System

Abstract

A system is provided.SOLUTION: A system comprising: sensor means for measuring a temperature in a refrigerator; sensor means for measuring a humidity in the refrigerator; sensor means for measuring a gas concentration in the refrigerator; communication means for transmitting data of the measured temperature, humidity and gas concentration to an analysis server; artificial intelligence means for optimizing a storage condition based on the data in the analysis server; and control means for transmitting the optimized storage condition to the refrigerator and automatically adjusting the storage condition in the refrigerator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Current household refrigerators have a limited shelf life for food, and food can deteriorate quickly if temperature, humidity, and gas conditions are not properly controlled. Furthermore, users often have to manually adjust the settings, making it difficult to maintain optimal storage conditions. This leads to increased food waste and time and financial losses. [Means for solving the problem]

[0005] To solve this problem, we propose a system that uses sensors to measure the temperature, humidity, and gas concentration inside the refrigerator and transmits the measurement data in real time to an analysis server. The analysis server then uses artificial intelligence to analyze this data and calculate the optimal storage conditions for food. Based on this, the system can be equipped with control means to automatically adjust the environment inside the refrigerator, minimizing food deterioration and extending the shelf life. In addition, a function to notify the user of the analysis results will be added, allowing the user to easily understand the appropriate storage conditions. Furthermore, by recording data, referencing past data, and performing statistical analysis, we will build a system that enables continuous optimization of the storage environment.

[0006] "Sensor means" refers to devices for measuring temperature, humidity, and gas concentrations within the refrigerator.

[0007] "Communication means" refers to an interface or protocol for transmitting data acquired from a sensor to an analysis server.

[0008] "Artificial intelligence means" refers to software that analyzes environmental data inside the refrigerator and executes algorithms and models to calculate optimal storage conditions.

[0009] "Control means" refers to the mechanical and electrical components for adjusting the environment within the refrigerator based on the optimized storage conditions received from the analysis server.

[0010] "Notification Means" refers to a display, audio alert, or mobile application that notifies the user of optimized storage conditions and other relevant information.

[0011] "Recording means" refers to a data storage system that stores environmental data and the history of storage condition adjustments for later reference and analysis. [Brief explanation of the drawings]

[0012] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a 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.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0026] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] MODE FOR CARRYING OUT THE INVENTION

[0034] The present invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof will be described below.

[0035] Overall system overview

[0036] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.The system components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, and a user notification function.

[0037] Program processing overview

[0038] The operation of this system will now be described.

[0039] Initialization

[0040] When the device starts up, it first initializes the sensors. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally.

[0041] Once initialization is complete, the terminal connects to the server and receives initial storage condition data, which establishes the basic operating conditions of the refrigerator.

[0042] Data Acquisition and Transmission

[0043] The sensors in the device measure the environment inside the refrigerator (temperature, humidity, gas concentration) at regular intervals, and this data is sent to the server in real time via the communication module.

[0044] Data analysis

[0045] The server uses AI to analyze the data it receives. This analysis takes into account the type and quantity of food and calculates the optimal temperature, humidity, and gas environment. The server stores a database of optimal storage conditions for each food item, and the analysis is based on this.

[0046] Sending results and adjusting the environment

[0047] The server calculates the optimal storage conditions and sends them to the terminal. The terminal receives this data and automatically adjusts the environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, and the humidity control module changes the set humidity. The gas control module also performs the necessary ventilation and gas filtering.

[0048] User Notifications

[0049] The device notifies the user of the analysis results and new storage conditions via the refrigerator's display or a connected smartphone app, allowing the user to confirm that the storage environment has been optimized.

[0050] Data Recording

[0051] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[0052] Example 1: Maintaining the quality of vegetables

[0053] 1. Initialization

[0054] The device starts up and initializes the sensor.

[0055] Connect to the server and receive the initial configuration.

[0056] 2. Data Acquisition and Transmission

[0057] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[0058] The data is sent to the server.

[0059] 3. Data analysis

[0060] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[0061] 4. Sending results and adjusting the environment

[0062] The server transmits the storage conditions to the terminal.

[0063] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[0064] 5. User Notices

[0065] The device notifies the user that "the cabbage storage environment has been optimized."

[0066] 6. Data Recording

[0067] The adjustment history is recorded and can be referenced later.

[0068] Example 2: Maintaining the quality of seafood

[0069] 1. Initialization

[0070] The device starts up and initializes the sensor.

[0071] Connect to the server and receive the initial configuration.

[0072] 2. Data Acquisition and Transmission

[0073] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[0074] The data is sent to the server.

[0075] 3. Data analysis

[0076] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[0077] 4. Sending results and adjusting the environment

[0078] The server transmits the storage conditions to the terminal.

[0079] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[0080] 5. User Notices

[0081] The device notifies the user that "the salmon storage environment has been optimized."

[0082] 6. Data Recording

[0083] The adjustment history is recorded and can be referenced later.

[0084] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[0088] Step 2:

[0089] The device connects to the server and receives the initial setting data. The server then sends the basic storage condition settings (e.g., the recommended temperature, humidity, and gas environment for the entire refrigerator) to the device. This data is recorded in the device and serves as the basis for future operation.

[0090] Step 3:

[0091] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[0092] Step 4:

[0093] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[0094] Step 5:

[0095] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[0096] Step 6:

[0097] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[0098] Step 7:

[0099] The terminal adjusts the environment inside the refrigerator based on the conditions received: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs any necessary ventilation or filtering.

[0100] Step 8:

[0101] The device will notify the user that the new storage conditions have been applied. This notification will be sent via the refrigerator display or a connected smartphone app. The message "The cabbage storage environment has been optimized" will be displayed.

[0102] Step 9:

[0103] The terminal and server each record environmental data and the history of storage condition adjustments. The recorded data can be referenced later and also used for long-term data analysis, making it possible to continuously optimize the storage environment.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In recent years, there has been a demand for optimizing food storage in home refrigerators. Conventional refrigerators have a single temperature setting, making it difficult to provide an optimal storage environment for each food item. This can lead to food quality deterioration or waste. Furthermore, users must manually adjust and change the storage environment, which is inconvenient. The objective of this invention is to provide a system that automatically and optimally adjusts the storage environment within a refrigerator, thereby maintaining food quality for an extended period of time.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] This invention provides the following means for optimizing the storage environment of a refrigerator. The server includes a communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means for optimizing the storage conditions based on the data in the analysis server, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, a data recording means for recording environmental data and adjustment history inside the refrigerator, and a notification means for notifying the user of the analysis results and new storage conditions. This allows the storage environment inside the refrigerator to be optimized in real time, reducing manual operation by the user, maintaining food quality, and reducing waste.

[0109] "Communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server" refers to technology for transmitting temperature, humidity, and gas concentration data obtained from various sensors inside the refrigerator to an analysis server using wireless or wired communication.

[0110] "Artificial intelligence means in the analytical server for optimizing storage conditions based on said data" refers to the artificial intelligence algorithms and models used to calculate optimal food storage conditions using the received temperature, humidity and gas concentration data.

[0111] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system that receives the optimized storage conditions sent from the server and automatically adjusts the temperature, humidity, and gas environment inside the refrigerator.

[0112] "Data recording means for recording environmental data and adjustment history inside the refrigerator" refers to technology including a memory or database for storing data on the temperature, humidity, and gas concentration inside the refrigerator and the adjustment history of these.

[0113] "Notification means for notifying the user of the analysis results and new storage conditions" refers to a system or device for notifying the user of the analysis results and updated storage conditions through the refrigerator's display device or a smartphone app.

[0114] This invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof are described below. This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time and transmits the obtained data to an analysis server to optimize the storage conditions. System components include various sensors (temperature sensor, humidity sensor, gas sensor) placed inside the refrigerator, a communications module, an analysis server, a control module, and a user notification function.

[0115] Sensor and Communication Modules

[0116] The terminal is equipped with multiple sensors, including a temperature sensor, humidity sensor, and gas sensor. When the terminal starts up, it performs a self-diagnosis to check whether each sensor is operating normally and records the results. This allows real-time data on the temperature, humidity, and gas concentration inside the refrigerator to be obtained. This data is sent to an analysis server via a communication module such as a Wi-Fi module.

[0117] Data analysis

[0118] The server stores the received temperature, humidity, and gas concentration data in a database and analyzes it using an AI model. Examples of generative AI models used include TensorFlow and PyTorch. This AI model references the optimal storage conditions (temperature, humidity, and gas environment) for each food item in the database and calculates the optimal storage conditions based on the received data.

[0119] Control Modules and Auto Tuning

[0120] The optimal storage conditions calculated by the server are sent to the control module inside the refrigerator. The control module automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received data. Specifically, the temperature control module controls the cooling system, the humidity control module controls the humidifier and dehumidifier, and the gas control module performs filtering and ventilation.

[0121] User Notifications

[0122] The device has a function to notify the user of the analysis results and new storage conditions. This notification is made on the refrigerator's display or via a connected smartphone app. The user can confirm that the storage environment inside the refrigerator has been optimized.

[0123] Data Recording

[0124] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[0125] Specific examples

[0126] Example 1: Maintaining the quality of vegetables

[0127] When storing cabbage in the refrigerator, the device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures ethylene concentration. The server uses this data to calculate the optimal storage conditions for the cabbage (for example, temperature 3°C, humidity 80%) and sends the results to the device. The device automatically adjusts the temperature inside the refrigerator to 3°C and humidity 80%, and notifies the user that "the cabbage storage environment has been optimized."

[0128] Example 2: Maintaining the quality of seafood

[0129] The same applies when storing salmon in a refrigerator. The device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition. Based on this data, the server calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%) and sends the results to the device. The device then adjusts the temperature inside the refrigerator to 1°C and humidity 70%, and notifies the user that "the storage environment for the salmon has been optimized."

[0130] Prompt Sentence Examples

[0131] "What temperature and humidity settings are appropriate for optimal storage of cabbage?"

[0132] "What are the optimal environmental conditions for storing salmon in a refrigerator for a long period of time?"

[0133] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1: Initialization

[0136] After starting up, the terminal first performs a self-diagnosis of the temperature sensor, humidity sensor, and gas sensor. This self-diagnosis checks whether each sensor is operating normally, and if there is an abnormality, it records an error message in the log. If it is confirmed that the sensor is operating normally, it proceeds to the next step. The internal status data of the sensor is used as input, and information on whether it is operating normally or not is obtained as output.

[0137] Specific behavior:

[0138] The device receives a wake-up signal.

[0139] Sends self-diagnosis commands to each sensor.

[0140] If there is an error, an error message is logged.

[0141] Step 2: Download communication and initial setting data

[0142] The terminal connects to the server and downloads the initial setting data for the storage conditions. The server receives this and establishes the basic operating conditions for the refrigerator. The input is the initial setting request data from the server, and the output is the initial setting data for the storage conditions.

[0143] Specific behavior:

[0144] The device sends a connection request to the server.

[0145] The server generates initial setting data and sends it to the terminal.

[0146] The device receives the initial setting data and stores it in its internal memory.

[0147] Step 3: Data Acquisition

[0148] The sensors in the device measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. This data is stored in memory at each measurement interval. The input is the physical environmental data inside the refrigerator, and the output is the measurement data of temperature, humidity, and gas concentration.

[0149] Specific behavior:

[0150] The temperature sensor measures the temperature and stores the value in memory.

[0151] The humidity sensor measures the humidity and stores the value in memory.

[0152] The gas sensor measures the gas concentration and stores the value in memory.

[0153] Step 4: Send data

[0154] The terminal transmits the acquired temperature, humidity, and gas concentration data to the server in real time. The data is transmitted via a communication module (e.g., Wi-Fi module). The input is the various measured data, and the output is the data transmitted to the server.

[0155] Specific behavior:

[0156] The terminal packetizes the measured data.

[0157] Send data to the server via the Wi-Fi module.

[0158] Wait for an acknowledgment after sending data.

[0159] Step 5: Data analysis

[0160] The server analyzes the received data using a generative AI model (e.g., TensorFlow) to calculate optimal storage conditions based on input data (temperature, humidity, gas concentration). The input is real-time environmental data, and the output is optimal storage conditions.

[0161] Specific behavior:

[0162] The server stores the received data in a database.

[0163] Input new data into the AI ​​model.

[0164] The AI ​​model performs analysis and outputs the optimal storage conditions.

[0165] Step 6: Submit optimal storage conditions

[0166] The server sends the optimal storage conditions to the terminal as the analysis results, allowing the terminal to receive the optimal storage conditions data as input and prepare to proceed to the next step as output.

[0167] Specific behavior:

[0168] The server generates optimal storage conditions.

[0169] The generated storage conditions are sent to the terminal.

[0170] The server waits for an acknowledgment from the terminal.

[0171] Step 7: Adjusting the environment

[0172] The terminal automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received storage conditions. The control module operates the temperature control system, humidity control system, and gas control system. The input is the optimal storage condition data, and the output is the adjusted environmental data inside the refrigerator.

[0173] Specific behavior:

[0174] The temperature control module regulates the cooling system.

[0175] The humidity control module operates the humidifier and dehumidifier.

[0176] The gas control module performs ventilation and filtering.

[0177] Step 8: User Notification

[0178] The device notifies the user of the analysis results and new storage conditions via a display or smartphone app. The input is the optimal storage condition information, and the output is a user notification message.

[0179] Specific behavior:

[0180] Storage conditions are displayed on the screen.

[0181] Sends push notifications to smartphone apps.

[0182] The user checks the notification content.

[0183] Step 9: Data recording

[0184] The terminal and server record the environmental data and storage condition adjustment history. This data is used for future reference and analysis. The input is the actual environmental data and storage condition data, and the output is the recorded history data.

[0185] Specific behavior:

[0186] Environmental data and storage condition data are stored in memory.

[0187] Sync data to the server.

[0188] Regular backups are performed.

[0189] This detailed processing step allows for optimal storage conditions in the refrigerator and maintains food quality.

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] While technology already exists to optimize food storage conditions in refrigerators, it is not practical in large-scale food management environments such as logistics centers. Furthermore, there is a lack of systems that allow workers to instantly check and respond to the status inside the refrigerator. Therefore, there is a need for technology that can monitor storage conditions in real time and instantly notify workers of the necessary information.

[0193] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0194] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and a display means for displaying the storage conditions and analysis results in real time on smart glasses. This enables workers in large-scale food management environments such as logistics centers to instantly check the status inside the refrigerator and take appropriate action. Furthermore, optimizing storage conditions can improve food quality maintenance and reduce waste.

[0195] "Sensor means for measuring the temperature inside the refrigerator" refers to a device installed to measure the internal temperature of the refrigerator in real time.

[0196] The "sensor means for measuring humidity inside a refrigerator" is a device installed to measure the internal humidity of a refrigerator in real time.

[0197] The "sensor means for measuring the gas concentration inside the refrigerator" is a device installed to measure the concentration of a specific gas, such as ethylene gas, present inside the refrigerator in real time.

[0198] The "communication means for transmitting the measured temperature, humidity, and gas concentration data to the analysis server" refers to a device or system for transmitting the data measured inside the refrigerator to the analysis server via a wireless or wired network.

[0199] "Artificial intelligence means for optimizing storage conditions based on the data in the analysis server" refers to artificial intelligence technology used to analyze the received temperature, humidity, and gas concentration data and calculate and determine the optimal storage conditions for each food item.

[0200] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system for automatically adjusting the internal environment of the refrigerator in accordance with the optimized storage conditions obtained from the analysis server.

[0201] The "display means for displaying storage conditions and analysis results in real time on smart glasses" is a system for instantly displaying the storage conditions and analysis results inside the refrigerator on smart glasses worn by workers.

[0202] This invention is a system for optimizing storage conditions in a refrigerator. In one embodiment, various sensors, a communication module, an analysis server, a control module, and smart glasses used by the user all work together in the refrigerator. The details are described below.

[0203] Overall system overview

[0204] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions. Each refrigerator is equipped with a temperature sensor, a humidity sensor, and a gas sensor. Data obtained from these sensors is sent to the analysis server in real time via a communication module. The analysis server uses this data to optimize storage conditions using an AI model. The optimized conditions are applied inside the refrigerator via a control module. Using smart glasses, workers can also check the status of storage conditions and any abnormalities in real time.

[0205] Program processing overview

[0206] The system operates as follows: First, the sensors are initialized and perform a self-diagnosis to ensure normal operation. Next, the temperature, humidity, and gas concentration data inside the refrigerator are collected at regular intervals. This data is sent to the server via the communications module, and the server uses an AI model to analyze and optimize the storage conditions. The results are then transmitted to the refrigerator, and the control module adjusts the refrigerator to the optimal storage conditions. These storage conditions and analysis results are displayed to the user in real time through the smart glasses.

[0207] Components and Data Processing

[0208] Hardware

[0209] Temperature sensor: STMicroelectronics refrigerator temperature sensor

[0210] Humidity sensor: Honeywell humidity sensor

[0211] Gas sensor: Figaro gas concentration sensor

[0212] Communication module: Qualcomm wireless communication module

[0213] Analysis server: High-performance cloud server (Google Cloud, AWS, etc.)

[0214] Smart Glasses: Google Glass Enterprise Edition

[0215] software

[0216] Data processing and calculation: Implementing AI models using Python and TensorFlow

[0217] Communication: Sending and receiving data using HTTP protocol and REST API

[0218] User interface: Smart glasses app for Android or iOS

[0219] Specific examples

[0220] The system operates as follows inside a refrigerator. For example, in a refrigerator containing cabbage, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration. This data is sent to a server, which uses an AI model to calculate the optimal storage conditions for the cabbage (for example, a temperature of 3°C and a humidity of 80%). The results are transmitted to the refrigerator, and the control module adjusts the temperature inside the refrigerator to 3°C and 80% humidity. At the same time, the smart glasses display the message, "The cabbage storage environment has been optimized."

[0221] Prompt example

[0222] "Please implement an AI algorithm to optimize storage conditions based on data from inside the refrigerator. In particular, please include a method for calculating optimal storage conditions that takes into account the type of food, amount, and acquired data (temperature, humidity, gas concentration)."

[0223] The above is a specific embodiment of the present invention. This system makes it possible to optimize food storage conditions, which can greatly contribute to maintaining quality and reducing waste.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] The terminal starts up and initializes the sensors. Specifically, it performs self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. The input is the self-diagnosis command for each sensor, and the output is the normal operating status of the sensor.

[0227] Step 2:

[0228] The sensors measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. The input is environmental data inside the refrigerator, and the output is a data set of temperature, humidity, and gas concentration. Specifically, the temperature sensor measures temperature data, the humidity sensor measures humidity data, and the gas sensor measures gas concentration data.

[0229] Step 3:

[0230] The acquired data is sent in real time to an analysis server via a communication module. The input is temperature, humidity, and gas concentration data, and the output is the data sent to the server. Specifically, each piece of data is sent to the server using the HTTP protocol.

[0231] Step 4:

[0232] The server uses the received data to analyze storage conditions using an AI model. The input is the acquired temperature, humidity, and gas concentration data, and the output is the optimal storage conditions. Specifically, the AI ​​model performs analysis taking into account the type and quantity of food, and calculates the optimal temperature, humidity, and gas environment.

[0233] Step 5:

[0234] The server sends the calculated optimal storage conditions to the terminal. The input is the optimal storage conditions, and the output is the storage condition data sent to the terminal. Specifically, the optimal storage conditions are sent again to the terminal using the HTTP protocol.

[0235] Step 6:

[0236] The terminal automatically adjusts the environment inside the refrigerator based on the optimal storage conditions received. The input is the optimal storage conditions, and the output is the adjusted temperature, humidity, and gas environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, the humidity control module changes the set humidity, and the gas control module performs the necessary ventilation and gas filtering.

[0237] Step 7:

[0238] The device notifies the user of the analysis results and new storage conditions via the smart glasses. The input is the analysis results and optimal storage condition data, and the output is the information displayed on the smart glasses display. Specifically, a message such as "The storage environment has been optimized" is displayed along with a list of the current status and adjustment details.

[0239] Step 8:

[0240] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. The input is the data generated at each step, and the output is the record stored in the database. Specifically, the history of storage conditions and the changes in environmental data are recorded in the log database so that they can be referenced later.

[0241] These are the specific processing steps of the system based on the application example. This system enables efficient monitoring and management of the storage environment, thereby maintaining food quality and reducing waste.

[0242] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0243] MODE FOR CARRYING OUT THE INVENTION

[0244] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[0245] Overall system overview

[0246] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and the emotion engine.

[0247] Program processing overview

[0248] The operation of this system will now be described.

[0249] Initialization

[0250] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[0251] After the initialization is complete, the device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[0252] Data Acquisition and Transmission

[0253] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[0254] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[0255] Data analysis and optimization of storage conditions

[0256] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[0257] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[0258] environmental adjustment

[0259] Based on the conditions received by the terminal, the environment inside the refrigerator is automatically adjusted: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[0260] User Notifications

[0261] The device notifies the user of the analysis results and new storage conditions. This notification is sent via the refrigerator's display or a connected smartphone app. A message such as "The cabbage storage environment has been optimized" is displayed.

[0262] Emotion recognition and response

[0263] The emotion engine recognizes the user's emotions and generates emotion data by analyzing the user's facial expressions and tone of voice.

[0264] The server then uses the emotion data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[0265] The system records the user's emotional data and adjusts notifications based on past emotional data. For example, when saving a food item that previously generated a happy emotion, a positive message will be displayed.

[0266] Data Recording

[0267] Environmental data and storage condition adjustment history are recorded on both the terminal and the server. The recorded data can be referenced later and also used for long-term data analysis, enabling continuous optimization of the storage environment.

[0268] Example 1: Maintaining vegetable quality and emotion recognition

[0269] 1. Initialization

[0270] The device starts up and initializes the sensor.

[0271] Connect to the server and receive the initial configuration.

[0272] 2. Data Acquisition and Transmission

[0273] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[0274] The data is sent to the server.

[0275] 3. Data analysis

[0276] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[0277] 4. Sending results and adjusting the environment

[0278] The server transmits the storage conditions to the terminal.

[0279] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[0280] 5. User Notices

[0281] The device notifies the user that "the cabbage storage environment has been optimized."

[0282] 6. Emotion Recognition and Response

[0283] The emotion engine recognizes the user's happy facial expression and displays the message, "The optimal storage environment has been set! We're glad to be of service to you."

[0284] 7. Data Recording

[0285] The adjustment history is recorded and can be referenced later.

[0286] Example 2: Maintaining seafood quality and emotion recognition

[0287] 1. Initialization

[0288] The device starts up and initializes the sensor.

[0289] Connect to the server and receive the initial configuration.

[0290] 2. Data Acquisition and Transmission

[0291] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[0292] The data is sent to the server.

[0293] 3. Data analysis

[0294] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[0295] 4. Sending results and adjusting the environment

[0296] The server transmits the storage conditions to the terminal.

[0297] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[0298] 5. User Notices

[0299] The device notifies the user that "the salmon storage environment has been optimized."

[0300] 6. Emotion Recognition and Response

[0301] The emotion engine recognizes the user's stress and displays the message, "The salmon storage conditions have been optimized. Please let it cool down and enjoy it."

[0302] 7. Data Recording

[0303] The adjustment history is recorded and can be referenced later.

[0304] This system provides optimal storage conditions for various foods, thereby maintaining their quality for a long period of time. It also responds flexibly to user preferences, making it a more personalized and user-friendly system.

[0305] The processing flow will be explained below.

[0306] Step 1:

[0307] When the terminal starts up, it first initializes the sensor means. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are normal, the sensors transition to an operational state.

[0308] Step 2:

[0309] The terminal connects to the server and receives the initial setting data. The server sends the basic settings of the storage conditions, and the terminal records this data and sets the basic operation.

[0310] Step 3:

[0311] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[0312] Step 4:

[0313] The device sends measurement data, including temperature, humidity, and gas concentration, to the server in real time, ensuring that the server always has up-to-date environmental information.

[0314] Step 5:

[0315] The server uses AI to analyze the received data, and then refers to the optimal storage conditions for food from a database and calculates the optimal temperature, humidity, and gas environment for each food item stored.

[0316] Step 6:

[0317] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[0318] Step 7:

[0319] The terminal adjusts the environment inside the refrigerator based on the storage conditions received. The temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[0320] Step 8:

[0321] The device notifies the user of the results of the environmental adjustments, either through the refrigerator display or a connected smartphone app, with a message saying, "The cabbage storage environment has been optimized."

[0322] Step 9:

[0323] The emotion engine recognizes the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice to generate emotion data.

[0324] Step 10:

[0325] The server then uses the emotional data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[0326] Step 11:

[0327] The user's emotional data is recorded and saved as historical data, and is reflected in future notifications and suggestions.

[0328] Step 12:

[0329] Environmental data and storage condition adjustment history are recorded on both the terminal and the server, making the historical data available for later reference and enabling long-term data analysis.

[0330] Example 2

[0331] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0332] Maintaining an appropriate storage environment is essential to keeping food fresh in a refrigerator for a longer period of time. At the same time, interactive environment settings that take into account the user's emotions and stress level are required to improve the user's comfortable use experience. However, conventional refrigerator systems lack a mechanism to address these factors in an integrated manner, which can lead to problems such as food storage conditions being left suboptimal or the burden of adjusting the settings themselves. Another issue is that the system does not provide appropriate suggestions that take the user's emotions into account, preventing it from providing a high level of convenience to users.

[0333] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, and a sensor means for measuring the gas concentration inside the refrigerator. This makes it possible to acquire and analyze environmental data inside the refrigerator in real time. In addition, by combining an emotion recognition means for recognizing the user's emotion and a suggestion means for suggesting adjustments to storage conditions based on the user's emotion data, flexible responses according to the user's emotion become possible. As a result, food storage conditions are optimized, and personalized suggestions are made to the user, improving convenience.

[0334] "Sensor means" refers to a device or function utilized to measure temperature, humidity, and gas concentrations within the refrigerator.

[0335] "Communication means" refers to the device or protocol used to transmit the measured data to a computer that performs the analysis.

[0336] "Artificial intelligence means" refers to software or algorithms that are used in the computer that performs the analysis to analyze the acquired data and calculate optimal storage conditions.

[0337] "Control means" refers to a device or software that communicates optimized storage conditions to the refrigerator and automatically adjusts the environment.

[0338] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and tone of voice and generating emotion data.

[0339] The "suggestion means" is a device or software for proposing adjustments to storage conditions based on the user's emotional data.

[0340] A "notification mechanism" is a device or protocol for communicating the system's analysis results and recommendations to the user.

[0341] MODE FOR CARRYING OUT THE INVENTION

[0342] The present invention is a system for optimizing storage conditions in a refrigerator and recognizing and responding to user emotions. Specific embodiments of the system will be described below.

[0343] Overall system overview

[0344] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and an emotion recognition engine.

[0345] Hardware and Software Configuration

[0346] 1. Sensor means

[0347] Temperature sensor: Used to measure the temperature inside the refrigerator.

[0348] Humidity sensor: Used to measure the humidity inside the refrigerator.

[0349] Gas sensors: Used to measure gas concentrations (e.g., ethylene concentration) inside a refrigerator.

[0350] 2. Means of communication

[0351] A communication module (e.g., Wi-Fi module) for transmitting measured data to an analysis server in real time.

[0352] 3. Analysis (server-side artificial intelligence means)

[0353] The analysis server has a generative AI model and food database for analyzing the received data. The AI ​​analyzes the data and calculates the optimal storage conditions.

[0354] 4. Control Measures

[0355] The device automatically adjusts the environment inside the refrigerator based on the storage conditions received by the terminal. It includes a temperature control module, humidity control module, and gas control module.

[0356] 5. User Notification Methods

[0357] A display and smartphone application to inform the user of the analysis results and suggestions for adjusting storage conditions.

[0358] 6. Emotion recognition means

[0359] A camera and microphone for analyzing the user's facial expressions and tone of voice to generate emotional data.

[0360] 7. Proposal method

[0361] A generative AI model that proposes adjustments to storage conditions based on user emotional data.

[0362] Specific operation example

[0363] 1. Maintaining vegetable quality and emotion recognition

[0364] Initialization

[0365] The device starts up and initializes the sensor.

[0366] Connect to the server and receive the initial configuration.

[0367] Data Acquisition and Transmission

[0368] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[0369] The data is sent to the server.

[0370] Data analysis and results transmission

[0371] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[0372] The server transmits the storage conditions to the terminal.

[0373] environmental adjustment

[0374] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 3°C and the humidity to 80%.

[0375] User Notifications

[0376] The device notifies the user that "the cabbage storage environment has been optimized."

[0377] Emotion Recognition and Response

[0378] The emotion recognition engine recognizes the user's joy and displays the message, "Optimal saving environment has been set! We're glad to help you."

[0379] Data Recording

[0380] Record your adjustment history for future reference.

[0381] Prompt Sentence Examples

[0382] "I've stored the cabbage in the refrigerator. Please set the optimal storage conditions."

[0383] 2. Maintaining seafood quality and emotion recognition

[0384] Initialization

[0385] The device starts up and initializes the sensor.

[0386] Connect to the server and receive the initial configuration.

[0387] Data Acquisition and Transmission

[0388] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[0389] The data is sent to the server.

[0390] Data analysis and results transmission

[0391] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[0392] The server transmits the storage conditions to the terminal.

[0393] environmental adjustment

[0394] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 1°C and the humidity to 70%.

[0395] User Notifications

[0396] The device notifies the user that "the salmon storage environment has been optimized."

[0397] Emotion Recognition and Response

[0398] The emotion recognition engine recognizes the user's stress and displays the message, "The storage conditions for the salmon have been optimized. Please let it cool down and enjoy it."

[0399] Data Recording

[0400] Record your adjustment history for future reference.

[0401] Prompt Sentence Examples

[0402] "I've stored the salmon in the refrigerator. Please set the optimal storage conditions."

[0403] This system can provide optimal storage conditions for various foods, maintaining their quality for a long period of time. It also allows for flexible responses that take into account the user's feelings, making it a more personalized and user-friendly system.

[0404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0405] Step 1: Initialization

[0406] When the device boots up, it automatically begins self-testing the temperature, humidity, and gas sensors, sending each sensor a test signal for a few seconds and evaluating its response.

[0407] Input: The refrigerator powers on.

[0408] Data calculation: Self-diagnosis to check whether the sensor is operating normally.

[0409] Output: The sensor is normal.

[0410] If the result is normal, the sensor transitions to an operational state, and the terminal connects to the analysis server to receive the initial setting data, which includes the initial storage conditions (e.g., temperature 5°C, humidity 60%).

[0411] Input: Sensor initialization complete, request to connect to server.

[0412] Data calculation: Connect to the server via a communication means and request setting data.

[0413] Output: Receives initial setting data (temperature 5°C, humidity 60%).

[0414] Step 2: Data Acquisition

[0415] Each sensor on the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes).

[0416] Input: Acquisition timing (every 5 minutes).

[0417] Data calculation: The temperature sensor measures the current temperature, the humidity sensor measures the current humidity, and the gas sensor measures the current gas concentration.

[0418] Output: Data for temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[0419] Step 3: Send data

[0420] The environmental data (temperature, humidity, gas concentration) acquired by the device is sent to an analysis server in real time.

[0421] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[0422] Data calculation: Data is packaged and sent to a server via a communication means.

[0423] Output: Data sent to analysis server completed.

[0424] Step 4: Data analysis

[0425] The server analyzes the received data, and the server's generative AI model uses this data to calculate the optimal storage conditions for the stored food.

[0426] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[0427] Data calculation: The generative AI model analyzes the data and refers to a food database to calculate the optimal storage conditions (e.g., for cabbage, a temperature of 3°C and humidity of 80%).

[0428] Output: Optimal storage conditions (temperature 3°C, humidity 80%).

[0429] Step 5: Submitting storage conditions

[0430] The server sends the calculated optimal storage conditions to the terminal.

[0431] Input: Optimal storage conditions (temperature 3°C, humidity 80%).

[0432] Data calculation: The storage conditions are packaged and sent to the terminal via a communication means.

[0433] Output: Save conditions sent to the device completed.

[0434] Step 6: Adjusting the environment

[0435] The terminal automatically adjusts the environment inside the refrigerator based on the storage conditions received: the temperature control module sets it to 3°C, and the humidity control module sets it to 80%.

[0436] Input: Optimal storage conditions (temperature 3°C, humidity 80%).

[0437] Data calculation: The internal control module sets the temperature and humidity.

[0438] Output: The environment inside the refrigerator has been adjusted.

[0439] Step 7: User Notification

[0440] The device notifies the user of the results of the storage condition adjustments via the refrigerator display or a connected smartphone app.

[0441] Input: Results of applying optimal storage conditions.

[0442] Data operation: Generates user notification data and displays or transmits it.

[0443] Output: Notification that "The cabbage storage environment has been optimized."

[0444] Step 8: Emotion Recognition

[0445] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to generate emotional data.

[0446] Input: User's facial expression image or voice data.

[0447] Data calculation: Analysis by emotion recognition algorithm.

[0448] Output: User emotion data (e.g., happiness).

[0449] Step 9: Emotionally Based Response

[0450] The server uses the emotion data to make suggestions that correspond to the user's emotions. If the server recognizes that the user is happy, it displays a message saying, "The optimal storage environment has been set up! We're glad to be of service to you."

[0451] Input: User emotion data (e.g., happiness).

[0452] Data calculation: Generate a proposal message.

[0453] Output: Notification saying "Optimal saving environment set up! Glad to help."

[0454] Step 10: Data recording

[0455] The device and server record all environmental data and adjustment history, and this data is stored for future improvement and analysis.

[0456] Input: Environmental data, adjustment history, emotion data.

[0457] Data calculation: Record each data in the appropriate format.

[0458] Output: Saved to database.

[0459] (Application example 2)

[0460] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0461] Conventional refrigerators do not automatically adjust to maintain optimal food storage conditions, resulting in a decline in food quality. Furthermore, they do not provide appropriate notifications or suggestions that take into account the user's emotions and psychology, resulting in a poor user experience. Even in physical stores, storage conditions are not properly optimized, resulting in food deterioration and waste.

[0462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0463] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and an emotion recognition engine for recognizing the user's emotions and providing suggestions and notifications for adjusting the storage conditions according to the emotions. This allows for the optimization of food storage conditions to maintain food quality while providing appropriate notifications and suggestions according to the user's emotions. This also improves the efficiency of food management in physical stores.

[0464] - "Temperature sensor means" means a sensor for measuring the temperature inside the refrigerator.

[0465] The "humidity sensor means" is a sensor for measuring the humidity inside the refrigerator.

[0466] The "gas sensor means" is a sensor for measuring the gas concentration inside the refrigerator.

[0467] The "communication means" is a communication module for transmitting the measured temperature, humidity, and gas concentration data to the analysis server.

[0468] "Artificial intelligence means" refers to functions and programs in the analysis server that optimize storage conditions based on the transmitted data.

[0469] The "control means" refers to a device or program that transmits the optimized storage conditions to the refrigerator and automatically adjusts the storage conditions within the refrigerator.

[0470] An "emotion recognition engine" is a system or program that recognizes a user's emotions and suggests and notifies them of adjustments to storage conditions based on those emotions.

[0471] The "notification means" is an application or a display device for notifying the user of the results of optimization of storage conditions or notifications based on emotion recognition.

[0472] "Storage conditions" refer to environmental conditions such as temperature, humidity, and gas concentration required to store food in an optimal state inside a refrigerator.

[0473] The "analysis server" is a server device that receives data sent from the refrigerator, analyzes it, and optimizes storage conditions.

[0474] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[0475] System configuration

[0476] This system includes various sensors installed inside the refrigerator, a communication module, an analysis server, a control module, an emotion recognition engine, and a notification means. Specific examples of the hardware and software used include:

[0477] Hardware

[0478] 1. Temperature sensor: A sensor that measures the temperature inside the refrigerator.

[0479] 2. Humidity sensor: A sensor that measures the humidity inside the refrigerator.

[0480] 3. Gas sensor: A sensor that measures the gas concentration inside the refrigerator.

[0481] 4. Communication module: A module that sends and receives data using wireless LAN or Bluetooth.

[0482] 5. Smart Glasses: Input devices for emotion recognition engines (e.g. smart glasses).

[0483] 6. Refrigerator control module: A module that regulates the temperature and humidity inside the refrigerator.

[0484] software

[0485] 1. Analysis server: Data analysis software running on a cloud server (e.g., AWS).

[0486] 2. Artificial intelligence means: AI models to analyze data and optimize storage conditions (e.g., OpenAI GPT-4).

[0487] 3. Emotion recognition engine: Software that analyzes user emotions (e.g., Microsoft Azure Face API).

[0488] 4. Notification method: An application (e.g., a smartphone app) that notifies the user of the results.

[0489] Data Acquisition and Optimization

[0490] The server collects data from temperature, humidity, and gas sensors installed inside the refrigerator and sends each measurement data to the analysis server at regular intervals. The analysis server uses an AI model based on the received data to calculate the optimal storage conditions. The calculated storage conditions are transmitted to the refrigerator's control module via the communication module, and the environment inside the refrigerator is automatically adjusted.

[0491] Examples:

[0492] 1. Storing cabbage: If the temperature, humidity, and gas concentration inside a refrigerator containing cabbage are 5°C, 50%, and 0.2%, respectively, the server receives this information and the AI ​​model calculates the optimal storage conditions (e.g., temperature 3°C, humidity 80%). Based on this, the refrigerator automatically adjusts.

[0493] Emotion Recognition and User Notification

[0494] The emotion recognition engine analyzes the user's emotions from their facial expressions and tone of voice. For example, smart glasses can recognize the user's facial expressions while standing in front of the refrigerator, and send that data to the server. The server then uses that emotional data to suggest adjustments to storage conditions or generate positive messages.

[0495] Examples:

[0496] 1. Emotion recognition and messaging: If the emotion recognition engine detects the user's joy, it will send a notification to the user's smartphone, such as "The storage environment has been optimized! We're glad you enjoyed it."

[0497] Prompt Sentence Examples

[0498] 1. Initialization phase:

[0499] "Perform initial setup of the sensor and check that it works properly."

[0500] 2. Data acquisition timing:

[0501] "Measure the temperature, humidity, and gas concentration inside the refrigerator every 5 minutes and send the data to the cloud server."

[0502] 3. Optimizing storage conditions:

[0503] "Calculate the optimal storage conditions for cabbage based on the received data."

[0504] 4. Environmental conditioning:

[0505] "Set the refrigerator temperature to 3°C and humidity to 80%."

[0506] 5. Emotion Recognition and Notification:

[0507] "Analyze customer facial expressions and create messages about the storage environment that correspond to their emotions."

[0508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0509] Step 1: Sensor initialization and self-diagnosis

[0510] When the terminal starts up, it first initializes each sensor. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are operating normally, the sensors enter an operational state.

[0511] Input: Sensor self-diagnosis data

[0512] Output: Status of the sensor, whether it is working properly or not

[0513] Specific operation: Check the self-diagnosis results of each sensor, and if no abnormalities are found, switch to operating state.

[0514] Step 2: Receive and record initial setup data

[0515] The device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[0516] Input: Initial setting data from the analysis server

[0517] Output: Initial settings for refrigerator storage conditions (temperature, humidity, etc.)

[0518] Specific operation: Receives initial setting data from the analysis server and saves it in the device.

[0519] Step 3: Measuring and transmitting environmental data

[0520] Each sensor in the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes). The measurement data is sent to an analysis server using a communication method.

[0521] Input: Temperature, humidity, and gas concentration measurements

[0522] Output: Sending data to the analysis server

[0523] Specific operation: Each sensor measures temperature, humidity, and gas concentration and sends the measurement data to the server.

[0524] Step 4: Data analysis and optimization

[0525] The server uses artificial intelligence to calculate the optimal storage conditions based on the data received, and optimizes storage conditions (temperature, humidity, etc.) by referring to a food database.

[0526] Input: Measurement data sent from each sensor

[0527] Output: Optimized storage conditions

[0528] What it does: It uses an AI model (e.g., OpenAI GPT-4) to calculate optimal storage conditions and generates instructions for setting those conditions in the refrigerator.

[0529] Step 5: Reflecting storage conditions in the refrigerator

[0530] The terminal automatically adjusts the temperature, humidity, and gas concentration inside the refrigerator based on the optimal storage conditions received from the server. The temperature, humidity, and gas control modules change their respective settings.

[0531] Input: Optimal storage conditions received from the server

[0532] Output: Adjustment of temperature, humidity and gas concentration inside the refrigerator

[0533] Specific operations: The temperature control module changes the set temperature, the humidity module changes the set humidity, and the gas control module performs ventilation and filtering.

[0534] Step 6: User Notification

[0535] The device notifies the user of the results of the optimization of storage conditions, and a message such as "The cabbage storage environment has been optimized" is displayed via a smartphone app or other device.

[0536] Input: Optimal storage conditions and their settings

[0537] Output: A message to inform the user

[0538] Specific operation: Using a notification method, the smartphone app is notified that the storage conditions have been optimized.

[0539] Step 7: Emotion Recognition and Suggestions

[0540] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, which the server uses to customize notifications and suggest adjustments to storage conditions.

[0541] Input: User's facial expressions and tone of voice (emotion data)

[0542] Output: A customized message or suggestion

[0543] How it works: An emotion recognition engine analyzes the user's emotions, and then processes that data with an AI model to generate appropriate messages and suggestions.

[0544] Step 8: Data recording

[0545] Environmental data and storage condition adjustment history are recorded on the server and on the terminal, and this data can be referenced later and used for long-term data analysis.

[0546] Input: Storage conditions and their adjustment history

[0547] Output: Recorded environmental data and adjustment history

[0548] Specific operation: Records storage conditions and adjustment history in a database (e.g., AWS DynamoDB) and uses it for long-term trend analysis.

[0549] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0550] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0552] [Second embodiment]

[0553] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0554] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0555] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0557] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0559] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0560] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0561] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0562] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0563] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0564] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0565] MODE FOR CARRYING OUT THE INVENTION

[0566] The present invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof will be described below.

[0567] Overall system overview

[0568] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.The system components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, and a user notification function.

[0569] Program processing overview

[0570] The operation of this system will now be described.

[0571] Initialization

[0572] When the device starts up, it first initializes the sensors. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally.

[0573] Once initialization is complete, the terminal connects to the server and receives initial storage condition data, which establishes the basic operating conditions of the refrigerator.

[0574] Data Acquisition and Transmission

[0575] The sensors in the device measure the environment inside the refrigerator (temperature, humidity, gas concentration) at regular intervals, and this data is sent to the server in real time via the communication module.

[0576] Data analysis

[0577] The server uses AI to analyze the data it receives. This analysis takes into account the type and quantity of food and calculates the optimal temperature, humidity, and gas environment. The server stores a database of optimal storage conditions for each food item, and the analysis is based on this.

[0578] Sending results and adjusting the environment

[0579] The server calculates the optimal storage conditions and sends them to the terminal. The terminal receives this data and automatically adjusts the environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, and the humidity control module changes the set humidity. The gas control module also performs the necessary ventilation and gas filtering.

[0580] User Notifications

[0581] The device notifies the user of the analysis results and new storage conditions via the refrigerator's display or a connected smartphone app, allowing the user to confirm that the storage environment has been optimized.

[0582] Data Recording

[0583] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[0584] Example 1: Maintaining the quality of vegetables

[0585] 1. Initialization

[0586] The device starts up and initializes the sensor.

[0587] Connect to the server and receive the initial configuration.

[0588] 2. Data Acquisition and Transmission

[0589] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[0590] The data is sent to the server.

[0591] 3. Data analysis

[0592] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[0593] 4. Sending results and adjusting the environment

[0594] The server transmits the storage conditions to the terminal.

[0595] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[0596] 5. User Notices

[0597] The device notifies the user that "the cabbage storage environment has been optimized."

[0598] 6. Data Recording

[0599] The adjustment history is recorded and can be referenced later.

[0600] Example 2: Maintaining the quality of seafood

[0601] 1. Initialization

[0602] The device starts up and initializes the sensor.

[0603] Connect to the server and receive the initial configuration.

[0604] 2. Data Acquisition and Transmission

[0605] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[0606] The data is sent to the server.

[0607] 3. Data analysis

[0608] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[0609] 4. Sending results and adjusting the environment

[0610] The server transmits the storage conditions to the terminal.

[0611] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[0612] 5. User Notices

[0613] The device notifies the user that "the salmon storage environment has been optimized."

[0614] 6. Data Recording

[0615] The adjustment history is recorded and can be referenced later.

[0616] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[0617] The processing flow will be explained below.

[0618] Step 1:

[0619] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[0620] Step 2:

[0621] The device connects to the server and receives the initial setting data. The server then sends the basic storage condition settings (e.g., the recommended temperature, humidity, and gas environment for the entire refrigerator) to the device. This data is recorded in the device and serves as the basis for future operation.

[0622] Step 3:

[0623] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[0624] Step 4:

[0625] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[0626] Step 5:

[0627] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[0628] Step 6:

[0629] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[0630] Step 7:

[0631] The terminal adjusts the environment inside the refrigerator based on the conditions received: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs any necessary ventilation or filtering.

[0632] Step 8:

[0633] The device will notify the user that the new storage conditions have been applied. This notification will be sent via the refrigerator display or a connected smartphone app. The message "The cabbage storage environment has been optimized" will be displayed.

[0634] Step 9:

[0635] The terminal and server each record environmental data and the history of storage condition adjustments. The recorded data can be referenced later and also used for long-term data analysis, making it possible to continuously optimize the storage environment.

[0636] Example 1

[0637] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0638] In recent years, there has been a demand for optimizing food storage in home refrigerators. Conventional refrigerators have a single temperature setting, making it difficult to provide an optimal storage environment for each food item. This can lead to food quality deterioration or waste. Furthermore, users must manually adjust and change the storage environment, which is inconvenient. The objective of this invention is to provide a system that automatically and optimally adjusts the storage environment within a refrigerator, thereby maintaining food quality for an extended period of time.

[0639] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0640] This invention provides the following means for optimizing the storage environment of a refrigerator. The server includes a communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means for optimizing the storage conditions based on the data in the analysis server, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, a data recording means for recording environmental data and adjustment history inside the refrigerator, and a notification means for notifying the user of the analysis results and new storage conditions. This allows the storage environment inside the refrigerator to be optimized in real time, reducing manual operation by the user, maintaining food quality, and reducing waste.

[0641] "Communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server" refers to technology for transmitting temperature, humidity, and gas concentration data obtained from various sensors inside the refrigerator to an analysis server using wireless or wired communication.

[0642] "Artificial intelligence means in the analytical server for optimizing storage conditions based on said data" refers to the artificial intelligence algorithms and models used to calculate optimal food storage conditions using the received temperature, humidity and gas concentration data.

[0643] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system that receives the optimized storage conditions sent from the server and automatically adjusts the temperature, humidity, and gas environment inside the refrigerator.

[0644] "Data recording means for recording environmental data and adjustment history inside the refrigerator" refers to technology including a memory or database for storing data on the temperature, humidity, and gas concentration inside the refrigerator and the adjustment history of these.

[0645] "Notification means for notifying the user of the analysis results and new storage conditions" refers to a system or device for notifying the user of the analysis results and updated storage conditions through the refrigerator's display device or a smartphone app.

[0646] This invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof are described below. This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time and transmits the obtained data to an analysis server to optimize the storage conditions. System components include various sensors (temperature sensor, humidity sensor, gas sensor) placed inside the refrigerator, a communications module, an analysis server, a control module, and a user notification function.

[0647] Sensor and Communication Modules

[0648] The terminal is equipped with multiple sensors, including a temperature sensor, humidity sensor, and gas sensor. When the terminal starts up, it performs a self-diagnosis to check whether each sensor is operating normally and records the results. This allows real-time data on the temperature, humidity, and gas concentration inside the refrigerator to be obtained. This data is sent to an analysis server via a communication module such as a Wi-Fi module.

[0649] Data analysis

[0650] The server stores the received temperature, humidity, and gas concentration data in a database and analyzes it using an AI model. Examples of generative AI models used include TensorFlow and PyTorch. This AI model references the optimal storage conditions (temperature, humidity, and gas environment) for each food item in the database and calculates the optimal storage conditions based on the received data.

[0651] Control Modules and Auto Tuning

[0652] The optimal storage conditions calculated by the server are sent to the control module inside the refrigerator. The control module automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received data. Specifically, the temperature control module controls the cooling system, the humidity control module controls the humidifier and dehumidifier, and the gas control module performs filtering and ventilation.

[0653] User Notifications

[0654] The device has a function to notify the user of the analysis results and new storage conditions. This notification is made on the refrigerator's display or via a connected smartphone app. The user can confirm that the storage environment inside the refrigerator has been optimized.

[0655] Data Recording

[0656] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[0657] Specific examples

[0658] Example 1: Maintaining the quality of vegetables

[0659] When storing cabbage in the refrigerator, the device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures ethylene concentration. The server uses this data to calculate the optimal storage conditions for the cabbage (for example, temperature 3°C, humidity 80%) and sends the results to the device. The device automatically adjusts the temperature inside the refrigerator to 3°C and humidity 80%, and notifies the user that "the cabbage storage environment has been optimized."

[0660] Example 2: Maintaining the quality of seafood

[0661] The same applies when storing salmon in a refrigerator. The device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition. Based on this data, the server calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%) and sends the results to the device. The device then adjusts the temperature inside the refrigerator to 1°C and humidity 70%, and notifies the user that "the storage environment for the salmon has been optimized."

[0662] Prompt Sentence Examples

[0663] "What temperature and humidity settings are appropriate for optimal storage of cabbage?"

[0664] "What are the optimal environmental conditions for storing salmon in a refrigerator for a long period of time?"

[0665] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[0666] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0667] Step 1: Initialization

[0668] After starting up, the terminal first performs a self-diagnosis of the temperature sensor, humidity sensor, and gas sensor. This self-diagnosis checks whether each sensor is operating normally, and if there is an abnormality, it records an error message in the log. If it is confirmed that the sensor is operating normally, it proceeds to the next step. The internal status data of the sensor is used as input, and information on whether it is operating normally or not is obtained as output.

[0669] Specific behavior:

[0670] The device receives a wake-up signal.

[0671] Sends self-diagnosis commands to each sensor.

[0672] If there is an error, an error message is logged.

[0673] Step 2: Download communication and initial setting data

[0674] The terminal connects to the server and downloads the initial setting data for the storage conditions. The server receives this and establishes the basic operating conditions for the refrigerator. The input is the initial setting request data from the server, and the output is the initial setting data for the storage conditions.

[0675] Specific behavior:

[0676] The device sends a connection request to the server.

[0677] The server generates initial setting data and sends it to the terminal.

[0678] The device receives the initial setting data and stores it in its internal memory.

[0679] Step 3: Data Acquisition

[0680] The sensors in the device measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. This data is stored in memory at each measurement interval. The input is the physical environmental data inside the refrigerator, and the output is the measurement data of temperature, humidity, and gas concentration.

[0681] Specific behavior:

[0682] The temperature sensor measures the temperature and stores the value in memory.

[0683] The humidity sensor measures the humidity and stores the value in memory.

[0684] The gas sensor measures the gas concentration and stores the value in memory.

[0685] Step 4: Send data

[0686] The terminal transmits the acquired temperature, humidity, and gas concentration data to the server in real time. The data is transmitted via a communication module (e.g., Wi-Fi module). The input is the various measured data, and the output is the data transmitted to the server.

[0687] Specific behavior:

[0688] The terminal packetizes the measured data.

[0689] Send data to the server via the Wi-Fi module.

[0690] Wait for an acknowledgment after sending data.

[0691] Step 5: Data analysis

[0692] The server analyzes the received data using a generative AI model (e.g., TensorFlow) to calculate optimal storage conditions based on input data (temperature, humidity, gas concentration). The input is real-time environmental data, and the output is optimal storage conditions.

[0693] Specific behavior:

[0694] The server stores the received data in a database.

[0695] Input new data into the AI ​​model.

[0696] The AI ​​model performs analysis and outputs the optimal storage conditions.

[0697] Step 6: Submit optimal storage conditions

[0698] The server sends the optimal storage conditions to the terminal as the analysis results, allowing the terminal to receive the optimal storage conditions data as input and prepare to proceed to the next step as output.

[0699] Specific behavior:

[0700] The server generates optimal storage conditions.

[0701] The generated storage conditions are sent to the terminal.

[0702] The server waits for an acknowledgment from the terminal.

[0703] Step 7: Adjusting the environment

[0704] The terminal automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received storage conditions. The control module operates the temperature control system, humidity control system, and gas control system. The input is the optimal storage condition data, and the output is the adjusted environmental data inside the refrigerator.

[0705] Specific behavior:

[0706] The temperature control module regulates the cooling system.

[0707] The humidity control module operates the humidifier and dehumidifier.

[0708] The gas control module performs ventilation and filtering.

[0709] Step 8: User Notification

[0710] The device notifies the user of the analysis results and new storage conditions via a display or smartphone app. The input is the optimal storage condition information, and the output is a user notification message.

[0711] Specific behavior:

[0712] Storage conditions are displayed on the screen.

[0713] Sends push notifications to smartphone apps.

[0714] The user checks the notification content.

[0715] Step 9: Data recording

[0716] The terminal and server record the environmental data and storage condition adjustment history. This data is used for future reference and analysis. The input is the actual environmental data and storage condition data, and the output is the recorded history data.

[0717] Specific behavior:

[0718] Environmental data and storage condition data are stored in memory.

[0719] Sync data to the server.

[0720] Regular backups are performed.

[0721] This detailed processing step allows for optimal storage conditions in the refrigerator and maintains food quality.

[0722] (Application example 1)

[0723] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0724] While technology already exists to optimize food storage conditions in refrigerators, it is not practical in large-scale food management environments such as logistics centers. Furthermore, there is a lack of systems that allow workers to instantly check and respond to the status inside the refrigerator. Therefore, there is a need for technology that can monitor storage conditions in real time and instantly notify workers of the necessary information.

[0725] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0726] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and a display means for displaying the storage conditions and analysis results in real time on smart glasses. This enables workers in large-scale food management environments such as logistics centers to instantly check the status inside the refrigerator and take appropriate action. Furthermore, optimizing storage conditions can improve food quality maintenance and reduce waste.

[0727] "Sensor means for measuring the temperature inside the refrigerator" refers to a device installed to measure the internal temperature of the refrigerator in real time.

[0728] The "sensor means for measuring humidity inside a refrigerator" is a device installed to measure the internal humidity of a refrigerator in real time.

[0729] The "sensor means for measuring the gas concentration inside the refrigerator" is a device installed to measure the concentration of a specific gas, such as ethylene gas, present inside the refrigerator in real time.

[0730] The "communication means for transmitting the measured temperature, humidity, and gas concentration data to the analysis server" refers to a device or system for transmitting the data measured inside the refrigerator to the analysis server via a wireless or wired network.

[0731] "Artificial intelligence means for optimizing storage conditions based on the data in the analysis server" refers to artificial intelligence technology used to analyze the received temperature, humidity, and gas concentration data and calculate and determine the optimal storage conditions for each food item.

[0732] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system for automatically adjusting the internal environment of the refrigerator in accordance with the optimized storage conditions obtained from the analysis server.

[0733] The "display means for displaying storage conditions and analysis results in real time on smart glasses" is a system for instantly displaying the storage conditions and analysis results inside the refrigerator on smart glasses worn by workers.

[0734] This invention is a system for optimizing storage conditions in a refrigerator. In one embodiment, various sensors, a communication module, an analysis server, a control module, and smart glasses used by the user all work together in the refrigerator. The details are described below.

[0735] Overall system overview

[0736] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions. Each refrigerator is equipped with a temperature sensor, a humidity sensor, and a gas sensor. Data obtained from these sensors is sent to the analysis server in real time via a communication module. The analysis server uses this data to optimize storage conditions using an AI model. The optimized conditions are applied inside the refrigerator via a control module. Using smart glasses, workers can also check the status of storage conditions and any abnormalities in real time.

[0737] Program processing overview

[0738] The system operates as follows: First, the sensors are initialized and perform a self-diagnosis to ensure normal operation. Next, the temperature, humidity, and gas concentration data inside the refrigerator are collected at regular intervals. This data is sent to the server via the communications module, and the server uses an AI model to analyze and optimize the storage conditions. The results are then transmitted to the refrigerator, and the control module adjusts the refrigerator to the optimal storage conditions. These storage conditions and analysis results are displayed to the user in real time through the smart glasses.

[0739] Components and Data Processing

[0740] Hardware

[0741] Temperature sensor: STMicroelectronics refrigerator temperature sensor

[0742] Humidity sensor: Honeywell humidity sensor

[0743] Gas sensor: Figaro gas concentration sensor

[0744] Communication module: Qualcomm wireless communication module

[0745] Analysis server: High-performance cloud server (Google Cloud, AWS, etc.)

[0746] Smart Glasses: Google Glass Enterprise Edition

[0747] software

[0748] Data processing and calculation: Implementing AI models using Python and TensorFlow

[0749] Communication: Sending and receiving data using HTTP protocol and REST API

[0750] User interface: Smart glasses app for Android or iOS

[0751] Specific examples

[0752] The system operates as follows inside a refrigerator. For example, in a refrigerator containing cabbage, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration. This data is sent to a server, which uses an AI model to calculate the optimal storage conditions for the cabbage (for example, a temperature of 3°C and a humidity of 80%). The results are transmitted to the refrigerator, and the control module adjusts the temperature inside the refrigerator to 3°C and 80% humidity. At the same time, the smart glasses display the message, "The cabbage storage environment has been optimized."

[0753] Prompt example

[0754] "Please implement an AI algorithm to optimize storage conditions based on data from inside the refrigerator. In particular, please include a method for calculating optimal storage conditions that takes into account the type of food, amount, and acquired data (temperature, humidity, gas concentration)."

[0755] The above is a specific embodiment of the present invention. This system makes it possible to optimize food storage conditions, which can greatly contribute to maintaining quality and reducing waste.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] The terminal starts up and initializes the sensors. Specifically, it performs self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. The input is the self-diagnosis command for each sensor, and the output is the normal operating status of the sensor.

[0759] Step 2:

[0760] The sensors measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. The input is environmental data inside the refrigerator, and the output is a data set of temperature, humidity, and gas concentration. Specifically, the temperature sensor measures temperature data, the humidity sensor measures humidity data, and the gas sensor measures gas concentration data.

[0761] Step 3:

[0762] The acquired data is sent in real time to an analysis server via a communication module. The input is temperature, humidity, and gas concentration data, and the output is the data sent to the server. Specifically, each piece of data is sent to the server using the HTTP protocol.

[0763] Step 4:

[0764] The server uses the received data to analyze storage conditions using an AI model. The input is the acquired temperature, humidity, and gas concentration data, and the output is the optimal storage conditions. Specifically, the AI ​​model performs analysis taking into account the type and quantity of food, and calculates the optimal temperature, humidity, and gas environment.

[0765] Step 5:

[0766] The server sends the calculated optimal storage conditions to the terminal. The input is the optimal storage conditions, and the output is the storage condition data sent to the terminal. Specifically, the optimal storage conditions are sent again to the terminal using the HTTP protocol.

[0767] Step 6:

[0768] The terminal automatically adjusts the environment inside the refrigerator based on the optimal storage conditions received. The input is the optimal storage conditions, and the output is the adjusted temperature, humidity, and gas environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, the humidity control module changes the set humidity, and the gas control module performs the necessary ventilation and gas filtering.

[0769] Step 7:

[0770] The device notifies the user of the analysis results and new storage conditions via the smart glasses. The input is the analysis results and optimal storage condition data, and the output is the information displayed on the smart glasses display. Specifically, a message such as "The storage environment has been optimized" is displayed along with a list of the current status and adjustment details.

[0771] Step 8:

[0772] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. The input is the data generated at each step, and the output is the record stored in the database. Specifically, the history of storage conditions and the changes in environmental data are recorded in the log database so that they can be referenced later.

[0773] These are the specific processing steps of the system based on the application example. This system enables efficient monitoring and management of the storage environment, thereby maintaining food quality and reducing waste.

[0774] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0775] MODE FOR CARRYING OUT THE INVENTION

[0776] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[0777] Overall system overview

[0778] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and the emotion engine.

[0779] Program processing overview

[0780] The operation of this system will now be described.

[0781] Initialization

[0782] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[0783] After the initialization is complete, the device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[0784] Data Acquisition and Transmission

[0785] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[0786] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[0787] Data analysis and optimization of storage conditions

[0788] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[0789] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[0790] environmental adjustment

[0791] Based on the conditions received by the terminal, the environment inside the refrigerator is automatically adjusted: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[0792] User Notifications

[0793] The device notifies the user of the analysis results and new storage conditions. This notification is sent via the refrigerator's display or a connected smartphone app. A message such as "The cabbage storage environment has been optimized" is displayed.

[0794] Emotion recognition and response

[0795] The emotion engine recognizes the user's emotions and generates emotion data by analyzing the user's facial expressions and tone of voice.

[0796] The server then uses the emotion data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[0797] The system records the user's emotional data and adjusts notifications based on past emotional data. For example, when saving a food item that previously generated a happy emotion, a positive message will be displayed.

[0798] Data Recording

[0799] Environmental data and storage condition adjustment history are recorded on both the terminal and the server. The recorded data can be referenced later and also used for long-term data analysis, enabling continuous optimization of the storage environment.

[0800] Example 1: Maintaining vegetable quality and emotion recognition

[0801] 1. Initialization

[0802] The device starts up and initializes the sensor.

[0803] Connect to the server and receive the initial configuration.

[0804] 2. Data Acquisition and Transmission

[0805] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[0806] The data is sent to the server.

[0807] 3. Data analysis

[0808] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[0809] 4. Sending results and adjusting the environment

[0810] The server transmits the storage conditions to the terminal.

[0811] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[0812] 5. User Notices

[0813] The device notifies the user that "the cabbage storage environment has been optimized."

[0814] 6. Emotion Recognition and Response

[0815] The emotion engine recognizes the user's happy facial expression and displays the message, "The optimal storage environment has been set! We're glad to be of service to you."

[0816] 7. Data Recording

[0817] The adjustment history is recorded and can be referenced later.

[0818] Example 2: Maintaining seafood quality and emotion recognition

[0819] 1. Initialization

[0820] The device starts up and initializes the sensor.

[0821] Connect to the server and receive the initial configuration.

[0822] 2. Data Acquisition and Transmission

[0823] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[0824] The data is sent to the server.

[0825] 3. Data analysis

[0826] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[0827] 4. Sending results and adjusting the environment

[0828] The server transmits the storage conditions to the terminal.

[0829] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[0830] 5. User Notices

[0831] The device notifies the user that "the salmon storage environment has been optimized."

[0832] 6. Emotion Recognition and Response

[0833] The emotion engine recognizes the user's stress and displays the message, "The salmon storage conditions have been optimized. Please let it cool down and enjoy it."

[0834] 7. Data Recording

[0835] The adjustment history is recorded and can be referenced later.

[0836] This system provides optimal storage conditions for various foods, thereby maintaining their quality for a long period of time. It also responds flexibly to user preferences, making it a more personalized and user-friendly system.

[0837] The processing flow will be explained below.

[0838] Step 1:

[0839] When the terminal starts up, it first initializes the sensor means. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are normal, the sensors transition to an operational state.

[0840] Step 2:

[0841] The terminal connects to the server and receives the initial setting data. The server sends the basic settings of the storage conditions, and the terminal records this data and sets the basic operation.

[0842] Step 3:

[0843] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[0844] Step 4:

[0845] The device sends measurement data, including temperature, humidity, and gas concentration, to the server in real time, ensuring that the server always has up-to-date environmental information.

[0846] Step 5:

[0847] The server uses AI to analyze the received data, and then refers to the optimal storage conditions for food from a database and calculates the optimal temperature, humidity, and gas environment for each food item stored.

[0848] Step 6:

[0849] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[0850] Step 7:

[0851] The terminal adjusts the environment inside the refrigerator based on the storage conditions received. The temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[0852] Step 8:

[0853] The device notifies the user of the results of the environmental adjustments, either through the refrigerator display or a connected smartphone app, with a message saying, "The cabbage storage environment has been optimized."

[0854] Step 9:

[0855] The emotion engine recognizes the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice to generate emotion data.

[0856] Step 10:

[0857] The server then uses the emotional data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[0858] Step 11:

[0859] The user's emotional data is recorded and saved as historical data, and is reflected in future notifications and suggestions.

[0860] Step 12:

[0861] Environmental data and storage condition adjustment history are recorded on both the terminal and the server, making the historical data available for later reference and enabling long-term data analysis.

[0862] Example 2

[0863] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0864] Maintaining an appropriate storage environment is essential to keeping food fresh in a refrigerator for a longer period of time. At the same time, interactive environment settings that take into account the user's emotions and stress level are required to improve the user's comfortable use experience. However, conventional refrigerator systems lack a mechanism to address these factors in an integrated manner, which can lead to problems such as food storage conditions being left suboptimal or the burden of adjusting the settings themselves. Another issue is that the system does not provide appropriate suggestions that take the user's emotions into account, preventing it from providing a high level of convenience to users.

[0865] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, and a sensor means for measuring the gas concentration inside the refrigerator. This makes it possible to acquire and analyze environmental data inside the refrigerator in real time. In addition, by combining an emotion recognition means for recognizing the user's emotion and a suggestion means for suggesting adjustments to storage conditions based on the user's emotion data, flexible responses according to the user's emotion become possible. As a result, food storage conditions are optimized, and personalized suggestions are made to the user, improving convenience.

[0866] "Sensor means" refers to a device or function utilized to measure temperature, humidity, and gas concentrations within the refrigerator.

[0867] "Communication means" refers to the device or protocol used to transmit the measured data to a computer that performs the analysis.

[0868] "Artificial intelligence means" refers to software or algorithms that are used in the computer that performs the analysis to analyze the acquired data and calculate optimal storage conditions.

[0869] "Control means" refers to a device or software that communicates optimized storage conditions to the refrigerator and automatically adjusts the environment.

[0870] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and tone of voice and generating emotion data.

[0871] The "suggestion means" is a device or software for proposing adjustments to storage conditions based on the user's emotional data.

[0872] A "notification mechanism" is a device or protocol for communicating the system's analysis results and recommendations to the user.

[0873] MODE FOR CARRYING OUT THE INVENTION

[0874] The present invention is a system for optimizing storage conditions in a refrigerator and recognizing and responding to user emotions. Specific embodiments of the system will be described below.

[0875] Overall system overview

[0876] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and an emotion recognition engine.

[0877] Hardware and Software Configuration

[0878] 1. Sensor means

[0879] Temperature sensor: Used to measure the temperature inside the refrigerator.

[0880] Humidity sensor: Used to measure the humidity inside the refrigerator.

[0881] Gas sensors: Used to measure gas concentrations (e.g., ethylene concentration) inside a refrigerator.

[0882] 2. Means of communication

[0883] A communication module (e.g., Wi-Fi module) for transmitting measured data to an analysis server in real time.

[0884] 3. Analysis (server-side artificial intelligence means)

[0885] The analysis server has a generative AI model and food database for analyzing the received data. The AI ​​analyzes the data and calculates the optimal storage conditions.

[0886] 4. Control Measures

[0887] The device automatically adjusts the environment inside the refrigerator based on the storage conditions received by the terminal. It includes a temperature control module, humidity control module, and gas control module.

[0888] 5. User Notification Methods

[0889] A display and smartphone application to inform the user of the analysis results and suggestions for adjusting storage conditions.

[0890] 6. Emotion recognition means

[0891] A camera and microphone for analyzing the user's facial expressions and tone of voice to generate emotional data.

[0892] 7. Proposal method

[0893] A generative AI model that proposes adjustments to storage conditions based on user emotional data.

[0894] Specific operation example

[0895] 1. Maintaining vegetable quality and emotion recognition

[0896] Initialization

[0897] The device starts up and initializes the sensor.

[0898] Connect to the server and receive the initial configuration.

[0899] Data Acquisition and Transmission

[0900] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[0901] The data is sent to the server.

[0902] Data analysis and results transmission

[0903] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[0904] The server transmits the storage conditions to the terminal.

[0905] environmental adjustment

[0906] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 3°C and the humidity to 80%.

[0907] User Notifications

[0908] The device notifies the user that "the cabbage storage environment has been optimized."

[0909] Emotion Recognition and Response

[0910] The emotion recognition engine recognizes the user's joy and displays the message, "Optimal saving environment has been set! We're glad to help you."

[0911] Data Recording

[0912] Record your adjustment history for future reference.

[0913] Prompt Sentence Examples

[0914] "I've stored the cabbage in the refrigerator. Please set the optimal storage conditions."

[0915] 2. Maintaining seafood quality and emotion recognition

[0916] Initialization

[0917] The device starts up and initializes the sensor.

[0918] Connect to the server and receive the initial configuration.

[0919] Data Acquisition and Transmission

[0920] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[0921] The data is sent to the server.

[0922] Data analysis and results transmission

[0923] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[0924] The server transmits the storage conditions to the terminal.

[0925] environmental adjustment

[0926] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 1°C and the humidity to 70%.

[0927] User Notifications

[0928] The device notifies the user that "the salmon storage environment has been optimized."

[0929] Emotion Recognition and Response

[0930] The emotion recognition engine recognizes the user's stress and displays the message, "The storage conditions for the salmon have been optimized. Please let it cool down and enjoy it."

[0931] Data Recording

[0932] Record your adjustment history for future reference.

[0933] Prompt Sentence Examples

[0934] "I've stored the salmon in the refrigerator. Please set the optimal storage conditions."

[0935] This system can provide optimal storage conditions for various foods, maintaining their quality for a long period of time. It also allows for flexible responses that take into account the user's feelings, making it a more personalized and user-friendly system.

[0936] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0937] Step 1: Initialization

[0938] When the device boots up, it automatically begins self-testing the temperature, humidity, and gas sensors, sending each sensor a test signal for a few seconds and evaluating its response.

[0939] Input: The refrigerator powers on.

[0940] Data calculation: Self-diagnosis to check whether the sensor is operating normally.

[0941] Output: The sensor is normal.

[0942] If the result is normal, the sensor transitions to an operational state, and the terminal connects to the analysis server to receive the initial setting data, which includes the initial storage conditions (e.g., temperature 5°C, humidity 60%).

[0943] Input: Sensor initialization complete, request to connect to server.

[0944] Data calculation: Connect to the server via a communication means and request setting data.

[0945] Output: Receives initial setting data (temperature 5°C, humidity 60%).

[0946] Step 2: Data Acquisition

[0947] Each sensor on the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes).

[0948] Input: Acquisition timing (every 5 minutes).

[0949] Data calculation: The temperature sensor measures the current temperature, the humidity sensor measures the current humidity, and the gas sensor measures the current gas concentration.

[0950] Output: Data for temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[0951] Step 3: Send data

[0952] The environmental data (temperature, humidity, gas concentration) acquired by the device is sent to an analysis server in real time.

[0953] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[0954] Data calculation: Data is packaged and sent to a server via a communication means.

[0955] Output: Data sent to analysis server completed.

[0956] Step 4: Data analysis

[0957] The server analyzes the received data, and the server's generative AI model uses this data to calculate the optimal storage conditions for the stored food.

[0958] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[0959] Data calculation: The generative AI model analyzes the data and refers to a food database to calculate the optimal storage conditions (e.g., for cabbage, a temperature of 3°C and humidity of 80%).

[0960] Output: Optimal storage conditions (temperature 3°C, humidity 80%).

[0961] Step 5: Submitting storage conditions

[0962] The server sends the calculated optimal storage conditions to the terminal.

[0963] Input: Optimal storage conditions (temperature 3°C, humidity 80%).

[0964] Data calculation: The storage conditions are packaged and sent to the terminal via a communication means.

[0965] Output: Save conditions sent to the device completed.

[0966] Step 6: Adjusting the environment

[0967] The terminal automatically adjusts the environment inside the refrigerator based on the storage conditions received: the temperature control module sets it to 3°C, and the humidity control module sets it to 80%.

[0968] Input: Optimal storage conditions (temperature 3°C, humidity 80%).

[0969] Data calculation: The internal control module sets the temperature and humidity.

[0970] Output: The environment inside the refrigerator has been adjusted.

[0971] Step 7: User Notification

[0972] The device notifies the user of the results of the storage condition adjustments via the refrigerator display or a connected smartphone app.

[0973] Input: Results of applying optimal storage conditions.

[0974] Data operation: Generates user notification data and displays or transmits it.

[0975] Output: Notification that "The cabbage storage environment has been optimized."

[0976] Step 8: Emotion Recognition

[0977] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to generate emotional data.

[0978] Input: User's facial expression image or voice data.

[0979] Data calculation: Analysis by emotion recognition algorithm.

[0980] Output: User emotion data (e.g., happiness).

[0981] Step 9: Emotionally Based Response

[0982] The server uses the emotion data to make suggestions that correspond to the user's emotions. If the server recognizes that the user is happy, it displays a message saying, "The optimal storage environment has been set up! We're glad to be of service to you."

[0983] Input: User emotion data (e.g., happiness).

[0984] Data calculation: Generate a proposal message.

[0985] Output: Notification saying "Optimal saving environment set up! Glad to help."

[0986] Step 10: Data recording

[0987] The device and server record all environmental data and adjustment history, and this data is stored for future improvement and analysis.

[0988] Input: Environmental data, adjustment history, emotion data.

[0989] Data calculation: Record each data in the appropriate format.

[0990] Output: Saved to database.

[0991] (Application example 2)

[0992] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0993] Conventional refrigerators do not automatically adjust to maintain optimal food storage conditions, resulting in a decline in food quality. Furthermore, they do not provide appropriate notifications or suggestions that take into account the user's emotions and psychology, resulting in a poor user experience. Even in physical stores, storage conditions are not properly optimized, resulting in food deterioration and waste.

[0994] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0995] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and an emotion recognition engine for recognizing the user's emotions and providing suggestions and notifications for adjusting the storage conditions according to the emotions. This allows for the optimization of food storage conditions to maintain food quality while providing appropriate notifications and suggestions according to the user's emotions. This also improves the efficiency of food management in physical stores.

[0996] - "Temperature sensor means" means a sensor for measuring the temperature inside the refrigerator.

[0997] The "humidity sensor means" is a sensor for measuring the humidity inside the refrigerator.

[0998] The "gas sensor means" is a sensor for measuring the gas concentration inside the refrigerator.

[0999] The "communication means" is a communication module for transmitting the measured temperature, humidity, and gas concentration data to the analysis server.

[1000] "Artificial intelligence means" refers to functions and programs in the analysis server that optimize storage conditions based on the transmitted data.

[1001] The "control means" refers to a device or program that transmits the optimized storage conditions to the refrigerator and automatically adjusts the storage conditions within the refrigerator.

[1002] An "emotion recognition engine" is a system or program that recognizes a user's emotions and suggests and notifies them of adjustments to storage conditions based on those emotions.

[1003] The "notification means" is an application or a display device for notifying the user of the results of optimization of storage conditions or notifications based on emotion recognition.

[1004] "Storage conditions" refer to environmental conditions such as temperature, humidity, and gas concentration required to store food in an optimal state inside a refrigerator.

[1005] The "analysis server" is a server device that receives data sent from the refrigerator, analyzes it, and optimizes storage conditions.

[1006] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[1007] System configuration

[1008] This system includes various sensors installed inside the refrigerator, a communication module, an analysis server, a control module, an emotion recognition engine, and a notification means. Specific examples of the hardware and software used include:

[1009] Hardware

[1010] 1. Temperature sensor: A sensor that measures the temperature inside the refrigerator.

[1011] 2. Humidity sensor: A sensor that measures the humidity inside the refrigerator.

[1012] 3. Gas sensor: A sensor that measures the gas concentration inside the refrigerator.

[1013] 4. Communication module: A module that sends and receives data using wireless LAN or Bluetooth.

[1014] 5. Smart Glasses: Input devices for emotion recognition engines (e.g. smart glasses).

[1015] 6. Refrigerator control module: A module that regulates the temperature and humidity inside the refrigerator.

[1016] software

[1017] 1. Analysis server: Data analysis software running on a cloud server (e.g., AWS).

[1018] 2. Artificial intelligence means: AI models to analyze data and optimize storage conditions (e.g., OpenAI GPT-4).

[1019] 3. Emotion recognition engine: Software that analyzes user emotions (e.g., Microsoft Azure Face API).

[1020] 4. Notification method: An application (e.g., a smartphone app) that notifies the user of the results.

[1021] Data Acquisition and Optimization

[1022] The server collects data from temperature, humidity, and gas sensors installed inside the refrigerator and sends each measurement data to the analysis server at regular intervals. The analysis server uses an AI model based on the received data to calculate the optimal storage conditions. The calculated storage conditions are transmitted to the refrigerator's control module via the communication module, and the environment inside the refrigerator is automatically adjusted.

[1023] Examples:

[1024] 1. Storing cabbage: If the temperature, humidity, and gas concentration inside a refrigerator containing cabbage are 5°C, 50%, and 0.2%, respectively, the server receives this information and the AI ​​model calculates the optimal storage conditions (e.g., temperature 3°C, humidity 80%). Based on this, the refrigerator automatically adjusts.

[1025] Emotion Recognition and User Notification

[1026] The emotion recognition engine analyzes the user's emotions from their facial expressions and tone of voice. For example, smart glasses can recognize the user's facial expressions while standing in front of the refrigerator, and send that data to the server. The server then uses that emotional data to suggest adjustments to storage conditions or generate positive messages.

[1027] Examples:

[1028] 1. Emotion recognition and messaging: If the emotion recognition engine detects the user's joy, it will send a notification to the user's smartphone, such as "The storage environment has been optimized! We're glad you enjoyed it."

[1029] Prompt Sentence Examples

[1030] 1. Initialization phase:

[1031] "Perform initial setup of the sensor and check that it works properly."

[1032] 2. Data acquisition timing:

[1033] "Measure the temperature, humidity, and gas concentration inside the refrigerator every 5 minutes and send the data to the cloud server."

[1034] 3. Optimizing storage conditions:

[1035] "Calculate the optimal storage conditions for cabbage based on the received data."

[1036] 4. Environmental conditioning:

[1037] "Set the refrigerator temperature to 3°C and humidity to 80%."

[1038] 5. Emotion Recognition and Notification:

[1039] "Analyze customer facial expressions and create messages about the storage environment that correspond to their emotions."

[1040] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1041] Step 1: Sensor initialization and self-diagnosis

[1042] When the terminal starts up, it first initializes each sensor. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are operating normally, the sensors enter an operational state.

[1043] Input: Sensor self-diagnosis data

[1044] Output: Status of the sensor, whether it is working properly or not

[1045] Specific operation: Check the self-diagnosis results of each sensor, and if no abnormalities are found, switch to operating state.

[1046] Step 2: Receive and record initial setup data

[1047] The device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[1048] Input: Initial setting data from the analysis server

[1049] Output: Initial settings for refrigerator storage conditions (temperature, humidity, etc.)

[1050] Specific operation: Receives initial setting data from the analysis server and saves it in the device.

[1051] Step 3: Measuring and transmitting environmental data

[1052] Each sensor in the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes). The measurement data is sent to an analysis server using a communication method.

[1053] Input: Temperature, humidity, and gas concentration measurements

[1054] Output: Sending data to the analysis server

[1055] Specific operation: Each sensor measures temperature, humidity, and gas concentration and sends the measurement data to the server.

[1056] Step 4: Data analysis and optimization

[1057] The server uses artificial intelligence to calculate the optimal storage conditions based on the data received, and optimizes storage conditions (temperature, humidity, etc.) by referring to a food database.

[1058] Input: Measurement data sent from each sensor

[1059] Output: Optimized storage conditions

[1060] What it does: It uses an AI model (e.g., OpenAI GPT-4) to calculate optimal storage conditions and generates instructions for setting those conditions in the refrigerator.

[1061] Step 5: Reflecting storage conditions in the refrigerator

[1062] The terminal automatically adjusts the temperature, humidity, and gas concentration inside the refrigerator based on the optimal storage conditions received from the server. The temperature, humidity, and gas control modules change their respective settings.

[1063] Input: Optimal storage conditions received from the server

[1064] Output: Adjustment of temperature, humidity and gas concentration inside the refrigerator

[1065] Specific operations: The temperature control module changes the set temperature, the humidity module changes the set humidity, and the gas control module performs ventilation and filtering.

[1066] Step 6: User Notification

[1067] The device notifies the user of the results of the optimization of storage conditions, and a message such as "The cabbage storage environment has been optimized" is displayed via a smartphone app or other device.

[1068] Input: Optimal storage conditions and their settings

[1069] Output: A message to inform the user

[1070] Specific operation: Using a notification method, the smartphone app is notified that the storage conditions have been optimized.

[1071] Step 7: Emotion Recognition and Suggestions

[1072] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, which the server uses to customize notifications and suggest adjustments to storage conditions.

[1073] Input: User's facial expressions and tone of voice (emotion data)

[1074] Output: A customized message or suggestion

[1075] How it works: An emotion recognition engine analyzes the user's emotions, and then processes that data with an AI model to generate appropriate messages and suggestions.

[1076] Step 8: Data recording

[1077] Environmental data and storage condition adjustment history are recorded on the server and on the terminal, and this data can be referenced later and used for long-term data analysis.

[1078] Input: Storage conditions and their adjustment history

[1079] Output: Recorded environmental data and adjustment history

[1080] Specific operation: Records storage conditions and adjustment history in a database (e.g., AWS DynamoDB) and uses it for long-term trend analysis.

[1081] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1083] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1084] [Third embodiment]

[1085] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1086] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1088] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1089] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1092] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1093] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1095] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1096] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1097] MODE FOR CARRYING OUT THE INVENTION

[1098] The present invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof will be described below.

[1099] Overall system overview

[1100] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.The system components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, and a user notification function.

[1101] Program processing overview

[1102] The operation of this system will now be described.

[1103] Initialization

[1104] When the device starts up, it first initializes the sensors. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally.

[1105] Once initialization is complete, the terminal connects to the server and receives initial storage condition data, which establishes the basic operating conditions of the refrigerator.

[1106] Data Acquisition and Transmission

[1107] The sensors in the device measure the environment inside the refrigerator (temperature, humidity, gas concentration) at regular intervals, and this data is sent to the server in real time via the communication module.

[1108] Data analysis

[1109] The server uses AI to analyze the data it receives. This analysis takes into account the type and quantity of food and calculates the optimal temperature, humidity, and gas environment. The server stores a database of optimal storage conditions for each food item, and the analysis is based on this.

[1110] Sending results and adjusting the environment

[1111] The server calculates the optimal storage conditions and sends them to the terminal. The terminal receives this data and automatically adjusts the environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, and the humidity control module changes the set humidity. The gas control module also performs the necessary ventilation and gas filtering.

[1112] User Notifications

[1113] The device notifies the user of the analysis results and new storage conditions via the refrigerator's display or a connected smartphone app, allowing the user to confirm that the storage environment has been optimized.

[1114] Data Recording

[1115] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[1116] Example 1: Maintaining the quality of vegetables

[1117] 1. Initialization

[1118] The device starts up and initializes the sensor.

[1119] Connect to the server and receive the initial configuration.

[1120] 2. Data Acquisition and Transmission

[1121] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[1122] The data is sent to the server.

[1123] 3. Data analysis

[1124] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[1125] 4. Sending results and adjusting the environment

[1126] The server transmits the storage conditions to the terminal.

[1127] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[1128] 5. User Notices

[1129] The device notifies the user that "the cabbage storage environment has been optimized."

[1130] 6. Data Recording

[1131] The adjustment history is recorded and can be referenced later.

[1132] Example 2: Maintaining the quality of seafood

[1133] 1. Initialization

[1134] The device starts up and initializes the sensor.

[1135] Connect to the server and receive the initial configuration.

[1136] 2. Data Acquisition and Transmission

[1137] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[1138] The data is sent to the server.

[1139] 3. Data analysis

[1140] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[1141] 4. Sending results and adjusting the environment

[1142] The server transmits the storage conditions to the terminal.

[1143] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[1144] 5. User Notices

[1145] The device notifies the user that "the salmon storage environment has been optimized."

[1146] 6. Data Recording

[1147] The adjustment history is recorded and can be referenced later.

[1148] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[1149] The processing flow will be explained below.

[1150] Step 1:

[1151] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[1152] Step 2:

[1153] The device connects to the server and receives the initial setting data. The server then sends the basic storage condition settings (e.g., the recommended temperature, humidity, and gas environment for the entire refrigerator) to the device. This data is recorded in the device and serves as the basis for future operation.

[1154] Step 3:

[1155] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[1156] Step 4:

[1157] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[1158] Step 5:

[1159] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[1160] Step 6:

[1161] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[1162] Step 7:

[1163] The terminal adjusts the environment inside the refrigerator based on the conditions received: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs any necessary ventilation or filtering.

[1164] Step 8:

[1165] The device will notify the user that the new storage conditions have been applied. This notification will be sent via the refrigerator display or a connected smartphone app. The message "The cabbage storage environment has been optimized" will be displayed.

[1166] Step 9:

[1167] The terminal and server each record environmental data and the history of storage condition adjustments. The recorded data can be referenced later and also used for long-term data analysis, making it possible to continuously optimize the storage environment.

[1168] Example 1

[1169] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1170] In recent years, there has been a demand for optimizing food storage in home refrigerators. Conventional refrigerators have a single temperature setting, making it difficult to provide an optimal storage environment for each food item. This can lead to food quality deterioration or waste. Furthermore, users must manually adjust and change the storage environment, which is inconvenient. The objective of this invention is to provide a system that automatically and optimally adjusts the storage environment within a refrigerator, thereby maintaining food quality for an extended period of time.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1172] This invention provides the following means for optimizing the storage environment of a refrigerator. The server includes a communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means for optimizing the storage conditions based on the data in the analysis server, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, a data recording means for recording environmental data and adjustment history inside the refrigerator, and a notification means for notifying the user of the analysis results and new storage conditions. This allows the storage environment inside the refrigerator to be optimized in real time, reducing manual operation by the user, maintaining food quality, and reducing waste.

[1173] "Communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server" refers to technology for transmitting temperature, humidity, and gas concentration data obtained from various sensors inside the refrigerator to an analysis server using wireless or wired communication.

[1174] "Artificial intelligence means in the analytical server for optimizing storage conditions based on said data" refers to the artificial intelligence algorithms and models used to calculate optimal food storage conditions using the received temperature, humidity and gas concentration data.

[1175] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system that receives the optimized storage conditions sent from the server and automatically adjusts the temperature, humidity, and gas environment inside the refrigerator.

[1176] "Data recording means for recording environmental data and adjustment history inside the refrigerator" refers to technology including a memory or database for storing data on the temperature, humidity, and gas concentration inside the refrigerator and the adjustment history of these.

[1177] "Notification means for notifying the user of the analysis results and new storage conditions" refers to a system or device for notifying the user of the analysis results and updated storage conditions through the refrigerator's display device or a smartphone app.

[1178] This invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof are described below. This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time and transmits the obtained data to an analysis server to optimize the storage conditions. System components include various sensors (temperature sensor, humidity sensor, gas sensor) placed inside the refrigerator, a communications module, an analysis server, a control module, and a user notification function.

[1179] Sensor and Communication Modules

[1180] The terminal is equipped with multiple sensors, including a temperature sensor, humidity sensor, and gas sensor. When the terminal starts up, it performs a self-diagnosis to check whether each sensor is operating normally and records the results. This allows real-time data on the temperature, humidity, and gas concentration inside the refrigerator to be obtained. This data is sent to an analysis server via a communication module such as a Wi-Fi module.

[1181] Data analysis

[1182] The server stores the received temperature, humidity, and gas concentration data in a database and analyzes it using an AI model. Examples of generative AI models used include TensorFlow and PyTorch. This AI model references the optimal storage conditions (temperature, humidity, and gas environment) for each food item in the database and calculates the optimal storage conditions based on the received data.

[1183] Control Modules and Auto Tuning

[1184] The optimal storage conditions calculated by the server are sent to the control module inside the refrigerator. The control module automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received data. Specifically, the temperature control module controls the cooling system, the humidity control module controls the humidifier and dehumidifier, and the gas control module performs filtering and ventilation.

[1185] User Notifications

[1186] The device has a function to notify the user of the analysis results and new storage conditions. This notification is made on the refrigerator's display or via a connected smartphone app. The user can confirm that the storage environment inside the refrigerator has been optimized.

[1187] Data Recording

[1188] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[1189] Specific examples

[1190] Example 1: Maintaining the quality of vegetables

[1191] When storing cabbage in the refrigerator, the device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures ethylene concentration. The server uses this data to calculate the optimal storage conditions for the cabbage (for example, temperature 3°C, humidity 80%) and sends the results to the device. The device automatically adjusts the temperature inside the refrigerator to 3°C and humidity 80%, and notifies the user that "the cabbage storage environment has been optimized."

[1192] Example 2: Maintaining the quality of seafood

[1193] The same applies when storing salmon in a refrigerator. The device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition. Based on this data, the server calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%) and sends the results to the device. The device then adjusts the temperature inside the refrigerator to 1°C and humidity 70%, and notifies the user that "the storage environment for the salmon has been optimized."

[1194] Prompt Sentence Examples

[1195] "What temperature and humidity settings are appropriate for optimal storage of cabbage?"

[1196] "What are the optimal environmental conditions for storing salmon in a refrigerator for a long period of time?"

[1197] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[1198] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1199] Step 1: Initialization

[1200] After starting up, the terminal first performs a self-diagnosis of the temperature sensor, humidity sensor, and gas sensor. This self-diagnosis checks whether each sensor is operating normally, and if there is an abnormality, it records an error message in the log. If it is confirmed that the sensor is operating normally, it proceeds to the next step. The internal status data of the sensor is used as input, and information on whether it is operating normally or not is obtained as output.

[1201] Specific behavior:

[1202] The device receives a wake-up signal.

[1203] Sends self-diagnosis commands to each sensor.

[1204] If there is an error, an error message is logged.

[1205] Step 2: Download communication and initial setting data

[1206] The terminal connects to the server and downloads the initial setting data for the storage conditions. The server receives this and establishes the basic operating conditions for the refrigerator. The input is the initial setting request data from the server, and the output is the initial setting data for the storage conditions.

[1207] Specific behavior:

[1208] The device sends a connection request to the server.

[1209] The server generates initial setting data and sends it to the terminal.

[1210] The device receives the initial setting data and stores it in its internal memory.

[1211] Step 3: Data Acquisition

[1212] The sensors in the device measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. This data is stored in memory at each measurement interval. The input is the physical environmental data inside the refrigerator, and the output is the measurement data of temperature, humidity, and gas concentration.

[1213] Specific behavior:

[1214] The temperature sensor measures the temperature and stores the value in memory.

[1215] The humidity sensor measures the humidity and stores the value in memory.

[1216] The gas sensor measures the gas concentration and stores the value in memory.

[1217] Step 4: Send data

[1218] The terminal transmits the acquired temperature, humidity, and gas concentration data to the server in real time. The data is transmitted via a communication module (e.g., Wi-Fi module). The input is the various measured data, and the output is the data transmitted to the server.

[1219] Specific behavior:

[1220] The terminal packetizes the measured data.

[1221] Send data to the server via the Wi-Fi module.

[1222] Wait for an acknowledgment after sending data.

[1223] Step 5: Data analysis

[1224] The server analyzes the received data using a generative AI model (e.g., TensorFlow) to calculate optimal storage conditions based on input data (temperature, humidity, gas concentration). The input is real-time environmental data, and the output is optimal storage conditions.

[1225] Specific behavior:

[1226] The server stores the received data in a database.

[1227] Input new data into the AI ​​model.

[1228] The AI ​​model performs analysis and outputs the optimal storage conditions.

[1229] Step 6: Submit optimal storage conditions

[1230] The server sends the optimal storage conditions to the terminal as the analysis results, allowing the terminal to receive the optimal storage conditions data as input and prepare to proceed to the next step as output.

[1231] Specific behavior:

[1232] The server generates optimal storage conditions.

[1233] The generated storage conditions are sent to the terminal.

[1234] The server waits for an acknowledgment from the terminal.

[1235] Step 7: Adjusting the environment

[1236] The terminal automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received storage conditions. The control module operates the temperature control system, humidity control system, and gas control system. The input is the optimal storage condition data, and the output is the adjusted environmental data inside the refrigerator.

[1237] Specific behavior:

[1238] The temperature control module regulates the cooling system.

[1239] The humidity control module operates the humidifier and dehumidifier.

[1240] The gas control module performs ventilation and filtering.

[1241] Step 8: User Notification

[1242] The device notifies the user of the analysis results and new storage conditions via a display or smartphone app. The input is the optimal storage condition information, and the output is a user notification message.

[1243] Specific behavior:

[1244] Storage conditions are displayed on the screen.

[1245] Sends push notifications to smartphone apps.

[1246] The user checks the notification content.

[1247] Step 9: Data recording

[1248] The terminal and server record the environmental data and storage condition adjustment history. This data is used for future reference and analysis. The input is the actual environmental data and storage condition data, and the output is the recorded history data.

[1249] Specific behavior:

[1250] Environmental data and storage condition data are stored in memory.

[1251] Sync data to the server.

[1252] Regular backups are performed.

[1253] This detailed processing step allows for optimal storage conditions in the refrigerator and maintains food quality.

[1254] (Application example 1)

[1255] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1256] While technology already exists to optimize food storage conditions in refrigerators, it is not practical in large-scale food management environments such as logistics centers. Furthermore, there is a lack of systems that allow workers to instantly check and respond to the status inside the refrigerator. Therefore, there is a need for technology that can monitor storage conditions in real time and instantly notify workers of the necessary information.

[1257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1258] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and a display means for displaying the storage conditions and analysis results in real time on smart glasses. This enables workers in large-scale food management environments such as logistics centers to instantly check the status inside the refrigerator and take appropriate action. Furthermore, optimizing storage conditions can improve food quality maintenance and reduce waste.

[1259] "Sensor means for measuring the temperature inside the refrigerator" refers to a device installed to measure the internal temperature of the refrigerator in real time.

[1260] The "sensor means for measuring humidity inside a refrigerator" is a device installed to measure the internal humidity of a refrigerator in real time.

[1261] The "sensor means for measuring the gas concentration inside the refrigerator" is a device installed to measure the concentration of a specific gas, such as ethylene gas, present inside the refrigerator in real time.

[1262] The "communication means for transmitting the measured temperature, humidity, and gas concentration data to the analysis server" refers to a device or system for transmitting the data measured inside the refrigerator to the analysis server via a wireless or wired network.

[1263] "Artificial intelligence means for optimizing storage conditions based on the data in the analysis server" refers to artificial intelligence technology used to analyze the received temperature, humidity, and gas concentration data and calculate and determine the optimal storage conditions for each food item.

[1264] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system for automatically adjusting the internal environment of the refrigerator in accordance with the optimized storage conditions obtained from the analysis server.

[1265] The "display means for displaying storage conditions and analysis results in real time on smart glasses" is a system for instantly displaying the storage conditions and analysis results inside the refrigerator on smart glasses worn by workers.

[1266] This invention is a system for optimizing storage conditions in a refrigerator. In one embodiment, various sensors, a communication module, an analysis server, a control module, and smart glasses used by the user all work together in the refrigerator. The details are described below.

[1267] Overall system overview

[1268] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions. Each refrigerator is equipped with a temperature sensor, a humidity sensor, and a gas sensor. Data obtained from these sensors is sent to the analysis server in real time via a communication module. The analysis server uses this data to optimize storage conditions using an AI model. The optimized conditions are applied inside the refrigerator via a control module. Using smart glasses, workers can also check the status of storage conditions and any abnormalities in real time.

[1269] Program processing overview

[1270] The system operates as follows: First, the sensors are initialized and perform a self-diagnosis to ensure normal operation. Next, the temperature, humidity, and gas concentration data inside the refrigerator are collected at regular intervals. This data is sent to the server via the communications module, and the server uses an AI model to analyze and optimize the storage conditions. The results are then transmitted to the refrigerator, and the control module adjusts the refrigerator to the optimal storage conditions. These storage conditions and analysis results are displayed to the user in real time through the smart glasses.

[1271] Components and Data Processing

[1272] Hardware

[1273] Temperature sensor: STMicroelectronics refrigerator temperature sensor

[1274] Humidity sensor: Honeywell humidity sensor

[1275] Gas sensor: Figaro gas concentration sensor

[1276] Communication module: Qualcomm wireless communication module

[1277] Analysis server: High-performance cloud server (Google Cloud, AWS, etc.)

[1278] Smart Glasses: Google Glass Enterprise Edition

[1279] software

[1280] Data processing and calculation: Implementing AI models using Python and TensorFlow

[1281] Communication: Sending and receiving data using HTTP protocol and REST API

[1282] User interface: Smart glasses app for Android or iOS

[1283] Specific examples

[1284] The system operates as follows inside a refrigerator. For example, in a refrigerator containing cabbage, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration. This data is sent to a server, which uses an AI model to calculate the optimal storage conditions for the cabbage (for example, a temperature of 3°C and a humidity of 80%). The results are transmitted to the refrigerator, and the control module adjusts the temperature inside the refrigerator to 3°C and 80% humidity. At the same time, the smart glasses display the message, "The cabbage storage environment has been optimized."

[1285] Prompt example

[1286] "Please implement an AI algorithm to optimize storage conditions based on data from inside the refrigerator. In particular, please include a method for calculating optimal storage conditions that takes into account the type of food, amount, and acquired data (temperature, humidity, gas concentration)."

[1287] The above is a specific embodiment of the present invention. This system makes it possible to optimize food storage conditions, which can greatly contribute to maintaining quality and reducing waste.

[1288] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1289] Step 1:

[1290] The terminal starts up and initializes the sensors. Specifically, it performs self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. The input is the self-diagnosis command for each sensor, and the output is the normal operating status of the sensor.

[1291] Step 2:

[1292] The sensors measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. The input is environmental data inside the refrigerator, and the output is a data set of temperature, humidity, and gas concentration. Specifically, the temperature sensor measures temperature data, the humidity sensor measures humidity data, and the gas sensor measures gas concentration data.

[1293] Step 3:

[1294] The acquired data is sent in real time to an analysis server via a communication module. The input is temperature, humidity, and gas concentration data, and the output is the data sent to the server. Specifically, each piece of data is sent to the server using the HTTP protocol.

[1295] Step 4:

[1296] The server uses the received data to analyze storage conditions using an AI model. The input is the acquired temperature, humidity, and gas concentration data, and the output is the optimal storage conditions. Specifically, the AI ​​model performs analysis taking into account the type and quantity of food, and calculates the optimal temperature, humidity, and gas environment.

[1297] Step 5:

[1298] The server sends the calculated optimal storage conditions to the terminal. The input is the optimal storage conditions, and the output is the storage condition data sent to the terminal. Specifically, the optimal storage conditions are sent again to the terminal using the HTTP protocol.

[1299] Step 6:

[1300] The terminal automatically adjusts the environment inside the refrigerator based on the optimal storage conditions received. The input is the optimal storage conditions, and the output is the adjusted temperature, humidity, and gas environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, the humidity control module changes the set humidity, and the gas control module performs the necessary ventilation and gas filtering.

[1301] Step 7:

[1302] The device notifies the user of the analysis results and new storage conditions via the smart glasses. The input is the analysis results and optimal storage condition data, and the output is the information displayed on the smart glasses display. Specifically, a message such as "The storage environment has been optimized" is displayed along with a list of the current status and adjustment details.

[1303] Step 8:

[1304] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. The input is the data generated at each step, and the output is the record stored in the database. Specifically, the history of storage conditions and the changes in environmental data are recorded in the log database so that they can be referenced later.

[1305] These are the specific processing steps of the system based on the application example. This system enables efficient monitoring and management of the storage environment, thereby maintaining food quality and reducing waste.

[1306] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1307] MODE FOR CARRYING OUT THE INVENTION

[1308] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[1309] Overall system overview

[1310] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and the emotion engine.

[1311] Program processing overview

[1312] The operation of this system will now be described.

[1313] Initialization

[1314] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[1315] After the initialization is complete, the device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[1316] Data Acquisition and Transmission

[1317] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[1318] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[1319] Data analysis and optimization of storage conditions

[1320] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[1321] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[1322] environmental adjustment

[1323] Based on the conditions received by the terminal, the environment inside the refrigerator is automatically adjusted: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[1324] User Notifications

[1325] The device notifies the user of the analysis results and new storage conditions. This notification is sent via the refrigerator's display or a connected smartphone app. A message such as "The cabbage storage environment has been optimized" is displayed.

[1326] Emotion recognition and response

[1327] The emotion engine recognizes the user's emotions and generates emotion data by analyzing the user's facial expressions and tone of voice.

[1328] The server then uses the emotion data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[1329] The system records the user's emotional data and adjusts notifications based on past emotional data. For example, when saving a food item that previously generated a happy emotion, a positive message will be displayed.

[1330] Data Recording

[1331] Environmental data and storage condition adjustment history are recorded on both the terminal and the server. The recorded data can be referenced later and also used for long-term data analysis, enabling continuous optimization of the storage environment.

[1332] Example 1: Maintaining vegetable quality and emotion recognition

[1333] 1. Initialization

[1334] The device starts up and initializes the sensor.

[1335] Connect to the server and receive the initial configuration.

[1336] 2. Data Acquisition and Transmission

[1337] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[1338] The data is sent to the server.

[1339] 3. Data analysis

[1340] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[1341] 4. Sending results and adjusting the environment

[1342] The server transmits the storage conditions to the terminal.

[1343] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[1344] 5. User Notices

[1345] The device notifies the user that "the cabbage storage environment has been optimized."

[1346] 6. Emotion Recognition and Response

[1347] The emotion engine recognizes the user's happy facial expression and displays the message, "The optimal storage environment has been set! We're glad to be of service to you."

[1348] 7. Data Recording

[1349] The adjustment history is recorded and can be referenced later.

[1350] Example 2: Maintaining seafood quality and emotion recognition

[1351] 1. Initialization

[1352] The device starts up and initializes the sensor.

[1353] Connect to the server and receive the initial configuration.

[1354] 2. Data Acquisition and Transmission

[1355] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[1356] The data is sent to the server.

[1357] 3. Data analysis

[1358] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[1359] 4. Sending results and adjusting the environment

[1360] The server transmits the storage conditions to the terminal.

[1361] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[1362] 5. User Notices

[1363] The device notifies the user that "the salmon storage environment has been optimized."

[1364] 6. Emotion Recognition and Response

[1365] The emotion engine recognizes the user's stress and displays the message, "The salmon storage conditions have been optimized. Please let it cool down and enjoy it."

[1366] 7. Data Recording

[1367] The adjustment history is recorded and can be referenced later.

[1368] This system provides optimal storage conditions for various foods, thereby maintaining their quality for a long period of time. It also responds flexibly to user preferences, making it a more personalized and user-friendly system.

[1369] The processing flow will be explained below.

[1370] Step 1:

[1371] When the terminal starts up, it first initializes the sensor means. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are normal, the sensors transition to an operational state.

[1372] Step 2:

[1373] The terminal connects to the server and receives the initial setting data. The server sends the basic settings of the storage conditions, and the terminal records this data and sets the basic operation.

[1374] Step 3:

[1375] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[1376] Step 4:

[1377] The device sends measurement data, including temperature, humidity, and gas concentration, to the server in real time, ensuring that the server always has up-to-date environmental information.

[1378] Step 5:

[1379] The server uses AI to analyze the received data, and then refers to the optimal storage conditions for food from a database and calculates the optimal temperature, humidity, and gas environment for each food item stored.

[1380] Step 6:

[1381] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[1382] Step 7:

[1383] The terminal adjusts the environment inside the refrigerator based on the storage conditions received. The temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[1384] Step 8:

[1385] The device notifies the user of the results of the environmental adjustments, either through the refrigerator display or a connected smartphone app, with a message saying, "The cabbage storage environment has been optimized."

[1386] Step 9:

[1387] The emotion engine recognizes the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice to generate emotion data.

[1388] Step 10:

[1389] The server then uses the emotional data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[1390] Step 11:

[1391] The user's emotional data is recorded and saved as historical data, and is reflected in future notifications and suggestions.

[1392] Step 12:

[1393] Environmental data and storage condition adjustment history are recorded on both the terminal and the server, making the historical data available for later reference and enabling long-term data analysis.

[1394] Example 2

[1395] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1396] Maintaining an appropriate storage environment is essential to keeping food fresh in a refrigerator for a longer period of time. At the same time, interactive environment settings that take into account the user's emotions and stress level are required to improve the user's comfortable use experience. However, conventional refrigerator systems lack a mechanism to address these factors in an integrated manner, which can lead to problems such as food storage conditions being left suboptimal or the burden of adjusting the settings themselves. Another issue is that the system does not provide appropriate suggestions that take the user's emotions into account, preventing it from providing a high level of convenience to users.

[1397] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, and a sensor means for measuring the gas concentration inside the refrigerator. This makes it possible to acquire and analyze environmental data inside the refrigerator in real time. In addition, by combining an emotion recognition means for recognizing the user's emotion and a suggestion means for suggesting adjustments to storage conditions based on the user's emotion data, flexible responses according to the user's emotion become possible. As a result, food storage conditions are optimized, and personalized suggestions are made to the user, improving convenience.

[1398] "Sensor means" refers to a device or function utilized to measure temperature, humidity, and gas concentrations within the refrigerator.

[1399] "Communication means" refers to the device or protocol used to transmit the measured data to a computer that performs the analysis.

[1400] "Artificial intelligence means" refers to software or algorithms that are used in the computer that performs the analysis to analyze the acquired data and calculate optimal storage conditions.

[1401] "Control means" refers to a device or software that communicates optimized storage conditions to the refrigerator and automatically adjusts the environment.

[1402] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and tone of voice and generating emotion data.

[1403] The "suggestion means" is a device or software for proposing adjustments to storage conditions based on the user's emotional data.

[1404] A "notification mechanism" is a device or protocol for communicating the system's analysis results and recommendations to the user.

[1405] MODE FOR CARRYING OUT THE INVENTION

[1406] The present invention is a system for optimizing storage conditions in a refrigerator and recognizing and responding to user emotions. Specific embodiments of the system will be described below.

[1407] Overall system overview

[1408] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and an emotion recognition engine.

[1409] Hardware and Software Configuration

[1410] 1. Sensor means

[1411] Temperature sensor: Used to measure the temperature inside the refrigerator.

[1412] Humidity sensor: Used to measure the humidity inside the refrigerator.

[1413] Gas sensors: Used to measure gas concentrations (e.g., ethylene concentration) inside a refrigerator.

[1414] 2. Means of communication

[1415] A communication module (e.g., Wi-Fi module) for transmitting measured data to an analysis server in real time.

[1416] 3. Analysis (server-side artificial intelligence means)

[1417] The analysis server has a generative AI model and food database for analyzing the received data. The AI ​​analyzes the data and calculates the optimal storage conditions.

[1418] 4. Control Measures

[1419] The device automatically adjusts the environment inside the refrigerator based on the storage conditions received by the terminal. It includes a temperature control module, humidity control module, and gas control module.

[1420] 5. User Notification Methods

[1421] A display and smartphone application to inform the user of the analysis results and suggestions for adjusting storage conditions.

[1422] 6. Emotion recognition means

[1423] A camera and microphone for analyzing the user's facial expressions and tone of voice to generate emotional data.

[1424] 7. Proposal method

[1425] A generative AI model that proposes adjustments to storage conditions based on user emotional data.

[1426] Specific operation example

[1427] 1. Maintaining vegetable quality and emotion recognition

[1428] Initialization

[1429] The device starts up and initializes the sensor.

[1430] Connect to the server and receive the initial configuration.

[1431] Data Acquisition and Transmission

[1432] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[1433] The data is sent to the server.

[1434] Data analysis and results transmission

[1435] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[1436] The server transmits the storage conditions to the terminal.

[1437] environmental adjustment

[1438] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 3°C and the humidity to 80%.

[1439] User Notifications

[1440] The device notifies the user that "the cabbage storage environment has been optimized."

[1441] Emotion Recognition and Response

[1442] The emotion recognition engine recognizes the user's joy and displays the message, "Optimal saving environment has been set! We're glad to help you."

[1443] Data Recording

[1444] Record your adjustment history for future reference.

[1445] Prompt Sentence Examples

[1446] "I've stored the cabbage in the refrigerator. Please set the optimal storage conditions."

[1447] 2. Maintaining seafood quality and emotion recognition

[1448] Initialization

[1449] The device starts up and initializes the sensor.

[1450] Connect to the server and receive the initial configuration.

[1451] Data Acquisition and Transmission

[1452] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[1453] The data is sent to the server.

[1454] Data analysis and results transmission

[1455] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[1456] The server transmits the storage conditions to the terminal.

[1457] environmental adjustment

[1458] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 1°C and the humidity to 70%.

[1459] User Notifications

[1460] The device notifies the user that "the salmon storage environment has been optimized."

[1461] Emotion Recognition and Response

[1462] The emotion recognition engine recognizes the user's stress and displays the message, "The storage conditions for the salmon have been optimized. Please let it cool down and enjoy it."

[1463] Data Recording

[1464] Record your adjustment history for future reference.

[1465] Prompt Sentence Examples

[1466] "I've stored the salmon in the refrigerator. Please set the optimal storage conditions."

[1467] This system can provide optimal storage conditions for various foods, maintaining their quality for a long period of time. It also allows for flexible responses that take into account the user's feelings, making it a more personalized and user-friendly system.

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Step 1: Initialization

[1470] When the device boots up, it automatically begins self-testing the temperature, humidity, and gas sensors, sending each sensor a test signal for a few seconds and evaluating its response.

[1471] Input: The refrigerator powers on.

[1472] Data calculation: Self-diagnosis to check whether the sensor is operating normally.

[1473] Output: The sensor is normal.

[1474] If the result is normal, the sensor transitions to an operational state, and the terminal connects to the analysis server to receive the initial setting data, which includes the initial storage conditions (e.g., temperature 5°C, humidity 60%).

[1475] Input: Sensor initialization complete, request to connect to server.

[1476] Data calculation: Connect to the server via a communication means and request setting data.

[1477] Output: Receives initial setting data (temperature 5°C, humidity 60%).

[1478] Step 2: Data Acquisition

[1479] Each sensor on the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes).

[1480] Input: Acquisition timing (every 5 minutes).

[1481] Data calculation: The temperature sensor measures the current temperature, the humidity sensor measures the current humidity, and the gas sensor measures the current gas concentration.

[1482] Output: Data for temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[1483] Step 3: Send data

[1484] The environmental data (temperature, humidity, gas concentration) acquired by the device is sent to an analysis server in real time.

[1485] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[1486] Data calculation: Data is packaged and sent to a server via a communication means.

[1487] Output: Data sent to analysis server completed.

[1488] Step 4: Data analysis

[1489] The server analyzes the received data, and the server's generative AI model uses this data to calculate the optimal storage conditions for the stored food.

[1490] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[1491] Data calculation: The generative AI model analyzes the data and refers to a food database to calculate the optimal storage conditions (e.g., for cabbage, a temperature of 3°C and humidity of 80%).

[1492] Output: Optimal storage conditions (temperature 3°C, humidity 80%).

[1493] Step 5: Submitting storage conditions

[1494] The server sends the calculated optimal storage conditions to the terminal.

[1495] Input: Optimal storage conditions (temperature 3°C, humidity 80%).

[1496] Data calculation: The storage conditions are packaged and sent to the terminal via a communication means.

[1497] Output: Save conditions sent to the device completed.

[1498] Step 6: Adjusting the environment

[1499] The terminal automatically adjusts the environment inside the refrigerator based on the storage conditions received: the temperature control module sets it to 3°C, and the humidity control module sets it to 80%.

[1500] Input: Optimal storage conditions (temperature 3°C, humidity 80%).

[1501] Data calculation: The internal control module sets the temperature and humidity.

[1502] Output: The environment inside the refrigerator has been adjusted.

[1503] Step 7: User Notification

[1504] The device notifies the user of the results of the storage condition adjustments via the refrigerator display or a connected smartphone app.

[1505] Input: Results of applying optimal storage conditions.

[1506] Data operation: Generates user notification data and displays or transmits it.

[1507] Output: Notification that "The cabbage storage environment has been optimized."

[1508] Step 8: Emotion Recognition

[1509] The device's emotion recognition engine analyzes the user's facial expressions and tone of voice to generate emotional data.

[1510] Input: User's facial expression image or voice data.

[1511] Data calculation: Analysis by emotion recognition algorithm.

[1512] Output: User emotion data (e.g., happiness).

[1513] Step 9: Emotionally Based Response

[1514] The server uses the emotion data to make suggestions that correspond to the user's emotions. If the server recognizes that the user is happy, it displays a message saying, "The optimal storage environment has been set up! We're glad to be of service to you."

[1515] Input: User emotion data (e.g., happiness).

[1516] Data calculation: Generate a proposal message.

[1517] Output: Notification saying "Optimal saving environment set up! Glad to help."

[1518] Step 10: Data recording

[1519] The device and server record all environmental data and adjustment history, and this data is stored for future improvement and analysis.

[1520] Input: Environmental data, adjustment history, emotion data.

[1521] Data calculation: Record each data in the appropriate format.

[1522] Output: Saved to database.

[1523] (Application example 2)

[1524] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1525] Conventional refrigerators do not automatically adjust to maintain optimal food storage conditions, resulting in a decline in food quality. Furthermore, they do not provide appropriate notifications or suggestions that take into account the user's emotions and psychology, resulting in a poor user experience. Even in physical stores, storage conditions are not properly optimized, resulting in food deterioration and waste.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1527] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and an emotion recognition engine for recognizing the user's emotions and providing suggestions and notifications for adjusting the storage conditions according to the emotions. This allows for the optimization of food storage conditions to maintain food quality while providing appropriate notifications and suggestions according to the user's emotions. This also improves the efficiency of food management in physical stores.

[1528] - "Temperature sensor means" means a sensor for measuring the temperature inside the refrigerator.

[1529] The "humidity sensor means" is a sensor for measuring the humidity inside the refrigerator.

[1530] The "gas sensor means" is a sensor for measuring the gas concentration inside the refrigerator.

[1531] The "communication means" is a communication module for transmitting the measured temperature, humidity, and gas concentration data to the analysis server.

[1532] "Artificial intelligence means" refers to functions and programs in the analysis server that optimize storage conditions based on the transmitted data.

[1533] The "control means" refers to a device or program that transmits the optimized storage conditions to the refrigerator and automatically adjusts the storage conditions within the refrigerator.

[1534] An "emotion recognition engine" is a system or program that recognizes a user's emotions and suggests and notifies them of adjustments to storage conditions based on those emotions.

[1535] The "notification means" is an application or a display device for notifying the user of the results of optimization of storage conditions or notifications based on emotion recognition.

[1536] "Storage conditions" refer to environmental conditions such as temperature, humidity, and gas concentration required to store food in an optimal state inside a refrigerator.

[1537] The "analysis server" is a server device that receives data sent from the refrigerator, analyzes it, and optimizes storage conditions.

[1538] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[1539] System configuration

[1540] This system includes various sensors installed inside the refrigerator, a communication module, an analysis server, a control module, an emotion recognition engine, and a notification means. Specific examples of the hardware and software used include:

[1541] Hardware

[1542] 1. Temperature sensor: A sensor that measures the temperature inside the refrigerator.

[1543] 2. Humidity sensor: A sensor that measures the humidity inside the refrigerator.

[1544] 3. Gas sensor: A sensor that measures the gas concentration inside the refrigerator.

[1545] 4. Communication module: A module that sends and receives data using wireless LAN or Bluetooth.

[1546] 5. Smart Glasses: Input devices for emotion recognition engines (e.g. smart glasses).

[1547] 6. Refrigerator control module: A module that regulates the temperature and humidity inside the refrigerator.

[1548] software

[1549] 1. Analysis server: Data analysis software running on a cloud server (e.g., AWS).

[1550] 2. Artificial intelligence means: AI models to analyze data and optimize storage conditions (e.g., OpenAI GPT-4).

[1551] 3. Emotion recognition engine: Software that analyzes user emotions (e.g., Microsoft Azure Face API).

[1552] 4. Notification method: An application (e.g., a smartphone app) that notifies the user of the results.

[1553] Data Acquisition and Optimization

[1554] The server collects data from temperature, humidity, and gas sensors installed inside the refrigerator and sends each measurement data to the analysis server at regular intervals. The analysis server uses an AI model based on the received data to calculate the optimal storage conditions. The calculated storage conditions are transmitted to the refrigerator's control module via the communication module, and the environment inside the refrigerator is automatically adjusted.

[1555] Examples:

[1556] 1. Storing cabbage: If the temperature, humidity, and gas concentration inside a refrigerator containing cabbage are 5°C, 50%, and 0.2%, respectively, the server receives this information and the AI ​​model calculates the optimal storage conditions (e.g., temperature 3°C, humidity 80%). Based on this, the refrigerator automatically adjusts.

[1557] Emotion Recognition and User Notification

[1558] The emotion recognition engine analyzes the user's emotions from their facial expressions and tone of voice. For example, smart glasses can recognize the user's facial expressions while standing in front of the refrigerator, and send that data to the server. The server then uses that emotional data to suggest adjustments to storage conditions or generate positive messages.

[1559] Examples:

[1560] 1. Emotion recognition and messaging: If the emotion recognition engine detects the user's joy, it will send a notification to the user's smartphone, such as "The storage environment has been optimized! We're glad you enjoyed it."

[1561] Prompt Sentence Examples

[1562] 1. Initialization phase:

[1563] "Perform initial setup of the sensor and check that it works properly."

[1564] 2. Data acquisition timing:

[1565] "Measure the temperature, humidity, and gas concentration inside the refrigerator every 5 minutes and send the data to the cloud server."

[1566] 3. Optimizing storage conditions:

[1567] "Calculate the optimal storage conditions for cabbage based on the received data."

[1568] 4. Environmental conditioning:

[1569] "Set the refrigerator temperature to 3°C and humidity to 80%."

[1570] 5. Emotion Recognition and Notification:

[1571] "Analyze customer facial expressions and create messages about the storage environment that correspond to their emotions."

[1572] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1573] Step 1: Sensor initialization and self-diagnosis

[1574] When the terminal starts up, it first initializes each sensor. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are operating normally, the sensors enter an operational state.

[1575] Input: Sensor self-diagnosis data

[1576] Output: Status of the sensor, whether it is working properly or not

[1577] Specific operation: Check the self-diagnosis results of each sensor, and if no abnormalities are found, switch to operating state.

[1578] Step 2: Receive and record initial setup data

[1579] The device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[1580] Input: Initial setting data from the analysis server

[1581] Output: Initial settings for refrigerator storage conditions (temperature, humidity, etc.)

[1582] Specific operation: Receives initial setting data from the analysis server and saves it in the device.

[1583] Step 3: Measuring and transmitting environmental data

[1584] Each sensor in the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes). The measurement data is sent to an analysis server using a communication method.

[1585] Input: Temperature, humidity, and gas concentration measurements

[1586] Output: Sending data to the analysis server

[1587] Specific operation: Each sensor measures temperature, humidity, and gas concentration and sends the measurement data to the server.

[1588] Step 4: Data analysis and optimization

[1589] The server uses artificial intelligence to calculate the optimal storage conditions based on the data received, and optimizes storage conditions (temperature, humidity, etc.) by referring to a food database.

[1590] Input: Measurement data sent from each sensor

[1591] Output: Optimized storage conditions

[1592] What it does: It uses an AI model (e.g., OpenAI GPT-4) to calculate optimal storage conditions and generates instructions for setting those conditions in the refrigerator.

[1593] Step 5: Reflecting storage conditions in the refrigerator

[1594] The terminal automatically adjusts the temperature, humidity, and gas concentration inside the refrigerator based on the optimal storage conditions received from the server. The temperature, humidity, and gas control modules change their respective settings.

[1595] Input: Optimal storage conditions received from the server

[1596] Output: Adjustment of temperature, humidity and gas concentration inside the refrigerator

[1597] Specific operations: The temperature control module changes the set temperature, the humidity module changes the set humidity, and the gas control module performs ventilation and filtering.

[1598] Step 6: User Notification

[1599] The device notifies the user of the results of the optimization of storage conditions, and a message such as "The cabbage storage environment has been optimized" is displayed via a smartphone app or other device.

[1600] Input: Optimal storage conditions and their settings

[1601] Output: A message to inform the user

[1602] Specific operation: Using a notification method, the smartphone app is notified that the storage conditions have been optimized.

[1603] Step 7: Emotion Recognition and Suggestions

[1604] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, which the server uses to customize notifications and suggest adjustments to storage conditions.

[1605] Input: User's facial expressions and tone of voice (emotion data)

[1606] Output: A customized message or suggestion

[1607] How it works: An emotion recognition engine analyzes the user's emotions, and then processes that data with an AI model to generate appropriate messages and suggestions.

[1608] Step 8: Data recording

[1609] Environmental data and storage condition adjustment history are recorded on the server and on the terminal, and this data can be referenced later and used for long-term data analysis.

[1610] Input: Storage conditions and their adjustment history

[1611] Output: Recorded environmental data and adjustment history

[1612] Specific operation: Records storage conditions and adjustment history in a database (e.g., AWS DynamoDB) and uses it for long-term trend analysis.

[1613] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1614] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1615] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1616] [Fourth embodiment]

[1617] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1618] 7, a 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.

[1619] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1621] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1623] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1624] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1625] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1626] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1627] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1628] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1629] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1630] MODE FOR CARRYING OUT THE INVENTION

[1631] The present invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof will be described below.

[1632] Overall system overview

[1633] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.The system components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, and a user notification function.

[1634] Program processing overview

[1635] The operation of this system will now be described.

[1636] Initialization

[1637] When the device starts up, it first initializes the sensors. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally.

[1638] Once initialization is complete, the terminal connects to the server and receives initial storage condition data, which establishes the basic operating conditions of the refrigerator.

[1639] Data Acquisition and Transmission

[1640] The sensors in the device measure the environment inside the refrigerator (temperature, humidity, gas concentration) at regular intervals, and this data is sent to the server in real time via the communication module.

[1641] Data analysis

[1642] The server uses AI to analyze the data it receives. This analysis takes into account the type and quantity of food and calculates the optimal temperature, humidity, and gas environment. The server stores a database of optimal storage conditions for each food item, and the analysis is based on this.

[1643] Sending results and adjusting the environment

[1644] The server calculates the optimal storage conditions and sends them to the terminal. The terminal receives this data and automatically adjusts the environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, and the humidity control module changes the set humidity. The gas control module also performs the necessary ventilation and gas filtering.

[1645] User Notifications

[1646] The device notifies the user of the analysis results and new storage conditions via the refrigerator's display or a connected smartphone app, allowing the user to confirm that the storage environment has been optimized.

[1647] Data Recording

[1648] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[1649] Example 1: Maintaining the quality of vegetables

[1650] 1. Initialization

[1651] The device starts up and initializes the sensor.

[1652] Connect to the server and receive the initial configuration.

[1653] 2. Data Acquisition and Transmission

[1654] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[1655] The data is sent to the server.

[1656] 3. Data analysis

[1657] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[1658] 4. Sending results and adjusting the environment

[1659] The server transmits the storage conditions to the terminal.

[1660] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[1661] 5. User Notices

[1662] The device notifies the user that "the cabbage storage environment has been optimized."

[1663] 6. Data Recording

[1664] The adjustment history is recorded and can be referenced later.

[1665] Example 2: Maintaining the quality of seafood

[1666] 1. Initialization

[1667] The device starts up and initializes the sensor.

[1668] Connect to the server and receive the initial configuration.

[1669] 2. Data Acquisition and Transmission

[1670] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[1671] The data is sent to the server.

[1672] 3. Data analysis

[1673] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[1674] 4. Sending results and adjusting the environment

[1675] The server transmits the storage conditions to the terminal.

[1676] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[1677] 5. User Notices

[1678] The device notifies the user that "the salmon storage environment has been optimized."

[1679] 6. Data Recording

[1680] The adjustment history is recorded and can be referenced later.

[1681] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[1682] The processing flow will be explained below.

[1683] Step 1:

[1684] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[1685] Step 2:

[1686] The device connects to the server and receives the initial setting data. The server then sends the basic storage condition settings (e.g., the recommended temperature, humidity, and gas environment for the entire refrigerator) to the device. This data is recorded in the device and serves as the basis for future operation.

[1687] Step 3:

[1688] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[1689] Step 4:

[1690] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[1691] Step 5:

[1692] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[1693] Step 6:

[1694] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[1695] Step 7:

[1696] The terminal adjusts the environment inside the refrigerator based on the conditions received: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs any necessary ventilation or filtering.

[1697] Step 8:

[1698] The device will notify the user that the new storage conditions have been applied. This notification will be sent via the refrigerator display or a connected smartphone app. The message "The cabbage storage environment has been optimized" will be displayed.

[1699] Step 9:

[1700] The terminal and server each record environmental data and the history of storage condition adjustments. The recorded data can be referenced later and also used for long-term data analysis, making it possible to continuously optimize the storage environment.

[1701] Example 1

[1702] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1703] In recent years, there has been a demand for optimizing food storage in home refrigerators. Conventional refrigerators have a single temperature setting, making it difficult to provide an optimal storage environment for each food item. This can lead to food quality deterioration or waste. Furthermore, users must manually adjust and change the storage environment, which is inconvenient. The objective of this invention is to provide a system that automatically and optimally adjusts the storage environment within a refrigerator, thereby maintaining food quality for an extended period of time.

[1704] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1705] This invention provides the following means for optimizing the storage environment of a refrigerator. The server includes a communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means for optimizing the storage conditions based on the data in the analysis server, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, a data recording means for recording environmental data and adjustment history inside the refrigerator, and a notification means for notifying the user of the analysis results and new storage conditions. This allows the storage environment inside the refrigerator to be optimized in real time, reducing manual operation by the user, maintaining food quality, and reducing waste.

[1706] "Communication means for transmitting measured temperature, humidity, and gas concentration data to an analysis server" refers to technology for transmitting temperature, humidity, and gas concentration data obtained from various sensors inside the refrigerator to an analysis server using wireless or wired communication.

[1707] "Artificial intelligence means in the analytical server for optimizing storage conditions based on said data" refers to the artificial intelligence algorithms and models used to calculate optimal food storage conditions using the received temperature, humidity and gas concentration data.

[1708] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system that receives the optimized storage conditions sent from the server and automatically adjusts the temperature, humidity, and gas environment inside the refrigerator.

[1709] "Data recording means for recording environmental data and adjustment history inside the refrigerator" refers to technology including a memory or database for storing data on the temperature, humidity, and gas concentration inside the refrigerator and the adjustment history of these.

[1710] "Notification means for notifying the user of the analysis results and new storage conditions" refers to a system or device for notifying the user of the analysis results and updated storage conditions through the refrigerator's display device or a smartphone app.

[1711] This invention is a system for optimizing storage conditions in a refrigerator, and specific embodiments thereof are described below. This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time and transmits the obtained data to an analysis server to optimize the storage conditions. System components include various sensors (temperature sensor, humidity sensor, gas sensor) placed inside the refrigerator, a communications module, an analysis server, a control module, and a user notification function.

[1712] Sensor and Communication Modules

[1713] The terminal is equipped with multiple sensors, including a temperature sensor, humidity sensor, and gas sensor. When the terminal starts up, it performs a self-diagnosis to check whether each sensor is operating normally and records the results. This allows real-time data on the temperature, humidity, and gas concentration inside the refrigerator to be obtained. This data is sent to an analysis server via a communication module such as a Wi-Fi module.

[1714] Data analysis

[1715] The server stores the received temperature, humidity, and gas concentration data in a database and analyzes it using an AI model. Examples of generative AI models used include TensorFlow and PyTorch. This AI model references the optimal storage conditions (temperature, humidity, and gas environment) for each food item in the database and calculates the optimal storage conditions based on the received data.

[1716] Control Modules and Auto Tuning

[1717] The optimal storage conditions calculated by the server are sent to the control module inside the refrigerator. The control module automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received data. Specifically, the temperature control module controls the cooling system, the humidity control module controls the humidifier and dehumidifier, and the gas control module performs filtering and ventilation.

[1718] User Notifications

[1719] The device has a function to notify the user of the analysis results and new storage conditions. This notification is made on the refrigerator's display or via a connected smartphone app. The user can confirm that the storage environment inside the refrigerator has been optimized.

[1720] Data Recording

[1721] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. This data can be referenced later to check what adjustments were made under specific conditions.

[1722] Specific examples

[1723] Example 1: Maintaining the quality of vegetables

[1724] When storing cabbage in the refrigerator, the device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures ethylene concentration. The server uses this data to calculate the optimal storage conditions for the cabbage (for example, temperature 3°C, humidity 80%) and sends the results to the device. The device automatically adjusts the temperature inside the refrigerator to 3°C and humidity 80%, and notifies the user that "the cabbage storage environment has been optimized."

[1725] Example 2: Maintaining the quality of seafood

[1726] The same applies when storing salmon in a refrigerator. The device sends data obtained from the temperature sensor, humidity sensor, and gas sensor to the server. For example, suppose the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition. Based on this data, the server calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%) and sends the results to the device. The device then adjusts the temperature inside the refrigerator to 1°C and humidity 70%, and notifies the user that "the storage environment for the salmon has been optimized."

[1727] Prompt Sentence Examples

[1728] "What temperature and humidity settings are appropriate for optimal storage of cabbage?"

[1729] "What are the optimal environmental conditions for storing salmon in a refrigerator for a long period of time?"

[1730] This system provides optimal storage conditions for a variety of foods, thereby maintaining food quality for a long period of time. It also allows users to efficiently manage the storage environment, reducing food waste and easing the economic burden.

[1731] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1732] Step 1: Initialization

[1733] After starting up, the terminal first performs a self-diagnosis of the temperature sensor, humidity sensor, and gas sensor. This self-diagnosis checks whether each sensor is operating normally, and if there is an abnormality, it records an error message in the log. If it is confirmed that the sensor is operating normally, it proceeds to the next step. The internal status data of the sensor is used as input, and information on whether it is operating normally or not is obtained as output.

[1734] Specific behavior:

[1735] The device receives a wake-up signal.

[1736] Sends self-diagnosis commands to each sensor.

[1737] If there is an error, an error message is logged.

[1738] Step 2: Download communication and initial setting data

[1739] The terminal connects to the server and downloads the initial setting data for the storage conditions. The server receives this and establishes the basic operating conditions for the refrigerator. The input is the initial setting request data from the server, and the output is the initial setting data for the storage conditions.

[1740] Specific behavior:

[1741] The device sends a connection request to the server.

[1742] The server generates initial setting data and sends it to the terminal.

[1743] The device receives the initial setting data and stores it in its internal memory.

[1744] Step 3: Data Acquisition

[1745] The sensors in the device measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. This data is stored in memory at each measurement interval. The input is the physical environmental data inside the refrigerator, and the output is the measurement data of temperature, humidity, and gas concentration.

[1746] Specific behavior:

[1747] The temperature sensor measures the temperature and stores the value in memory.

[1748] The humidity sensor measures the humidity and stores the value in memory.

[1749] The gas sensor measures the gas concentration and stores the value in memory.

[1750] Step 4: Send data

[1751] The terminal transmits the acquired temperature, humidity, and gas concentration data to the server in real time. The data is transmitted via a communication module (e.g., Wi-Fi module). The input is the various measured data, and the output is the data transmitted to the server.

[1752] Specific behavior:

[1753] The terminal packetizes the measured data.

[1754] Send data to the server via the Wi-Fi module.

[1755] Wait for an acknowledgment after sending data.

[1756] Step 5: Data analysis

[1757] The server analyzes the received data using a generative AI model (e.g., TensorFlow) to calculate optimal storage conditions based on input data (temperature, humidity, gas concentration). The input is real-time environmental data, and the output is optimal storage conditions.

[1758] Specific behavior:

[1759] The server stores the received data in a database.

[1760] Input new data into the AI ​​model.

[1761] The AI ​​model performs analysis and outputs the optimal storage conditions.

[1762] Step 6: Submit optimal storage conditions

[1763] The server sends the optimal storage conditions to the terminal as the analysis results, allowing the terminal to receive the optimal storage conditions data as input and prepare to proceed to the next step as output.

[1764] Specific behavior:

[1765] The server generates optimal storage conditions.

[1766] The generated storage conditions are sent to the terminal.

[1767] The server waits for an acknowledgment from the terminal.

[1768] Step 7: Adjusting the environment

[1769] The terminal automatically adjusts the temperature, humidity, and gas environment inside the refrigerator based on the received storage conditions. The control module operates the temperature control system, humidity control system, and gas control system. The input is the optimal storage condition data, and the output is the adjusted environmental data inside the refrigerator.

[1770] Specific behavior:

[1771] The temperature control module regulates the cooling system.

[1772] The humidity control module operates the humidifier and dehumidifier.

[1773] The gas control module performs ventilation and filtering.

[1774] Step 8: User Notification

[1775] The device notifies the user of the analysis results and new storage conditions via a display or smartphone app. The input is the optimal storage condition information, and the output is a user notification message.

[1776] Specific behavior:

[1777] Storage conditions are displayed on the screen.

[1778] Sends push notifications to smartphone apps.

[1779] The user checks the notification content.

[1780] Step 9: Data recording

[1781] The terminal and server record the environmental data and storage condition adjustment history. This data is used for future reference and analysis. The input is the actual environmental data and storage condition data, and the output is the recorded history data.

[1782] Specific behavior:

[1783] Environmental data and storage condition data are stored in memory.

[1784] Sync data to the server.

[1785] Regular backups are performed.

[1786] This detailed processing step allows for optimal storage conditions in the refrigerator and maintains food quality.

[1787] (Application example 1)

[1788] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1789] While technology already exists to optimize food storage conditions in refrigerators, it is not practical in large-scale food management environments such as logistics centers. Furthermore, there is a lack of systems that allow workers to instantly check and respond to the status inside the refrigerator. Therefore, there is a need for technology that can monitor storage conditions in real time and instantly notify workers of the necessary information.

[1790] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1791] In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, a sensor means for measuring the gas concentration inside the refrigerator, a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server, an artificial intelligence means in the analysis server for optimizing storage conditions based on the data, a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator, and a display means for displaying the storage conditions and analysis results in real time on smart glasses. This enables workers in large-scale food management environments such as logistics centers to instantly check the status inside the refrigerator and take appropriate action. Furthermore, optimizing storage conditions can improve food quality maintenance and reduce waste.

[1792] "Sensor means for measuring the temperature inside the refrigerator" refers to a device installed to measure the internal temperature of the refrigerator in real time.

[1793] The "sensor means for measuring humidity inside a refrigerator" is a device installed to measure the internal humidity of a refrigerator in real time.

[1794] The "sensor means for measuring the gas concentration inside the refrigerator" is a device installed to measure the concentration of a specific gas, such as ethylene gas, present inside the refrigerator in real time.

[1795] The "communication means for transmitting the measured temperature, humidity, and gas concentration data to the analysis server" refers to a device or system for transmitting the data measured inside the refrigerator to the analysis server via a wireless or wired network.

[1796] "Artificial intelligence means for optimizing storage conditions based on the data in the analysis server" refers to artificial intelligence technology used to analyze the received temperature, humidity, and gas concentration data and calculate and determine the optimal storage conditions for each food item.

[1797] "Control means for transmitting optimized storage conditions to the refrigerator and automatically adjusting the storage conditions inside the refrigerator" refers to a device or system for automatically adjusting the internal environment of the refrigerator in accordance with the optimized storage conditions obtained from the analysis server.

[1798] The "display means for displaying storage conditions and analysis results in real time on smart glasses" is a system for instantly displaying the storage conditions and analysis results inside the refrigerator on smart glasses worn by workers.

[1799] This invention is a system for optimizing storage conditions in a refrigerator. In one embodiment, various sensors, a communication module, an analysis server, a control module, and smart glasses used by the user all work together in the refrigerator. The details are described below.

[1800] Overall system overview

[1801] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions. Each refrigerator is equipped with a temperature sensor, a humidity sensor, and a gas sensor. Data obtained from these sensors is sent to the analysis server in real time via a communication module. The analysis server uses this data to optimize storage conditions using an AI model. The optimized conditions are applied inside the refrigerator via a control module. Using smart glasses, workers can also check the status of storage conditions and any abnormalities in real time.

[1802] Program processing overview

[1803] The system operates as follows: First, the sensors are initialized and perform a self-diagnosis to ensure normal operation. Next, the temperature, humidity, and gas concentration data inside the refrigerator are collected at regular intervals. This data is sent to the server via the communications module, and the server uses an AI model to analyze and optimize the storage conditions. The results are then transmitted to the refrigerator, and the control module adjusts the refrigerator to the optimal storage conditions. These storage conditions and analysis results are displayed to the user in real time through the smart glasses.

[1804] Components and Data Processing

[1805] Hardware

[1806] Temperature sensor: STMicroelectronics refrigerator temperature sensor

[1807] Humidity sensor: Honeywell humidity sensor

[1808] Gas sensor: Figaro gas concentration sensor

[1809] Communication module: Qualcomm wireless communication module

[1810] Analysis server: High-performance cloud server (Google Cloud, AWS, etc.)

[1811] Smart Glasses: Google Glass Enterprise Edition

[1812] software

[1813] Data processing and calculation: Implementing AI models using Python and TensorFlow

[1814] Communication: Sending and receiving data using HTTP protocol and REST API

[1815] User interface: Smart glasses app for Android or iOS

[1816] Specific examples

[1817] The system operates as follows inside a refrigerator. For example, in a refrigerator containing cabbage, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration. This data is sent to a server, which uses an AI model to calculate the optimal storage conditions for the cabbage (for example, a temperature of 3°C and a humidity of 80%). The results are transmitted to the refrigerator, and the control module adjusts the temperature inside the refrigerator to 3°C and 80% humidity. At the same time, the smart glasses display the message, "The cabbage storage environment has been optimized."

[1818] Prompt example

[1819] "Please implement an AI algorithm to optimize storage conditions based on data from inside the refrigerator. In particular, please include a method for calculating optimal storage conditions that takes into account the type of food, amount, and acquired data (temperature, humidity, gas concentration)."

[1820] The above is a specific embodiment of the present invention. This system makes it possible to optimize food storage conditions, which can greatly contribute to maintaining quality and reducing waste.

[1821] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1822] Step 1:

[1823] The terminal starts up and initializes the sensors. Specifically, it performs self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. The input is the self-diagnosis command for each sensor, and the output is the normal operating status of the sensor.

[1824] Step 2:

[1825] The sensors measure the temperature, humidity, and gas concentration inside the refrigerator at regular intervals. The input is environmental data inside the refrigerator, and the output is a data set of temperature, humidity, and gas concentration. Specifically, the temperature sensor measures temperature data, the humidity sensor measures humidity data, and the gas sensor measures gas concentration data.

[1826] Step 3:

[1827] The acquired data is sent in real time to an analysis server via a communication module. The input is temperature, humidity, and gas concentration data, and the output is the data sent to the server. Specifically, each piece of data is sent to the server using the HTTP protocol.

[1828] Step 4:

[1829] The server uses the received data to analyze storage conditions using an AI model. The input is the acquired temperature, humidity, and gas concentration data, and the output is the optimal storage conditions. Specifically, the AI ​​model performs analysis taking into account the type and quantity of food, and calculates the optimal temperature, humidity, and gas environment.

[1830] Step 5:

[1831] The server sends the calculated optimal storage conditions to the terminal. The input is the optimal storage conditions, and the output is the storage condition data sent to the terminal. Specifically, the optimal storage conditions are sent again to the terminal using the HTTP protocol.

[1832] Step 6:

[1833] The terminal automatically adjusts the environment inside the refrigerator based on the optimal storage conditions received. The input is the optimal storage conditions, and the output is the adjusted temperature, humidity, and gas environment inside the refrigerator. Specifically, the temperature control module changes the set temperature, the humidity control module changes the set humidity, and the gas control module performs the necessary ventilation and gas filtering.

[1834] Step 7:

[1835] The device notifies the user of the analysis results and new storage conditions via the smart glasses. The input is the analysis results and optimal storage condition data, and the output is the information displayed on the smart glasses display. Specifically, a message such as "The storage environment has been optimized" is displayed along with a list of the current status and adjustment details.

[1836] Step 8:

[1837] The environmental data and storage condition adjustment history are recorded on both the terminal and the server. The input is the data generated at each step, and the output is the record stored in the database. Specifically, the history of storage conditions and the changes in environmental data are recorded in the log database so that they can be referenced later.

[1838] These are the specific processing steps of the system based on the application example. This system enables efficient monitoring and management of the storage environment, thereby maintaining food quality and reducing waste.

[1839] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1840] MODE FOR CARRYING OUT THE INVENTION

[1841] The present invention is a system that optimizes storage conditions in a refrigerator and recognizes and responds to user emotions. Specific embodiments of the system will be described below.

[1842] Overall system overview

[1843] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and the emotion engine.

[1844] Program processing overview

[1845] The operation of this system will now be described.

[1846] Initialization

[1847] When the terminal starts up, it first initializes the sensor means. Specifically, it performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally. If the diagnosis result is normal, the sensors transition to an operational state.

[1848] After the initialization is complete, the device connects to the server and receives the initial setting data for the storage conditions. This data is recorded in the device and becomes the basis for future operations.

[1849] Data Acquisition and Transmission

[1850] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[1851] The device sends measurement data, including temperature, humidity, and gas concentration, to the server. This data is sent in real time, allowing the server to maintain up-to-date environmental information.

[1852] Data analysis and optimization of storage conditions

[1853] The server analyzes the received data. The artificial intelligence in the server analyzes the data and calculates the optimal storage conditions for the stored food. The server has a food database, and calculations are made based on the optimal storage conditions for each food.

[1854] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[1855] environmental adjustment

[1856] Based on the conditions received by the terminal, the environment inside the refrigerator is automatically adjusted: the temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[1857] User Notifications

[1858] The device notifies the user of the analysis results and new storage conditions. This notification is sent via the refrigerator's display or a connected smartphone app. A message such as "The cabbage storage environment has been optimized" is displayed.

[1859] Emotion recognition and response

[1860] The emotion engine recognizes the user's emotions and generates emotion data by analyzing the user's facial expressions and tone of voice.

[1861] The server then uses the emotion data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[1862] The system records the user's emotional data and adjusts notifications based on past emotional data. For example, when saving a food item that previously generated a happy emotion, a positive message will be displayed.

[1863] Data Recording

[1864] Environmental data and storage condition adjustment history are recorded on both the terminal and the server. The recorded data can be referenced later and also used for long-term data analysis, enabling continuous optimization of the storage environment.

[1865] Example 1: Maintaining vegetable quality and emotion recognition

[1866] 1. Initialization

[1867] The device starts up and initializes the sensor.

[1868] Connect to the server and receive the initial configuration.

[1869] 2. Data Acquisition and Transmission

[1870] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[1871] The data is sent to the server.

[1872] 3. Data analysis

[1873] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[1874] 4. Sending results and adjusting the environment

[1875] The server transmits the storage conditions to the terminal.

[1876] The device will adjust the temperature inside the refrigerator to 3°C and the humidity to 80%.

[1877] 5. User Notices

[1878] The device notifies the user that "the cabbage storage environment has been optimized."

[1879] 6. Emotion Recognition and Response

[1880] The emotion engine recognizes the user's happy facial expression and displays the message, "The optimal storage environment has been set! We're glad to be of service to you."

[1881] 7. Data Recording

[1882] The adjustment history is recorded and can be referenced later.

[1883] Example 2: Maintaining seafood quality and emotion recognition

[1884] 1. Initialization

[1885] The device starts up and initializes the sensor.

[1886] Connect to the server and receive the initial configuration.

[1887] 2. Data Acquisition and Transmission

[1888] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[1889] The data is sent to the server.

[1890] 3. Data analysis

[1891] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[1892] 4. Sending results and adjusting the environment

[1893] The server transmits the storage conditions to the terminal.

[1894] The device will adjust the temperature inside the refrigerator to 1°C and the humidity to 70%.

[1895] 5. User Notices

[1896] The device notifies the user that "the salmon storage environment has been optimized."

[1897] 6. Emotion Recognition and Response

[1898] The emotion engine recognizes the user's stress and displays the message, "The salmon storage conditions have been optimized. Please let it cool down and enjoy it."

[1899] 7. Data Recording

[1900] The adjustment history is recorded and can be referenced later.

[1901] This system provides optimal storage conditions for various foods, thereby maintaining their quality for a long period of time. It also responds flexibly to user preferences, making it a more personalized and user-friendly system.

[1902] The processing flow will be explained below.

[1903] Step 1:

[1904] When the terminal starts up, it first initializes the sensor means. It performs a self-diagnosis to check whether the temperature sensor, humidity sensor, and gas sensor are operating normally, and if they are normal, the sensors transition to an operational state.

[1905] Step 2:

[1906] The terminal connects to the server and receives the initial setting data. The server sends the basic settings of the storage conditions, and the terminal records this data and sets the basic operation.

[1907] Step 3:

[1908] Sensors in the device measure environmental data inside the refrigerator at regular intervals (for example, every 5 minutes). The temperature sensor measures the current temperature, the humidity sensor measures the humidity, and the gas sensor measures the gas concentration.

[1909] Step 4:

[1910] The device sends measurement data, including temperature, humidity, and gas concentration, to the server in real time, ensuring that the server always has up-to-date environmental information.

[1911] Step 5:

[1912] The server uses AI to analyze the received data, and then refers to the optimal storage conditions for food from a database and calculates the optimal temperature, humidity, and gas environment for each food item stored.

[1913] Step 6:

[1914] The server calculates the optimal storage conditions and sends them to the terminal. For example, if the optimal storage conditions for cabbage are calculated to be a temperature of 3°C and a humidity of 80%, these conditions are sent to the terminal.

[1915] Step 7:

[1916] The terminal adjusts the environment inside the refrigerator based on the storage conditions received. The temperature control module changes the set temperature to 3°C, the humidity control module changes the set humidity to 80%, and the gas control module performs the necessary ventilation and filtering.

[1917] Step 8:

[1918] The device notifies the user of the results of the environmental adjustments, either through the refrigerator display or a connected smartphone app, with a message saying, "The cabbage storage environment has been optimized."

[1919] Step 9:

[1920] The emotion engine recognizes the user's emotions. It uses a camera and microphone to analyze the user's facial expressions and tone of voice to generate emotion data.

[1921] Step 10:

[1922] The server then uses the emotional data to suggest adjustments to storage conditions. For example, if the user is feeling stressed, the server might suggest organizing the refrigerator or prioritize displaying foods with long shelf lives.

[1923] Step 11:

[1924] The user's emotional data is recorded and saved as historical data, and is reflected in future notifications and suggestions.

[1925] Step 12:

[1926] Environmental data and storage condition adjustment history are recorded on both the terminal and the server, making the historical data available for later reference and enabling long-term data analysis.

[1927] Example 2

[1928] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1929] Maintaining an appropriate storage environment is essential to keeping food fresh in a refrigerator for a longer period of time. At the same time, interactive environment settings that take into account the user's emotions and stress level are required to improve the user's comfortable use experience. However, conventional refrigerator systems lack a mechanism to address these factors in an integrated manner, which can lead to problems such as food storage conditions being left suboptimal or the burden of adjusting the settings themselves. Another issue is that the system does not provide appropriate suggestions that take the user's emotions into account, preventing it from providing a high level of convenience to users.

[1930] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for measuring the temperature inside the refrigerator, a sensor means for measuring the humidity inside the refrigerator, and a sensor means for measuring the gas concentration inside the refrigerator. This makes it possible to acquire and analyze environmental data inside the refrigerator in real time. In addition, by combining an emotion recognition means for recognizing the user's emotion and a suggestion means for suggesting adjustments to storage conditions based on the user's emotion data, flexible responses according to the user's emotion become possible. As a result, food storage conditions are optimized, and personalized suggestions are made to the user, improving convenience.

[1931] "Sensor means" refers to a device or function utilized to measure temperature, humidity, and gas concentrations within the refrigerator.

[1932] "Communication means" refers to the device or protocol used to transmit the measured data to a computer that performs the analysis.

[1933] "Artificial intelligence means" refers to software or algorithms that are used in the computer that performs the analysis to analyze the acquired data and calculate optimal storage conditions.

[1934] "Control means" refers to a device or software that communicates optimized storage conditions to the refrigerator and automatically adjusts the environment.

[1935] The "emotion recognition means" is a device or software for analyzing the user's facial expressions and tone of voice and generating emotion data.

[1936] The "suggestion means" is a device or software for proposing adjustments to storage conditions based on the user's emotional data.

[1937] A "notification mechanism" is a device or protocol for communicating the system's analysis results and recommendations to the user.

[1938] MODE FOR CARRYING OUT THE INVENTION

[1939] The present invention is a system for optimizing storage conditions in a refrigerator and recognizing and responding to user emotions. Specific embodiments of the system will be described below.

[1940] Overall system overview

[1941] This system monitors the temperature, humidity, and gas concentration inside the refrigerator in real time, and sends the obtained data to an analysis server to optimize storage conditions.It also uses an emotion engine to recognize the user's emotions and proposes and notifies the user to adjust storage conditions based on those emotions.The system's components include various sensors placed inside the refrigerator (temperature sensor, humidity sensor, gas sensor), a communication module, an analysis server, a control module, a user notification function, and an emotion recognition engine.

[1942] Hardware and Software Configuration

[1943] 1. Sensor means

[1944] Temperature sensor: Used to measure the temperature inside the refrigerator.

[1945] Humidity sensor: Used to measure the humidity inside the refrigerator.

[1946] Gas sensors: Used to measure gas concentrations (e.g., ethylene concentration) inside a refrigerator.

[1947] 2. Means of communication

[1948] A communication module (e.g., Wi-Fi module) for transmitting measured data to an analysis server in real time.

[1949] 3. Analysis (server-side artificial intelligence means)

[1950] The analysis server has a generative AI model and food database for analyzing the received data. The AI ​​analyzes the data and calculates the optimal storage conditions.

[1951] 4. Control Measures

[1952] The device automatically adjusts the environment inside the refrigerator based on the storage conditions received by the terminal. It includes a temperature control module, humidity control module, and gas control module.

[1953] 5. User Notification Methods

[1954] A display and smartphone application to inform the user of the analysis results and suggestions for adjusting storage conditions.

[1955] 6. Emotion recognition means

[1956] A camera and microphone for analyzing the user's facial expressions and tone of voice to generate emotional data.

[1957] 7. Proposal method

[1958] A generative AI model that proposes adjustments to storage conditions based on user emotional data.

[1959] Specific operation example

[1960] 1. Maintaining vegetable quality and emotion recognition

[1961] Initialization

[1962] The device starts up and initializes the sensor.

[1963] Connect to the server and receive the initial configuration.

[1964] Data Acquisition and Transmission

[1965] Inside the refrigerator where the cabbage is stored, the temperature sensor measures 5°C, the humidity sensor measures 50%, and the gas sensor measures the ethylene concentration.

[1966] The data is sent to the server.

[1967] Data analysis and results transmission

[1968] The server receives the data and calculates the optimal storage conditions for the cabbage (for example, a temperature of 3°C and humidity of 80%).

[1969] The server transmits the storage conditions to the terminal.

[1970] environmental adjustment

[1971] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 3°C and the humidity to 80%.

[1972] User Notifications

[1973] The device notifies the user that "the cabbage storage environment has been optimized."

[1974] Emotion Recognition and Response

[1975] The emotion recognition engine recognizes the user's joy and displays the message, "Optimal saving environment has been set! We're glad to help you."

[1976] Data Recording

[1977] Record your adjustment history for future reference.

[1978] Prompt Sentence Examples

[1979] "I've stored the cabbage in the refrigerator. Please set the optimal storage conditions."

[1980] 2. Maintaining seafood quality and emotion recognition

[1981] Initialization

[1982] The device starts up and initializes the sensor.

[1983] Connect to the server and receive the initial configuration.

[1984] Data Acquisition and Transmission

[1985] Inside the refrigerator where the salmon is stored, the temperature sensor measures 2°C, the humidity sensor measures 60%, and the gas sensor measures the appropriate air composition.

[1986] The data is sent to the server.

[1987] Data analysis and results transmission

[1988] The server receives the data and calculates the optimal storage conditions for the salmon (for example, temperature 1°C, humidity 70%).

[1989] The server transmits the storage conditions to the terminal.

[1990] environmental adjustment

[1991] Based on the conditions received by the terminal, the temperature inside the refrigerator is adjusted to 1°C and the humidity to 70%.

[1992] User Notifications

[1993] The device notifies the user that "the salmon storage environment has been optimized."

[1994] Emotion Recognition and Response

[1995] The emotion recognition engine recognizes the user's stress and displays the message, "The storage conditions for the salmon have been optimized. Please let it cool down and enjoy it."

[1996] Data Recording

[1997] Record your adjustment history for future reference.

[1998] Prompt Sentence Examples

[1999] "I've stored the salmon in the refrigerator. Please set the optimal storage conditions."

[2000] This system can provide optimal storage conditions for various foods, maintaining their quality for a long period of time. It also allows for flexible responses that take into account the user's feelings, making it a more personalized and user-friendly system.

[2001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2002] Step 1: Initialization

[2003] When the device boots up, it automatically begins self-testing the temperature, humidity, and gas sensors, sending each sensor a test signal for a few seconds and evaluating its response.

[2004] Input: The refrigerator powers on.

[2005] Data calculation: Self-diagnosis to check whether the sensor is operating normally.

[2006] Output: The sensor is normal.

[2007] If the result is normal, the sensor transitions to an operational state, and the terminal connects to the analysis server to receive the initial setting data, which includes the initial storage conditions (e.g., temperature 5°C, humidity 60%).

[2008] Input: Sensor initialization complete, request to connect to server.

[2009] Data calculation: Connect to the server via a communication means and request setting data.

[2010] Output: Receives initial setting data (temperature 5°C, humidity 60%).

[2011] Step 2: Data Acquisition

[2012] Each sensor on the device measures the temperature, humidity, and gas concentration inside the refrigerator at regular intervals (for example, every 5 minutes).

[2013] Input: Acquisition timing (every 5 minutes).

[2014] Data calculation: The temperature sensor measures the current temperature, the humidity sensor measures the current humidity, and the gas sensor measures the current gas concentration.

[2015] Output: Data for temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[2016] Step 3: Send data

[2017] The environmental data (temperature, humidity, gas concentration) acquired by the device is sent to an analysis server in real time.

[2018] Input: Temperature 5°C, humidity 50%, ethylene concentration 0.02 ppm.

[2019] Data calculation: Data is packaged and sent to a server via a communication means.

[2020] Output: Data sent to analysis server completed.

[2021] Step 4: Data analysis

[2022] The server analyzes the received data, and the server's...

Claims

1. sensor means for measuring the temperature within the refrigerator; sensor means for measuring humidity within the refrigerator; sensor means for measuring gas concentrations within the refrigerator; a communication means for transmitting the measured temperature, humidity, and gas concentration data to an analysis server; an artificial intelligence means in the analysis server for optimizing storage conditions based on the data; a control means for transmitting the optimized storage conditions to the refrigerator and automatically adjusting the storage conditions within the refrigerator; A system including:

2. The system according to claim 1, wherein the temperature, humidity, or gas environment inside the refrigerator is adjusted based on the optimized storage conditions.

3. The system according to claim 1 , further comprising a notification means for notifying a user of the optimization result of the storage conditions.

4. The system according to claim 1 , further comprising a recording means for recording the data and for performing historical data reference or statistical analysis.

Images