and operates the actual equipment via environmental adjustment means.

[0759] A distinctive feature of this invention is that the server is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice through a camera and microphone to identify their current emotions. This emotion data is fed back and helps to dynamically adjust the system's operability and user interface.

[0760] For example, if a process is delayed and the system determines that the user is experiencing stress, the emotion engine recognizes that emotion. Based on this information, the device improves the user interface display and shows support messages and solutions that clearly explain the situation to the user. This increases the user's sense of security and improves the overall system user experience.

[0761] Furthermore, the server accumulates emotional history and provides emotional data analysis tools to support long-term improvement of user satisfaction. This generates suggestions for improving the user experience and contributes to continuous process optimization.

[0762] Overall, this system not only automatically optimizes the microbial growth environment but also achieves advanced process management that takes the user experience into consideration.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The server uses various sensors to collect environmental data, acquiring information such as temperature, pH, and nutrient concentration. This data is collected in real time and stored in a database on the server.

[0766] Step 2:

[0767] The server preprocesses the collected data, detecting and removing outliers and imputing missing values. Statistical methods are used for preprocessing to generate a reliable dataset.

[0768] Step 3:

[0769] The server uses an AI agent to perform data analysis based on pre-processed data. The AI ​​agent applies machine learning algorithms to estimate the optimal environmental conditions for microbial growth. The results of this analysis provide the information necessary for environmental adjustment.

[0770] Step 4:

[0771] The server performs inferences based on the analysis results and generates specific environmental adjustment instructions. This inference and condition generation means formulates signals to, for example, temperature control devices and pH balancers.

[0772] Step 5:

[0773] The server sends control instructions to the device and operates the actual equipment via environmental adjustment means. This ensures that the process environment is maintained in an optimized state.

[0774] Step 6:

[0775] The terminal displays current environmental data and adjustment status to the user in an easy-to-understand manner through its user interface. Through this interface, the user can monitor processes and perform manual adjustments.

[0776] Step 7:

[0777] The server uses an emotion engine to recognize the user's emotions through facial recognition and voice analysis. Based on this emotion information, it adapts the system's operation methods and interface to optimize the user experience.

[0778] Step 8:

[0779] The server records the user's emotional history and uses this to suggest improvements to long-term user satisfaction. These suggestions include custom messages and notifications tailored to system usage.

[0780] Step 9:

[0781] Based on the feedback and suggestions provided, users can take actions to deepen their understanding of the system and improve their confidence in it.

[0782] (Example 2)

[0783] 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".

[0784] While current biofuel production systems optimize environmental conditions, they lack dynamic system adjustments that take into account user emotions and experiences, leading to problems such as user stress and a poor user experience. Furthermore, long-term improvements in user satisfaction and the lack of improvement suggestions based on emotional data were also challenges.

[0785] 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.

[0786] In this invention, the server includes measurement means for collecting environmental data, data processing means for preprocessing the collected information and removing abnormal values, and information analysis means for analyzing the preprocessed information and optimizing the biological environment. This makes it possible to recognize the user's emotional data in real time and dynamically adjust the operation and interface of the device.

[0787] "Measurement means" refers to devices and systems used to accurately acquire environmental data, such as temperature, pH, and nutrient concentration, which are collected in real time through sensors.

[0788] "Data processing means" refers to methods and devices for converting acquired raw data into an analyzable format, including removing outliers and supplementing missing data.

[0789] "Information analysis means" refers to algorithms and processing methods for optimizing the biological environment based on pre-processed data, and is intended to efficiently estimate the growth conditions of microorganisms.

[0790] "Inference and condition generation means" refers to methods and processes for generating specific control instructions to adjust environmental conditions based on the results of information analysis.

[0791] "Environmental manipulation means" refers to mechanisms or systems for physically controlling a device based on generated conditions, and for adjusting environmental parameters.

[0792] "Emotional analysis means" refers to systems and technologies for analyzing and recognizing a user's emotions in real time, identifying emotional states from facial expressions and voice data.

[0793] "Interface adjustment means" refers to technologies and methods for dynamically customizing the user interface based on user sentiment data, thereby improving the user experience.

[0794] "Display means" refers to display systems and technologies that visually present the operating status and progress of a device to the user, thereby enhancing a sense of security through the provision of information.

[0795] This invention provides a system for optimizing a biofuel production process utilizing microorganisms and improving the user experience. The main components are a measurement means for collecting environmental data, a data processing means for preprocessing the collected data, an information analysis means for analyzing the preprocessed data, an inference and condition generation means, an environmental manipulation means, an emotion analysis means, an interface adjustment means, and a display means.

[0796] The server collects environmental data in real time using devices such as temperature sensors, pH sensors, and nutrient concentration sensors. This data is processed to remove outliers and noise, preparing it for analysis. After the data is cleaned, the server uses information analysis tools that implement machine learning algorithms to estimate the optimal environmental conditions for microorganisms. Random forests and neural networks are applied to these algorithms.

[0797] Based on the analysis results, the server generates control instructions for environmental manipulation using inference and condition generation means, and adjusts the device using automated environmental manipulation means. This optimizes the growth conditions for microorganisms.

[0798] Furthermore, the server uses emotion analysis tools to analyze the user's facial expressions and voice characteristics via the camera and microphone, recognizing the user's emotions in real time. This allows for appropriate feedback and interface adjustments depending on whether the user is stressed or happy.

[0799] The device uses sentiment data from the server to dynamically adjust the user interface according to the situation. For example, if the user is feeling anxious, the device displays a supportive message that clearly explains the situation, helping to improve the user experience.

[0800] For example, consider a scenario where a user experiences anxiety due to a delay in a microbial process. In this case, the emotion analysis system recognizes the user's emotion, and the terminal immediately displays an explanatory message. This reduces the user's anxiety.

[0801] Examples of prompts for generative AI models

[0802] Prompt: "Explain what measures can be taken to address user stress while optimizing the microbial biofuel production process."

[0803] This invention enables advanced process management that optimizes environmental conditions while also considering the user experience.

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] The server collects environmental data in real time using temperature sensors, pH sensors, and nutrient concentration sensors. This data is obtained in the form of temperature, pH value, nutrient concentration, etc. It receives sensor signals as input, digitizes them, and stores them in a database.

[0807] Step 2:

[0808] The server preprocesses the collected raw data using data processing tools. The input is the environmental data collected in step 1. Specifically, statistical methods are used to detect outliers, and z-scores are calculated to remove abnormal data points. Missing values ​​are linearly imputed using past data history. The output is the cleaned data.

[0809] Step 3:

[0810] The server passes the cleaning results to the AI ​​agent, which uses information analysis tools to estimate the optimal growth conditions for microorganisms. The input is the pre-processed data from step 2. Specifically, machine learning algorithms such as random forests and neural networks are applied to estimate efficient environmental conditions. The output is the estimated optimal environmental parameters.

[0811] Step 4:

[0812] The server generates control instructions using inference and condition generation means based on the estimated results. The input is the optimal parameters obtained in step 3. Specifically, control instructions are generated, and signals are created to control regulating devices such as valves and heaters. The output is the control signal.

[0813] Step 5:

[0814] The server recognizes the user's emotions in real time using emotion analysis tools. Inputs include user facial and voice data obtained via camera and microphone. Specifically, it uses image recognition and voice analysis technologies to identify the user's emotional state. The output is the identified user's emotion data.

[0815] Step 6:

[0816] The terminal dynamically adjusts the user interface using interface adjustment means based on emotional data. The input is the emotional data obtained in step 5. Specifically, if the user is experiencing stress, adjustments are made such as displaying a situational explanation message or simplifying the operation menu. The output is the adjusted interface for the user.

[0817] Step 7:

[0818] The server uses a display to show the device's operating status and the user's emotional state in real time. The inputs are the control signals in step 4 and the emotional data in step 5. Specifically, the display screen provides graphs and text messages indicating the process's progress, providing the user with a sense of security. The output is real-time visual information.

[0819] (Application Example 2)

[0820] 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".

[0821] In biofuel production processes, it is necessary not only to optimize environmental conditions but also to improve the user experience by considering the emotional state of workers, thereby simultaneously increasing work efficiency and satisfaction. However, conventional systems have struggled to recognize workers' emotional states in real time and provide dynamic environmental adjustments and feedback based on them.

[0822] 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.

[0823] In this invention, the server includes information gathering means for collecting environmental information, information preprocessing means for preprocessing the collected information and removing outliers, information analysis means for analyzing the preprocessed information and optimizing environmental conditions, inference and condition generation means for adjusting environmental parameters based on the analysis results, environment adjustment means for controlling the actual device based on the generated conditions, emotion recognition means for analyzing the user's emotions, and experience adjustment means for dynamically adjusting the user experience based on the recognized emotions. This enables process optimization in accordance with the worker's emotions and improvement of the user experience.

[0824] "Environmental information" refers to data on physical and chemical conditions related to the biofuel production process, such as temperature, pH, and nutrient concentration.

[0825] "Information gathering means" refers to devices or technologies for acquiring environmental information and transmitting it to a server.

[0826] "Information preprocessing means" refers to a processing device or method for removing outliers and missing values ​​from collected information and preparing it in a format useful for analysis.

[0827] "Information analysis means" refers to a device or program that performs analysis to optimize environmental conditions based on pre-processed information.

[0828] "Inference and condition generation means" refers to an apparatus or method that determines the optimal environmental parameters based on the analysis results and creates the conditions for those parameters.

[0829] "Environmental adjustment means" refers to a device or system for controlling an actual device and adjusting the environment based on generated conditions.

[0830] "Emotion recognition means" refers to technology that uses input devices such as cameras and microphones to analyze a user's facial expressions and tone of voice to identify their emotions.

[0831] "Experience adjustment means" refers to a device or method that dynamically adjusts the interface and operating environment based on the user's emotions to improve the user experience.

[0832] The embodiments for carrying out the invention are described below.

[0833] This invention is a system aimed at optimizing the biofuel production process and improving the user experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0834] The server integrates various sensors for collecting environmental information. This allows for the efficient acquisition of various data such as temperature, pH, and nutrient concentration. The collected data is preprocessed by information preprocessing means to remove outliers and impute missing values. Subsequently, based on the preprocessed data, information analysis means analyze the optimal environmental conditions, and inference and condition generation means determine the environmental parameters. Specifically, this process is realized using a generative AI model that utilizes machine learning algorithms.

[0835] The device is equipped with emotion recognition technology to recognize the user's emotions, analyzing their facial expressions and tone of voice using a camera and microphone. The emotional state is grasped in real time, and the user interface is dynamically changed by an experience adjustment mechanism. This allows the user to monitor the process while reducing stress and confusion.

[0836] Users can check environmental information and adjustment status through the interface and receive appropriate feedback as needed. For example, if a delay occurs in process progress, a message such as "The process is currently behind schedule, but the environment settings are optimized. We will suggest measures to resolve this situation." will be displayed, providing reassurance to the user.

[0837] In this way, the efficiency of the biofuel production process and the user experience are optimized through the collaboration of the server, terminal, and user. The program to realize this system uses software such as Python, OpenCV, and TensorFlow, as well as hardware such as smart glasses. An example of a prompt message is, "There is a delay in the process progress, but does the user understand the current situation? Calculate stress indicators from the worker's facial expressions and voice, and create a plan of action."

[0838] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0839] Step 1:

[0840] The server acquires environmental information from various sensors. Inputs are raw data such as temperature, pH, and nutrient concentration, while outputs are aggregated versions of this data. The acquired raw data is collected using various data collection methods.

[0841] Step 2:

[0842] The server preprocesses the collected data using information preprocessing means. The input is the raw data obtained in step 1. Outliers are removed and missing values ​​are imputed, and the output is data in a state suitable for analysis.

[0843] Step 3:

[0844] The server analyzes the preprocessed data using information analysis tools. The input is the preprocessed data obtained in step 2. The environmental conditions are optimized using a generative AI model. The output is the optimal environmental parameters.

[0845] Step 4:

[0846] The server generates control commands based on optimized environmental parameters using inference and condition generation means. The input is the optimal environmental parameters obtained in step 3. The output is the control command, which is sent to the environment adjustment means.

[0847] Step 5:

[0848] The device uses emotion recognition to monitor the user's emotions in real time. Input is data on facial expressions and voice tone acquired through the camera and microphone. Output is the result of the analysis of the user's emotional state.

[0849] Step 6:

[0850] The server uses experience adjustment mechanisms to adjust the user interface in response to the user's emotions. The input is the result of the emotional state analysis obtained in step 5. The user interface is dynamically changed, and the output is the content displayed to the visitor.

[0851] Step 7:

[0852] The user checks support messages and process status in real time through the terminal. The input is the information displayed in the interface adjusted in step 6. The output is the user's understanding of the process and sense of security.

[0853] Step 8:

[0854] The server uses example prompts and, if necessary, provides support from a generating AI model. The input is a prompt appropriate to the situation. An example prompt is, "There is a delay in process progress, but does the user understand the current situation?" The output is the generated countermeasures and additional messages.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] Data collection means for collecting environmental data,

[0879] A data preprocessing means for preprocessing collected data and removing outliers,

[0880] A data analysis means for analyzing pre-processed data and optimizing environmental conditions,

[0881] An inference and condition generation means for adjusting environmental parameters based on the analysis results,

[0882] An environment adjustment means for controlling the actual device based on the generated conditions,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, further comprising an improvement suggestion generation means for making long-term process improvement suggestions based on past data history.

[0886] (Claim 3)

[0887] The system according to claim 1, further comprising a feedback means for displaying environmental data and adjustment status to the user in real time via a user interface.

[0888] "Example 1"

[0889] (Claim 1)

[0890] Means for acquiring environmental information,

[0891] Information preprocessing means for preprocessing acquired information and removing anomalies,

[0892] A machine learning method for analyzing pre-processed information and estimating optimal environment variables,

[0893] Instruction generation means for generating environmental adjustment instructions based on analysis results,

[0894] Device control means that adjusts the actual equipment based on the generated instructions,

[0895] A means for generating improvement measures that automatically generates process improvement measures based on past information,

[0896] A feedback mechanism that displays environmental information and adjustment status to the user in real time via a user interface,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, which generates improvement measures for long-term process improvement.

[0900] (Claim 3)

[0901] The system according to claim 1, which provides real-time information to users.

[0902] "Application Example 1"

[0903] (Claim 1)

[0904] Information gathering methods for acquiring environmental data,

[0905] Information preprocessing means for preprocessing acquired information and removing outliers,

[0906] Information analysis means for analyzing pre-processed information and optimizing environmental conditions,

[0907] An inference and condition generation means for adjusting the characteristics of the environment based on the analysis results,

[0908] An environmental adjustment means for controlling the actual device based on the generated conditions,

[0909] Control means for real-time adjustment in the microbial production environment,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising an improvement suggestion generation means for making long-term method improvement suggestions based on past information history.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising a feedback means for displaying environmental information and adjustment status to the user in real time via a user interface.

[0915] "Example 2 of combining an emotion engine"

[0916] (Claim 1)

[0917] Measurement means for collecting environmental data,

[0918] A data processing means that preprocesses the collected information and removes abnormal values,

[0919] Information analysis means for analyzing pre-processed information and optimizing the biological environment,

[0920] An inference and condition generation means for adjusting operating conditions based on analysis results,

[0921] Environmental manipulation means for controlling the device based on the generated conditions,

[0922] A means of sentiment analysis for recognizing and providing feedback on user emotions,

[0923] Interface adjustment means for dynamically adjusting the user interface based on emotional data,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, further comprising a means for generating improvement suggestions that provide suggestions for long-term improvements to the user experience based on past emotional history.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising a display means for displaying the user and device operating status in real time to enhance a sense of security.

[0929] "Application example 2 when combining with an emotional engine"

[0930] (Claim 1)

[0931] Information gathering methods for collecting environmental information,

[0932] Information preprocessing means for preprocessing collected information and removing outliers,

[0933] Information analysis means for analyzing pre-processed information and optimizing environmental conditions,

[0934] An inference and condition generation means for adjusting environmental parameters based on the analysis results,

[0935] An environmental adjustment means for controlling the actual device based on the generated conditions,

[0936] A means of recognizing emotions to analyze user emotions,

[0937] An experience adjustment mechanism for dynamically adjusting the user experience based on recognized emotions,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, further comprising a proposal generation means for making suggestions for long-term process improvements based on past emotional history and data history.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising a feedback means for displaying environmental information, emotional states, and situation adjustments to the user in real time via a user interface. [Explanation of symbols]

[0943] 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. Data collection means for collecting environmental data, A data preprocessing means for preprocessing collected data and removing outliers, A data analysis means for analyzing pre-processed data and optimizing environmental conditions, An inference and condition generation means for adjusting environmental parameters based on the analysis results, An environment adjustment means for controlling the actual device based on the generated conditions, A system that includes this.

2. The system according to claim 1, further comprising an improvement suggestion generation means for making long-term process improvement suggestions based on past data history.

3. The system according to claim 1, further comprising a feedback means for displaying environmental data and adjustment status to the user in real time via a user interface.

Images