system

Abstract

We provide the system. [Solution] A means for measuring the environmental conditions inside the culture device using sensors and recording the data, A computational method using artificial intelligence that analyzes recorded data in real time and optimizes the environment, Means for adjusting the physical conditions of the culture apparatus based on the analysis results, A means of predicting anomalies from data and issuing warnings, 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] The cell culture process is very delicate, and environmental factors such as temperature, pH, oxygen concentration, and nutrients have an impact. Therefore, manually managing this is cumbersome and time-consuming, and it is difficult to obtain stable results. Also, there is a problem that post-event response is common until an abnormality occurs, and rapid response cannot be performed. Furthermore, there is also a problem that it is difficult to consistently improve the quality of culture and production efficiency.

Means for Solving the Problems

[0005] This invention provides a system that measures the environmental conditions within a cell culture apparatus using sensors, records the data, and analyzes it in real time. The recorded data is processed by artificial intelligence, and adjustments to optimize the culture environment are performed automatically. Furthermore, by using machine learning models, it is possible to learn abnormal patterns from past data and predict them in advance. The system also includes an interface for manual adjustments by the user, making user intervention easy and providing flexible control. As a result, it is possible to achieve consistent, high-quality cell culture and significantly improve work efficiency.

[0006] A "culture device" is a piece of mechanical equipment that provides and manages a specific environment for growing cells.

[0007] A "sensor" is a device that measures specific aspects of a physical environment or process and outputs that data as an electronic signal.

[0008] "Environmental conditions" is a general term for the physical and chemical conditions that affect cell culture, such as temperature, pH, oxygen concentration, and nutrient levels within the culture device.

[0009] "Means for recording data" refers to a device or system that stores measurement data transmitted from a sensor so that it can be analyzed later.

[0010] "Artificial intelligence" refers to computer programs or systems designed to mimic or extend human intellectual work.

[0011] A "computational means" is a device or program that, following instructions for processing, calculates various input data to derive a specific result.

[0012] "Means of predicting anomalies" refers to methods or techniques for analyzing past data to predict future irregular or undesirable events.

[0013] An "interface" is a means of enabling interaction between a user and a system, and for inputting and outputting data. [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 numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

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

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, 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 relates to a system for automatically optimizing cell culture processes. The system operates with a configuration including sensors that constitute the culture environment, a server for processing data, and a user interface. Each sensor measures temperature, pH, oxygen concentration, and nutrient levels within the culture environment in real time and transmits this information to the server.

[0036] The server has the ability to record received data in a database and analyze it in real time using artificial intelligence. This analysis evaluates whether the environment is optimal for cells and issues instructions to automatically adjust it if necessary. In addition, the server learns from past data history to predict potential anomalies and unexpected changes and plan appropriate countermeasures.

[0037] Users can access the system via a terminal and use the main interface to check environmental conditions and system performance metrics. For example, they can visually monitor the history of environmental adjustments according to the cell growth stage and data logs of past anomaly detections. Users can also directly give instructions to the system, providing an interactive method for manually adjusting the cell culture environment.

[0038] For example, when culturing a particular cell line, the system first compares data from sensors with pre-set temperature and oxygen concentration information. If the conditions deviate, the server automatically controls the heater, maintaining the optimal temperature while adjusting the oxygen concentration as needed based on the predicted growth pattern. Users can monitor these processes on their terminal and perform manual operations as needed.

[0039] In this way, the complex and diverse processes of cell culture can be managed in an integrated manner, achieving the consistent maintenance of high quality and efficient productivity improvements that are hallmarks of the invention.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives environmental data such as temperature, pH, oxygen concentration, and nutrient levels in real time from sensors. This data is recorded in a database.

[0043] Step 2:

[0044] The server analyzes the received data and uses an artificial intelligence model to evaluate whether the current environment is optimized. This evaluation also includes trend analysis of past culture data.

[0045] Step 3:

[0046] The server outputs instructions to automatically adjust environmental conditions as needed. These adjustment instructions are sent to the heater, cooler, gas supply system, and other components within the culture apparatus.

[0047] Step 4:

[0048] The AI ​​uses historical data to predict anomalies. If an anomaly is likely to occur, the server automatically sends a warning to the user.

[0049] Step 5:

[0050] Users can access the dashboard via their device to view system analysis results and adjustment history. Users can also manually control the system as needed.

[0051] (Example 1)

[0052] 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."

[0053] Conventional cell culture systems have faced challenges in achieving efficient and high-quality cell culture due to difficulties in optimizing the environment, predicting anomalies, and providing intuitive user operation. This necessitates manual monitoring and adjustment, resulting in time-consuming and labor-intensive processes. Furthermore, it is difficult to respond quickly to unexpected environmental changes, potentially negatively impacting productivity and culture consistency.

[0054] 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.

[0055] In this invention, the server includes means for acquiring the environmental characteristics of the culture system using a measuring device and storing the information, a computing device using an intelligent algorithm that immediately analyzes the stored information and performs environmental optimization, and means for correcting the physical characteristics of the culture system based on the analysis results. This enables real-time monitoring and optimization of the culture environment. Furthermore, it enables the realization of a rapid and efficient cell culture process through the prediction of anomalies and interactive operation by the user.

[0056] A "culture system" is a device or mechanism that provides a specific environment for growing biomaterials such as cells and microorganisms.

[0057] "Environmental characteristics" refer to the physical and chemical conditions that affect culture, such as temperature, pH, oxygen concentration, and nutrient levels within the culture system.

[0058] A "measuring device" is a device used to measure environmental characteristics and acquire them as data, and includes sensors and other such devices.

[0059] "Means for storing information" refers to devices or methods for recording and managing data acquired from measuring devices, and includes databases, etc.

[0060] A "computer using intelligent algorithms" is a device that analyzes received information and uses programs or models to derive optimal environmental conditions, utilizing artificial intelligence and machine learning.

[0061] "Means for modifying physical properties" refer to devices or methods for adjusting the environmental characteristics within a culture system based on the analysis results of a computing device.

[0062] "Anomaly prediction" is the process of predicting irregular changes that may occur in the future, based on patterns learned from past data.

[0063] A "user interface" refers to the means by which a user interacts with a system, and includes screens and devices that enable the display and manipulation of information.

[0064] A "means of issuing warnings" refers to a system for notifying users of an abnormality or emergency when such an event is detected.

[0065] This invention provides specific means for optimizing the environment of a culture system and achieving efficient and high-quality cell culture. The server, terminals, and users cooperate to effectively operate this system.

[0066] First, the server plays a central role in the culture system, receiving environmental characteristic data sent from measurement devices. This data includes temperature, pH, oxygen concentration, and nutrient levels. The server stores this data in a database and performs detailed analysis. Intelligent algorithms are used for this analysis, and generative AI models are specifically employed. The server leverages machine learning frameworks such as TENSORFLOW® and PyTorch to analyze the received data in real time and also predict anomalies. Specifically, it learns from past data history and applies time series analysis models to predict future environmental changes.

[0067] Next, the terminal provides a means for the user to interact with the system. The terminal interface visually displays the current environmental state and past data history, allowing the user to intuitively understand the situation. Furthermore, through the user interface, the user can manually input information into the system and instruct specific environmental changes.

[0068] The user monitors and controls the culture system using a terminal. If an abnormality is detected, the user receives an alarm notification and intervenes as necessary. For example, by entering a prompt message such as, "Please tell me how to properly adjust the temperature and oxygen concentration when culturing a certain cell line," the server will infer the most effective adjustment method and provide it to the user.

[0069] This invention enables automated optimization of the culture environment and consistent quality control, as well as rapid response to unexpected environmental changes.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server receives real-time data on the culture environment from the measuring devices. This includes various environmental characteristics such as temperature, pH, oxygen concentration, and nutrient levels. This data is acquired as input and stored in a temporary storage area on the server. This enables rapid and efficient initial data processing. Specifically, the server aggregates data from each sensor, standardizes the format, and prepares it for the next processing step.

[0073] Step 2:

[0074] The server records the accumulated data in a database. This recorded data is efficiently managed using an SQL-based management system. The input here is the real-time data obtained in the previous step, and the output is structured database entries. The server applies appropriate indexes to ensure that subsequent queries and analyses of the data can be performed smoothly.

[0075] Step 3:

[0076] The server immediately analyzes the data using a generative AI model. The AI ​​model compares historical data with current data and performs predictive analysis. The input is the dataset constructed in step 2, and the output is predicted data on the optimization status and anomalies of the analyzed environment. Specifically, the server executes the machine learning model using frameworks such as TensorFlow and PyTorch.

[0077] Step 4:

[0078] The server automatically adjusts the physical characteristics of the culture system based on the analysis results. This adjustment includes controlling the heater and oxygen supply device. The input is the optimization command obtained in step 3, and the output is the actual hardware control signal. The server performs the specific actions of communicating instructions to the hardware via a PLC (Programmable Logic Controller).

[0079] Step 5:

[0080] The terminal visually displays data obtained from the server. Users can use the terminal to check the current environmental status and analysis results. The input is analysis result data from the server, and the output is a visualized display screen. Specifically, the terminal displays graphs and charts through a GUI, allowing users to easily understand the situation.

[0081] Step 6:

[0082] The user can issue supply instructions to the system via the terminal interface. If necessary, the user can manually adjust the environment settings. The input in this process is the user's operational instructions, and the output is the system's response based on those instructions. The user makes fine adjustments by specifically manipulating buttons and sliders on the screen.

[0083] (Application Example 1)

[0084] 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."

[0085] In modern cell culture processes, maintaining optimal environmental conditions at all times is crucial, but currently, manual adjustments are prevalent, making efficient management difficult. Furthermore, early detection of abnormalities is challenging, potentially impacting productivity and quality. In addition, limitations on real-time monitoring and adjustment from remote locations pose another challenge.

[0086] 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.

[0087] In this invention, the server includes means for measuring the environmental conditions within the culture device using a detector and recording the data; computation means using an intelligent system that analyzes the recorded data in real time and optimizes the environment; and means for providing the environmental conditions and analysis results to a remotely operated information terminal device, which can be manually adjusted. This enables efficient optimization of the cell culture environment, early detection of abnormalities, and remote monitoring and adjustment.

[0088] A "culture device" is a device that provides and monitors the environment necessary for cell growth and maintenance.

[0089] A "detector" is a device used to measure the state of the environment and record it as data.

[0090] "Means of recording data" refers to methods or devices for storing measured data and making it available for later analysis or reference.

[0091] "Real-time analysis" is a process of processing data immediately as it is generated and gaining insights from it.

[0092] An "intelligent system" is a system that utilizes artificial intelligence to analyze data and automatically optimize the environment.

[0093] A "remote control information terminal device" is an electronic device that enables monitoring and operation of cell culture processes from a distance.

[0094] "Environmental conditions" refer to various physical conditions related to cell culture, such as temperature, pH, oxygen concentration, and nutrient levels.

[0095] "Predicting anomalies" means identifying in advance, through data analysis, situations that may deviate from normal patterns.

[0096] "Manually adjustable means" refers to functions or interfaces that allow users to directly change environmental conditions at their own discretion.

[0097] One embodiment of this invention is a system aimed at the efficient management and optimization of a cell culture process. This system includes an intelligent system that monitors the environmental conditions within the culture apparatus in real time and automatically adjusts them accordingly.

[0098] The server collects data from multiple detectors (sensors) and records it in a database. Data such as temperature, pH, oxygen concentration, and nutrient levels are stored in real time in databases like MySQL® or PostgreSQL. The server then analyzes the data using intelligent systems such as TensorFlow or PyTorch to optimize the environment.

[0099] Based on the analysis results, the server controls the culture device and automatically adjusts the physical conditions. Furthermore, when predicting anomalies, it utilizes machine learning models to evaluate past data patterns and proactively detect potential anomalies.

[0100] Users can monitor the situation in real time at all times using remote control terminals (smartphones and tablets). They can receive visualized information on the environmental conditions and analysis results from intelligent systems, and make manual adjustments.

[0101] For example, if the temperature of the culture process exceeds the set range, the server immediately sends an anomaly detection alert to the terminal. At this time, the application displays a prompt message such as, "The temperature of the culture environment is 5% above the set value. Do you want to adjust the heater?", allowing the user to quickly select the appropriate action.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The sensors measure environmental data (temperature, pH, oxygen concentration, nutrient levels) within the culture device. This data is transmitted to a server via an IoT device. Input data is recorded in real time, and numerical values ​​such as temperature and nutrient levels are stored as output.

[0105] Step 2:

[0106] The server saves the received data to a database such as MySQL or PostgreSQL. Once the data has been written to the database, it is organized and ready for later analysis.

[0107] Step 3:

[0108] The server uses TensorFlow and PyTorch to analyze the data in real time. It processes environmental data obtained from a database as input and calculates the optimal conditions for cell growth. The output generates optimized settings and abnormal data based on the analysis results.

[0109] Step 4:

[0110] The server automatically adjusts the parameters of the culture device based on the instructions obtained from the analysis results. For example, if the analysis indicates that the temperature is too high, the server will issue a command to stop heating and return to the set temperature.

[0111] Step 5:

[0112] The server also runs machine learning models to detect anomalies using historical data. It identifies data patterns that are predicted to be anomalous and outputs them to the user as alerts. Using generative AI models improves the ability to identify complex patterns.

[0113] Step 6:

[0114] The user receives notifications from the information terminal and checks the status. Based on the prompt displayed on the terminal, they can decide and input whether to manually adjust the environment. For example, a prompt such as "The culture environment temperature is 5% above the set value. Do you want to adjust the heater?" might be displayed.

[0115] 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.

[0116] This invention incorporates emotion recognition capabilities into an autonomous system aimed at optimizing cell culture processes. This system not only automates environmental adjustment and anomaly prediction within the culture apparatus, but also recognizes the user's emotional state and provides a corresponding interface adaptation. The system consists of a group of sensors attached to the culture apparatus, a server that manages and analyzes this data, and a terminal for connecting with the user.

[0117] The server collects and records environmental data from sensors in real time, and analyzes this data using artificial intelligence algorithms. Based on this data, the AI ​​adjusts conditions to optimize the environmental state. It also builds machine learning models that learn from past data and predict anomalies in advance.

[0118] Furthermore, an emotion engine is incorporated, which analyzes data collected from the camera and microphone when the user interacts with the device to determine the user's emotional state. For example, it can identify the user's current emotions from changes in voice tone and facial expressions. Based on this emotional information, the system dynamically adjusts the interface. Specifically, when the user is feeling stressed, the system adjusts the content and tone of the information and warning messages it presents to reduce the user's burden.

[0119] For example, if a user experiences anxiety while monitoring a cell culture process, the camera and microphone on the device recognize this state. Based on the analysis results from the emotion engine, the server automatically adjusts the interface to provide more concise and easy-to-understand information. It can also play relaxation music or guides for the user.

[0120] This system allows users to work in a more comfortable and efficient environment, enabling consistent quality control and increased efficiency in the cell culture process.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The server receives real-time culture environment data from sensors, including temperature, pH, oxygen concentration, and nutrient levels. This data is recorded in a database and prepared for later analysis.

[0124] Step 2:

[0125] The server uses artificial intelligence to analyze the received data in real time and determine whether each element of the environment is within the optimal range. Based on this analysis, it issues instructions to automatically adjust the physical conditions of the culture device as needed.

[0126] Step 3:

[0127] The server utilizes a machine learning model to learn anomaly patterns from past data. This model is used to predict probabilistic anomalies and generate warnings in advance.

[0128] Step 4:

[0129] The emotion engine recognizes the user's emotional state by collecting facial expressions and voice data through the device's camera and microphone. The server analyzes this data to determine the user's current emotional state.

[0130] Step 5:

[0131] The server makes adjustments based on the user's emotions and dynamically changes the terminal's interface. For example, if the terminal determines that the user is stressed, it changes how it presents information, displaying only essential information in a softer tone.

[0132] Step 6:

[0133] Users monitor the current state of the environment and system responses on a dashboard provided by the system via their terminal. Users can manually provide additional instructions and customize operations as needed.

[0134] (Example 2)

[0135] 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".

[0136] In cell culture processes, inoptimized environmental conditions negatively impact cell growth, leading to a decline in quality. Furthermore, the stress and anxiety experienced by users during the process affect efficiency. Conventional systems have struggled to integrate multiple elements, including environmental adjustment, anomaly detection, and the provision of an interface that considers the user's emotional state.

[0137] 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.

[0138] In this invention, the server includes means for acquiring environmental conditions within the culture device using a detection device and recording the information; means for performing calculations using artificial intelligence to analyze the recorded information in real time and optimize the environment; means for adjusting the physical conditions of the culture device based on the analysis results; and means for performing emotional analysis and dynamically changing the settings of the display device based on the user's emotional state. This makes it possible to maintain an optimal cell culture environment at all times while providing appropriate information according to the user's emotional state.

[0139] A "cell culture device" is a device used to cultivate cells and provide them with an appropriate growth environment.

[0140] "Environmental conditions" refer to factors that determine the state of the cell culture environment, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0141] "Detection equipment" refers to sensors and related devices used to measure environmental conditions within a culture device and collect the data.

[0142] "Means of recording information" refers to functions and technologies that store data obtained from detection devices and make it available for later reference.

[0143] "Computational means using artificial intelligence" refers to a computer system that uses machine learning and data analysis techniques to analyze collected data and optimize environmental conditions.

[0144] "Means for adjusting physical conditions" refer to control devices and operating mechanisms for changing the environmental conditions within the culture apparatus based on the analysis results.

[0145] "Emotional analysis" is a process that estimates a user's emotions and mental state by analyzing data obtained from sensors such as cameras and microphones.

[0146] "Means for dynamically changing the settings of a display device" refers to functions and technologies for adjusting the displayed information and interface in real time according to the user's emotional state.

[0147] This invention streamlines the cell culture process by incorporating emotion recognition functionality into a system that optimizes the environment within the culture apparatus. The server, terminal, and user each play their respective roles, forming the overall system.

[0148] The server first collects environmental data such as temperature, humidity, oxygen concentration, and carbon dioxide concentration in real time from sensors installed in the culture device. This data is analyzed in real time using the Python library TensorFlow to calculate the optimal environmental conditions. Based on the analysis results, the physical conditions of the culture device are adjusted via the control device. Furthermore, past data is analyzed using the machine learning algorithm Random Forest to predict potential failures in advance.

[0149] The device uses a camera and microphone to recognize the user's emotions. The data collected by these devices is emotionally analyzed to determine the user's emotional state. Libraries such as OpenCV and LibROSA are used for the analysis. If it is determined that the user is experiencing stress, the interface settings are dynamically changed to provide the user with more concise and user-friendly information.

[0150] For example, if a user feels anxious while monitoring cell culture operations, the device's camera and microphone will detect this state. Once stress is detected, the server will provide a more user-friendly interface and play relaxation music to reduce the user's stress.

[0151] An example of a prompt using a generative AI model might be, "What prompt would you use to design a cell culture monitoring system that takes into account the user's emotional state?" This would provide the system with insights into emotion recognition and interface adjustment, allowing for further improvements. By incorporating these elements, the system achieves consistent quality control of the cell culture environment and improves user work efficiency.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server collects environmental data from sensors attached to the culture device. The sensors measure temperature, humidity, oxygen concentration, and carbon dioxide concentration, and transmit this data to the server in real time. The server records the received data in a database and prepares it for the next analysis step.

[0155] Step 2:

[0156] The server analyzes the collected environmental data using TensorFlow, a Python library. This process takes various environmental values ​​as input and outputs appropriate environmental condition adjustment parameters as analysis results. Specifically, the AI ​​calculates settings to optimize the environment and determines which physical conditions should be adjusted.

[0157] Step 3:

[0158] The server automatically adjusts the physical conditions of the culture device based on the analysis results. For example, if it determines that temperature adjustment is necessary, the server sends a command to the culture device's heater to adjust it to the set temperature. This maintains the optimal environment for cell growth.

[0159] Step 4:

[0160] The server uses a random forest algorithm based on historical data to predict anomalies. This process learns from past data patterns and outputs the likelihood of an anomaly occurring. When signs of an anomaly are detected, a warning is issued, allowing for appropriate action to be taken in advance.

[0161] Step 5:

[0162] The device collects the user's facial expressions and voice using its camera and microphone. This data is sent to a server for use in emotional analysis. The device performs specific actions such as taking photos and recording voice every second.

[0163] Step 6:

[0164] The server uses OpenCV and LibROSA to analyze the user's emotional state. This process takes facial image and audio data as input and outputs the user's emotional state (e.g., stress or anxiety). Once the emotional state is determined, the interface is dynamically changed accordingly.

[0165] Step 7:

[0166] The user inputs prompts into the generated AI model to gain ideas for further system improvements. These prompts might include questions such as, "What prompts would you use to design a cell culture monitoring system that takes the user's emotional state into account?" The generated ideas are then used to improve the system.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In factories, systems that rely solely on optimizing environmental data without considering the impact of workers' emotional states on production efficiency and safety have the problem of not being sufficient to improve the work environment or prevent abnormal situations.

[0170] 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.

[0171] In this invention, the server includes means for measuring the environmental conditions within the culture device and recording data, computation means using artificial intelligence to analyze the recorded data, and emotion recognition means for detecting the emotional state of the worker. This enables dynamic optimization of the environment based on the emotional state of the worker.

[0172] A "culture device" is a device that provides optimal environmental conditions for the growth and maintenance of cells, microorganisms, and other organisms.

[0173] "Environmental conditions" refer to physical and chemical conditions within the culture device, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0174] A "sensor" is a device that measures physical or chemical environmental parameters and outputs changes in them as electronic signals.

[0175] "Artificial intelligence" is a computing system that analyzes data collected from sensors and uses efficient methods to optimize the environment and predict anomalies.

[0176] "Computational means" refers to a processing unit that performs analysis based on measured data and makes appropriate adjustments.

[0177] An "emotion recognition system" is a system that possesses technology to determine the emotions and psychological state of a worker, and acquires this information using devices such as cameras and microphones.

[0178] An "interface" refers to a display or operating device that allows a user and a system to exchange information with each other.

[0179] The system for realizing this application consists of a sensor array, a server, and a user terminal. The sensors measure the environmental conditions of the factory in real time and transmit the data to the server. The server receives this data and analyzes it using artificial intelligence algorithms. The artificial intelligence used includes an emotion recognition engine and a machine learning model, which are used to optimize the environment and predict anomalies.

[0180] The server can collect emotional information in real time by detecting the emotional state of workers using cameras and microphones. This allows for the identification of workers' stress and fatigue levels, and dynamic interface adjustments to improve the work environment. Based on these analysis results, the user terminal appropriately adjusts the interface to provide information with minimal burden on the user. For example, if a high level of stress is detected, the terminal will automatically play relaxation music to alleviate the work environment.

[0181] This system allows workers to perform their tasks safely and efficiently in a better working environment. For example, in a factory assembly line, if stress is detected from a worker's voice, the system adjusts the environment on the spot and changes the interface information display to be more user-friendly.

[0182] An example of a prompt would be: "Explain how to monitor the emotional state of workers on a factory production line and automatically adjust the environment accordingly."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] Sensors measure various physical conditions of the factory environment (temperature, humidity, light intensity, etc.). The measurement data is transmitted directly to the server. The input is environmental data from the sensors, and the output is raw environmental data stored on the server.

[0186] Step 2:

[0187] The server performs real-time analysis of the environmental data it receives using an artificial intelligence algorithm. The analysis involves data processing to identify changes necessary for optimizing the surrounding physical conditions. The input is the raw environmental data acquired in step 1, and the output is adjustment command information for optimization.

[0188] Step 3:

[0189] The terminal collects data from the worker's facial expressions and voice and sends it to the emotion recognition engine. The input is facial expression data and voice data from the worker captured by the camera and microphone, and the output is information about the worker's emotional state, which is sent to the server.

[0190] Step 4:

[0191] The server analyzes the worker's emotional data using an emotion recognition engine. The analysis determines the type of emotional state (stress, exhilaration, fatigue, etc.). The input is the emotional state information obtained in step 3, and the output is the emotional state evaluation result for interface adjustment.

[0192] Step 5:

[0193] The server generates interface adjustment commands based on the environment optimization commands from step 2 and the sentiment evaluation results from step 4. The generated commands are sent to the terminal, and the interface content and tone are dynamically adjusted. The inputs are the environment optimization commands and sentiment evaluation results, and the output is the optimized interface display content and adjusted audio information for the user.

[0194] Step 6:

[0195] Based on commands received from the server, the terminal adjusts the interface for the user and plays relaxation music as needed. The input is the optimized commands generated in step 5, and the output is the improved interface environment that the user can view and interact with.

[0196] Step 7:

[0197] Users experience improved productivity or reduced stress by using the terminal's optimized interface. The input is a streamlined interface environment, and the output is improved user work efficiency and emotional stability.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention relates to a system for automatically optimizing cell culture processes. The system operates with a configuration including sensors that constitute the culture environment, a server for processing data, and a user interface. Each sensor measures temperature, pH, oxygen concentration, and nutrient levels within the culture environment in real time and transmits this information to the server.

[0215] The server has the ability to record received data in a database and analyze it in real time using artificial intelligence. This analysis evaluates whether the environment is optimal for cells and issues instructions to automatically adjust it if necessary. In addition, the server learns from past data history to predict potential anomalies and unexpected changes and plan appropriate countermeasures.

[0216] Users can access the system via a terminal and use the main interface to check environmental conditions and system performance metrics. For example, they can visually monitor the history of environmental adjustments according to the cell growth stage and data logs of past anomaly detections. Users can also directly give instructions to the system, providing an interactive method for manually adjusting the cell culture environment.

[0217] For example, when culturing a particular cell line, the system first compares data from sensors with pre-set temperature and oxygen concentration information. If the conditions deviate, the server automatically controls the heater, maintaining the optimal temperature while adjusting the oxygen concentration as needed based on the predicted growth pattern. Users can monitor these processes on their terminal and perform manual operations as needed.

[0218] In this way, the complex and diverse processes of cell culture can be managed in an integrated manner, achieving the consistent maintenance of high quality and efficient productivity improvements that are hallmarks of the invention.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server receives environmental data such as temperature, pH, oxygen concentration, and nutrient levels in real time from sensors. This data is recorded in a database.

[0222] Step 2:

[0223] The server analyzes the received data and uses an artificial intelligence model to evaluate whether the current environment is optimized. This evaluation also includes trend analysis of past culture data.

[0224] Step 3:

[0225] The server outputs instructions to automatically adjust environmental conditions as needed. These adjustment instructions are sent to the heater, cooler, gas supply system, and other components within the culture apparatus.

[0226] Step 4:

[0227] The AI ​​uses historical data to predict anomalies. If an anomaly is likely to occur, the server automatically sends a warning to the user.

[0228] Step 5:

[0229] Users can access the dashboard via their device to view system analysis results and adjustment history. Users can also manually control the system as needed.

[0230] (Example 1)

[0231] 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."

[0232] Conventional cell culture systems have faced challenges in achieving efficient and high-quality cell culture due to difficulties in optimizing the environment, predicting anomalies, and providing intuitive user operation. This necessitates manual monitoring and adjustment, resulting in time-consuming and labor-intensive processes. Furthermore, it is difficult to respond quickly to unexpected environmental changes, potentially negatively impacting productivity and culture consistency.

[0233] 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.

[0234] In this invention, the server includes means for acquiring the environmental characteristics of the culture system using a measuring device and storing the information, a computing device using an intelligent algorithm that immediately analyzes the stored information and performs environmental optimization, and means for correcting the physical characteristics of the culture system based on the analysis results. This enables real-time monitoring and optimization of the culture environment. Furthermore, it enables the realization of a rapid and efficient cell culture process through the prediction of anomalies and interactive operation by the user.

[0235] A "culture system" is a device or mechanism that provides a specific environment for growing biomaterials such as cells and microorganisms.

[0236] "Environmental characteristics" refer to the physical and chemical conditions that affect culture, such as temperature, pH, oxygen concentration, and nutrient levels within the culture system.

[0237] A "measuring device" is a device used to measure environmental characteristics and acquire them as data, and includes sensors and other such devices.

[0238] "Means for storing information" refers to devices or methods for recording and managing data acquired from measuring devices, and includes databases, etc.

[0239] A "computer using intelligent algorithms" is a device that analyzes received information and uses programs or models to derive optimal environmental conditions, utilizing artificial intelligence and machine learning.

[0240] "Means for modifying physical properties" refer to devices or methods for adjusting the environmental characteristics within a culture system based on the analysis results of a computing device.

[0241] "Anomaly prediction" is the process of predicting irregular changes that may occur in the future, based on patterns learned from past data.

[0242] A "user interface" refers to the means by which a user interacts with a system, and includes screens and devices that enable the display and manipulation of information.

[0243] A "means of issuing warnings" refers to a system for notifying users of an abnormality or emergency when such an event is detected.

[0244] This invention provides specific means for optimizing the environment of a culture system and achieving efficient and high-quality cell culture. The server, terminals, and users cooperate to effectively operate this system.

[0245] First, the server plays a central role in the culture system, receiving environmental characteristic data sent from measurement devices. This data includes temperature, pH, oxygen concentration, and nutrient levels. The server stores this data in a database and performs detailed analysis. Intelligent algorithms are used for this analysis, and generative AI models are specifically employed. The server leverages machine learning frameworks such as TensorFlow and PyTorch to analyze the received data in real time and also predict anomalies. Specifically, it learns from past data history and applies time-series analysis models to predict future environmental changes.

[0246] Next, the terminal provides a means for the user to interact with the system. The terminal interface visually displays the current environmental state and past data history, allowing the user to intuitively understand the situation. Furthermore, through the user interface, the user can manually input information into the system and instruct specific environmental changes.

[0247] The user monitors and controls the culture system using a terminal. If an abnormality is detected, the user receives an alarm notification and intervenes as necessary. For example, by entering a prompt message such as, "Please tell me how to properly adjust the temperature and oxygen concentration when culturing a certain cell line," the server will infer the most effective adjustment method and provide it to the user.

[0248] This invention enables automated optimization of the culture environment and consistent quality control, as well as rapid response to unexpected environmental changes.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The server receives real-time data on the culture environment from the measuring devices. This includes various environmental characteristics such as temperature, pH, oxygen concentration, and nutrient levels. This data is acquired as input and stored in a temporary storage area on the server. This enables rapid and efficient initial data processing. Specifically, the server aggregates data from each sensor, standardizes the format, and prepares it for the next processing step.

[0252] Step 2:

[0253] The server records the accumulated data in a database. This recorded data is efficiently managed using an SQL-based management system. The input here is the real-time data obtained in the previous step, and the output is structured database entries. The server applies appropriate indexes to ensure that subsequent queries and analyses of the data can be performed smoothly.

[0254] Step 3:

[0255] The server immediately analyzes the data using a generative AI model. The AI ​​model compares historical data with current data and performs predictive analysis. The input is the dataset constructed in step 2, and the output is predicted data on the optimization status and anomalies of the analyzed environment. Specifically, the server executes the machine learning model using frameworks such as TensorFlow and PyTorch.

[0256] Step 4:

[0257] The server automatically adjusts the physical characteristics of the culture system based on the analysis results. This adjustment includes controlling the heater and oxygen supply device. The input is the optimization command obtained in step 3, and the output is the actual hardware control signal. The server performs the specific actions of communicating instructions to the hardware via a PLC (Programmable Logic Controller).

[0258] Step 5:

[0259] The terminal visually displays data obtained from the server. Users can use the terminal to check the current environmental status and analysis results. The input is analysis result data from the server, and the output is a visualized display screen. Specifically, the terminal displays graphs and charts through a GUI, allowing users to easily understand the situation.

[0260] Step 6:

[0261] The user can issue supply instructions to the system via the terminal interface. If necessary, the user can manually adjust the environment settings. The input in this process is the user's operational instructions, and the output is the system's response based on those instructions. The user makes fine adjustments by specifically manipulating buttons and sliders on the screen.

[0262] (Application Example 1)

[0263] 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."

[0264] In modern cell culture processes, maintaining optimal environmental conditions at all times is crucial, but currently, manual adjustments are prevalent, making efficient management difficult. Furthermore, early detection of abnormalities is challenging, potentially impacting productivity and quality. In addition, limitations on real-time monitoring and adjustment from remote locations pose another challenge.

[0265] 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.

[0266] In this invention, the server includes means for measuring the environmental conditions within the culture device using a detector and recording the data; computation means using an intelligent system that analyzes the recorded data in real time and optimizes the environment; and means for providing the environmental conditions and analysis results to a remotely operated information terminal device, which can be manually adjusted. This enables efficient optimization of the cell culture environment, early detection of abnormalities, and remote monitoring and adjustment.

[0267] A "culture device" is a device that provides and monitors the environment necessary for cell growth and maintenance.

[0268] A "detector" is a device used to measure the state of the environment and record it as data.

[0269] "Means of recording data" refers to methods or devices for storing measured data and making it available for later analysis or reference.

[0270] "Real-time analysis" is a process of processing data immediately as it is generated and gaining insights from it.

[0271] An "intelligent system" is a system that utilizes artificial intelligence to analyze data and automatically optimize the environment.

[0272] A "remote control information terminal device" is an electronic device that enables monitoring and operation of cell culture processes from a distance.

[0273] "Environmental conditions" refer to various physical conditions related to cell culture, such as temperature, pH, oxygen concentration, and nutrient levels.

[0274] "Predicting anomalies" means identifying in advance, through data analysis, situations that may deviate from normal patterns.

[0275] "Manually adjustable means" refers to functions or interfaces that allow users to directly change environmental conditions at their own discretion.

[0276] One embodiment of this invention is a system aimed at the efficient management and optimization of a cell culture process. This system includes an intelligent system that monitors the environmental conditions within the culture apparatus in real time and automatically adjusts them accordingly.

[0277] The server collects data from multiple detectors (sensors) and records it in a database. Data such as temperature, pH, oxygen concentration, and nutrient levels are stored in real time in databases like MySQL or PostgreSQL. The server then analyzes the data using intelligent systems such as TensorFlow or PyTorch to optimize the environment.

[0278] Based on the analysis results, the server controls the culture device and automatically adjusts the physical conditions. Furthermore, when predicting anomalies, it utilizes machine learning models to evaluate past data patterns and proactively detect potential anomalies.

[0279] Users can monitor the situation in real time at all times using remote control terminals (smartphones and tablets). They can receive visualized information on the environmental conditions and analysis results from intelligent systems, and make manual adjustments.

[0280] For example, if the temperature of the culture process exceeds the set range, the server immediately sends an anomaly detection alert to the terminal. At this time, the application displays a prompt message such as, "The temperature of the culture environment is 5% above the set value. Do you want to adjust the heater?", allowing the user to quickly select the appropriate action.

[0281] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0282] Step 1:

[0283] The sensor measures the environmental state data (temperature, pH, oxygen concentration, nutrient level) inside the culture device. These data are sent to the server via the IoT device. The input data is recorded in real time, and values such as temperature and nutrient levels are accumulated as output.

[0284] Step 2:

[0285] The server saves the received data in a database such as MySQL or PostgreSQL. When the writing to the database is completed, the data is neatly managed in preparation for later analysis.

[0286] Step 3:

[0287] The server uses TensorFlow or PyTorch to analyze the data in real time. It processes the environmental data obtained from the database as input and calculates the optimal conditions for cell growth. As output, optimization settings and abnormal data based on the analysis results are generated.

[0288] Step 4:

[0289] Based on the instructions obtained from the analysis results, the server automatically adjusts the parameters of the culture device. As an example, if the temperature is analyzed to be high, the server outputs a command to return to the set temperature by stopping the heating.

[0290] Step 5:

[0291] The server also runs a machine learning model to detect anomalies using past data. It identifies data patterns predicted as anomalies and outputs them as alerts to the user. By using a generative AI model, the ability to identify complex patterns is improved.

[0292] Step 6:

[0293] The user receives notifications from the information terminal and checks the status. Based on the prompt displayed on the terminal, they can decide and input whether to manually adjust the environment. For example, a prompt such as "The culture environment temperature is 5% above the set value. Do you want to adjust the heater?" might be displayed.

[0294] 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.

[0295] This invention incorporates emotion recognition capabilities into an autonomous system aimed at optimizing cell culture processes. This system not only automates environmental adjustment and anomaly prediction within the culture apparatus, but also recognizes the user's emotional state and provides a corresponding interface adaptation. The system consists of a group of sensors attached to the culture apparatus, a server that manages and analyzes this data, and a terminal for connecting with the user.

[0296] The server collects and records environmental data from sensors in real time, and analyzes this data using artificial intelligence algorithms. Based on this data, the AI ​​adjusts conditions to optimize the environmental state. It also builds machine learning models that learn from past data and predict anomalies in advance.

[0297] Furthermore, an emotion engine is incorporated, which analyzes data collected from the camera and microphone when the user interacts with the device to determine the user's emotional state. For example, it can identify the user's current emotions from changes in voice tone and facial expressions. Based on this emotional information, the system dynamically adjusts the interface. Specifically, when the user is feeling stressed, the system adjusts the content and tone of the information and warning messages it presents to reduce the user's burden.

[0298] As a specific example, when a user feels anxious while monitoring the cell culture process, the camera and microphone equipped on the terminal recognize this state. Based on the analysis results by the emotion engine, the server automatically adjusts the interface to provide more concise and understandable information. It is also possible to play music or guides for relaxation to the user.

[0299] With this system, the user can work in a more comfortable and efficient environment, and consistent quality control and work efficiency improvement of the cell culture process are realized.

[0300] The following describes the process flow.

[0301] Step 1:

[0302] The server receives in real-time culture environment data such as temperature, pH, oxygen concentration, and nutrient level from the sensors. The data is recorded in the database and prepared for later analysis.

[0303] Step 2:

[0304] The server analyzes the received data in real-time using artificial intelligence and determines whether each element of the environment is within the optimal range. Based on this analysis, instructions for automatically adjusting the physical conditions of the culture device are transmitted as needed.

[0305] Step 3:

[0306] The server utilizes a machine learning model to learn abnormal patterns from past data. This model is used to predict probabilistic abnormal occurrences and generate warnings in advance.

[0307] Step 4:

[0308] The emotion engine recognizes the user's emotional state by collecting facial expressions and voice data through the device's camera and microphone. The server analyzes this data to determine the user's current emotional state.

[0309] Step 5:

[0310] The server makes adjustments based on the user's emotions and dynamically changes the terminal's interface. For example, if the terminal determines that the user is stressed, it changes how it presents information, displaying only essential information in a softer tone.

[0311] Step 6:

[0312] Users monitor the current state of the environment and system responses on a dashboard provided by the system via their terminal. Users can manually provide additional instructions and customize operations as needed.

[0313] (Example 2)

[0314] 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".

[0315] In cell culture processes, inoptimized environmental conditions negatively impact cell growth, leading to a decline in quality. Furthermore, the stress and anxiety experienced by users during the process affect efficiency. Conventional systems have struggled to integrate multiple elements, including environmental adjustment, anomaly detection, and the provision of an interface that considers the user's emotional state.

[0316] 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.

[0317] In this invention, the server includes means for acquiring environmental conditions within the culture device using a detection device and recording the information; means for performing calculations using artificial intelligence to analyze the recorded information in real time and optimize the environment; means for adjusting the physical conditions of the culture device based on the analysis results; and means for performing emotional analysis and dynamically changing the settings of the display device based on the user's emotional state. This makes it possible to maintain an optimal cell culture environment at all times while providing appropriate information according to the user's emotional state.

[0318] A "cell culture device" is a device used to cultivate cells and provide them with an appropriate growth environment.

[0319] "Environmental conditions" refer to factors that determine the state of the cell culture environment, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0320] "Detection equipment" refers to sensors and related devices used to measure environmental conditions within a culture device and collect the data.

[0321] "Means of recording information" refers to functions and technologies that store data obtained from detection devices and make it available for later reference.

[0322] "Computational means using artificial intelligence" refers to a computer system that uses machine learning and data analysis techniques to analyze collected data and optimize environmental conditions.

[0323] "Means for adjusting physical conditions" refer to control devices and operating mechanisms for changing the environmental conditions within the culture apparatus based on the analysis results.

[0324] "Emotional analysis" is a process that estimates a user's emotions and mental state by analyzing data obtained from sensors such as cameras and microphones.

[0325] "Means for dynamically changing the settings of a display device" refers to functions and technologies for adjusting the displayed information and interface in real time according to the user's emotional state.

[0326] This invention streamlines the cell culture process by incorporating emotion recognition functionality into a system that optimizes the environment within the culture apparatus. The server, terminal, and user each play their respective roles, forming the overall system.

[0327] The server first collects environmental data such as temperature, humidity, oxygen concentration, and carbon dioxide concentration in real time from sensors installed in the culture device. This data is analyzed in real time using the Python library TensorFlow to calculate the optimal environmental conditions. Based on the analysis results, the physical conditions of the culture device are adjusted via the control device. Furthermore, past data is analyzed using the machine learning algorithm Random Forest to predict potential failures in advance.

[0328] The device uses a camera and microphone to recognize the user's emotions. The data collected by these devices is emotionally analyzed to determine the user's emotional state. Libraries such as OpenCV and LibROSA are used for the analysis. If it is determined that the user is experiencing stress, the interface settings are dynamically changed to provide the user with more concise and user-friendly information.

[0329] For example, if a user feels anxious while monitoring cell culture operations, the device's camera and microphone will detect this state. Once stress is detected, the server will provide a more user-friendly interface and play relaxation music to reduce the user's stress.

[0330] An example of a prompt using a generative AI model might be, "What prompt would you use to design a cell culture monitoring system that takes into account the user's emotional state?" This would provide the system with insights into emotion recognition and interface adjustment, allowing for further improvements. By incorporating these elements, the system achieves consistent quality control of the cell culture environment and improves user work efficiency.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The server collects environmental data from sensors attached to the culture device. The sensors measure temperature, humidity, oxygen concentration, and carbon dioxide concentration, and transmit this data to the server in real time. The server records the received data in a database and prepares it for the next analysis step.

[0334] Step 2:

[0335] The server analyzes the collected environmental data using TensorFlow, a Python library. This process takes various environmental values ​​as input and outputs appropriate environmental condition adjustment parameters as analysis results. Specifically, the AI ​​calculates settings to optimize the environment and determines which physical conditions should be adjusted.

[0336] Step 3:

[0337] The server automatically adjusts the physical conditions of the culture device based on the analysis results. For example, if it determines that temperature adjustment is necessary, the server sends a command to the culture device's heater to adjust it to the set temperature. This maintains the optimal environment for cell growth.

[0338] Step 4:

[0339] The server uses a random forest algorithm based on historical data to predict anomalies. This process learns from past data patterns and outputs the likelihood of an anomaly occurring. When signs of an anomaly are detected, a warning is issued, allowing for appropriate action to be taken in advance.

[0340] Step 5:

[0341] The device collects the user's facial expressions and voice using its camera and microphone. This data is sent to a server for use in emotional analysis. The device performs specific actions such as taking photos and recording voice every second.

[0342] Step 6:

[0343] The server uses OpenCV and LibROSA to analyze the user's emotional state. This process takes facial image and audio data as input and outputs the user's emotional state (e.g., stress or anxiety). Once the emotional state is determined, the interface is dynamically changed accordingly.

[0344] Step 7:

[0345] The user inputs prompts into the generated AI model to gain ideas for further system improvements. These prompts might include questions such as, "What prompts would you use to design a cell culture monitoring system that takes the user's emotional state into account?" The generated ideas are then used to improve the system.

[0346] (Application Example 2)

[0347] 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."

[0348] In factories, systems that rely solely on optimizing environmental data without considering the impact of workers' emotional states on production efficiency and safety have the problem of not being sufficient to improve the work environment or prevent abnormal situations.

[0349] 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.

[0350] In this invention, the server includes means for measuring the environmental conditions within the culture device and recording data, computation means using artificial intelligence to analyze the recorded data, and emotion recognition means for detecting the emotional state of the worker. This enables dynamic optimization of the environment based on the emotional state of the worker.

[0351] A "culture device" is a device that provides optimal environmental conditions for the growth and maintenance of cells, microorganisms, and other organisms.

[0352] "Environmental conditions" refer to physical and chemical conditions within the culture device, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0353] A "sensor" is a device that measures physical or chemical environmental parameters and outputs changes in them as electronic signals.

[0354] "Artificial intelligence" is a computing system that analyzes data collected from sensors and uses efficient methods to optimize the environment and predict anomalies.

[0355] "Computational means" refers to a processing unit that performs analysis based on measured data and makes appropriate adjustments.

[0356] An "emotion recognition system" is a system that possesses technology to determine the emotions and psychological state of a worker, and acquires this information using devices such as cameras and microphones.

[0357] An "interface" refers to a display or operating device that allows a user and a system to exchange information with each other.

[0358] The system for realizing this application consists of a sensor array, a server, and a user terminal. The sensors measure the environmental conditions of the factory in real time and transmit the data to the server. The server receives this data and analyzes it using artificial intelligence algorithms. The artificial intelligence used includes an emotion recognition engine and a machine learning model, which are used to optimize the environment and predict anomalies.

[0359] The server can collect emotional information in real time by detecting the emotional state of workers using cameras and microphones. This allows for the identification of workers' stress and fatigue levels, and dynamic interface adjustments to improve the work environment. Based on these analysis results, the user terminal appropriately adjusts the interface to provide information with minimal burden on the user. For example, if a high level of stress is detected, the terminal will automatically play relaxation music to alleviate the work environment.

[0360] This system allows workers to perform their tasks safely and efficiently in a better working environment. For example, in a factory assembly line, if stress is detected from a worker's voice, the system adjusts the environment on the spot and changes the interface information display to be more user-friendly.

[0361] An example of a prompt would be: "Explain how to monitor the emotional state of workers on a factory production line and automatically adjust the environment accordingly."

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] Sensors measure various physical conditions of the factory environment (temperature, humidity, light intensity, etc.). The measurement data is transmitted directly to the server. The input is environmental data from the sensors, and the output is raw environmental data stored on the server.

[0365] Step 2:

[0366] The server performs real-time analysis of the environmental data it receives using an artificial intelligence algorithm. The analysis involves data processing to identify changes necessary for optimizing the surrounding physical conditions. The input is the raw environmental data acquired in step 1, and the output is adjustment command information for optimization.

[0367] Step 3:

[0368] The terminal collects data from the worker's facial expressions and voice and sends it to the emotion recognition engine. The input is facial expression data and voice data from the worker captured by the camera and microphone, and the output is information about the worker's emotional state, which is sent to the server.

[0369] Step 4:

[0370] The server analyzes the worker's emotional data using an emotion recognition engine. The analysis determines the type of emotional state (stress, exhilaration, fatigue, etc.). The input is the emotional state information obtained in step 3, and the output is the emotional state evaluation result for interface adjustment.

[0371] Step 5:

[0372] The server generates interface adjustment commands based on the environment optimization commands from step 2 and the sentiment evaluation results from step 4. The generated commands are sent to the terminal, and the interface content and tone are dynamically adjusted. The inputs are the environment optimization commands and sentiment evaluation results, and the output is the optimized interface display content and adjusted audio information for the user.

[0373] Step 6:

[0374] Based on commands received from the server, the terminal adjusts the interface for the user and plays relaxation music as needed. The input is the optimized commands generated in step 5, and the output is the improved interface environment that the user can view and interact with.

[0375] Step 7:

[0376] Users experience improved productivity or reduced stress by using the terminal's optimized interface. The input is a streamlined interface environment, and the output is improved user work efficiency and emotional stability.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] This invention relates to a system for automatically optimizing cell culture processes. The system operates with a configuration including sensors that constitute the culture environment, a server for processing data, and a user interface. Each sensor measures temperature, pH, oxygen concentration, and nutrient levels within the culture environment in real time and transmits this information to the server.

[0394] The server has the ability to record received data in a database and analyze it in real time using artificial intelligence. This analysis evaluates whether the environment is optimal for cells and issues instructions to automatically adjust it if necessary. In addition, the server learns from past data history to predict potential anomalies and unexpected changes and plan appropriate countermeasures.

[0395] Users can access the system via a terminal and use the main interface to check environmental conditions and system performance metrics. For example, they can visually monitor the history of environmental adjustments according to the cell growth stage and data logs of past anomaly detections. Users can also directly give instructions to the system, providing an interactive method for manually adjusting the cell culture environment.

[0396] For example, when culturing a particular cell line, the system first compares data from sensors with pre-set temperature and oxygen concentration information. If the conditions deviate, the server automatically controls the heater, maintaining the optimal temperature while adjusting the oxygen concentration as needed based on the predicted growth pattern. Users can monitor these processes on their terminal and perform manual operations as needed.

[0397] In this way, the complex and diverse processes of cell culture can be managed in an integrated manner, achieving the consistent maintenance of high quality and efficient productivity improvements that are hallmarks of the invention.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The server receives environmental data such as temperature, pH, oxygen concentration, and nutrient levels in real time from sensors. This data is recorded in a database.

[0401] Step 2:

[0402] The server analyzes the received data and uses an artificial intelligence model to evaluate whether the current environment is optimized. This evaluation also includes trend analysis of past culture data.

[0403] Step 3:

[0404] The server outputs instructions to automatically adjust environmental conditions as needed. These adjustment instructions are sent to the heater, cooler, gas supply system, and other components within the culture apparatus.

[0405] Step 4:

[0406] The AI ​​uses historical data to predict anomalies. If an anomaly is likely to occur, the server automatically sends a warning to the user.

[0407] Step 5:

[0408] Users can access the dashboard via their device to view system analysis results and adjustment history. Users can also manually control the system as needed.

[0409] (Example 1)

[0410] 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."

[0411] Conventional cell culture systems have faced challenges in achieving efficient and high-quality cell culture due to difficulties in optimizing the environment, predicting anomalies, and providing intuitive user operation. This necessitates manual monitoring and adjustment, resulting in time-consuming and labor-intensive processes. Furthermore, it is difficult to respond quickly to unexpected environmental changes, potentially negatively impacting productivity and culture consistency.

[0412] 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.

[0413] In this invention, the server includes means for acquiring the environmental characteristics of the culture system using a measuring device and storing the information, a computing device using an intelligent algorithm that immediately analyzes the stored information and performs environmental optimization, and means for correcting the physical characteristics of the culture system based on the analysis results. This enables real-time monitoring and optimization of the culture environment. Furthermore, it enables the realization of a rapid and efficient cell culture process through the prediction of anomalies and interactive operation by the user.

[0414] A "culture system" is a device or mechanism that provides a specific environment for growing biomaterials such as cells and microorganisms.

[0415] "Environmental characteristics" refer to the physical and chemical conditions that affect culture, such as temperature, pH, oxygen concentration, and nutrient levels within the culture system.

[0416] A "measuring device" is a device used to measure environmental characteristics and acquire them as data, and includes sensors and other such devices.

[0417] "Means for storing information" refers to devices or methods for recording and managing data acquired from measuring devices, and includes databases, etc.

[0418] A "computer using intelligent algorithms" is a device that analyzes received information and uses programs or models to derive optimal environmental conditions, utilizing artificial intelligence and machine learning.

[0419] "Means for modifying physical properties" refer to devices or methods for adjusting the environmental characteristics within a culture system based on the analysis results of a computing device.

[0420] "Anomaly prediction" is the process of predicting irregular changes that may occur in the future, based on patterns learned from past data.

[0421] A "user interface" refers to the means by which a user interacts with a system, and includes screens and devices that enable the display and manipulation of information.

[0422] A "means of issuing warnings" refers to a system for notifying users of an abnormality or emergency when such an event is detected.

[0423] This invention provides specific means for optimizing the environment of a culture system and achieving efficient and high-quality cell culture. The server, terminals, and users cooperate to effectively operate this system.

[0424] First, the server plays a central role in the culture system, receiving environmental characteristic data sent from measurement devices. This data includes temperature, pH, oxygen concentration, and nutrient levels. The server stores this data in a database and performs detailed analysis. Intelligent algorithms are used for this analysis, and generative AI models are specifically employed. The server leverages machine learning frameworks such as TensorFlow and PyTorch to analyze the received data in real time and also predict anomalies. Specifically, it learns from past data history and applies time-series analysis models to predict future environmental changes.

[0425] Next, the terminal provides a means for the user to interact with the system. The terminal interface visually displays the current environmental state and past data history, allowing the user to intuitively understand the situation. Furthermore, through the user interface, the user can manually input information into the system and instruct specific environmental changes.

[0426] The user monitors and controls the culture system using a terminal. If an abnormality is detected, the user receives an alarm notification and intervenes as necessary. For example, by entering a prompt message such as, "Please tell me how to properly adjust the temperature and oxygen concentration when culturing a certain cell line," the server will infer the most effective adjustment method and provide it to the user.

[0427] This invention enables automated optimization of the culture environment and consistent quality control, as well as rapid response to unexpected environmental changes.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The server receives real-time data on the culture environment from the measuring devices. This includes various environmental characteristics such as temperature, pH, oxygen concentration, and nutrient levels. This data is acquired as input and stored in a temporary storage area on the server. This enables rapid and efficient initial data processing. Specifically, the server aggregates data from each sensor, standardizes the format, and prepares it for the next processing step.

[0431] Step 2:

[0432] The server records the accumulated data in a database. This recorded data is efficiently managed using an SQL-based management system. The input here is the real-time data obtained in the previous step, and the output is structured database entries. The server applies appropriate indexes to ensure that subsequent queries and analyses of the data can be performed smoothly.

[0433] Step 3:

[0434] The server immediately analyzes the data using a generative AI model. The AI ​​model compares historical data with current data and performs predictive analysis. The input is the dataset constructed in step 2, and the output is predicted data on the optimization status and anomalies of the analyzed environment. Specifically, the server executes the machine learning model using frameworks such as TensorFlow and PyTorch.

[0435] Step 4:

[0436] The server automatically adjusts the physical characteristics of the culture system based on the analysis results. This adjustment includes controlling the heater and oxygen supply device. The input is the optimization command obtained in step 3, and the output is the actual hardware control signal. The server performs the specific actions of communicating instructions to the hardware via a PLC (Programmable Logic Controller).

[0437] Step 5:

[0438] The terminal visually displays data obtained from the server. Users can use the terminal to check the current environmental status and analysis results. The input is analysis result data from the server, and the output is a visualized display screen. Specifically, the terminal displays graphs and charts through a GUI, allowing users to easily understand the situation.

[0439] Step 6:

[0440] The user can issue supply instructions to the system via the terminal interface. If necessary, the user can manually adjust the environment settings. The input in this process is the user's operational instructions, and the output is the system's response based on those instructions. The user makes fine adjustments by specifically manipulating buttons and sliders on the screen.

[0441] (Application Example 1)

[0442] 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."

[0443] In modern cell culture processes, maintaining optimal environmental conditions at all times is crucial, but currently, manual adjustments are prevalent, making efficient management difficult. Furthermore, early detection of abnormalities is challenging, potentially impacting productivity and quality. In addition, limitations on real-time monitoring and adjustment from remote locations pose another challenge.

[0444] 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.

[0445] In this invention, the server includes means for measuring the environmental conditions within the culture device using a detector and recording the data; computation means using an intelligent system that analyzes the recorded data in real time and optimizes the environment; and means for providing the environmental conditions and analysis results to a remotely operated information terminal device, which can be manually adjusted. This enables efficient optimization of the cell culture environment, early detection of abnormalities, and remote monitoring and adjustment.

[0446] A "culture device" is a device that provides and monitors the environment necessary for cell growth and maintenance.

[0447] A "detector" is a device used to measure the state of the environment and record it as data.

[0448] "Means of recording data" refers to methods or devices for storing measured data and making it available for later analysis or reference.

[0449] "Real-time analysis" is a process of processing data immediately as it is generated and gaining insights from it.

[0450] An "intelligent system" is a system that utilizes artificial intelligence to analyze data and automatically optimize the environment.

[0451] A "remote control information terminal device" is an electronic device that enables monitoring and operation of cell culture processes from a distance.

[0452] "Environmental conditions" refer to various physical conditions related to cell culture, such as temperature, pH, oxygen concentration, and nutrient levels.

[0453] "Predicting anomalies" means identifying in advance, through data analysis, situations that may deviate from normal patterns.

[0454] "Manually adjustable means" refers to functions or interfaces that allow users to directly change environmental conditions at their own discretion.

[0455] One embodiment of this invention is a system aimed at the efficient management and optimization of a cell culture process. This system includes an intelligent system that monitors the environmental conditions within the culture apparatus in real time and automatically adjusts them accordingly.

[0456] The server collects data from multiple detectors (sensors) and records it in a database. Data such as temperature, pH, oxygen concentration, and nutrient levels are stored in real time in databases like MySQL or PostgreSQL. The server then analyzes the data using intelligent systems such as TensorFlow or PyTorch to optimize the environment.

[0457] Based on the analysis results, the server controls the culture device and automatically adjusts the physical conditions. Furthermore, when predicting anomalies, it utilizes machine learning models to evaluate past data patterns and proactively detect potential anomalies.

[0458] Users can monitor the situation in real time at all times using remote control terminals (smartphones and tablets). They can receive visualized information on the environmental conditions and analysis results from intelligent systems, and make manual adjustments.

[0459] For example, if the temperature of the culture process exceeds the set range, the server immediately sends an anomaly detection alert to the terminal. At this time, the application displays a prompt message such as, "The temperature of the culture environment is 5% above the set value. Do you want to adjust the heater?", allowing the user to quickly select the appropriate action.

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The sensors measure environmental data (temperature, pH, oxygen concentration, nutrient levels) within the culture device. This data is transmitted to a server via an IoT device. Input data is recorded in real time, and numerical values ​​such as temperature and nutrient levels are stored as output.

[0463] Step 2:

[0464] The server saves the received data to a database such as MySQL or PostgreSQL. Once the data has been written to the database, it is organized and ready for later analysis.

[0465] Step 3:

[0466] The server uses TensorFlow and PyTorch to analyze the data in real time. It processes environmental data obtained from a database as input and calculates the optimal conditions for cell growth. The output generates optimized settings and abnormal data based on the analysis results.

[0467] Step 4:

[0468] The server automatically adjusts the parameters of the culture device based on the instructions obtained from the analysis results. For example, if the analysis indicates that the temperature is too high, the server will issue a command to stop heating and return to the set temperature.

[0469] Step 5:

[0470] The server also runs machine learning models to detect anomalies using historical data. It identifies data patterns that are predicted to be anomalous and outputs them to the user as alerts. Using generative AI models improves the ability to identify complex patterns.

[0471] Step 6:

[0472] The user receives notifications from the information terminal and checks the status. Based on the prompt displayed on the terminal, they can decide and input whether to manually adjust the environment. For example, a prompt such as "The culture environment temperature is 5% above the set value. Do you want to adjust the heater?" might be displayed.

[0473] 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.

[0474] This invention incorporates emotion recognition capabilities into an autonomous system aimed at optimizing cell culture processes. This system not only automates environmental adjustment and anomaly prediction within the culture apparatus, but also recognizes the user's emotional state and provides a corresponding interface adaptation. The system consists of a group of sensors attached to the culture apparatus, a server that manages and analyzes this data, and a terminal for connecting with the user.

[0475] The server collects and records environmental data from sensors in real time, and analyzes this data using artificial intelligence algorithms. Based on this data, the AI ​​adjusts conditions to optimize the environmental state. It also builds machine learning models that learn from past data and predict anomalies in advance.

[0476] Furthermore, an emotion engine is incorporated, which analyzes data collected from the camera and microphone when the user interacts with the device to determine the user's emotional state. For example, it can identify the user's current emotions from changes in voice tone and facial expressions. Based on this emotional information, the system dynamically adjusts the interface. Specifically, when the user is feeling stressed, the system adjusts the content and tone of the information and warning messages it presents to reduce the user's burden.

[0477] For example, if a user experiences anxiety while monitoring a cell culture process, the camera and microphone on the device recognize this state. Based on the analysis results from the emotion engine, the server automatically adjusts the interface to provide more concise and easy-to-understand information. It can also play relaxation music or guides for the user.

[0478] This system allows users to work in a more comfortable and efficient environment, enabling consistent quality control and increased efficiency in the cell culture process.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The server receives real-time culture environment data from sensors, including temperature, pH, oxygen concentration, and nutrient levels. This data is recorded in a database and prepared for later analysis.

[0482] Step 2:

[0483] The server uses artificial intelligence to analyze the received data in real time and determine whether each element of the environment is within the optimal range. Based on this analysis, it issues instructions to automatically adjust the physical conditions of the culture device as needed.

[0484] Step 3:

[0485] The server utilizes a machine learning model to learn anomaly patterns from past data. This model is used to predict probabilistic anomalies and generate warnings in advance.

[0486] Step 4:

[0487] The emotion engine recognizes the user's emotional state by collecting facial expressions and voice data through the device's camera and microphone. The server analyzes this data to determine the user's current emotional state.

[0488] Step 5:

[0489] The server makes adjustments based on the user's emotions and dynamically changes the terminal's interface. For example, if the terminal determines that the user is stressed, it changes how it presents information, displaying only essential information in a softer tone.

[0490] Step 6:

[0491] Users monitor the current state of the environment and system responses on a dashboard provided by the system via their terminal. Users can manually provide additional instructions and customize operations as needed.

[0492] (Example 2)

[0493] 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."

[0494] In cell culture processes, inoptimized environmental conditions negatively impact cell growth, leading to a decline in quality. Furthermore, the stress and anxiety experienced by users during the process affect efficiency. Conventional systems have struggled to integrate multiple elements, including environmental adjustment, anomaly detection, and the provision of an interface that considers the user's emotional state.

[0495] 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.

[0496] In this invention, the server includes means for acquiring environmental conditions within the culture device using a detection device and recording the information; means for performing calculations using artificial intelligence to analyze the recorded information in real time and optimize the environment; means for adjusting the physical conditions of the culture device based on the analysis results; and means for performing emotional analysis and dynamically changing the settings of the display device based on the user's emotional state. This makes it possible to maintain an optimal cell culture environment at all times while providing appropriate information according to the user's emotional state.

[0497] A "cell culture device" is a device used to cultivate cells and provide them with an appropriate growth environment.

[0498] "Environmental conditions" refer to factors that determine the state of the cell culture environment, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0499] "Detection equipment" refers to sensors and related devices used to measure environmental conditions within a culture device and collect the data.

[0500] "Means of recording information" refers to functions and technologies that store data obtained from detection devices and make it available for later reference.

[0501] "Computational means using artificial intelligence" refers to a computer system that uses machine learning and data analysis techniques to analyze collected data and optimize environmental conditions.

[0502] "Means for adjusting physical conditions" refer to control devices and operating mechanisms for changing the environmental conditions within the culture apparatus based on the analysis results.

[0503] "Emotional analysis" is a process that estimates a user's emotions and mental state by analyzing data obtained from sensors such as cameras and microphones.

[0504] "Means for dynamically changing the settings of a display device" refers to functions and technologies for adjusting the displayed information and interface in real time according to the user's emotional state.

[0505] This invention streamlines the cell culture process by incorporating emotion recognition functionality into a system that optimizes the environment within the culture apparatus. The server, terminal, and user each play their respective roles, forming the overall system.

[0506] The server first collects environmental data such as temperature, humidity, oxygen concentration, and carbon dioxide concentration in real time from sensors installed in the culture device. This data is analyzed in real time using the Python library TensorFlow to calculate the optimal environmental conditions. Based on the analysis results, the physical conditions of the culture device are adjusted via the control device. Furthermore, past data is analyzed using the machine learning algorithm Random Forest to predict potential failures in advance.

[0507] The device uses a camera and microphone to recognize the user's emotions. The data collected by these devices is emotionally analyzed to determine the user's emotional state. Libraries such as OpenCV and LibROSA are used for the analysis. If it is determined that the user is experiencing stress, the interface settings are dynamically changed to provide the user with more concise and user-friendly information.

[0508] For example, if a user feels anxious while monitoring cell culture operations, the device's camera and microphone will detect this state. Once stress is detected, the server will provide a more user-friendly interface and play relaxation music to reduce the user's stress.

[0509] An example of a prompt using a generative AI model might be, "What prompt would you use to design a cell culture monitoring system that takes into account the user's emotional state?" This would provide the system with insights into emotion recognition and interface adjustment, allowing for further improvements. By incorporating these elements, the system achieves consistent quality control of the cell culture environment and improves user work efficiency.

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

[0511] Step 1:

[0512] The server collects environmental data from sensors attached to the culture device. The sensors measure temperature, humidity, oxygen concentration, and carbon dioxide concentration, and transmit this data to the server in real time. The server records the received data in a database and prepares it for the next analysis step.

[0513] Step 2:

[0514] The server analyzes the collected environmental data using TensorFlow, a Python library. This process takes various environmental values ​​as input and outputs appropriate environmental condition adjustment parameters as analysis results. Specifically, the AI ​​calculates settings to optimize the environment and determines which physical conditions should be adjusted.

[0515] Step 3:

[0516] The server automatically adjusts the physical conditions of the culture device based on the analysis results. For example, if it determines that temperature adjustment is necessary, the server sends a command to the culture device's heater to adjust it to the set temperature. This maintains the optimal environment for cell growth.

[0517] Step 4:

[0518] The server uses a random forest algorithm based on historical data to predict anomalies. This process learns from past data patterns and outputs the likelihood of an anomaly occurring. When signs of an anomaly are detected, a warning is issued, allowing for appropriate action to be taken in advance.

[0519] Step 5:

[0520] The device collects the user's facial expressions and voice using its camera and microphone. This data is sent to a server for use in emotional analysis. The device performs specific actions such as taking photos and recording voice every second.

[0521] Step 6:

[0522] The server uses OpenCV and LibROSA to analyze the user's emotional state. This process takes facial image and audio data as input and outputs the user's emotional state (e.g., stress or anxiety). Once the emotional state is determined, the interface is dynamically changed accordingly.

[0523] Step 7:

[0524] The user inputs prompts into the generated AI model to gain ideas for further system improvements. These prompts might include questions such as, "What prompts would you use to design a cell culture monitoring system that takes the user's emotional state into account?" The generated ideas are then used to improve the system.

[0525] (Application Example 2)

[0526] 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."

[0527] In factories, systems that rely solely on optimizing environmental data without considering the impact of workers' emotional states on production efficiency and safety have the problem of not being sufficient to improve the work environment or prevent abnormal situations.

[0528] 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.

[0529] In this invention, the server includes means for measuring the environmental conditions within the culture device and recording data, computation means using artificial intelligence to analyze the recorded data, and emotion recognition means for detecting the emotional state of the worker. This enables dynamic optimization of the environment based on the emotional state of the worker.

[0530] A "culture device" is a device that provides optimal environmental conditions for the growth and maintenance of cells, microorganisms, and other organisms.

[0531] "Environmental conditions" refer to physical and chemical conditions within the culture device, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0532] A "sensor" is a device that measures physical or chemical environmental parameters and outputs changes in them as electronic signals.

[0533] "Artificial intelligence" is a computing system that analyzes data collected from sensors and uses efficient methods to optimize the environment and predict anomalies.

[0534] "Computational means" refers to a processing unit that performs analysis based on measured data and makes appropriate adjustments.

[0535] An "emotion recognition system" is a system that possesses technology to determine the emotions and psychological state of a worker, and acquires this information using devices such as cameras and microphones.

[0536] An "interface" refers to a display or operating device that allows a user and a system to exchange information with each other.

[0537] The system for realizing this application consists of a sensor array, a server, and a user terminal. The sensors measure the environmental conditions of the factory in real time and transmit the data to the server. The server receives this data and analyzes it using artificial intelligence algorithms. The artificial intelligence used includes an emotion recognition engine and a machine learning model, which are used to optimize the environment and predict anomalies.

[0538] The server can collect emotional information in real time by detecting the emotional state of workers using cameras and microphones. This allows for the identification of workers' stress and fatigue levels, and dynamic interface adjustments to improve the work environment. Based on these analysis results, the user terminal appropriately adjusts the interface to provide information with minimal burden on the user. For example, if a high level of stress is detected, the terminal will automatically play relaxation music to alleviate the work environment.

[0539] This system allows workers to perform their tasks safely and efficiently in a better working environment. For example, in a factory assembly line, if stress is detected from a worker's voice, the system adjusts the environment on the spot and changes the interface information display to be more user-friendly.

[0540] An example of a prompt would be: "Explain how to monitor the emotional state of workers on a factory production line and automatically adjust the environment accordingly."

[0541] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0542] Step 1:

[0543] Sensors measure various physical conditions of the factory environment (temperature, humidity, light intensity, etc.). The measurement data is transmitted directly to the server. The input is environmental data from the sensors, and the output is raw environmental data stored on the server.

[0544] Step 2:

[0545] The server performs real-time analysis of the environmental data it receives using an artificial intelligence algorithm. The analysis involves data processing to identify changes necessary for optimizing the surrounding physical conditions. The input is the raw environmental data acquired in step 1, and the output is adjustment command information for optimization.

[0546] Step 3:

[0547] The terminal collects data from the worker's facial expressions and voice and sends it to the emotion recognition engine. The input is facial expression data and voice data from the worker captured by the camera and microphone, and the output is information about the worker's emotional state, which is sent to the server.

[0548] Step 4:

[0549] The server analyzes the worker's emotional data using an emotion recognition engine. The analysis determines the type of emotional state (stress, exhilaration, fatigue, etc.). The input is the emotional state information obtained in step 3, and the output is the emotional state evaluation result for interface adjustment.

[0550] Step 5:

[0551] The server generates interface adjustment commands based on the environment optimization commands from step 2 and the sentiment evaluation results from step 4. The generated commands are sent to the terminal, and the interface content and tone are dynamically adjusted. The inputs are the environment optimization commands and sentiment evaluation results, and the output is the optimized interface display content and adjusted audio information for the user.

[0552] Step 6:

[0553] Based on commands received from the server, the terminal adjusts the interface for the user and plays relaxation music as needed. The input is the optimized commands generated in step 5, and the output is the improved interface environment that the user can view and interact with.

[0554] Step 7:

[0555] Users experience improved productivity or reduced stress by using the terminal's optimized interface. The input is a streamlined interface environment, and the output is improved user work efficiency and emotional stability.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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".

[0573] This invention relates to a system for automatically optimizing cell culture processes. The system operates with a configuration including sensors that constitute the culture environment, a server for processing data, and a user interface. Each sensor measures temperature, pH, oxygen concentration, and nutrient levels within the culture environment in real time and transmits this information to the server.

[0574] The server has the ability to record received data in a database and analyze it in real time using artificial intelligence. This analysis evaluates whether the environment is optimal for cells and issues instructions to automatically adjust it if necessary. In addition, the server learns from past data history to predict potential anomalies and unexpected changes and plan appropriate countermeasures.

[0575] Users can access the system via a terminal and use the main interface to check environmental conditions and system performance metrics. For example, they can visually monitor the history of environmental adjustments according to the cell growth stage and data logs of past anomaly detections. Users can also directly give instructions to the system, providing an interactive method for manually adjusting the cell culture environment.

[0576] For example, when culturing a particular cell line, the system first compares data from sensors with pre-set temperature and oxygen concentration information. If the conditions deviate, the server automatically controls the heater, maintaining the optimal temperature while adjusting the oxygen concentration as needed based on the predicted growth pattern. Users can monitor these processes on their terminal and perform manual operations as needed.

[0577] In this way, the complex and diverse processes of cell culture can be managed in an integrated manner, achieving the consistent maintenance of high quality and efficient productivity improvements that are hallmarks of the invention.

[0578] The following describes the processing flow.

[0579] Step 1:

[0580] The server receives environmental data such as temperature, pH, oxygen concentration, and nutrient levels in real time from sensors. This data is recorded in a database.

[0581] Step 2:

[0582] The server analyzes the received data and uses an artificial intelligence model to evaluate whether the current environment is optimized. This evaluation also includes trend analysis of past culture data.

[0583] Step 3:

[0584] The server outputs instructions to automatically adjust environmental conditions as needed. These adjustment instructions are sent to the heater, cooler, gas supply system, and other components within the culture apparatus.

[0585] Step 4:

[0586] The AI ​​uses historical data to predict anomalies. If an anomaly is likely to occur, the server automatically sends a warning to the user.

[0587] Step 5:

[0588] Users can access the dashboard via their device to view system analysis results and adjustment history. Users can also manually control the system as needed.

[0589] (Example 1)

[0590] 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".

[0591] Conventional cell culture systems have faced challenges in achieving efficient and high-quality cell culture due to difficulties in optimizing the environment, predicting anomalies, and providing intuitive user operation. This necessitates manual monitoring and adjustment, resulting in time-consuming and labor-intensive processes. Furthermore, it is difficult to respond quickly to unexpected environmental changes, potentially negatively impacting productivity and culture consistency.

[0592] 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.

[0593] In this invention, the server includes means for acquiring the environmental characteristics of the culture system using a measuring device and storing the information, a computing device using an intelligent algorithm that immediately analyzes the stored information and performs environmental optimization, and means for correcting the physical characteristics of the culture system based on the analysis results. This enables real-time monitoring and optimization of the culture environment. Furthermore, it enables the realization of a rapid and efficient cell culture process through the prediction of anomalies and interactive operation by the user.

[0594] A "culture system" is a device or mechanism that provides a specific environment for growing biomaterials such as cells and microorganisms.

[0595] "Environmental characteristics" refer to the physical and chemical conditions that affect culture, such as temperature, pH, oxygen concentration, and nutrient levels within the culture system.

[0596] A "measuring device" is a device used to measure environmental characteristics and acquire them as data, and includes sensors and other such devices.

[0597] "Means for storing information" refers to devices or methods for recording and managing data acquired from measuring devices, and includes databases, etc.

[0598] A "computer using intelligent algorithms" is a device that analyzes received information and uses programs or models to derive optimal environmental conditions, utilizing artificial intelligence and machine learning.

[0599] "Means for modifying physical properties" refer to devices or methods for adjusting the environmental characteristics within a culture system based on the analysis results of a computing device.

[0600] "Anomaly prediction" is the process of predicting irregular changes that may occur in the future, based on patterns learned from past data.

[0601] A "user interface" refers to the means by which a user interacts with a system, and includes screens and devices that enable the display and manipulation of information.

[0602] A "means of issuing warnings" refers to a system for notifying users of an abnormality or emergency when such an event is detected.

[0603] This invention provides specific means for optimizing the environment of a culture system and achieving efficient and high-quality cell culture. The server, terminals, and users cooperate to effectively operate this system.

[0604] First, the server plays a central role in the culture system, receiving environmental characteristic data sent from measurement devices. This data includes temperature, pH, oxygen concentration, and nutrient levels. The server stores this data in a database and performs detailed analysis. Intelligent algorithms are used for this analysis, and generative AI models are specifically employed. The server leverages machine learning frameworks such as TensorFlow and PyTorch to analyze the received data in real time and also predict anomalies. Specifically, it learns from past data history and applies time-series analysis models to predict future environmental changes.

[0605] Next, the terminal provides a means for the user to interact with the system. The terminal interface visually displays the current environmental state and past data history, allowing the user to intuitively understand the situation. Furthermore, through the user interface, the user can manually input information into the system and instruct specific environmental changes.

[0606] The user monitors and controls the culture system using a terminal. If an abnormality is detected, the user receives an alarm notification and intervenes as necessary. For example, by entering a prompt message such as, "Please tell me how to properly adjust the temperature and oxygen concentration when culturing a certain cell line," the server will infer the most effective adjustment method and provide it to the user.

[0607] This invention enables automated optimization of the culture environment and consistent quality control, as well as rapid response to unexpected environmental changes.

[0608] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0609] Step 1:

[0610] The server receives real-time data on the culture environment from the measuring devices. This includes various environmental characteristics such as temperature, pH, oxygen concentration, and nutrient levels. This data is acquired as input and stored in a temporary storage area on the server. This enables rapid and efficient initial data processing. Specifically, the server aggregates data from each sensor, standardizes the format, and prepares it for the next processing step.

[0611] Step 2:

[0612] The server records the accumulated data in a database. This recorded data is efficiently managed using an SQL-based management system. The input here is the real-time data obtained in the previous step, and the output is structured database entries. The server applies appropriate indexes to ensure that subsequent queries and analyses of the data can be performed smoothly.

[0613] Step 3:

[0614] The server immediately analyzes the data using a generative AI model. The AI ​​model compares historical data with current data and performs predictive analysis. The input is the dataset constructed in step 2, and the output is predicted data on the optimization status and anomalies of the analyzed environment. Specifically, the server executes the machine learning model using frameworks such as TensorFlow and PyTorch.

[0615] Step 4:

[0616] The server automatically adjusts the physical characteristics of the culture system based on the analysis results. This adjustment includes controlling the heater and oxygen supply device. The input is the optimization command obtained in step 3, and the output is the actual hardware control signal. The server performs the specific actions of communicating instructions to the hardware via a PLC (Programmable Logic Controller).

[0617] Step 5:

[0618] The terminal visually displays data obtained from the server. Users can use the terminal to check the current environmental status and analysis results. The input is analysis result data from the server, and the output is a visualized display screen. Specifically, the terminal displays graphs and charts through a GUI, allowing users to easily understand the situation.

[0619] Step 6:

[0620] The user can issue supply instructions to the system via the terminal interface. If necessary, the user can manually adjust the environment settings. The input in this process is the user's operational instructions, and the output is the system's response based on those instructions. The user makes fine adjustments by specifically manipulating buttons and sliders on the screen.

[0621] (Application Example 1)

[0622] 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".

[0623] In modern cell culture processes, maintaining optimal environmental conditions at all times is crucial, but currently, manual adjustments are prevalent, making efficient management difficult. Furthermore, early detection of abnormalities is challenging, potentially impacting productivity and quality. In addition, limitations on real-time monitoring and adjustment from remote locations pose another challenge.

[0624] 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.

[0625] In this invention, the server includes means for measuring the environmental conditions within the culture device using a detector and recording the data; computation means using an intelligent system that analyzes the recorded data in real time and optimizes the environment; and means for providing the environmental conditions and analysis results to a remotely operated information terminal device, which can be manually adjusted. This enables efficient optimization of the cell culture environment, early detection of abnormalities, and remote monitoring and adjustment.

[0626] A "culture device" is a device that provides and monitors the environment necessary for cell growth and maintenance.

[0627] A "detector" is a device used to measure the state of the environment and record it as data.

[0628] "Means of recording data" refers to methods or devices for storing measured data and making it available for later analysis or reference.

[0629] "Real-time analysis" is a process of processing data immediately as it is generated and gaining insights from it.

[0630] An "intelligent system" is a system that utilizes artificial intelligence to analyze data and automatically optimize the environment.

[0631] A "remote control information terminal device" is an electronic device that enables monitoring and operation of cell culture processes from a distance.

[0632] "Environmental conditions" refer to various physical conditions related to cell culture, such as temperature, pH, oxygen concentration, and nutrient levels.

[0633] "Predicting anomalies" means identifying in advance, through data analysis, situations that may deviate from normal patterns.

[0634] "Manually adjustable means" refers to functions or interfaces that allow users to directly change environmental conditions at their own discretion.

[0635] One embodiment of this invention is a system aimed at the efficient management and optimization of a cell culture process. This system includes an intelligent system that monitors the environmental conditions within the culture apparatus in real time and automatically adjusts them accordingly.

[0636] The server collects data from multiple detectors (sensors) and records it in a database. Data such as temperature, pH, oxygen concentration, and nutrient levels are stored in real time in databases like MySQL or PostgreSQL. The server then analyzes the data using intelligent systems such as TensorFlow or PyTorch to optimize the environment.

[0637] Based on the analysis results, the server controls the culture device and automatically adjusts the physical conditions. Furthermore, when predicting anomalies, it utilizes machine learning models to evaluate past data patterns and proactively detect potential anomalies.

[0638] Users can monitor the situation in real time at all times using remote control terminals (smartphones and tablets). They can receive visualized information on the environmental conditions and analysis results from intelligent systems, and make manual adjustments.

[0639] For example, if the temperature of the culture process exceeds the set range, the server immediately sends an anomaly detection alert to the terminal. At this time, the application displays a prompt message such as, "The temperature of the culture environment is 5% above the set value. Do you want to adjust the heater?", allowing the user to quickly select the appropriate action.

[0640] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0641] Step 1:

[0642] The sensors measure environmental data (temperature, pH, oxygen concentration, nutrient levels) within the culture device. This data is transmitted to a server via an IoT device. Input data is recorded in real time, and numerical values ​​such as temperature and nutrient levels are stored as output.

[0643] Step 2:

[0644] The server saves the received data to a database such as MySQL or PostgreSQL. Once the data has been written to the database, it is organized and ready for later analysis.

[0645] Step 3:

[0646] The server uses TensorFlow and PyTorch to analyze the data in real time. It processes environmental data obtained from a database as input and calculates the optimal conditions for cell growth. The output generates optimized settings and abnormal data based on the analysis results.

[0647] Step 4:

[0648] The server automatically adjusts the parameters of the culture device based on the instructions obtained from the analysis results. For example, if the analysis indicates that the temperature is too high, the server will issue a command to stop heating and return to the set temperature.

[0649] Step 5:

[0650] The server also runs machine learning models to detect anomalies using historical data. It identifies data patterns that are predicted to be anomalous and outputs them to the user as alerts. Using generative AI models improves the ability to identify complex patterns.

[0651] Step 6:

[0652] The user receives notifications from the information terminal and checks the status. Based on the prompt displayed on the terminal, they can decide and input whether to manually adjust the environment. For example, a prompt such as "The culture environment temperature is 5% above the set value. Do you want to adjust the heater?" might be displayed.

[0653] 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.

[0654] This invention incorporates emotion recognition capabilities into an autonomous system aimed at optimizing cell culture processes. This system not only automates environmental adjustment and anomaly prediction within the culture apparatus, but also recognizes the user's emotional state and provides a corresponding interface adaptation. The system consists of a group of sensors attached to the culture apparatus, a server that manages and analyzes this data, and a terminal for connecting with the user.

[0655] The server collects and records environmental data from sensors in real time, and analyzes this data using artificial intelligence algorithms. Based on this data, the AI ​​adjusts conditions to optimize the environmental state. It also builds machine learning models that learn from past data and predict anomalies in advance.

[0656] Furthermore, an emotion engine is incorporated, which analyzes data collected from the camera and microphone when the user interacts with the device to determine the user's emotional state. For example, it can identify the user's current emotions from changes in voice tone and facial expressions. Based on this emotional information, the system dynamically adjusts the interface. Specifically, when the user is feeling stressed, the system adjusts the content and tone of the information and warning messages it presents to reduce the user's burden.

[0657] For example, if a user experiences anxiety while monitoring a cell culture process, the camera and microphone on the device recognize this state. Based on the analysis results from the emotion engine, the server automatically adjusts the interface to provide more concise and easy-to-understand information. It can also play relaxation music or guides for the user.

[0658] This system allows users to work in a more comfortable and efficient environment, enabling consistent quality control and increased efficiency in the cell culture process.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The server receives real-time culture environment data from sensors, including temperature, pH, oxygen concentration, and nutrient levels. This data is recorded in a database and prepared for later analysis.

[0662] Step 2:

[0663] The server uses artificial intelligence to analyze the received data in real time and determine whether each element of the environment is within the optimal range. Based on this analysis, it issues instructions to automatically adjust the physical conditions of the culture device as needed.

[0664] Step 3:

[0665] The server utilizes a machine learning model to learn anomaly patterns from past data. This model is used to predict probabilistic anomalies and generate warnings in advance.

[0666] Step 4:

[0667] The emotion engine recognizes the user's emotional state by collecting facial expressions and voice data through the device's camera and microphone. The server analyzes this data to determine the user's current emotional state.

[0668] Step 5:

[0669] The server makes adjustments based on the user's emotions and dynamically changes the terminal's interface. For example, if the terminal determines that the user is stressed, it changes how it presents information, displaying only essential information in a softer tone.

[0670] Step 6:

[0671] Users monitor the current state of the environment and system responses on a dashboard provided by the system via their terminal. Users can manually provide additional instructions and customize operations as needed.

[0672] (Example 2)

[0673] 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".

[0674] In cell culture processes, inoptimized environmental conditions negatively impact cell growth, leading to a decline in quality. Furthermore, the stress and anxiety experienced by users during the process affect efficiency. Conventional systems have struggled to integrate multiple elements, including environmental adjustment, anomaly detection, and the provision of an interface that considers the user's emotional state.

[0675] 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.

[0676] In this invention, the server includes means for acquiring environmental conditions within the culture device using a detection device and recording the information; means for performing calculations using artificial intelligence to analyze the recorded information in real time and optimize the environment; means for adjusting the physical conditions of the culture device based on the analysis results; and means for performing emotional analysis and dynamically changing the settings of the display device based on the user's emotional state. This makes it possible to maintain an optimal cell culture environment at all times while providing appropriate information according to the user's emotional state.

[0677] A "cell culture device" is a device used to cultivate cells and provide them with an appropriate growth environment.

[0678] "Environmental conditions" refer to factors that determine the state of the cell culture environment, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0679] "Detection equipment" refers to sensors and related devices used to measure environmental conditions within a culture device and collect the data.

[0680] "Means of recording information" refers to functions and technologies that store data obtained from detection devices and make it available for later reference.

[0681] "Computational means using artificial intelligence" refers to a computer system that uses machine learning and data analysis techniques to analyze collected data and optimize environmental conditions.

[0682] "Means for adjusting physical conditions" refer to control devices and operating mechanisms for changing the environmental conditions within the culture apparatus based on the analysis results.

[0683] "Emotional analysis" is a process that estimates a user's emotions and mental state by analyzing data obtained from sensors such as cameras and microphones.

[0684] "Means for dynamically changing the settings of a display device" refers to functions and technologies for adjusting the displayed information and interface in real time according to the user's emotional state.

[0685] This invention streamlines the cell culture process by incorporating emotion recognition functionality into a system that optimizes the environment within the culture apparatus. The server, terminal, and user each play their respective roles, forming the overall system.

[0686] The server first collects environmental data such as temperature, humidity, oxygen concentration, and carbon dioxide concentration in real time from sensors installed in the culture device. This data is analyzed in real time using the Python library TensorFlow to calculate the optimal environmental conditions. Based on the analysis results, the physical conditions of the culture device are adjusted via the control device. Furthermore, past data is analyzed using the machine learning algorithm Random Forest to predict potential failures in advance.

[0687] The device uses a camera and microphone to recognize the user's emotions. The data collected by these devices is emotionally analyzed to determine the user's emotional state. Libraries such as OpenCV and LibROSA are used for the analysis. If it is determined that the user is experiencing stress, the interface settings are dynamically changed to provide the user with more concise and user-friendly information.

[0688] For example, if a user feels anxious while monitoring cell culture operations, the device's camera and microphone will detect this state. Once stress is detected, the server will provide a more user-friendly interface and play relaxation music to reduce the user's stress.

[0689] An example of a prompt using a generative AI model might be, "What prompt would you use to design a cell culture monitoring system that takes into account the user's emotional state?" This would provide the system with insights into emotion recognition and interface adjustment, allowing for further improvements. By incorporating these elements, the system achieves consistent quality control of the cell culture environment and improves user work efficiency.

[0690] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0691] Step 1:

[0692] The server collects environmental data from sensors attached to the culture device. The sensors measure temperature, humidity, oxygen concentration, and carbon dioxide concentration, and transmit this data to the server in real time. The server records the received data in a database and prepares it for the next analysis step.

[0693] Step 2:

[0694] The server analyzes the collected environmental data using TensorFlow, a Python library. This process takes various environmental values ​​as input and outputs appropriate environmental condition adjustment parameters as analysis results. Specifically, the AI ​​calculates settings to optimize the environment and determines which physical conditions should be adjusted.

[0695] Step 3:

[0696] The server automatically adjusts the physical conditions of the culture device based on the analysis results. For example, if it determines that temperature adjustment is necessary, the server sends a command to the culture device's heater to adjust it to the set temperature. This maintains the optimal environment for cell growth.

[0697] Step 4:

[0698] The server uses a random forest algorithm based on historical data to predict anomalies. This process learns from past data patterns and outputs the likelihood of an anomaly occurring. When signs of an anomaly are detected, a warning is issued, allowing for appropriate action to be taken in advance.

[0699] Step 5:

[0700] The device collects the user's facial expressions and voice using its camera and microphone. This data is sent to a server for use in emotional analysis. The device performs specific actions such as taking photos and recording voice every second.

[0701] Step 6:

[0702] The server uses OpenCV and LibROSA to analyze the user's emotional state. This process takes facial image and audio data as input and outputs the user's emotional state (e.g., stress or anxiety). Once the emotional state is determined, the interface is dynamically changed accordingly.

[0703] Step 7:

[0704] The user inputs prompts into the generated AI model to gain ideas for further system improvements. These prompts might include questions such as, "What prompts would you use to design a cell culture monitoring system that takes the user's emotional state into account?" The generated ideas are then used to improve the system.

[0705] (Application Example 2)

[0706] 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".

[0707] In factories, systems that rely solely on optimizing environmental data without considering the impact of workers' emotional states on production efficiency and safety have the problem of not being sufficient to improve the work environment or prevent abnormal situations.

[0708] 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.

[0709] In this invention, the server includes means for measuring the environmental conditions within the culture device and recording data, computation means using artificial intelligence to analyze the recorded data, and emotion recognition means for detecting the emotional state of the worker. This enables dynamic optimization of the environment based on the emotional state of the worker.

[0710] A "culture device" is a device that provides optimal environmental conditions for the growth and maintenance of cells, microorganisms, and other organisms.

[0711] "Environmental conditions" refer to physical and chemical conditions within the culture device, such as temperature, humidity, oxygen concentration, and carbon dioxide concentration.

[0712] A "sensor" is a device that measures physical or chemical environmental parameters and outputs changes in them as electronic signals.

[0713] "Artificial intelligence" is a computing system that analyzes data collected from sensors and uses efficient methods to optimize the environment and predict anomalies.

[0714] "Computational means" refers to a processing unit that performs analysis based on measured data and makes appropriate adjustments.

[0715] An "emotion recognition system" is a system that possesses technology to determine the emotions and psychological state of a worker, and acquires this information using devices such as cameras and microphones.

[0716] An "interface" refers to a display or operating device that allows a user and a system to exchange information with each other.

[0717] The system for realizing this application consists of a sensor array, a server, and a user terminal. The sensors measure the environmental conditions of the factory in real time and transmit the data to the server. The server receives this data and analyzes it using artificial intelligence algorithms. The artificial intelligence used includes an emotion recognition engine and a machine learning model, which are used to optimize the environment and predict anomalies.

[0718] The server can collect emotional information in real time by detecting the emotional state of workers using cameras and microphones. This allows for the identification of workers' stress and fatigue levels, and dynamic interface adjustments to improve the work environment. Based on these analysis results, the user terminal appropriately adjusts the interface to provide information with minimal burden on the user. For example, if a high level of stress is detected, the terminal will automatically play relaxation music to alleviate the work environment.

[0719] This system allows workers to perform their tasks safely and efficiently in a better working environment. For example, in a factory assembly line, if stress is detected from a worker's voice, the system adjusts the environment on the spot and changes the interface information display to be more user-friendly.

[0720] An example of a prompt would be: "Explain how to monitor the emotional state of workers on a factory production line and automatically adjust the environment accordingly."

[0721] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0722] Step 1:

[0723] Sensors measure various physical conditions of the factory environment (temperature, humidity, light intensity, etc.). The measurement data is transmitted directly to the server. The input is environmental data from the sensors, and the output is raw environmental data stored on the server.

[0724] Step 2:

[0725] The server performs real-time analysis of the environmental data it receives using an artificial intelligence algorithm. The analysis involves data processing to identify changes necessary for optimizing the surrounding physical conditions. The input is the raw environmental data acquired in step 1, and the output is adjustment command information for optimization.

[0726] Step 3:

[0727] The terminal collects data from the worker's facial expressions and voice and sends it to the emotion recognition engine. The input is facial expression data and voice data from the worker captured by the camera and microphone, and the output is information about the worker's emotional state, which is sent to the server.

[0728] Step 4:

[0729] The server analyzes the worker's emotional data using an emotion recognition engine. The analysis determines the type of emotional state (stress, exhilaration, fatigue, etc.). The input is the emotional state information obtained in step 3, and the output is the emotional state evaluation result for interface adjustment.

[0730] Step 5:

[0731] The server generates interface adjustment commands based on the environment optimization commands from step 2 and the sentiment evaluation results from step 4. The generated commands are sent to the terminal, and the interface content and tone are dynamically adjusted. The inputs are the environment optimization commands and sentiment evaluation results, and the output is the optimized interface display content and adjusted audio information for the user.

[0732] Step 6:

[0733] Based on commands received from the server, the terminal adjusts the interface for the user and plays relaxation music as needed. The input is the optimized commands generated in step 5, and the output is the improved interface environment that the user can view and interact with.

[0734] Step 7:

[0735] Users experience improved productivity or reduced stress by using the terminal's optimized interface. The input is a streamlined interface environment, and the output is improved user work efficiency and emotional stability.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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."

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0757] The following is further disclosed regarding the embodiments described above.

[0758] (Claim 1)

[0759] A means for measuring the environmental conditions inside the culture device using sensors and recording the data,

[0760] A computational method using artificial intelligence that analyzes recorded data in real time and optimizes the environment,

[0761] Means for adjusting the physical conditions of the culture apparatus based on the analysis results,

[0762] A means of predicting anomalies from data and issuing warnings,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, comprising means for learning patterns from past data using a machine learning model to predict anomalies.

[0766] (Claim 3)

[0767] The system according to claim 1, comprising a user-adjustable interface and means for performing user control.

[0768] "Example 1"

[0769] (Claim 1)

[0770] A means for acquiring and accumulating information on the environmental characteristics of the culture system using a measuring device,

[0771] A computing device that uses intelligent algorithms to instantly analyze accumulated information and optimize the environment,

[0772] A means for modifying the physical properties of the culture system based on the analysis results,

[0773] A means of predicting anomalies from information and issuing alarms,

[0774] A means of learning from past information history and predicting future irregular changes,

[0775] A means of providing a terminal that visually displays data and accepts manual user input,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, which has means for identifying patterns from past information using a machine learning algorithm.

[0779] (Claim 3)

[0780] The system according to claim 1, comprising a user interface that allows the user to manually adjust the physical environment and means for carrying out the user's instructions.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] A means for measuring the environmental conditions inside the culture device using a detector and recording the data,

[0784] A computing means using an intelligent system that analyzes recorded data in real time and optimizes the environment,

[0785] Means for adjusting the physical conditions of the culture apparatus based on the analysis results,

[0786] A means of predicting anomalies from data and issuing warnings,

[0787] The information terminal device for remote operation provides environmental conditions and analysis results, and has means for manual adjustment.

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, further comprising means for learning patterns from past data using a learning model to predict anomalies.

[0791] (Claim 3)

[0792] The system according to claim 1, comprising a communication device that can be manually adjusted by the user, and means for performing control by the user.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] A means for acquiring environmental conditions inside the culture device using a detection device and recording the information,

[0796] A computational means using artificial intelligence to analyze recorded information in real time and optimize the environment,

[0797] A means for adjusting the physical conditions of the culture apparatus based on the analysis results,

[0798] A means of predicting and notifying about failures based on information,

[0799] A means for performing emotional analysis and dynamically changing the settings of a display device based on the user's emotional state,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, which has means for learning trends from past information using a machine learning algorithm to predict failures.

[0803] (Claim 3)

[0804] The system according to claim 1, comprising a display device that can be manually adjusted by the user and means for operation by the user.

[0805] "Application example 2 when combining with an emotional engine"

[0806] (Claim 1)

[0807] A means for measuring the environmental conditions inside the culture device using sensors and recording the data,

[0808] A computational method using artificial intelligence that analyzes recorded data in real time and optimizes the environment,

[0809] Means for adjusting the physical conditions of the culture apparatus based on the analysis results,

[0810] A means of predicting anomalies from data and issuing warnings,

[0811] An emotion recognition means for detecting the emotional state of a worker,

[0812] A means of dynamically adjusting the interface based on emotional state,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, comprising means for learning patterns from past data using a machine learning model to predict anomalies.

[0816] (Claim 3)

[0817] The system according to claim 1, comprising a user-adjustable interface and means for performing user control. [Explanation of symbols]

[0818] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for measuring the environmental conditions inside the culture device using sensors and recording the data, A computational method using artificial intelligence that analyzes recorded data in real time and optimizes the environment, Means for adjusting the physical conditions of the culture apparatus based on the analysis results, A means of predicting anomalies from data and issuing warnings, A system that includes this.

2. The system according to claim 1, which has means for learning patterns from past data using a machine learning model to predict anomalies.

3. The system according to claim 1, comprising an interface that can be manually adjusted by a user and means for performing user control.

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