system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining the growth status and environmental conditions of microorganisms in real time, An analysis means that analyzes the data obtained from the data acquisition means and generates an optimal environmental adjustment command, An environmental control means that automatically adjusts environmental conditions based on commands generated by the analysis means, An information provision method that provides analysis results to a mobile device, allowing users to visually confirm the adjustment of environmental 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional biofuel production processes require manual monitoring and adjustment of microbial growth and environmental conditions, which pose problems of reducing production efficiency and increasing production costs. Also, it is difficult to find optimized environmental conditions, and it is difficult to respond promptly when abnormalities occur. There is a need for means to solve such problems and efficiently produce high-quality biofuels.

Means for Solving the Problems

[0005] The present invention provides a system comprising: a data acquisition means for acquiring the growth state of microorganisms and environmental conditions in real time; an analysis means for analyzing the data obtained from the data acquisition means and generating optimal environmental adjustment commands; and an environmental control means for automatically adjusting environmental conditions based on the commands generated by the analysis means. Furthermore, the analysis means is equipped with a function to learn from past data and propose future process improvements, and a function to detect anomalies and send alerts to the user, thereby enabling process automation and immediate response, improving production efficiency and reducing costs.

[0006] "Microbial growth state" refers to an indicator that shows the process and progress of how microorganisms multiply and become active in a culture environment.

[0007] "Environmental conditions" is a general term for the physical or chemical factors necessary for the growth of microorganisms, such as temperature, pH, and nutrient concentration.

[0008] "Data acquisition means" refers to a method or apparatus for collecting real-time data on the growth status of microorganisms and environmental conditions using sensors or other devices.

[0009] "Analysis means" refers to technical methods and algorithms that use acquired data to analyze microbial growth and environmental optimization, and generate necessary adjustment commands.

[0010] "Environmental control means" refers to a device or system for adjusting culture conditions based on commands generated by the analysis means.

[0011] "Learning from past data" is the process of analyzing trends and patterns using previously acquired data to help improve future processes.

[0012] An "anomaly detection method" is a technology that uses analysis methods to identify abnormal conditions that deviate from normal data patterns and generate necessary alerts.

[0013] An "alert" is a notification that informs the user of an anomaly or situation requiring attention that has occurred within a system or process. [Brief explanation of the drawing]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for optimizing the biofuel production process using microorganisms. This system acquires the growth status of microorganisms and related environmental conditions in real time and has a function to automatically adjust the environmental conditions based on AI-based data analysis. The specific embodiment of the invention is implemented with the following configuration.

[0036] The server plays a central role in processing real-time data received from terminals. This data includes environmental conditions such as temperature, pH, and nutrient concentration, as well as the growth status of microorganisms. The server's AI agent analyzes this information and generates optimal environmental conditions. Based on these results, it creates commands such as "increase the temperature by a specific degree" or "add a specific nutrient."

[0037] The terminal operates the actual sensors and actuators within the culture device. The sensors monitor the growth status of microorganisms and environmental conditions, and transmit the data to the server. Upon receiving commands from the server, the terminal makes adjustments, such as controlling heaters to regulate the temperature or adding the appropriate amount of nutrients.

[0038] Users monitor and control the entire system and develop improvements to further enhance process efficiency. They access analysis results and suggestions provided by the server to consider responses to detected anomalies and new strategies. Users also leverage system alert notifications to enable rapid responses to changes in environmental conditions and abnormal situations.

[0039] As a specific example, in a certain biofuel production plant, if the server detects that microorganisms are growing more rapidly than usual, analysis identifies a temperature increase that supports this anomaly. The server quickly sends a "cool down" command to the terminal, which reduces the heater output and activates the cooling system. This restores the culture environment to a proper state, allowing efficient biofuel production to continue. In this way, the system maintains an optimized process at all times, enabling the production of high-quality, cost-effective biofuels.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal uses sensors installed inside the culture device to collect real-time data such as temperature, pH, nutrient concentration, and microbial growth status.

[0043] Step 2:

[0044] The terminal sends the collected data to the server at predetermined intervals. This data includes information necessary for status monitoring.

[0045] Step 3:

[0046] The server stores data received from the terminals and uses an AI agent to perform data analysis. This analysis determines whether the current culture environment is optimal.

[0047] Step 4:

[0048] The server's AI agent compares past and current data to assess whether adjustments are needed to maintain the optimal culture environment. If anomalies or trends are detected, the agent analyzes their nature.

[0049] Step 5:

[0050] The server generates commands to adjust the necessary environmental conditions based on the analysis results. Specific commands might include "lower the temperature by 1 degree" or "add nutrient A."

[0051] Step 6:

[0052] The server sends the generated command to the terminal and instructs it to execute.

[0053] Step 7:

[0054] The terminal operates temperature control devices and nutrient supply devices within the culture apparatus based on commands received from the server, thereby actually adjusting the environmental conditions.

[0055] Step 8:

[0056] Users monitor the system status by checking real-time analysis results and environment adjustment history provided by the server. They continuously monitor for any problems and intervene manually as needed.

[0057] Step 9:

[0058] Users refer to future improvement suggestions from the server and develop plans to further optimize the process. In this way, the entire system is ensured to operate efficiently at all times.

[0059] (Example 1)

[0060] 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."

[0061] In biofuel production processes utilizing microorganisms, it is necessary to appropriately manage the growth state of microorganisms and environmental conditions, but doing so efficiently and in real time is difficult. Furthermore, the insufficient ability to quickly detect and respond to abnormal situations is a factor that hinders the achievement of optimal production efficiency. There is a need to build a system that can solve these problems and realize high-quality, cost-effective biofuel production.

[0062] 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.

[0063] In this invention, the server includes an information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time, an information analysis means for analyzing the information obtained from the information gathering means and generating an optimal environmental adjustment command, and an environmental adjustment means for automatically adjusting the environmental conditions based on the command generated by the information analysis means. This enables efficient management of the growth state of microorganisms and environmental conditions, allowing for rapid response to abnormal situations and improved production efficiency.

[0064] "Information gathering means" refers to a device or mechanism used to acquire the growth state and environmental conditions of microorganisms in real time.

[0065] "Information analysis means" refers to a device or function that analyzes data obtained from information collection means and generates optimal environmental adjustment commands.

[0066] "Environmental adjustment means" refers to a device or mechanism that automatically adjusts environmental conditions based on commands generated by information analysis means.

[0067] "Communication means" refers to a device or function for receiving information transmitted from a terminal and transmitting generated commands to the terminal.

[0068] "User interface means" refers to a device or function that presents analysis results and command logs to the user and assists in monitoring and controlling the process.

[0069] A "learning tool" is a device or function that learns from past information and makes suggestions for future process improvements.

[0070] An "anomaly detection means" is a device or function for detecting an anomaly and sending a warning to the user.

[0071] This invention is a system for carrying out a biofuel production process using microorganisms with a certain level of efficiency. This system is composed of a combination of information gathering means, information analysis means, environmental adjustment means, communication means, and user interface means.

[0072] The server acts as a means of information analysis, analyzing data on the growth status of microorganisms and environmental conditions received from terminals. The server performs the analysis using dedicated software and a generative AI model, thereby creating instructions to generate optimal environmental conditions.

[0073] The terminal functions as both an information gathering and environmental adjustment device. Using sensors installed within the culture apparatus, the terminal monitors environmental data such as temperature, pH, and nutrient concentration in real time and transmits this data to the server. Upon receiving commands from the server, it operates actuators to adjust environmental conditions. Specific hardware examples include temperature sensors, pH meters, and nutrient pump control devices.

[0074] Users monitor the entire system through a user interface. They also review data and command logs analyzed by the server, supporting effective management and efficiency improvements of the biofuel production process. If an anomaly is detected, users receive an alert and can input prompt messages into an AI model to obtain appropriate countermeasures.

[0075] As a concrete example, a user can input a prompt message into the AI ​​model such as, "If microbial growth is faster than normal, please suggest an action plan to return to optimal environmental conditions," and receive appropriate countermeasures. This allows the system to constantly maintain an optimal environment, enabling stable biofuel production.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The terminal acquires environmental data such as temperature, pH, and nutrient concentration using multiple sensors installed within the culture device. This data is aggregated in the terminal's control unit and becomes input data transmitted to the server at regular intervals. In this process, the terminal performs specific operations such as reading the data and converting it to a standard format.

[0079] Step 2:

[0080] The server receives environmental data transmitted from the terminal. To analyze the received data, it uses a generative AI model to calculate the optimal environmental conditions. Based on this analysis, it performs specific data processing to generate environmental adjustment commands, including temperature and pH adjustment values, from the raw data obtained as input.

[0081] Step 3:

[0082] The server sends the commands obtained through analysis to the terminal. Specifically, detailed commands such as "lower the temperature by 2 degrees" or "add 5 mg / L of a specific nutrient" are output, and the server performs a communication operation to send these commands to the terminal.

[0083] Step 4:

[0084] The terminal controls the actuators based on environmental adjustment commands received from the server. Specifically, it analyzes the commands and performs output operations such as adjusting the heater output settings or adding a specified amount of nutrients using a pump.

[0085] Step 5:

[0086] The user accesses analysis results and command history provided by the server. This allows them to monitor the current state of the process, input new prompts into the generated AI model as needed, and receive suggestions for further process improvement. Specifically, they use the user interface to review and evaluate the visualized data.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In biofuel production processes, monitoring microbial growth and environmental conditions in real time and making optimal environmental adjustments is a challenging task. Furthermore, there is a lack of visualization tools to enable on-site workers to understand the data and respond quickly, thus necessitating process efficiency improvements.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes data acquisition means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the data obtained from the data acquisition means and generating optimal environmental adjustment commands; environmental control means for automatically adjusting the environmental conditions based on the commands generated by the analysis means; and information provision means for providing the analysis results to a portable information terminal so that the user can visually confirm the adjustment of the environmental conditions. This enables process efficiency and rapid response.

[0092] "Microorganisms" is a general term for tiny organisms that can only be observed with a microscope.

[0093] "Growth status" is an indicator that shows how much a microorganism is growing under specific environmental conditions.

[0094] "Environmental conditions" refer to external physical and chemical factors that affect the growth of microorganisms.

[0095] "Data acquisition means" refers to a device or method for measuring and recording the growth state of microorganisms and environmental conditions in real time.

[0096] "Analysis means" refers to a system or method for determining optimal environmental conditions based on collected data and generating adjustment commands.

[0097] "Environmental control means" refers to a device or mechanism that automatically adjusts the growth environment of microorganisms in accordance with commands generated by an analysis means.

[0098] "Information provision means" refers to a system or method that notifies the user of the analyzed results and provides a visualization of changes and adjustments to environmental conditions.

[0099] An "anomaly detection system" is a mechanism that recognizes unusual conditions or malfunctions and notifies the user of a warning.

[0100] A "portable information terminal" is a portable electronic device, such as a smartphone or tablet, used for displaying and operating information.

[0101] Embodiments of this invention are systems designed to optimize the biofuel production process. Specific embodiments are described below.

[0102] The server primarily manages data acquisition methods for obtaining real-time information on the growth status and environmental conditions of microorganisms. These data acquisition methods include temperature and pH sensors, as well as sensors that measure nutrient concentrations. Information from these sensors is aggregated on the server. On the server, this data is analyzed using a generative AI model to generate optimized commands. Software libraries such as TENSORFLOW® are used, and predictions and adjustments to optimize growth conditions are made based on the accumulated data through machine learning.

[0103] The terminal receives commands from the server and is responsible for making adjustments within the actual culture environment. Specifically, it includes actuators that automatically perform operations such as temperature control and nutrient addition. This makes it possible to promote optimal microbial growth while maintaining the efficiency of the entire process.

[0104] The user monitors the analysis results using a mobile device and oversees the entire process. Through information provision methods, the user can learn about the data and system status in real time, and the data is visualized on smartphones and tablets. In addition, anomaly detection means that when an anomaly occurs, an alert is immediately sent to the mobile device and a prompt is displayed to prompt additional action. For example, a prompt message such as "The AI ​​system has detected a temperature rise. Should you activate the cooling system immediately?" can be given. With such a system, the user can efficiently manage and control the process and improve it.

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

[0106] Step 1:

[0107] The server acquires real-time data on the growth status of microorganisms and environmental conditions. It receives temperature, pH, and nutrient concentration data from sensors and transmits it to the server. The input is sensor data, and the output is aggregated information from this data.

[0108] Step 2:

[0109] The server analyzes the received data using a generation AI model. Using the TensorFlow library, it calculates optimal environmental conditions by referencing historical data. The input to this process is aggregated sensor data, and the output is a command regarding the optimized environmental conditions.

[0110] Step 3:

[0111] Based on the analysis results, the server generates environmental adjustment commands and sends them to the terminal. Specific actions include commands for temperature adjustment and nutrient addition. The input consists of commands regarding optimized environmental conditions, while the output is command data sent to the terminal.

[0112] Step 4:

[0113] The terminal automatically adjusts the culture environment according to the received commands. It uses actuators to control temperature and add necessary nutrients. The input is command data from the server, and the output is the result of the actual environment adjustment.

[0114] Step 5:

[0115] Users use their mobile devices to check the system status and analysis results. The data is visualized on a smartphone via an information provision system. Input consists of analysis results and status data from the server, while output is the visualized information provided to the user.

[0116] Step 6:

[0117] If an anomaly is detected, the server immediately sends an alert to the user's mobile device. It displays a prompt message to encourage the user to take action. For example, it might display in the format, "The AI ​​system has detected a temperature rise. Do you want to activate the cooling system immediately?" The input is data related to the anomaly detection, and the output is an alert notification and prompt message to the user.

[0118] 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.

[0119] This invention provides a system that combines a biofuel generation system utilizing microbial growth with an emotion engine that recognizes user emotions. The system includes data acquisition means, analysis means, environmental control means, and an emotion engine, thereby enabling real-time data collection, analysis, environmental control, and optimization of the user experience.

[0120] The server receives real-time data transmitted from the terminal and performs data analysis using an AI agent. This analysis includes evaluating the growth status of microorganisms and environmental conditions, and generates commands to adjust environmental conditions as needed. Furthermore, by utilizing an emotion engine to analyze user emotions, the server can offer suggestions and adjust the interface to reduce user stress and anxiety.

[0121] The device uses sensors to measure environmental conditions and transmits that data to a server. Upon receiving commands from the server, it performs actual environmental adjustments, specifically temperature control and nutrient addition. User emotion information obtained by the emotion engine is used to improve the device's usability and customize the user interface.

[0122] The user monitors the system's status and reviews the analysis results and emotion engine feedback provided by the server. For example, if the emotion engine recognizes the user's stress level, the system will simplify the operating procedures and adjust the interface based on the suggestions, improving the user's work efficiency and enhancing the overall process effectiveness.

[0123] As a concrete example, in a system operated by a plant operator, if the emotion engine detects that the operator is experiencing stress, the server automatically proposes environmental adjustment procedures and issues commands to automate some manual operations. This flexible response improves the overall performance of the operation. By making improvements based on emotion data, the burden felt by the user is reduced, and the matching with the system is optimized, resulting in efficient and smooth biofuel production.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The device uses sensors within the culture system to collect environmental conditions such as temperature, pH, and nutrient concentration, as well as microbial growth data, in real time. It also acquires emotional data through a device that records user interactions.

[0127] Step 2:

[0128] The terminal collects environmental and emotional data and sends it to the server at regular intervals. This allows the server to aggregate the latest information on the system's operational status and the user's emotional state.

[0129] Step 3:

[0130] The server analyzes the data received from the terminal and evaluates the environmental conditions necessary for optimal microbial growth. Based on this analysis, it determines whether the environment is suitable and generates adjustment commands as needed.

[0131] Step 4:

[0132] The server's emotion engine analyzes the user's emotional data. If the emotion engine detects stress or anxiety, it suggests interface adjustments or automation to reduce the user's burden.

[0133] Step 5:

[0134] The server sends environment adjustment commands and operation suggestions and interface adjustment proposals based on the user's emotional state to the terminal.

[0135] Step 6:

[0136] Upon receiving commands from the server, the terminal performs environmental adjustments and modifies device settings. This includes changing the temperature and adjusting nutrient dosages. Furthermore, the user interface is adapted based on suggestions and customized for ease of use.

[0137] Step 7:

[0138] Users review real-time analysis results and interface adjustments provided by the emotion engine from the server, and provide further instructions as needed. User feedback is used for future adjustments.

[0139] Step 8:

[0140] Users will continuously monitor the entire system to ensure a comfortable operating environment, allowing them to confidently participate in plant operations. If necessary, they will accept suggestions from the emotional engine to help the process proceed efficiently.

[0141] (Example 2)

[0142] 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".

[0143] Conventional biofuel production systems utilizing microorganisms have suffered from problems such as the inability to quickly adjust environmental conditions, resulting in unoptimized microbial growth. Furthermore, high user workload and increased stress led to a decrease in overall efficiency. Therefore, there is a need for a system that can adjust environmental conditions in real time, optimize the interface in response to user emotions, and enable efficient biofuel production.

[0144] 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.

[0145] In this invention, the server includes data acquisition means, analysis means, environment control means, emotion analysis means, and interface adjustment means. This makes it possible to provide an optimal environment for microbial growth in real time, reduce user stress, and improve overall generation efficiency.

[0146] "Data acquisition means" refers to devices or technologies that observe the growth state of microorganisms and environmental conditions in real time and collect necessary data.

[0147] "Analysis means" refers to a device or technology that processes information obtained from data acquisition means, identifies the optimal environmental conditions for microbial growth, and generates necessary adjustment commands.

[0148] "Environmental control means" refers to a device or technology that automatically adjusts environmental conditions and promotes microbial growth based on commands generated by an analysis means.

[0149] "Emotional analysis means" refers to a device or technology that collects and analyzes a user's emotional state and uses the results to optimize the user interface or improve the efficiency of operations.

[0150] "Interface adjustment means" refers to a device or technology that adjusts the system's user interface based on user emotion data obtained from emotion analysis means, thereby reducing user stress and improving operability.

[0151] In order to implement this invention, it is necessary for the server, terminal, and user to play their respective roles and cooperate with each other.

[0152] The server comprises data acquisition means, analysis means, environmental control means, emotion analysis means, and interface adjustment means. These systems are supported by appropriate hardware and software. The data acquisition means receives environmental data transmitted from the terminal, which includes temperature, humidity, and light intensity related to microbial growth. The analysis means analyzes this data using a generative AI model to derive the optimal conditions for microbial growth. Based on the analysis results, the environmental control means automatically adjusts the environment, and the emotion analysis means evaluates the user's emotions. Based on this data, the interface adjustment means optimizes the user interface.

[0153] The terminal uses a data acquisition system composed of sensors to collect environmental data that affects microbial growth and transmits it to a server. In response to environmental adjustment commands received from the server, the terminal performs specific control actions, such as operating a cooling device or adding nutrients. Furthermore, based on the results derived by the emotion analysis system, the user interface on the terminal is adjusted to make it easier to operate.

[0154] The user reviews the analysis results from the server and suggestions in the interface, monitors the system, and takes instructions as needed. The results obtained from sentiment analysis serve as guidelines to improve the user's work efficiency and reduce stress. For example, when microbial growth is poor, the user receives improvement suggestions from the analysis, enabling efficient response through optimized operations. An example of a prompt would be, "Please suggest ways to optimize microbial growth." This question allows the generative AI model to provide new approaches and ideas.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The device uses sensors to acquire environmental data such as temperature, humidity, and light intensity in real time. This data is then formatted and sent to a server. Inputs are analog or digital data from each environmental sensor, and outputs are digital signals containing this data. Specifically, the device processes the signals from the sensors using a microcontroller and transmits the data via Wi-Fi or a wired network.

[0158] Step 2:

[0159] The server processes the received environmental data using analytical tools. It utilizes a generative AI model to evaluate the optimal environmental conditions for microbial growth. The input is formatted data sent from the terminal, and the output is environmental adjustment commands necessary for growth promotion. Specifically, the AI ​​model analyzes the data and determines the optimal temperature and humidity settings.

[0160] Step 3:

[0161] The server generates commands to automate environmental control based on the adjustment commands obtained from the analysis and sends them to the terminal. The input is the analysis results from the AI, and the output is the specific commands sent to the control device. Specific actions include, for example, issuing commands to activate a cooling system or start a heater.

[0162] Step 4:

[0163] The terminal receives commands from the server and adjusts the actual environmental conditions. The input is the environmental adjustment command from the server, and the output is the adjusted environment. Specific actions include operating a motor to improve ventilation or automatically adding nutrients from a nutrient tank.

[0164] Step 5:

[0165] The server collects and analyzes real-time emotional data from users using emotion analysis tools. Inputs include the user's facial expressions and voice obtained from cameras, microphones, etc., while outputs are information about the user's emotional state. Specifically, it evaluates the user's stress levels and concentration using facial recognition technology and voice analysis.

[0166] Step 6:

[0167] The server generates and presents suggestions to the user for adjusting the user interface and improving operational efficiency based on the sentiment analysis results. The input is emotional state data obtained through sentiment analysis, and the output is optimized interface settings and operational suggestions. Specific actions include simplifying screen displays and rearranging button layouts.

[0168] Step 7:

[0169] The user reviews the analysis results and suggestions provided by the server and performs actions as needed. The input is the feedback and suggestions from the server, and the output is the result of the user's actions. Specifically, this involves accepting the server's suggestions and manually changing the settings.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0172] The present invention aims to provide a system that optimizes environmental control in biofuel production while simultaneously reducing stress and improving efficiency in response to user emotions. The challenge is to provide a more comfortable and effective operating environment for the user by not only optimizing microbial growth but also adjusting the interface to take into account the user's emotional state.

[0173] 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.

[0174] In this invention, the server includes information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the information obtained from the information gathering means and generating optimal environmental adjustment commands; situation control means for automatically adjusting environmental conditions based on the commands generated by the analysis means; and emotion analysis means for recognizing the user's emotional state and adjusting the user interface. This enables optimization of the biofuel production process and customized stress reduction based on the user's emotions.

[0175] "Microorganisms" are tiny organisms composed of single or multicellular cells that grow under specific environmental conditions and are used in the production of biofuels.

[0176] "Growth status" refers to information indicating the extent to which microorganisms are growing, and it is a dynamic state that is influenced by environmental conditions.

[0177] "Environmental conditions" refer to the surrounding physical and chemical factors that affect the growth of microorganisms, including temperature and nutrient supply.

[0178] "Information gathering means" refers to hardware and software components for acquiring the growth status and environmental conditions of microorganisms in real time.

[0179] "Analysis means" refers to data processing and analysis procedures for generating optimal environmental adjustment commands based on acquired information.

[0180] "Condition control means" are components of a system for maintaining or adjusting environmental conditions suitable for the growth of microorganisms based on commands generated by the analysis means.

[0181] An "emotion analysis tool" is an analytical tool that recognizes the user's emotional state and adjusts the user interface based on the results.

[0182] The system for implementing this invention integrates various hardware and software components to generate biofuels and manage user emotions. The server first uses multiple sensors to acquire real-time data on microbial growth and surrounding environmental conditions. This information is processed by an analysis system running on the server. This analysis system utilizes generative AI models such as Google Cloud AI, enabling complex data analysis.

[0183] The terminal automatically adjusts environmental conditions according to commands sent from the server. This adjustment utilizes smart home appliance control devices and temperature sensors, among other things. Furthermore, it can recognize the user's real-time emotional state through emotion analysis and optimize the terminal's user interface based on the results. For example, if a user feels stressed while working, the terminal will implement stress reduction measures such as playing relaxation music or adjusting the brightness of the lighting.

[0184] Users can receive these adjustments and suggestions via their smartphones or smart glasses, allowing them to maintain an optimal work environment. The collected emotional data is also used to analyze the user's stress levels and emotional tendencies, and to provide appropriate lifestyle improvement suggestions.

[0185] For example, if stress is detected in a user during morning teleworking, the system will recommend appropriate music and change the room lighting to warmer colors to provide a relaxing environment. Another example of a prompt for the generating AI model is, "Describe the app's function of adjusting lighting and music to provide a relaxing environment based on the user's stress level and environmental data." In this way, the system makes adjustments that take into account the interaction between the user and the environment, achieving more efficient and human-centered biofuel production.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server acquires data from sensors in real time regarding the growth status of microorganisms and environmental conditions. This input data includes temperature, humidity, and nutrient levels. The server first stores this data in a database and then transmits it to the analysis system.

[0189] Step 2:

[0190] The server's analysis method uses a generative AI model to analyze the data acquired in Step 1. This analysis generates environmental adjustment commands to optimize microbial growth. The output includes adjustment commands such as the optimal temperature setting and the amount of nutrients required.

[0191] Step 3:

[0192] The terminal receives environmental adjustment commands sent from the server. Based on the received commands, the terminal operates smart home appliance control devices to adjust temperature and nutrient supply. Specifically, temperature controllers and automatic water supply systems are involved in these adjustments.

[0193] Step 4:

[0194] The server acquires emotional data through the smartphone's camera and microphone to understand the user's emotional state. This data is input into an emotion analysis system to analyze changes in emotions. This analysis determines the user's stress level and emotional state.

[0195] Step 5:

[0196] The server's emotion analysis system generates commands to adjust the user interface based on the obtained emotion data. These commands include suggestions aimed at stress reduction, such as playing relaxation music or adjusting the lighting color. The output of these commands is sent to the terminal.

[0197] Step 6:

[0198] The terminal adjusts the user interface and environment according to commands sent from the server. Specifically, the music player plays soothing music, and the smart lighting switches to warm colors. This process provides the user with a more comfortable operating environment.

[0199] Step 7:

[0200] Users receive feedback from their devices and evaluate their own comfort level. Furthermore, they contribute to improving the system's accuracy by using example prompts and feeding back new emotional patterns to the server. Through this process, the system continuously learns and improves the user experience.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention is a system for optimizing the biofuel production process using microorganisms. This system acquires the growth status of microorganisms and related environmental conditions in real time and has a function to automatically adjust the environmental conditions based on AI-based data analysis. The specific embodiment of the invention is implemented with the following configuration.

[0218] The server plays a central role in processing real-time data received from terminals. This data includes environmental conditions such as temperature, pH, and nutrient concentration, as well as the growth status of microorganisms. The server's AI agent analyzes this information and generates optimal environmental conditions. Based on these results, it creates commands such as "increase the temperature by a specific degree" or "add a specific nutrient."

[0219] The terminal operates the actual sensors and actuators within the culture device. The sensors monitor the growth status of microorganisms and environmental conditions, and transmit the data to the server. Upon receiving commands from the server, the terminal makes adjustments, such as controlling heaters to regulate the temperature or adding the appropriate amount of nutrients.

[0220] Users monitor and control the entire system and develop improvements to further enhance process efficiency. They access analysis results and suggestions provided by the server to consider responses to detected anomalies and new strategies. Users also leverage system alert notifications to enable rapid responses to changes in environmental conditions and abnormal situations.

[0221] As a specific example, in a certain biofuel production plant, if the server detects that microorganisms are growing more rapidly than usual, analysis identifies a temperature increase that supports this anomaly. The server quickly sends a "cool down" command to the terminal, which reduces the heater output and activates the cooling system. This restores the culture environment to a proper state, allowing efficient biofuel production to continue. In this way, the system maintains an optimized process at all times, enabling the production of high-quality, cost-effective biofuels.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The terminal uses sensors installed inside the culture device to collect real-time data such as temperature, pH, nutrient concentration, and microbial growth status.

[0225] Step 2:

[0226] The terminal sends the collected data to the server at predetermined intervals. This data includes information necessary for status monitoring.

[0227] Step 3:

[0228] The server stores data received from the terminals and uses an AI agent to perform data analysis. This analysis determines whether the current culture environment is optimal.

[0229] Step 4:

[0230] The server's AI agent compares past and current data to assess whether adjustments are needed to maintain the optimal culture environment. If anomalies or trends are detected, the agent analyzes their nature.

[0231] Step 5:

[0232] The server generates commands to adjust the necessary environmental conditions based on the analysis results. Specific commands might include "lower the temperature by 1 degree" or "add nutrient A."

[0233] Step 6:

[0234] The server sends the generated command to the terminal and instructs it to execute.

[0235] Step 7:

[0236] The terminal operates temperature control devices and nutrient supply devices within the culture apparatus based on commands received from the server, thereby actually adjusting the environmental conditions.

[0237] Step 8:

[0238] Users monitor the system status by checking real-time analysis results and environment adjustment history provided by the server. They continuously monitor for any problems and intervene manually as needed.

[0239] Step 9:

[0240] Users refer to future improvement suggestions from the server and develop plans to further optimize the process. In this way, the entire system is ensured to operate efficiently at all times.

[0241] (Example 1)

[0242] 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."

[0243] In biofuel production processes utilizing microorganisms, it is necessary to appropriately manage the growth state of microorganisms and environmental conditions, but doing so efficiently and in real time is difficult. Furthermore, the insufficient ability to quickly detect and respond to abnormal situations is a factor that hinders the achievement of optimal production efficiency. There is a need to build a system that can solve these problems and realize high-quality, cost-effective biofuel production.

[0244] 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.

[0245] In this invention, the server includes an information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time, an information analysis means for analyzing the information obtained from the information gathering means and generating an optimal environmental adjustment command, and an environmental adjustment means for automatically adjusting the environmental conditions based on the command generated by the information analysis means. This enables efficient management of the growth state of microorganisms and environmental conditions, allowing for rapid response to abnormal situations and improved production efficiency.

[0246] "Information gathering means" refers to a device or mechanism used to acquire the growth state and environmental conditions of microorganisms in real time.

[0247] "Information analysis means" refers to a device or function that analyzes data obtained from information collection means and generates optimal environmental adjustment commands.

[0248] "Environmental adjustment means" refers to a device or mechanism that automatically adjusts environmental conditions based on commands generated by information analysis means.

[0249] "Communication means" refers to a device or function for receiving information transmitted from a terminal and transmitting generated commands to the terminal.

[0250] "User interface means" refers to a device or function that presents analysis results and command logs to the user and assists in monitoring and controlling the process.

[0251] A "learning tool" is a device or function that learns from past information and makes suggestions for future process improvements.

[0252] An "anomaly detection means" is a device or function for detecting an anomaly and sending a warning to the user.

[0253] This invention is a system for carrying out a biofuel production process using microorganisms with a certain level of efficiency. This system is composed of a combination of information gathering means, information analysis means, environmental adjustment means, communication means, and user interface means.

[0254] The server acts as a means of information analysis, analyzing data on the growth status of microorganisms and environmental conditions received from terminals. The server performs the analysis using dedicated software and a generative AI model, thereby creating instructions to generate optimal environmental conditions.

[0255] The terminal functions as both an information gathering and environmental adjustment device. Using sensors installed within the culture apparatus, the terminal monitors environmental data such as temperature, pH, and nutrient concentration in real time and transmits this data to the server. Upon receiving commands from the server, it operates actuators to adjust environmental conditions. Specific hardware examples include temperature sensors, pH meters, and nutrient pump control devices.

[0256] Users monitor the entire system through a user interface. They also review data and command logs analyzed by the server, supporting effective management and efficiency improvements of the biofuel production process. If an anomaly is detected, users receive an alert and can input prompt messages into an AI model to obtain appropriate countermeasures.

[0257] As a concrete example, a user can input a prompt message into the AI ​​model such as, "If microbial growth is faster than normal, please suggest an action plan to return to optimal environmental conditions," and receive appropriate countermeasures. This allows the system to constantly maintain an optimal environment, enabling stable biofuel production.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The terminal acquires environmental data such as temperature, pH, and nutrient concentration using multiple sensors installed within the culture device. This data is aggregated in the terminal's control unit and becomes input data transmitted to the server at regular intervals. In this process, the terminal performs specific operations such as reading the data and converting it to a standard format.

[0261] Step 2:

[0262] The server receives environmental data transmitted from the terminal. To analyze the received data, it uses a generative AI model to calculate the optimal environmental conditions. Based on this analysis, it performs specific data processing to generate environmental adjustment commands, including temperature and pH adjustment values, from the raw data obtained as input.

[0263] Step 3:

[0264] The server sends the commands obtained through analysis to the terminal. Specifically, detailed commands such as "lower the temperature by 2 degrees" or "add 5 mg / L of a specific nutrient" are output, and the server performs a communication operation to send these commands to the terminal.

[0265] Step 4:

[0266] The terminal controls the actuators based on environmental adjustment commands received from the server. Specifically, it analyzes the commands and performs output operations such as adjusting the heater output settings or adding a specified amount of nutrients using a pump.

[0267] Step 5:

[0268] The user accesses analysis results and command history provided by the server. This allows them to monitor the current state of the process, input new prompts into the generated AI model as needed, and receive suggestions for further process improvement. Specifically, they use the user interface to review and evaluate the visualized data.

[0269] (Application Example 1)

[0270] 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."

[0271] In biofuel production processes, monitoring microbial growth and environmental conditions in real time and making optimal environmental adjustments is a challenging task. Furthermore, there is a lack of visualization tools to enable on-site workers to understand the data and respond quickly, thus necessitating process efficiency improvements.

[0272] 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.

[0273] In this invention, the server includes data acquisition means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the data obtained from the data acquisition means and generating optimal environmental adjustment commands; environmental control means for automatically adjusting the environmental conditions based on the commands generated by the analysis means; and information provision means for providing the analysis results to a portable information terminal so that the user can visually confirm the adjustment of the environmental conditions. This enables process efficiency and rapid response.

[0274] "Microorganisms" is a general term for tiny organisms that can only be observed with a microscope.

[0275] "Growth status" is an indicator that shows how much a microorganism is growing under specific environmental conditions.

[0276] "Environmental conditions" refer to external physical and chemical factors that affect the growth of microorganisms.

[0277] "Data acquisition means" refers to a device or method for measuring and recording the growth state of microorganisms and environmental conditions in real time.

[0278] "Analysis means" refers to a system or method for determining optimal environmental conditions based on collected data and generating adjustment commands.

[0279] "Environmental control means" refers to a device or mechanism that automatically adjusts the growth environment of microorganisms in accordance with commands generated by an analysis means.

[0280] The "information providing means" is a system or method that notifies the user of the analyzed results and provides visualization of changes and adjustments in environmental conditions.

[0281] The "abnormality detection means" is a mechanism that recognizes a state or defect different from normal and notifies the user of a warning.

[0282] The "portable information terminal" is a portable electronic device such as a smartphone or tablet, which is used for displaying and operating information.

[0283] An embodiment of this invention is a system designed to optimize the biofuel production process. Specific embodiments thereof will be described below.

[0284] The server mainly manages data acquisition means for acquiring the growth state of microorganisms and environmental conditions in real time. The data acquisition means includes temperature and pH sensors, and sensors for measuring nutrient concentrations, and the information obtained from these sensors is aggregated on the server. On the server, analysis is performed on these data using a generated AI model to generate an optimized command. Software libraries such as TensorFlow are used, and predictions and adjustments for optimizing growth conditions are made based on the data accumulated by machine learning.

[0285] The terminal receives the command transmitted from the server and is responsible for adjustments in the actual culture environment. Specifically, it includes an actuator that automatically performs operations such as temperature control and addition of nutrients. Thereby, it is possible to promote the optimal growth of microorganisms while maintaining the efficiency of the entire process.

[0286] The user monitors the analysis results using a mobile information terminal and has the role of monitoring the entire process. Through the information providing means, the user can know the data and the system state in real time, and the data is visualized on a smartphone or tablet. Also, by the anomaly detection means, when an anomaly occurs, an alert is immediately notified to the mobile information terminal, and a prompt for prompting additional actions is displayed. As a specific example, a prompt sentence such as "The AI system has detected a temperature rise. Should the cooling system be immediately activated?" can be cited as an example. With such a system, the user can efficiently perform management and control and can attempt to improve the process.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server acquires the growth state of microorganisms and environmental conditions in real time. It receives data on temperature, pH, and nutrient concentration from the sensor and transmits it to the server. The input is sensor data, and the output is aggregated information of these data.

[0290] Step 2:

[0291] The server analyzes the received data using the generated AI model. Using the TensorFlow library and referring to past data, it calculates the optimal environmental conditions. The input of this process is aggregated information of sensor data, and the output is a command regarding the optimized environmental conditions.

[0292] Step 3:

[0293] Based on the analysis results, the server generates an environmental adjustment command and transmits it to the terminal. Specific actions include temperature adjustment and nutrient addition commands. The input is a command regarding the optimized environmental conditions, and the output is command data to the terminal.

[0294] Step 4:

[0295] The terminal automatically adjusts the culture environment according to the received commands. It uses actuators to control temperature and add necessary nutrients. The input is command data from the server, and the output is the result of the actual environment adjustment.

[0296] Step 5:

[0297] Users use their mobile devices to check the system status and analysis results. The data is visualized on a smartphone via an information provision system. Input consists of analysis results and status data from the server, while output is the visualized information provided to the user.

[0298] Step 6:

[0299] If an anomaly is detected, the server immediately sends an alert to the user's mobile device. It displays a prompt message to encourage the user to take action. For example, it might display in the format, "The AI ​​system has detected a temperature rise. Do you want to activate the cooling system immediately?" The input is data related to the anomaly detection, and the output is an alert notification and prompt message to the user.

[0300] 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.

[0301] This invention provides a system that combines a biofuel generation system utilizing microbial growth with an emotion engine that recognizes user emotions. The system includes data acquisition means, analysis means, environmental control means, and an emotion engine, thereby enabling real-time data collection, analysis, environmental control, and optimization of the user experience.

[0302] The server receives the real-time data sent from the terminal and performs data analysis by the AI agent. The data analysis includes the evaluation of the growth state of microorganisms and environmental conditions, and generates adjustment instructions for environmental conditions as needed. Also, by utilizing the emotion engine and analyzing the user's emotions, it is possible to make proposals and adjust the interface to alleviate the user's stress and anxiety.

[0303] The terminal measures environmental conditions using sensors and sends the data to the server. Also, when receiving a command from the server, it performs actual environmental adjustments. Specifically, it is responsible for temperature control and addition of nutrients. The user's emotion information obtained by the emotion engine is used to improve the operability of the terminal and customize the user interface.

[0304] The user monitors the state of the system and checks the analysis results provided by the server and the feedback of the emotion engine. For example, when the emotion engine recognizes the user's stress level, the system simplifies the operation procedure and adjusts the interface based on the proposal, improving the user's work efficiency and enhancing the overall process effect.

[0305] As a specific example, in a system handled by a plant operator, when the emotion engine detects that the operator is feeling stressed, the server automatically proposes an environmental adjustment procedure and issues a command to automate some manual operations. This flexible response can improve the performance of the entire operation. By improving based on emotion data, the burden felt by the user is reduced, and by optimizing the matching with the system, efficient and smooth biofuel production is realized.

[0306] The following explains the processing flow.

[0307] Step 1:

[0308] The device uses sensors within the culture system to collect environmental conditions such as temperature, pH, and nutrient concentration, as well as microbial growth data, in real time. It also acquires emotional data through a device that records user interactions.

[0309] Step 2:

[0310] The terminal collects environmental and emotional data and sends it to the server at regular intervals. This allows the server to aggregate the latest information on the system's operational status and the user's emotional state.

[0311] Step 3:

[0312] The server analyzes the data received from the terminal and evaluates the environmental conditions necessary for optimal microbial growth. Based on this analysis, it determines whether the environment is suitable and generates adjustment commands as needed.

[0313] Step 4:

[0314] The server's emotion engine analyzes the user's emotional data. If the emotion engine detects stress or anxiety, it suggests interface adjustments or automation to reduce the user's burden.

[0315] Step 5:

[0316] The server sends environment adjustment commands and operation suggestions and interface adjustment proposals based on the user's emotional state to the terminal.

[0317] Step 6:

[0318] Upon receiving commands from the server, the terminal performs environmental adjustments and modifies device settings. This includes changing the temperature and adjusting nutrient dosages. Furthermore, the user interface is adapted based on suggestions and customized for ease of use.

[0319] Step 7:

[0320] Users review real-time analysis results and interface adjustments provided by the emotion engine from the server, and provide further instructions as needed. User feedback is used for future adjustments.

[0321] Step 8:

[0322] Users will continuously monitor the entire system to ensure a comfortable operating environment, allowing them to confidently participate in plant operations. If necessary, they will accept suggestions from the emotional engine to help the process proceed efficiently.

[0323] (Example 2)

[0324] 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".

[0325] Conventional biofuel production systems utilizing microorganisms have suffered from problems such as the inability to quickly adjust environmental conditions, resulting in unoptimized microbial growth. Furthermore, high user workload and increased stress led to a decrease in overall efficiency. Therefore, there is a need for a system that can adjust environmental conditions in real time, optimize the interface in response to user emotions, and enable efficient biofuel production.

[0326] 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.

[0327] In this invention, the server includes data acquisition means, analysis means, environment control means, emotion analysis means, and interface adjustment means. This makes it possible to provide an optimal environment for microbial growth in real time, reduce user stress, and improve overall generation efficiency.

[0328] "Data acquisition means" refers to devices or technologies that observe the growth state of microorganisms and environmental conditions in real time and collect necessary data.

[0329] "Analysis means" refers to a device or technology that processes information obtained from data acquisition means, identifies the optimal environmental conditions for microbial growth, and generates necessary adjustment commands.

[0330] "Environmental control means" refers to a device or technology that automatically adjusts environmental conditions and promotes microbial growth based on commands generated by an analysis means.

[0331] "Emotional analysis means" refers to a device or technology that collects and analyzes a user's emotional state and uses the results to optimize the user interface or improve the efficiency of operations.

[0332] "Interface adjustment means" refers to a device or technology that adjusts the system's user interface based on user emotion data obtained from emotion analysis means, thereby reducing user stress and improving operability.

[0333] In order to implement this invention, it is necessary for the server, terminal, and user to play their respective roles and cooperate with each other.

[0334] The server comprises data acquisition means, analysis means, environmental control means, emotion analysis means, and interface adjustment means. These systems are supported by appropriate hardware and software. The data acquisition means receives environmental data transmitted from the terminal, which includes temperature, humidity, and light intensity related to microbial growth. The analysis means analyzes this data using a generative AI model to derive the optimal conditions for microbial growth. Based on the analysis results, the environmental control means automatically adjusts the environment, and the emotion analysis means evaluates the user's emotions. Based on this data, the interface adjustment means optimizes the user interface.

[0335] The terminal uses a data acquisition system composed of sensors to collect environmental data that affects microbial growth and transmits it to a server. In response to environmental adjustment commands received from the server, the terminal performs specific control actions, such as operating a cooling device or adding nutrients. Furthermore, based on the results derived by the emotion analysis system, the user interface on the terminal is adjusted to make it easier to operate.

[0336] The user reviews the analysis results from the server and suggestions in the interface, monitors the system, and takes instructions as needed. The results obtained from sentiment analysis serve as guidelines to improve the user's work efficiency and reduce stress. For example, when microbial growth is poor, the user receives improvement suggestions from the analysis, enabling efficient response through optimized operations. An example of a prompt would be, "Please suggest ways to optimize microbial growth." This question allows the generative AI model to provide new approaches and ideas.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The device uses sensors to acquire environmental data such as temperature, humidity, and light intensity in real time. This data is then formatted and sent to a server. Inputs are analog or digital data from each environmental sensor, and outputs are digital signals containing this data. Specifically, the device processes the signals from the sensors using a microcontroller and transmits the data via Wi-Fi or a wired network.

[0340] Step 2:

[0341] The server processes the received environmental data using analytical tools. It utilizes a generative AI model to evaluate the optimal environmental conditions for microbial growth. The input is formatted data sent from the terminal, and the output is environmental adjustment commands necessary for growth promotion. Specifically, the AI ​​model analyzes the data and determines the optimal temperature and humidity settings.

[0342] Step 3:

[0343] The server generates commands to automate environmental control based on the adjustment commands obtained from the analysis and sends them to the terminal. The input is the analysis results from the AI, and the output is the specific commands sent to the control device. Specific actions include, for example, issuing commands to activate a cooling system or start a heater.

[0344] Step 4:

[0345] The terminal receives commands from the server and adjusts the actual environmental conditions. The input is the environmental adjustment command from the server, and the output is the adjusted environment. Specific actions include operating a motor to improve ventilation or automatically adding nutrients from a nutrient tank.

[0346] Step 5:

[0347] The server collects and analyzes real-time emotional data from users using emotion analysis tools. Inputs include the user's facial expressions and voice obtained from cameras, microphones, etc., while outputs are information about the user's emotional state. Specifically, it evaluates the user's stress levels and concentration using facial recognition technology and voice analysis.

[0348] Step 6:

[0349] The server generates and presents suggestions to the user for adjusting the user interface and improving operational efficiency based on the sentiment analysis results. The input is emotional state data obtained through sentiment analysis, and the output is optimized interface settings and operational suggestions. Specific actions include simplifying screen displays and rearranging button layouts.

[0350] Step 7:

[0351] The user reviews the analysis results and suggestions provided by the server and performs actions as needed. The input is the feedback and suggestions from the server, and the output is the result of the user's actions. Specifically, this involves accepting the server's suggestions and manually changing the settings.

[0352] (Application Example 2)

[0353] 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."

[0354] The present invention aims to provide a system that optimizes environmental control in biofuel production while simultaneously reducing stress and improving efficiency in response to user emotions. The challenge is to provide a more comfortable and effective operating environment for the user by not only optimizing microbial growth but also adjusting the interface to take into account the user's emotional state.

[0355] 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.

[0356] In this invention, the server includes information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the information obtained from the information gathering means and generating optimal environmental adjustment commands; situation control means for automatically adjusting environmental conditions based on the commands generated by the analysis means; and emotion analysis means for recognizing the user's emotional state and adjusting the user interface. This enables optimization of the biofuel production process and customized stress reduction based on the user's emotions.

[0357] "Microorganisms" are tiny organisms composed of single or multicellular cells that grow under specific environmental conditions and are used in the production of biofuels.

[0358] "Growth status" refers to information indicating the extent to which microorganisms are growing, and it is a dynamic state that is influenced by environmental conditions.

[0359] "Environmental conditions" refer to the surrounding physical and chemical factors that affect the growth of microorganisms, including temperature and nutrient supply.

[0360] "Information gathering means" refers to hardware and software components for acquiring the growth status and environmental conditions of microorganisms in real time.

[0361] "Analysis means" refers to data processing and analysis procedures for generating optimal environmental adjustment commands based on acquired information.

[0362] "Condition control means" are components of a system for maintaining or adjusting environmental conditions suitable for the growth of microorganisms based on commands generated by the analysis means.

[0363] An "emotion analysis tool" is an analytical tool that recognizes the user's emotional state and adjusts the user interface based on the results.

[0364] The system for implementing this invention integrates various hardware and software components to generate biofuels and manage user emotions. The server first uses multiple sensors to acquire real-time data on microbial growth and surrounding environmental conditions. This information is processed by an analysis system running on the server. This analysis system utilizes generative AI models such as Google Cloud AI, enabling complex data analysis.

[0365] The terminal automatically adjusts environmental conditions according to commands sent from the server. This adjustment utilizes smart home appliance control devices and temperature sensors, among other things. Furthermore, it can recognize the user's real-time emotional state through emotion analysis and optimize the terminal's user interface based on the results. For example, if a user feels stressed while working, the terminal will implement stress reduction measures such as playing relaxation music or adjusting the brightness of the lighting.

[0366] Users can receive these adjustments and suggestions via their smartphones or smart glasses, allowing them to maintain an optimal work environment. The collected emotional data is also used to analyze the user's stress levels and emotional tendencies, and to provide appropriate lifestyle improvement suggestions.

[0367] For example, if stress is detected in a user during morning teleworking, the system will recommend appropriate music and change the room lighting to warmer colors to provide a relaxing environment. Another example of a prompt for the generating AI model is, "Describe the app's function of adjusting lighting and music to provide a relaxing environment based on the user's stress level and environmental data." In this way, the system makes adjustments that take into account the interaction between the user and the environment, achieving more efficient and human-centered biofuel production.

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The server acquires data from sensors in real time regarding the growth status of microorganisms and environmental conditions. This input data includes temperature, humidity, and nutrient levels. The server first stores this data in a database and then transmits it to the analysis system.

[0371] Step 2:

[0372] The server's analysis method uses a generative AI model to analyze the data acquired in Step 1. This analysis generates environmental adjustment commands to optimize microbial growth. The output includes adjustment commands such as the optimal temperature setting and the amount of nutrients required.

[0373] Step 3:

[0374] The terminal receives environmental adjustment commands sent from the server. Based on the received commands, the terminal operates smart home appliance control devices to adjust temperature and nutrient supply. Specifically, temperature controllers and automatic water supply systems are involved in these adjustments.

[0375] Step 4:

[0376] The server acquires emotional data through the smartphone's camera and microphone to understand the user's emotional state. This data is input into an emotion analysis system to analyze changes in emotions. This analysis determines the user's stress level and emotional state.

[0377] Step 5:

[0378] The server's emotion analysis system generates commands to adjust the user interface based on the obtained emotion data. These commands include suggestions aimed at stress reduction, such as playing relaxation music or adjusting the lighting color. The output of these commands is sent to the terminal.

[0379] Step 6:

[0380] The terminal adjusts the user interface and environment according to commands sent from the server. Specifically, the music player plays soothing music, and the smart lighting switches to warm colors. This process provides the user with a more comfortable operating environment.

[0381] Step 7:

[0382] Users receive feedback from their devices and evaluate their own comfort level. Furthermore, they contribute to improving the system's accuracy by using example prompts and feeding back new emotional patterns to the server. Through this process, the system continuously learns and improves the user experience.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] This invention is a system for optimizing the biofuel production process using microorganisms. This system acquires the growth status of microorganisms and related environmental conditions in real time and has a function to automatically adjust the environmental conditions based on AI-based data analysis. The specific embodiment of the invention is implemented with the following configuration.

[0400] The server plays a central role in processing real-time data received from terminals. This data includes environmental conditions such as temperature, pH, and nutrient concentration, as well as the growth status of microorganisms. The server's AI agent analyzes this information and generates optimal environmental conditions. Based on these results, it creates commands such as "increase the temperature by a specific degree" or "add a specific nutrient."

[0401] The terminal operates the actual sensors and actuators within the culture device. The sensors monitor the growth status of microorganisms and environmental conditions, and transmit the data to the server. Upon receiving commands from the server, the terminal makes adjustments, such as controlling heaters to regulate the temperature or adding the appropriate amount of nutrients.

[0402] Users monitor and control the entire system and develop improvements to further enhance process efficiency. They access analysis results and suggestions provided by the server to consider responses to detected anomalies and new strategies. Users also leverage system alert notifications to enable rapid responses to changes in environmental conditions and abnormal situations.

[0403] As a specific example, in a certain biofuel production plant, if the server detects that microorganisms are growing more rapidly than usual, analysis identifies a temperature increase that supports this anomaly. The server quickly sends a "cool down" command to the terminal, which reduces the heater output and activates the cooling system. This restores the culture environment to a proper state, allowing efficient biofuel production to continue. In this way, the system maintains an optimized process at all times, enabling the production of high-quality, cost-effective biofuels.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The terminal uses sensors installed inside the culture device to collect real-time data such as temperature, pH, nutrient concentration, and microbial growth status.

[0407] Step 2:

[0408] The terminal sends the collected data to the server at predetermined intervals. This data includes information necessary for status monitoring.

[0409] Step 3:

[0410] The server stores data received from the terminals and uses an AI agent to perform data analysis. This analysis determines whether the current culture environment is optimal.

[0411] Step 4:

[0412] The server's AI agent compares past and current data to assess whether adjustments are needed to maintain the optimal culture environment. If anomalies or trends are detected, the agent analyzes their nature.

[0413] Step 5:

[0414] The server generates commands to adjust the necessary environmental conditions based on the analysis results. Specific commands might include "lower the temperature by 1 degree" or "add nutrient A."

[0415] Step 6:

[0416] The server sends the generated command to the terminal and instructs it to execute.

[0417] Step 7:

[0418] The terminal operates temperature control devices and nutrient supply devices within the culture apparatus based on commands received from the server, thereby actually adjusting the environmental conditions.

[0419] Step 8:

[0420] Users monitor the system status by checking real-time analysis results and environment adjustment history provided by the server. They continuously monitor for any problems and intervene manually as needed.

[0421] Step 9:

[0422] Users refer to future improvement suggestions from the server and develop plans to further optimize the process. In this way, the entire system is ensured to operate efficiently at all times.

[0423] (Example 1)

[0424] 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."

[0425] In biofuel production processes utilizing microorganisms, it is necessary to appropriately manage the growth state of microorganisms and environmental conditions, but doing so efficiently and in real time is difficult. Furthermore, the insufficient ability to quickly detect and respond to abnormal situations is a factor that hinders the achievement of optimal production efficiency. There is a need to build a system that can solve these problems and realize high-quality, cost-effective biofuel production.

[0426] 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.

[0427] In this invention, the server includes an information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time, an information analysis means for analyzing the information obtained from the information gathering means and generating an optimal environmental adjustment command, and an environmental adjustment means for automatically adjusting the environmental conditions based on the command generated by the information analysis means. This enables efficient management of the growth state of microorganisms and environmental conditions, allowing for rapid response to abnormal situations and improved production efficiency.

[0428] "Information gathering means" refers to a device or mechanism used to acquire the growth state and environmental conditions of microorganisms in real time.

[0429] "Information analysis means" refers to a device or function that analyzes data obtained from information collection means and generates optimal environmental adjustment commands.

[0430] "Environmental adjustment means" refers to a device or mechanism that automatically adjusts environmental conditions based on commands generated by information analysis means.

[0431] "Communication means" refers to a device or function for receiving information transmitted from a terminal and transmitting generated commands to the terminal.

[0432] "User interface means" refers to a device or function that presents analysis results and command logs to the user and assists in monitoring and controlling the process.

[0433] A "learning tool" is a device or function that learns from past information and makes suggestions for future process improvements.

[0434] An "anomaly detection means" is a device or function for detecting an anomaly and sending a warning to the user.

[0435] This invention is a system for carrying out a biofuel production process using microorganisms with a certain level of efficiency. This system is composed of a combination of information gathering means, information analysis means, environmental adjustment means, communication means, and user interface means.

[0436] The server acts as a means of information analysis, analyzing data on the growth status of microorganisms and environmental conditions received from terminals. The server performs the analysis using dedicated software and a generative AI model, thereby creating instructions to generate optimal environmental conditions.

[0437] The terminal functions as both an information gathering and environmental adjustment device. Using sensors installed within the culture apparatus, the terminal monitors environmental data such as temperature, pH, and nutrient concentration in real time and transmits this data to the server. Upon receiving commands from the server, it operates actuators to adjust environmental conditions. Specific hardware examples include temperature sensors, pH meters, and nutrient pump control devices.

[0438] Users monitor the entire system through a user interface. They also review data and command logs analyzed by the server, supporting effective management and efficiency improvements of the biofuel production process. If an anomaly is detected, users receive an alert and can input prompt messages into an AI model to obtain appropriate countermeasures.

[0439] As a concrete example, a user can input a prompt message into the AI ​​model such as, "If microbial growth is faster than normal, please suggest an action plan to return to optimal environmental conditions," and receive appropriate countermeasures. This allows the system to constantly maintain an optimal environment, enabling stable biofuel production.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The terminal acquires environmental data such as temperature, pH, and nutrient concentration using multiple sensors installed within the culture device. This data is aggregated in the terminal's control unit and becomes input data transmitted to the server at regular intervals. In this process, the terminal performs specific operations such as reading the data and converting it to a standard format.

[0443] Step 2:

[0444] The server receives environmental data transmitted from the terminal. To analyze the received data, it uses a generative AI model to calculate the optimal environmental conditions. Based on this analysis, it performs specific data processing to generate environmental adjustment commands, including temperature and pH adjustment values, from the raw data obtained as input.

[0445] Step 3:

[0446] The server sends the commands obtained through analysis to the terminal. Specifically, detailed commands such as "lower the temperature by 2 degrees" or "add 5 mg / L of a specific nutrient" are output, and the server performs a communication operation to send these commands to the terminal.

[0447] Step 4:

[0448] The terminal controls the actuators based on environmental adjustment commands received from the server. Specifically, it analyzes the commands and performs output operations such as adjusting the heater output settings or adding a specified amount of nutrients using a pump.

[0449] Step 5:

[0450] The user accesses analysis results and command history provided by the server. This allows them to monitor the current state of the process, input new prompts into the generated AI model as needed, and receive suggestions for further process improvement. Specifically, they use the user interface to review and evaluate the visualized data.

[0451] (Application Example 1)

[0452] 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."

[0453] In biofuel production processes, monitoring microbial growth and environmental conditions in real time and making optimal environmental adjustments is a challenging task. Furthermore, there is a lack of visualization tools to enable on-site workers to understand the data and respond quickly, thus necessitating process efficiency improvements.

[0454] 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.

[0455] In this invention, the server includes data acquisition means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the data obtained from the data acquisition means and generating optimal environmental adjustment commands; environmental control means for automatically adjusting the environmental conditions based on the commands generated by the analysis means; and information provision means for providing the analysis results to a portable information terminal so that the user can visually confirm the adjustment of the environmental conditions. This enables process efficiency and rapid response.

[0456] "Microorganisms" is a general term for tiny organisms that can only be observed with a microscope.

[0457] "Growth status" is an indicator that shows how much a microorganism is growing under specific environmental conditions.

[0458] "Environmental conditions" refer to external physical and chemical factors that affect the growth of microorganisms.

[0459] "Data acquisition means" refers to a device or method for measuring and recording the growth state of microorganisms and environmental conditions in real time.

[0460] "Analysis means" refers to a system or method for determining optimal environmental conditions based on collected data and generating adjustment commands.

[0461] "Environmental control means" refers to a device or mechanism that automatically adjusts the growth environment of microorganisms in accordance with commands generated by an analysis means.

[0462] "Information provision means" refers to a system or method that notifies the user of the analyzed results and provides a visualization of changes and adjustments to environmental conditions.

[0463] An "anomaly detection system" is a mechanism that recognizes unusual conditions or malfunctions and notifies the user of a warning.

[0464] A "portable information terminal" is a portable electronic device, such as a smartphone or tablet, used for displaying and operating information.

[0465] Embodiments of this invention are systems designed to optimize the biofuel production process. Specific embodiments are described below.

[0466] The server primarily manages data acquisition methods for obtaining real-time information on the growth status and environmental conditions of microorganisms. These data acquisition methods include temperature and pH sensors, as well as sensors that measure nutrient concentrations. Information from these sensors is aggregated on the server. On the server, this data is analyzed using a generative AI model to generate optimized commands. Software libraries such as TensorFlow are used, and predictions and adjustments to optimize growth conditions are made based on the accumulated data through machine learning.

[0467] The terminal receives commands from the server and is responsible for making adjustments within the actual culture environment. Specifically, it includes actuators that automatically perform operations such as temperature control and nutrient addition. This makes it possible to promote optimal microbial growth while maintaining the efficiency of the entire process.

[0468] The user monitors the analysis results using a mobile device and oversees the entire process. Through information provision methods, the user can learn about the data and system status in real time, and the data is visualized on smartphones and tablets. In addition, anomaly detection means that when an anomaly occurs, an alert is immediately sent to the mobile device and a prompt is displayed to prompt additional action. For example, a prompt message such as "The AI ​​system has detected a temperature rise. Should you activate the cooling system immediately?" can be given. With such a system, the user can efficiently manage and control the process and improve it.

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] The server acquires real-time data on the growth status of microorganisms and environmental conditions. It receives temperature, pH, and nutrient concentration data from sensors and transmits it to the server. The input is sensor data, and the output is aggregated information from this data.

[0472] Step 2:

[0473] The server analyzes the received data using a generation AI model. Using the TensorFlow library, it calculates optimal environmental conditions by referencing historical data. The input to this process is aggregated sensor data, and the output is a command regarding the optimized environmental conditions.

[0474] Step 3:

[0475] Based on the analysis results, the server generates environmental adjustment commands and sends them to the terminal. Specific actions include commands for temperature adjustment and nutrient addition. The input consists of commands regarding optimized environmental conditions, while the output is command data sent to the terminal.

[0476] Step 4:

[0477] The terminal automatically adjusts the culture environment according to the received commands. It uses actuators to control temperature and add necessary nutrients. The input is command data from the server, and the output is the result of the actual environment adjustment.

[0478] Step 5:

[0479] Users use their mobile devices to check the system status and analysis results. The data is visualized on a smartphone via an information provision system. Input consists of analysis results and status data from the server, while output is the visualized information provided to the user.

[0480] Step 6:

[0481] If an anomaly is detected, the server immediately sends an alert to the user's mobile device. It displays a prompt message to encourage the user to take action. For example, it might display in the format, "The AI ​​system has detected a temperature rise. Do you want to activate the cooling system immediately?" The input is data related to the anomaly detection, and the output is an alert notification and prompt message to the user.

[0482] 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.

[0483] This invention provides a system that combines a biofuel generation system utilizing microbial growth with an emotion engine that recognizes user emotions. The system includes data acquisition means, analysis means, environmental control means, and an emotion engine, thereby enabling real-time data collection, analysis, environmental control, and optimization of the user experience.

[0484] The server receives real-time data transmitted from the terminal and performs data analysis using an AI agent. This analysis includes evaluating the growth status of microorganisms and environmental conditions, and generates commands to adjust environmental conditions as needed. Furthermore, by utilizing an emotion engine to analyze user emotions, the server can offer suggestions and adjust the interface to reduce user stress and anxiety.

[0485] The device uses sensors to measure environmental conditions and transmits that data to a server. Upon receiving commands from the server, it performs actual environmental adjustments, specifically temperature control and nutrient addition. User emotion information obtained by the emotion engine is used to improve the device's usability and customize the user interface.

[0486] The user monitors the system's status and reviews the analysis results and emotion engine feedback provided by the server. For example, if the emotion engine recognizes the user's stress level, the system will simplify the operating procedures and adjust the interface based on the suggestions, improving the user's work efficiency and enhancing the overall process effectiveness.

[0487] As a concrete example, in a system operated by a plant operator, if the emotion engine detects that the operator is experiencing stress, the server automatically proposes environmental adjustment procedures and issues commands to automate some manual operations. This flexible response improves the overall performance of the operation. By making improvements based on emotion data, the burden felt by the user is reduced, and the matching with the system is optimized, resulting in efficient and smooth biofuel production.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The device uses sensors within the culture system to collect environmental conditions such as temperature, pH, and nutrient concentration, as well as microbial growth data, in real time. It also acquires emotional data through a device that records user interactions.

[0491] Step 2:

[0492] The terminal collects environmental and emotional data and sends it to the server at regular intervals. This allows the server to aggregate the latest information on the system's operational status and the user's emotional state.

[0493] Step 3:

[0494] The server analyzes the data received from the terminal and evaluates the environmental conditions necessary for optimal microbial growth. Based on this analysis, it determines whether the environment is suitable and generates adjustment commands as needed.

[0495] Step 4:

[0496] The server's emotion engine analyzes the user's emotional data. If the emotion engine detects stress or anxiety, it suggests interface adjustments or automation to reduce the user's burden.

[0497] Step 5:

[0498] The server sends environment adjustment commands and operation suggestions and interface adjustment proposals based on the user's emotional state to the terminal.

[0499] Step 6:

[0500] Upon receiving commands from the server, the terminal performs environmental adjustments and modifies device settings. This includes changing the temperature and adjusting nutrient dosages. Furthermore, the user interface is adapted based on suggestions and customized for ease of use.

[0501] Step 7:

[0502] Users review real-time analysis results and interface adjustments provided by the emotion engine from the server, and provide further instructions as needed. User feedback is used for future adjustments.

[0503] Step 8:

[0504] Users will continuously monitor the entire system to ensure a comfortable operating environment, allowing them to confidently participate in plant operations. If necessary, they will accept suggestions from the emotional engine to help the process proceed efficiently.

[0505] (Example 2)

[0506] 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."

[0507] Conventional biofuel production systems utilizing microorganisms have suffered from problems such as the inability to quickly adjust environmental conditions, resulting in unoptimized microbial growth. Furthermore, high user workload and increased stress led to a decrease in overall efficiency. Therefore, there is a need for a system that can adjust environmental conditions in real time, optimize the interface in response to user emotions, and enable efficient biofuel production.

[0508] 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.

[0509] In this invention, the server includes data acquisition means, analysis means, environment control means, emotion analysis means, and interface adjustment means. This makes it possible to provide an optimal environment for microbial growth in real time, reduce user stress, and improve overall generation efficiency.

[0510] "Data acquisition means" refers to devices or technologies that observe the growth state of microorganisms and environmental conditions in real time and collect necessary data.

[0511] "Analysis means" refers to a device or technology that processes information obtained from data acquisition means, identifies the optimal environmental conditions for microbial growth, and generates necessary adjustment commands.

[0512] "Environmental control means" refers to a device or technology that automatically adjusts environmental conditions and promotes microbial growth based on commands generated by an analysis means.

[0513] "Emotional analysis means" refers to a device or technology that collects and analyzes a user's emotional state and uses the results to optimize the user interface or improve the efficiency of operations.

[0514] "Interface adjustment means" refers to a device or technology that adjusts the system's user interface based on user emotion data obtained from emotion analysis means, thereby reducing user stress and improving operability.

[0515] In order to implement this invention, it is necessary for the server, terminal, and user to play their respective roles and cooperate with each other.

[0516] The server comprises data acquisition means, analysis means, environmental control means, emotion analysis means, and interface adjustment means. These systems are supported by appropriate hardware and software. The data acquisition means receives environmental data transmitted from the terminal, which includes temperature, humidity, and light intensity related to microbial growth. The analysis means analyzes this data using a generative AI model to derive the optimal conditions for microbial growth. Based on the analysis results, the environmental control means automatically adjusts the environment, and the emotion analysis means evaluates the user's emotions. Based on this data, the interface adjustment means optimizes the user interface.

[0517] The terminal uses a data acquisition system composed of sensors to collect environmental data that affects microbial growth and transmits it to a server. In response to environmental adjustment commands received from the server, the terminal performs specific control actions, such as operating a cooling device or adding nutrients. Furthermore, based on the results derived by the emotion analysis system, the user interface on the terminal is adjusted to make it easier to operate.

[0518] The user reviews the analysis results from the server and suggestions in the interface, monitors the system, and takes instructions as needed. The results obtained from sentiment analysis serve as guidelines to improve the user's work efficiency and reduce stress. For example, when microbial growth is poor, the user receives improvement suggestions from the analysis, enabling efficient response through optimized operations. An example of a prompt would be, "Please suggest ways to optimize microbial growth." This question allows the generative AI model to provide new approaches and ideas.

[0519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0520] Step 1:

[0521] The device uses sensors to acquire environmental data such as temperature, humidity, and light intensity in real time. This data is then formatted and sent to a server. Inputs are analog or digital data from each environmental sensor, and outputs are digital signals containing this data. Specifically, the device processes the signals from the sensors using a microcontroller and transmits the data via Wi-Fi or a wired network.

[0522] Step 2:

[0523] The server processes the received environmental data using analytical tools. It utilizes a generative AI model to evaluate the optimal environmental conditions for microbial growth. The input is formatted data sent from the terminal, and the output is environmental adjustment commands necessary for growth promotion. Specifically, the AI ​​model analyzes the data and determines the optimal temperature and humidity settings.

[0524] Step 3:

[0525] The server generates commands to automate environmental control based on the adjustment commands obtained from the analysis and sends them to the terminal. The input is the analysis results from the AI, and the output is the specific commands sent to the control device. Specific actions include, for example, issuing commands to activate a cooling system or start a heater.

[0526] Step 4:

[0527] The terminal receives commands from the server and adjusts the actual environmental conditions. The input is the environmental adjustment command from the server, and the output is the adjusted environment. Specific actions include operating a motor to improve ventilation or automatically adding nutrients from a nutrient tank.

[0528] Step 5:

[0529] The server collects and analyzes real-time emotional data from users using emotion analysis tools. Inputs include the user's facial expressions and voice obtained from cameras, microphones, etc., while outputs are information about the user's emotional state. Specifically, it evaluates the user's stress levels and concentration using facial recognition technology and voice analysis.

[0530] Step 6:

[0531] The server generates and presents suggestions to the user for adjusting the user interface and improving operational efficiency based on the sentiment analysis results. The input is emotional state data obtained through sentiment analysis, and the output is optimized interface settings and operational suggestions. Specific actions include simplifying screen displays and rearranging button layouts.

[0532] Step 7:

[0533] The user reviews the analysis results and suggestions provided by the server and performs actions as needed. The input is the feedback and suggestions from the server, and the output is the result of the user's actions. Specifically, this involves accepting the server's suggestions and manually changing the settings.

[0534] (Application Example 2)

[0535] 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."

[0536] The present invention aims to provide a system that optimizes environmental control in biofuel production while simultaneously reducing stress and improving efficiency in response to user emotions. The challenge is to provide a more comfortable and effective operating environment for the user by not only optimizing microbial growth but also adjusting the interface to take into account the user's emotional state.

[0537] 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.

[0538] In this invention, the server includes information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the information obtained from the information gathering means and generating optimal environmental adjustment commands; situation control means for automatically adjusting environmental conditions based on the commands generated by the analysis means; and emotion analysis means for recognizing the user's emotional state and adjusting the user interface. This enables optimization of the biofuel production process and customized stress reduction based on the user's emotions.

[0539] "Microorganisms" are tiny organisms composed of single or multicellular cells that grow under specific environmental conditions and are used in the production of biofuels.

[0540] "Growth status" refers to information indicating the extent to which microorganisms are growing, and it is a dynamic state that is influenced by environmental conditions.

[0541] "Environmental conditions" refer to the surrounding physical and chemical factors that affect the growth of microorganisms, including temperature and nutrient supply.

[0542] "Information gathering means" refers to hardware and software components for acquiring the growth status and environmental conditions of microorganisms in real time.

[0543] "Analysis means" refers to data processing and analysis procedures for generating optimal environmental adjustment commands based on acquired information.

[0544] "Condition control means" are components of a system for maintaining or adjusting environmental conditions suitable for the growth of microorganisms based on commands generated by the analysis means.

[0545] An "emotion analysis tool" is an analytical tool that recognizes the user's emotional state and adjusts the user interface based on the results.

[0546] The system for implementing this invention integrates various hardware and software components to generate biofuels and manage user emotions. The server first uses multiple sensors to acquire real-time data on microbial growth and surrounding environmental conditions. This information is processed by an analysis system running on the server. This analysis system utilizes generative AI models such as Google Cloud AI, enabling complex data analysis.

[0547] The terminal automatically adjusts environmental conditions according to commands sent from the server. This adjustment utilizes smart home appliance control devices and temperature sensors, among other things. Furthermore, it can recognize the user's real-time emotional state through emotion analysis and optimize the terminal's user interface based on the results. For example, if a user feels stressed while working, the terminal will implement stress reduction measures such as playing relaxation music or adjusting the brightness of the lighting.

[0548] Users can receive these adjustments and suggestions via their smartphones or smart glasses, allowing them to maintain an optimal work environment. The collected emotional data is also used to analyze the user's stress levels and emotional tendencies, and to provide appropriate lifestyle improvement suggestions.

[0549] For example, if stress is detected in a user during morning teleworking, the system will recommend appropriate music and change the room lighting to warmer colors to provide a relaxing environment. Another example of a prompt for the generating AI model is, "Describe the app's function of adjusting lighting and music to provide a relaxing environment based on the user's stress level and environmental data." In this way, the system makes adjustments that take into account the interaction between the user and the environment, achieving more efficient and human-centered biofuel production.

[0550] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0551] Step 1:

[0552] The server acquires data from sensors in real time regarding the growth status of microorganisms and environmental conditions. This input data includes temperature, humidity, and nutrient levels. The server first stores this data in a database and then transmits it to the analysis system.

[0553] Step 2:

[0554] The server's analysis method uses a generative AI model to analyze the data acquired in Step 1. This analysis generates environmental adjustment commands to optimize microbial growth. The output includes adjustment commands such as the optimal temperature setting and the amount of nutrients required.

[0555] Step 3:

[0556] The terminal receives environmental adjustment commands sent from the server. Based on the received commands, the terminal operates smart home appliance control devices to adjust temperature and nutrient supply. Specifically, temperature controllers and automatic water supply systems are involved in these adjustments.

[0557] Step 4:

[0558] The server acquires emotional data through the smartphone's camera and microphone to understand the user's emotional state. This data is input into an emotion analysis system to analyze changes in emotions. This analysis determines the user's stress level and emotional state.

[0559] Step 5:

[0560] The server's emotion analysis system generates commands to adjust the user interface based on the obtained emotion data. These commands include suggestions aimed at stress reduction, such as playing relaxation music or adjusting the lighting color. The output of these commands is sent to the terminal.

[0561] Step 6:

[0562] The terminal adjusts the user interface and environment according to commands sent from the server. Specifically, the music player plays soothing music, and the smart lighting switches to warm colors. This process provides the user with a more comfortable operating environment.

[0563] Step 7:

[0564] Users receive feedback from their devices and evaluate their own comfort level. Furthermore, they contribute to improving the system's accuracy by using example prompts and feeding back new emotional patterns to the server. Through this process, the system continuously learns and improves the user experience.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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".

[0582] This invention is a system for optimizing the biofuel production process using microorganisms. This system acquires the growth status of microorganisms and related environmental conditions in real time and has a function to automatically adjust the environmental conditions based on AI-based data analysis. The specific embodiment of the invention is implemented with the following configuration.

[0583] The server plays a central role in processing real-time data received from terminals. This data includes environmental conditions such as temperature, pH, and nutrient concentration, as well as the growth status of microorganisms. The server's AI agent analyzes this information and generates optimal environmental conditions. Based on these results, it creates commands such as "increase the temperature by a specific degree" or "add a specific nutrient."

[0584] The terminal operates the actual sensors and actuators within the culture device. The sensors monitor the growth status of microorganisms and environmental conditions, and transmit the data to the server. Upon receiving commands from the server, the terminal makes adjustments, such as controlling heaters to regulate the temperature or adding the appropriate amount of nutrients.

[0585] Users monitor and control the entire system and develop improvements to further enhance process efficiency. They access analysis results and suggestions provided by the server to consider responses to detected anomalies and new strategies. Users also leverage system alert notifications to enable rapid responses to changes in environmental conditions and abnormal situations.

[0586] As a specific example, in a certain biofuel production plant, if the server detects that microorganisms are growing more rapidly than usual, analysis identifies a temperature increase that supports this anomaly. The server quickly sends a "cool down" command to the terminal, which reduces the heater output and activates the cooling system. This restores the culture environment to a proper state, allowing efficient biofuel production to continue. In this way, the system maintains an optimized process at all times, enabling the production of high-quality, cost-effective biofuels.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The terminal uses sensors installed inside the culture device to collect real-time data such as temperature, pH, nutrient concentration, and microbial growth status.

[0590] Step 2:

[0591] The terminal sends the collected data to the server at predetermined intervals. This data includes information necessary for status monitoring.

[0592] Step 3:

[0593] The server stores data received from the terminals and uses an AI agent to perform data analysis. This analysis determines whether the current culture environment is optimal.

[0594] Step 4:

[0595] The server's AI agent compares past and current data to assess whether adjustments are needed to maintain the optimal culture environment. If anomalies or trends are detected, the agent analyzes their nature.

[0596] Step 5:

[0597] The server generates commands to adjust the necessary environmental conditions based on the analysis results. Specific commands might include "lower the temperature by 1 degree" or "add nutrient A."

[0598] Step 6:

[0599] The server sends the generated command to the terminal and instructs it to execute.

[0600] Step 7:

[0601] The terminal operates temperature control devices and nutrient supply devices within the culture apparatus based on commands received from the server, thereby actually adjusting the environmental conditions.

[0602] Step 8:

[0603] Users monitor the system status by checking real-time analysis results and environment adjustment history provided by the server. They continuously monitor for any problems and intervene manually as needed.

[0604] Step 9:

[0605] Users refer to future improvement suggestions from the server and develop plans to further optimize the process. In this way, the entire system is ensured to operate efficiently at all times.

[0606] (Example 1)

[0607] 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".

[0608] In biofuel production processes utilizing microorganisms, it is necessary to appropriately manage the growth state of microorganisms and environmental conditions, but doing so efficiently and in real time is difficult. Furthermore, the insufficient ability to quickly detect and respond to abnormal situations is a factor that hinders the achievement of optimal production efficiency. There is a need to build a system that can solve these problems and realize high-quality, cost-effective biofuel production.

[0609] 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.

[0610] In this invention, the server includes an information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time, an information analysis means for analyzing the information obtained from the information gathering means and generating an optimal environmental adjustment command, and an environmental adjustment means for automatically adjusting the environmental conditions based on the command generated by the information analysis means. This enables efficient management of the growth state of microorganisms and environmental conditions, allowing for rapid response to abnormal situations and improved production efficiency.

[0611] "Information gathering means" refers to a device or mechanism used to acquire the growth state and environmental conditions of microorganisms in real time.

[0612] "Information analysis means" refers to a device or function that analyzes data obtained from information collection means and generates optimal environmental adjustment commands.

[0613] "Environmental adjustment means" refers to a device or mechanism that automatically adjusts environmental conditions based on commands generated by information analysis means.

[0614] "Communication means" refers to a device or function for receiving information transmitted from a terminal and transmitting generated commands to the terminal.

[0615] "User interface means" refers to a device or function that presents analysis results and command logs to the user and assists in monitoring and controlling the process.

[0616] A "learning tool" is a device or function that learns from past information and makes suggestions for future process improvements.

[0617] An "anomaly detection means" is a device or function for detecting an anomaly and sending a warning to the user.

[0618] This invention is a system for carrying out a biofuel production process using microorganisms with a certain level of efficiency. This system is composed of a combination of information gathering means, information analysis means, environmental adjustment means, communication means, and user interface means.

[0619] The server acts as a means of information analysis, analyzing data on the growth status of microorganisms and environmental conditions received from terminals. The server performs the analysis using dedicated software and a generative AI model, thereby creating instructions to generate optimal environmental conditions.

[0620] The terminal functions as both an information gathering and environmental adjustment device. Using sensors installed within the culture apparatus, the terminal monitors environmental data such as temperature, pH, and nutrient concentration in real time and transmits this data to the server. Upon receiving commands from the server, it operates actuators to adjust environmental conditions. Specific hardware examples include temperature sensors, pH meters, and nutrient pump control devices.

[0621] Users monitor the entire system through a user interface. They also review data and command logs analyzed by the server, supporting effective management and efficiency improvements of the biofuel production process. If an anomaly is detected, users receive an alert and can input prompt messages into an AI model to obtain appropriate countermeasures.

[0622] As a concrete example, a user can input a prompt message into the AI ​​model such as, "If microbial growth is faster than normal, please suggest an action plan to return to optimal environmental conditions," and receive appropriate countermeasures. This allows the system to constantly maintain an optimal environment, enabling stable biofuel production.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] The terminal acquires environmental data such as temperature, pH, and nutrient concentration using multiple sensors installed within the culture device. This data is aggregated in the terminal's control unit and becomes input data transmitted to the server at regular intervals. In this process, the terminal performs specific operations such as reading the data and converting it to a standard format.

[0626] Step 2:

[0627] The server receives environmental data transmitted from the terminal. To analyze the received data, it uses a generative AI model to calculate the optimal environmental conditions. Based on this analysis, it performs specific data processing to generate environmental adjustment commands, including temperature and pH adjustment values, from the raw data obtained as input.

[0628] Step 3:

[0629] The server sends the commands obtained through analysis to the terminal. Specifically, detailed commands such as "lower the temperature by 2 degrees" or "add 5 mg / L of a specific nutrient" are output, and the server performs a communication operation to send these commands to the terminal.

[0630] Step 4:

[0631] The terminal controls the actuators based on environmental adjustment commands received from the server. Specifically, it analyzes the commands and performs output operations such as adjusting the heater output settings or adding a specified amount of nutrients using a pump.

[0632] Step 5:

[0633] The user accesses analysis results and command history provided by the server. This allows them to monitor the current state of the process, input new prompts into the generated AI model as needed, and receive suggestions for further process improvement. Specifically, they use the user interface to review and evaluate the visualized data.

[0634] (Application Example 1)

[0635] 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".

[0636] In biofuel production processes, monitoring microbial growth and environmental conditions in real time and making optimal environmental adjustments is a challenging task. Furthermore, there is a lack of visualization tools to enable on-site workers to understand the data and respond quickly, thus necessitating process efficiency improvements.

[0637] 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.

[0638] In this invention, the server includes data acquisition means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the data obtained from the data acquisition means and generating optimal environmental adjustment commands; environmental control means for automatically adjusting the environmental conditions based on the commands generated by the analysis means; and information provision means for providing the analysis results to a portable information terminal so that the user can visually confirm the adjustment of the environmental conditions. This enables process efficiency and rapid response.

[0639] "Microorganisms" is a general term for tiny organisms that can only be observed with a microscope.

[0640] "Growth status" is an indicator that shows how much a microorganism is growing under specific environmental conditions.

[0641] "Environmental conditions" refer to external physical and chemical factors that affect the growth of microorganisms.

[0642] "Data acquisition means" refers to a device or method for measuring and recording the growth state of microorganisms and environmental conditions in real time.

[0643] "Analysis means" refers to a system or method for determining optimal environmental conditions based on collected data and generating adjustment commands.

[0644] "Environmental control means" refers to a device or mechanism that automatically adjusts the growth environment of microorganisms in accordance with commands generated by an analysis means.

[0645] "Information provision means" refers to a system or method that notifies the user of the analyzed results and provides a visualization of changes and adjustments to environmental conditions.

[0646] An "anomaly detection system" is a mechanism that recognizes unusual conditions or malfunctions and notifies the user of a warning.

[0647] A "portable information terminal" is a portable electronic device, such as a smartphone or tablet, used for displaying and operating information.

[0648] Embodiments of this invention are systems designed to optimize the biofuel production process. Specific embodiments are described below.

[0649] The server primarily manages data acquisition methods for obtaining real-time information on the growth status and environmental conditions of microorganisms. These data acquisition methods include temperature and pH sensors, as well as sensors that measure nutrient concentrations. Information from these sensors is aggregated on the server. On the server, this data is analyzed using a generative AI model to generate optimized commands. Software libraries such as TensorFlow are used, and predictions and adjustments to optimize growth conditions are made based on the accumulated data through machine learning.

[0650] The terminal receives commands from the server and is responsible for making adjustments within the actual culture environment. Specifically, it includes actuators that automatically perform operations such as temperature control and nutrient addition. This makes it possible to promote optimal microbial growth while maintaining the efficiency of the entire process.

[0651] The user monitors the analysis results using a mobile device and oversees the entire process. Through information provision methods, the user can learn about the data and system status in real time, and the data is visualized on smartphones and tablets. In addition, anomaly detection means that when an anomaly occurs, an alert is immediately sent to the mobile device and a prompt is displayed to prompt additional action. For example, a prompt message such as "The AI ​​system has detected a temperature rise. Should you activate the cooling system immediately?" can be given. With such a system, the user can efficiently manage and control the process and improve it.

[0652] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0653] Step 1:

[0654] The server acquires real-time data on the growth status of microorganisms and environmental conditions. It receives temperature, pH, and nutrient concentration data from sensors and transmits it to the server. The input is sensor data, and the output is aggregated information from this data.

[0655] Step 2:

[0656] The server analyzes the received data using a generation AI model. Using the TensorFlow library, it calculates optimal environmental conditions by referencing historical data. The input to this process is aggregated sensor data, and the output is a command regarding the optimized environmental conditions.

[0657] Step 3:

[0658] Based on the analysis results, the server generates environmental adjustment commands and sends them to the terminal. Specific actions include commands for temperature adjustment and nutrient addition. The input consists of commands regarding optimized environmental conditions, while the output is command data sent to the terminal.

[0659] Step 4:

[0660] The terminal automatically adjusts the culture environment according to the received commands. It uses actuators to control temperature and add necessary nutrients. The input is command data from the server, and the output is the result of the actual environment adjustment.

[0661] Step 5:

[0662] Users use their mobile devices to check the system status and analysis results. The data is visualized on a smartphone via an information provision system. Input consists of analysis results and status data from the server, while output is the visualized information provided to the user.

[0663] Step 6:

[0664] If an anomaly is detected, the server immediately sends an alert to the user's mobile device. It displays a prompt message to encourage the user to take action. For example, it might display in the format, "The AI ​​system has detected a temperature rise. Do you want to activate the cooling system immediately?" The input is data related to the anomaly detection, and the output is an alert notification and prompt message to the user.

[0665] 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.

[0666] This invention provides a system that combines a biofuel generation system utilizing microbial growth with an emotion engine that recognizes user emotions. The system includes data acquisition means, analysis means, environmental control means, and an emotion engine, thereby enabling real-time data collection, analysis, environmental control, and optimization of the user experience.

[0667] The server receives real-time data transmitted from the terminal and performs data analysis using an AI agent. This analysis includes evaluating the growth status of microorganisms and environmental conditions, and generates commands to adjust environmental conditions as needed. Furthermore, by utilizing an emotion engine to analyze user emotions, the server can offer suggestions and adjust the interface to reduce user stress and anxiety.

[0668] The device uses sensors to measure environmental conditions and transmits that data to a server. Upon receiving commands from the server, it performs actual environmental adjustments, specifically temperature control and nutrient addition. User emotion information obtained by the emotion engine is used to improve the device's usability and customize the user interface.

[0669] The user monitors the system's status and reviews the analysis results and emotion engine feedback provided by the server. For example, if the emotion engine recognizes the user's stress level, the system will simplify the operating procedures and adjust the interface based on the suggestions, improving the user's work efficiency and enhancing the overall process effectiveness.

[0670] As a concrete example, in a system operated by a plant operator, if the emotion engine detects that the operator is experiencing stress, the server automatically proposes environmental adjustment procedures and issues commands to automate some manual operations. This flexible response improves the overall performance of the operation. By making improvements based on emotion data, the burden felt by the user is reduced, and the matching with the system is optimized, resulting in efficient and smooth biofuel production.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The device uses sensors within the culture system to collect environmental conditions such as temperature, pH, and nutrient concentration, as well as microbial growth data, in real time. It also acquires emotional data through a device that records user interactions.

[0674] Step 2:

[0675] The terminal collects environmental and emotional data and sends it to the server at regular intervals. This allows the server to aggregate the latest information on the system's operational status and the user's emotional state.

[0676] Step 3:

[0677] The server analyzes the data received from the terminal and evaluates the environmental conditions necessary for optimal microbial growth. Based on this analysis, it determines whether the environment is suitable and generates adjustment commands as needed.

[0678] Step 4:

[0679] The server's emotion engine analyzes the user's emotional data. If the emotion engine detects stress or anxiety, it suggests interface adjustments or automation to reduce the user's burden.

[0680] Step 5:

[0681] The server sends environment adjustment commands and operation suggestions and interface adjustment proposals based on the user's emotional state to the terminal.

[0682] Step 6:

[0683] Upon receiving commands from the server, the terminal performs environmental adjustments and modifies device settings. This includes changing the temperature and adjusting nutrient dosages. Furthermore, the user interface is adapted based on suggestions and customized for ease of use.

[0684] Step 7:

[0685] Users review real-time analysis results and interface adjustments provided by the emotion engine from the server, and provide further instructions as needed. User feedback is used for future adjustments.

[0686] Step 8:

[0687] Users will continuously monitor the entire system to ensure a comfortable operating environment, allowing them to confidently participate in plant operations. If necessary, they will accept suggestions from the emotional engine to help the process proceed efficiently.

[0688] (Example 2)

[0689] 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".

[0690] Conventional biofuel production systems utilizing microorganisms have suffered from problems such as the inability to quickly adjust environmental conditions, resulting in unoptimized microbial growth. Furthermore, high user workload and increased stress led to a decrease in overall efficiency. Therefore, there is a need for a system that can adjust environmental conditions in real time, optimize the interface in response to user emotions, and enable efficient biofuel production.

[0691] 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.

[0692] In this invention, the server includes data acquisition means, analysis means, environment control means, emotion analysis means, and interface adjustment means. This makes it possible to provide an optimal environment for microbial growth in real time, reduce user stress, and improve overall generation efficiency.

[0693] "Data acquisition means" refers to devices or technologies that observe the growth state of microorganisms and environmental conditions in real time and collect necessary data.

[0694] "Analysis means" refers to a device or technology that processes information obtained from data acquisition means, identifies the optimal environmental conditions for microbial growth, and generates necessary adjustment commands.

[0695] "Environmental control means" refers to a device or technology that automatically adjusts environmental conditions and promotes microbial growth based on commands generated by an analysis means.

[0696] "Emotional analysis means" refers to a device or technology that collects and analyzes a user's emotional state and uses the results to optimize the user interface or improve the efficiency of operations.

[0697] "Interface adjustment means" refers to a device or technology that adjusts the system's user interface based on user emotion data obtained from emotion analysis means, thereby reducing user stress and improving operability.

[0698] In order to implement this invention, it is necessary for the server, terminal, and user to play their respective roles and cooperate with each other.

[0699] The server comprises data acquisition means, analysis means, environmental control means, emotion analysis means, and interface adjustment means. These systems are supported by appropriate hardware and software. The data acquisition means receives environmental data transmitted from the terminal, which includes temperature, humidity, and light intensity related to microbial growth. The analysis means analyzes this data using a generative AI model to derive the optimal conditions for microbial growth. Based on the analysis results, the environmental control means automatically adjusts the environment, and the emotion analysis means evaluates the user's emotions. Based on this data, the interface adjustment means optimizes the user interface.

[0700] The terminal uses a data acquisition system composed of sensors to collect environmental data that affects microbial growth and transmits it to a server. In response to environmental adjustment commands received from the server, the terminal performs specific control actions, such as operating a cooling device or adding nutrients. Furthermore, based on the results derived by the emotion analysis system, the user interface on the terminal is adjusted to make it easier to operate.

[0701] The user reviews the analysis results from the server and suggestions in the interface, monitors the system, and takes instructions as needed. The results obtained from sentiment analysis serve as guidelines to improve the user's work efficiency and reduce stress. For example, when microbial growth is poor, the user receives improvement suggestions from the analysis, enabling efficient response through optimized operations. An example of a prompt would be, "Please suggest ways to optimize microbial growth." This question allows the generative AI model to provide new approaches and ideas.

[0702] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0703] Step 1:

[0704] The device uses sensors to acquire environmental data such as temperature, humidity, and light intensity in real time. This data is then formatted and sent to a server. Inputs are analog or digital data from each environmental sensor, and outputs are digital signals containing this data. Specifically, the device processes the signals from the sensors using a microcontroller and transmits the data via Wi-Fi or a wired network.

[0705] Step 2:

[0706] The server processes the received environmental data using analytical tools. It utilizes a generative AI model to evaluate the optimal environmental conditions for microbial growth. The input is formatted data sent from the terminal, and the output is environmental adjustment commands necessary for growth promotion. Specifically, the AI ​​model analyzes the data and determines the optimal temperature and humidity settings.

[0707] Step 3:

[0708] The server generates commands to automate environmental control based on the adjustment commands obtained from the analysis and sends them to the terminal. The input is the analysis results from the AI, and the output is the specific commands sent to the control device. Specific actions include, for example, issuing commands to activate a cooling system or start a heater.

[0709] Step 4:

[0710] The terminal receives commands from the server and adjusts the actual environmental conditions. The input is the environmental adjustment command from the server, and the output is the adjusted environment. Specific actions include operating a motor to improve ventilation or automatically adding nutrients from a nutrient tank.

[0711] Step 5:

[0712] The server collects and analyzes real-time emotional data from users using emotion analysis tools. Inputs include the user's facial expressions and voice obtained from cameras, microphones, etc., while outputs are information about the user's emotional state. Specifically, it evaluates the user's stress levels and concentration using facial recognition technology and voice analysis.

[0713] Step 6:

[0714] The server generates and presents suggestions to the user for adjusting the user interface and improving operational efficiency based on the sentiment analysis results. The input is emotional state data obtained through sentiment analysis, and the output is optimized interface settings and operational suggestions. Specific actions include simplifying screen displays and rearranging button layouts.

[0715] Step 7:

[0716] The user reviews the analysis results and suggestions provided by the server and performs actions as needed. The input is the feedback and suggestions from the server, and the output is the result of the user's actions. Specifically, this involves accepting the server's suggestions and manually changing the settings.

[0717] (Application Example 2)

[0718] 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".

[0719] The present invention aims to provide a system that optimizes environmental control in biofuel production while simultaneously reducing stress and improving efficiency in response to user emotions. The challenge is to provide a more comfortable and effective operating environment for the user by not only optimizing microbial growth but also adjusting the interface to take into account the user's emotional state.

[0720] 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.

[0721] In this invention, the server includes information gathering means for acquiring the growth state of microorganisms and environmental conditions in real time; analysis means for analyzing the information obtained from the information gathering means and generating optimal environmental adjustment commands; situation control means for automatically adjusting environmental conditions based on the commands generated by the analysis means; and emotion analysis means for recognizing the user's emotional state and adjusting the user interface. This enables optimization of the biofuel production process and customized stress reduction based on the user's emotions.

[0722] "Microorganisms" are tiny organisms composed of single or multicellular cells that grow under specific environmental conditions and are used in the production of biofuels.

[0723] "Growth status" refers to information indicating the extent to which microorganisms are growing, and it is a dynamic state that is influenced by environmental conditions.

[0724] "Environmental conditions" refer to the surrounding physical and chemical factors that affect the growth of microorganisms, including temperature and nutrient supply.

[0725] "Information gathering means" refers to hardware and software components for acquiring the growth status and environmental conditions of microorganisms in real time.

[0726] "Analysis means" refers to data processing and analysis procedures for generating optimal environmental adjustment commands based on acquired information.

[0727] "Condition control means" are components of a system for maintaining or adjusting environmental conditions suitable for the growth of microorganisms based on commands generated by the analysis means.

[0728] An "emotion analysis tool" is an analytical tool that recognizes the user's emotional state and adjusts the user interface based on the results.

[0729] The system for implementing this invention integrates various hardware and software components to generate biofuels and manage user emotions. The server first uses multiple sensors to acquire real-time data on microbial growth and surrounding environmental conditions. This information is processed by an analysis system running on the server. This analysis system utilizes generative AI models such as Google Cloud AI, enabling complex data analysis.

[0730] The terminal automatically adjusts environmental conditions according to commands sent from the server. This adjustment utilizes smart home appliance control devices and temperature sensors, among other things. Furthermore, it can recognize the user's real-time emotional state through emotion analysis and optimize the terminal's user interface based on the results. For example, if a user feels stressed while working, the terminal will implement stress reduction measures such as playing relaxation music or adjusting the brightness of the lighting.

[0731] Users can receive these adjustments and suggestions via their smartphones or smart glasses, allowing them to maintain an optimal work environment. The collected emotional data is also used to analyze the user's stress levels and emotional tendencies, and to provide appropriate lifestyle improvement suggestions.

[0732] For example, if stress is detected in a user during morning teleworking, the system will recommend appropriate music and change the room lighting to warmer colors to provide a relaxing environment. Another example of a prompt for the generating AI model is, "Describe the app's function of adjusting lighting and music to provide a relaxing environment based on the user's stress level and environmental data." In this way, the system makes adjustments that take into account the interaction between the user and the environment, achieving more efficient and human-centered biofuel production.

[0733] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0734] Step 1:

[0735] The server acquires data from sensors in real time regarding the growth status of microorganisms and environmental conditions. This input data includes temperature, humidity, and nutrient levels. The server first stores this data in a database and then transmits it to the analysis system.

[0736] Step 2:

[0737] The server's analysis method uses a generative AI model to analyze the data acquired in Step 1. This analysis generates environmental adjustment commands to optimize microbial growth. The output includes adjustment commands such as the optimal temperature setting and the amount of nutrients required.

[0738] Step 3:

[0739] The terminal receives environmental adjustment commands sent from the server. Based on the received commands, the terminal operates smart home appliance control devices to adjust temperature and nutrient supply. Specifically, temperature controllers and automatic water supply systems are involved in these adjustments.

[0740] Step 4:

[0741] The server acquires emotional data through the smartphone's camera and microphone to understand the user's emotional state. This data is input into an emotion analysis system to analyze changes in emotions. This analysis determines the user's stress level and emotional state.

[0742] Step 5:

[0743] The server's emotion analysis system generates commands to adjust the user interface based on the obtained emotion data. These commands include suggestions aimed at stress reduction, such as playing relaxation music or adjusting the lighting color. The output of these commands is sent to the terminal.

[0744] Step 6:

[0745] The terminal adjusts the user interface and environment according to commands sent from the server. Specifically, the music player plays soothing music, and the smart lighting switches to warm colors. This process provides the user with a more comfortable operating environment.

[0746] Step 7:

[0747] Users receive feedback from their devices and evaluate their own comfort level. Furthermore, they contribute to improving the system's accuracy by using example prompts and feeding back new emotional patterns to the server. Through this process, the system continuously learns and improves the user experience.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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."

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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 as being incorporated by reference.

[0769] The following is further disclosed regarding the embodiments described above.

[0770] (Claim 1)

[0771] A data acquisition method for obtaining the growth status and environmental conditions of microorganisms in real time,

[0772] An analysis means that analyzes the data obtained from the data acquisition means and generates an optimal environmental adjustment command,

[0773] An environmental control means that automatically adjusts environmental conditions based on commands generated by the analysis means,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, wherein the analysis means is further comprising a learning means that learns past data and makes suggestions for future process improvements.

[0777] (Claim 3)

[0778] The system according to claim 1, further comprising an anomaly detection means that detects an anomaly in the analysis means and sends an alert to the user.

[0779] "Example 1"

[0780] (Claim 1)

[0781] A means of collecting information to acquire the growth status of microorganisms and environmental conditions in real time,

[0782] Information analysis means that analyzes the information obtained from the information gathering means and generates an optimal environmental adjustment command,

[0783] An environmental adjustment means that automatically adjusts environmental conditions based on commands generated by the information analysis means,

[0784] A communication means that receives information transmitted from a terminal and transmits a generated command to the terminal,

[0785] A user interface means that presents analysis results and command logs to the user and assists in monitoring and controlling the process,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, further comprising a learning means that learns past information and makes suggestions for future process improvements.

[0789] (Claim 3)

[0790] The system according to claim 1, further comprising an anomaly detection means that detects an anomaly in the information analysis means and sends a warning to the user.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A data acquisition method for obtaining the growth status and environmental conditions of microorganisms in real time,

[0794] An analysis means that analyzes the data obtained from the data acquisition means and generates an optimal environmental adjustment command,

[0795] An environmental control means that automatically adjusts environmental conditions based on commands generated by the analysis means,

[0796] An information provision method that provides analysis results to a mobile device, allowing users to visually confirm the adjustment of environmental conditions,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the analysis means is further comprising a learning means that learns past data and makes suggestions for future process improvements.

[0800] (Claim 3)

[0801] The system according to claim 1, comprising an anomaly detection means that detects an anomaly and sends an alert to the user, and the alert content is displayed by an augmented reality display device.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A data acquisition method for obtaining the growth status and environmental conditions of microorganisms in real time,

[0805] An analysis means that analyzes the data obtained from the data acquisition means and generates an optimal environmental adjustment command,

[0806] An environmental control means that automatically adjusts environmental conditions based on commands generated by the analysis means,

[0807] An emotion analysis method that analyzes the user's emotional state and makes suggestions to improve the efficiency of system operation,

[0808] Interface adjustment means that adjusts the user interface based on the generated emotion analysis results,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the analysis means is further comprising a learning means that learns past data and makes suggestions for future process improvements.

[0812] (Claim 3)

[0813] The system according to claim 1, further comprising an anomaly detection means that detects an anomaly in the analysis means and sends an alert to the user.

[0814] "Application example 2 when combining with an emotional engine"

[0815] (Claim 1)

[0816] A means of collecting information to acquire the growth status of microorganisms and environmental conditions in real time,

[0817] An analysis means that analyzes the information obtained from the information gathering means and generates an optimal environmental adjustment command,

[0818] A situation control means that automatically adjusts environmental conditions based on commands generated by the analysis means,

[0819] A means for analyzing emotions to recognize the user's emotional state and adjust the user interface,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, comprising: a learning means that learns past data and makes suggestions for future process improvements; and a suggestion means that makes suggestions for lifestyle improvements based on emotional data.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising: an anomaly detection means that detects an anomaly and sends an alert to the user; and an emotion response means that adjusts the environment to reduce stress based on the emotion analysis results. [Explanation of Symbols]

[0825] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data acquisition method for obtaining the growth status and environmental conditions of microorganisms in real time, An analysis means that analyzes the data obtained from the data acquisition means and generates an optimal environmental adjustment command, An environmental control means that automatically adjusts environmental conditions based on commands generated by the analysis means, An information provision method that provides analysis results to a mobile device, allowing users to visually confirm the adjustment of environmental conditions, A system that includes this.

2. The system according to claim 1, wherein the analysis means is further comprising a learning means that learns past data and makes suggestions for future process improvements.

3. The system according to claim 1, wherein the analysis means includes an anomaly detection means that detects an anomaly and sends an alert to the user, and the alert content is displayed by an augmented reality display device.

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