system

Abstract

We provide the system. [Solution] An acquisition means for measuring environmental data inside a refrigerator and obtaining measurement information, A transmission means for transmitting the aforementioned measurement information to a terminal device, The terminal device includes a storage means for storing the measurement information in a database on the cloud, An analysis means that acquires the measurement information from the aforementioned cloud database and performs analysis, A generation means for generating guidance information for energy conservation based on the analysis results, A notification means for transmitting the aforementioned instruction information to the terminal device and notifying the user, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the operation of a household refrigerator is 24 hours, its power consumption is large, which is a factor increasing the electricity bill of the household. There is a need to provide a system that realizes reduction of the electricity bill and improvement of energy conservation awareness by suppressing wasteful power consumption of the refrigerator and promoting efficient energy utilization.

Means for Solving the Problems

[0005] This invention comprises means for acquiring environmental data inside a refrigerator, means for transmitting the measurement information to a terminal device, and means for storing the measurement information in a cloud-based database. Furthermore, it provides analysis and generation means for analyzing the data acquired from the cloud-based database and generating guidance information for energy conservation. By transmitting this generated guidance information to the terminal device and notifying the user, the invention aims to improve the user's energy-saving behavior and reduce power consumption.

[0006] "Acquisition means" refers to a device or method for measuring and acquiring environmental data such as the temperature inside a refrigerator and the state of opening and closing the refrigerator door.

[0007] "Transmission means" refers to a device or method for transmitting acquired measurement information to a terminal device.

[0008] "Storage means" refers to a system or method for storing measurement information received by a terminal device in a cloud-based database.

[0009] "Analysis means" refers to a device or method that has the function of analyzing refrigerator usage patterns using information obtained from a database on the cloud.

[0010] "Generation means" refers to a system or method that generates specific guidance information and suggestions for energy conservation based on the analysis results obtained by the analysis means.

[0011] "Notification means" refers to a device or method that transmits generated instructional information to the user's terminal device and informs the user. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

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

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

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

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

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0033] This invention is implemented as a system for effectively managing the power consumption of a refrigerator and promoting energy conservation. A measuring device installed inside the refrigerator acquires temperature changes and door open / closed status in real time and periodically transmits this information to a smartphone (terminal). Upon receiving the data, the terminal saves it to a cloud-based database via the internet.

[0034] The server analyzes data collected in the cloud to analyze refrigerator usage. Specifically, it evaluates temperature fluctuations and door opening / closing frequency over time and detects how these affect power consumption.

[0035] If the analysis reveals unnecessary power consumption, the server generates specific energy-saving advice for the user. This advice is sent as a push notification to the user's smartphone, prompting them to review their own habits.

[0036] For example, if the server detects that a user's refrigerator is being opened and closed frequently at night, it will advise them to refrain from doing so. The user receives this advice via a notification on their smartphone and can reduce power consumption by improving their habits.

[0037] Thus, the system of the present invention aims to reduce household electricity costs by providing concrete and continuous support for energy-saving behavior through the linkage of measuring devices, terminals, and the cloud.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] A measuring device installed inside the refrigerator senses the temperature and the status of the door opening and closing in real time. The sensed data is detected and collected as a sample every 5 minutes.

[0041] Step 2:

[0042] The device receives data from the measuring device via Bluetooth or Wi-Fi. The received data is stored in the device's temporary storage.

[0043] Step 3:

[0044] The device periodically connects to the cloud service and uploads the collected data. The data is stored in a cloud database along with the date and time.

[0045] Step 4:

[0046] The server periodically checks the cloud database to retrieve new data. The server uses this data to analyze the refrigerator's temperature changes and opening / closing patterns.

[0047] Step 5:

[0048] The analysis detects energy waste during specific time periods. The server identifies the abnormal usage patterns and pinpoints areas for improvement in user behavior.

[0049] Step 6:

[0050] Based on the analysis results, the server generates specific advice for energy saving. This advice may include suggestions such as "reduce opening and closing the refrigerator at night."

[0051] Step 7:

[0052] The device receives advice sent from the server and notifies the user. The notification is sent via push, making it easy for the user to access.

[0053] Step 8:

[0054] Users check notifications from their devices, take action based on energy-saving advice, and promote energy conservation in their homes.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] There is a need to effectively manage the power consumption of refrigerated storage equipment and promote resource conservation. However, conventional systems have made it difficult to grasp the specific usage status within refrigerated storage equipment in real time and provide energy-saving guidance based on that information. The present invention aims to solve this problem and provide a system that provides clear and actionable resource-saving guidance to users.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes a collection means, a communication means, and a storage means. This makes it possible to grasp the usage status of the refrigerated storage device in real time, generate specific guidance information for resource conservation, and provide it to the user.

[0060] "Collection means" refers to devices or functions used to measure and acquire environmental information within a refrigerated storage device.

[0061] "Communication means" refers to devices or functions used to transmit acquired information to an information processing device.

[0062] "Storage means" refers to devices or functions for saving information acquired by an information processing device to a recording area on a base.

[0063] "Evaluation means" refers to devices or functions that analyze information acquired from the recording area on the substrate to analyze environmental changes and operation frequency.

[0064] "Generation means" refers to devices or functions that generate guidance information for resource conservation based on evaluation results.

[0065] "Notification means" refers to a device or function that transmits generated guidance information to an information processing device and notifies the user.

[0066] This invention is a system for managing the power consumption of refrigerated storage equipment and promoting resource conservation. This system tracks environmental information in real time, generates specific guidance information for resource conservation based on that information, and provides it to the user.

[0067] A measuring device installed inside the refrigerated storage unit collects environmental information such as temperature and door open / closed status. This information is transmitted to the user's information processing device via Bluetooth or Wi-Fi. The information processing device stores the received data in a storage area on the base via the internet. Cloud services such as Amazon Web Services and Google Cloud are used in this process.

[0068] The server retrieves data from the storage area on the infrastructure and performs analysis using data analysis software such as Python or R. The server analyzes hourly temperature changes and the frequency of door opening and closing, and evaluates users' energy consumption behavior based on the results. This evaluation identifies unnecessary power consumption.

[0069] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. This generation process uses the prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" as input and provides appropriate action suggestions.

[0070] For example, if the server detects through analysis that the door of a refrigerated storage unit is frequently opened and closed at night, the AI ​​model generates specific advice such as "It is recommended to reduce the frequency of opening and closing the door at night." This advice is then notified to the user via the information processing device. The user can then review this notification and use it to improve their resource-saving behavior.

[0071] The overall objective of this system is to improve the resource-efficient operation of refrigeration equipment and support users in easily implementing concrete energy-saving actions through the processes of data collection, storage, analysis, guidance generation, and notification.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal acquires environmental information (temperature, door open / closed status) from a measuring device installed inside the refrigerated storage unit. This input information is collected at time intervals, and the terminal receives this data. Specifically, the terminal polls the data using Bluetooth or Wi-Fi and checks for newly acquired data. At this stage, the input is temperature data and door open / closed status from the measuring device, and the output is sensor data received by the terminal.

[0075] Step 2:

[0076] The terminal transmits and stores acquired environmental information in a storage area on a cloud infrastructure via the internet. The terminal then packets the received data and sends it to cloud services such as Amazon Web Services or Google Cloud. The data received as input is stored as output in a database on the cloud. Specifically, the terminal transfers a certain amount of data in batches at regular intervals.

[0077] Step 3:

[0078] The server retrieves environmental information from storage in the cloud and performs data analysis. The server utilizes data analysis libraries in Python and R to analyze environmental information and evaluate temperature changes and door opening / closing frequency. This process receives raw data stored in the cloud as input and generates evaluation results reporting trends and outliers as output. Specifically, the server performs data analysis using scheduled jobs.

[0079] Step 4:

[0080] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. The prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" is used as input. The output consists of actionable advice for the user. Specifically, the server calls the generation AI model and creates optimal guidance information based on the analysis results.

[0081] Step 5:

[0082] The server sends the generated guidance information to the terminal and notifies the user via push notification. The input is the generated guidance information, which is then transferred to the information processing device, and the user receives the notification as output. Specifically, notifications are made using services such as Firebase Cloud Messaging. Users can check the notification on their smartphone and use it to improve their resource-saving behaviors.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Effectively managing energy consumption within cooling systems and minimizing household electricity usage is crucial from the perspective of environmental protection and cost reduction. However, conventional devices merely maintain temperature and lack efficient energy management methods or means of providing users with appropriate warnings for abnormal behavior. This invention aims to solve these problems and achieve improved energy efficiency and enhanced security.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for measuring environmental data within the cooling device and acquiring measurement information, means for transmitting the measurement information to an information processing device, and means for acquiring the measurement information from an online storage device and performing analysis. This makes it possible to effectively manage energy consumption in the home by generating guidance information to optimize the energy consumption of the cooling device and identifying and warning about abnormal behavior.

[0088] A "cooling device" is an electric device used to preserve food and goods at low temperatures.

[0089] "Environmental data" refers to a collection of measurable information such as temperature, humidity, and the open / closed state of devices.

[0090] "Acquisition means" refers to functions for collecting physical data using sensors and measuring instruments.

[0091] An "information processing device" is a general term for a machine such as a computer or smart device used to receive and process data.

[0092] "Online storage devices" refer to data storage locations accessible via the internet, including cloud storage.

[0093] "Analysis tools" refer to algorithms and programs used to perform analysis based on certain rules using collected data.

[0094] "Generative means" refers to a function that creates specific information or instructional content based on analysis results.

[0095] "Notification means" refers to technologies used to inform users of generated information, specifically including push notifications and email.

[0096] "Monitoring measures" refer to mechanisms and methods for continuously observing a situation and collecting data.

[0097] A "warning mechanism" is a system that issues a warning to the user to draw their attention when an abnormality or malfunction is detected.

[0098] The system that realizes this application effectively manages data within the cooling device, aiming to improve energy efficiency and security. Specifically, it is implemented using the following technologies.

[0099] The cooling system is equipped with various sensors (temperature sensors, on / off sensors, etc.) to measure environmental data. A small computer such as a Raspberry Pi is used to acquire data from these sensors and transmit it to an information processing device at regular intervals. For example, it can be equipped with a function to transmit data in real time when the temperature exceeds a set range.

[0100] Smartphones and PCs, which are information processing devices, save received data to the cloud via the internet. In this process, online storage services such as Firebase are used to ensure secure and efficient data storage. The stored data is then used for analysis on the cloud.

[0101] The server uses analytics platforms such as Google Cloud AI to analyze the collected data. Based on the results, specific guidance information for energy conservation is generated. This guidance information may include, for example, advice on how often to open and close cooling system doors.

[0102] The generated guidance information is notified to the information processing device via Firebase Cloud Messaging, etc., and displayed to the user as advice. This allows the user to review how they are using the cooling device. In addition, if abnormal behavior is detected, a warning is issued, prompting appropriate action.

[0103] For example, if the cooling unit door is left open for more than an hour, a warning message will be sent to the user stating, "The door is left open. Please close it immediately." This system not only prevents energy waste but also identifies security issues in daily operations and supports improved user behavior.

[0104] In generative AI models, the following prompt statements are given as examples.

[0105] "Identify wasted energy consumption based on recent usage patterns of cooling equipment and provide specific advice on how to reduce it."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal acquires data from sensors attached to the cooling system. The input is an analog or digital signal from the sensor, which is processed using a built-in data conversion program to convert it into specific data such as temperature or door open / closed status. The output is periodically generated environmental data.

[0109] Step 2:

[0110] The device transmits the acquired environmental data to a cloud storage device via Wi-Fi or a wired network. The input is the environmental data generated earlier, and the output is this data stored in the cloud. In this process, the device compresses and encrypts the data for secure transmission.

[0111] Step 3:

[0112] The server periodically retrieves environmental data stored in the cloud. The input is environmental data stored in a database on the cloud, and the output is a dataset to be analyzed. The server then organizes the listed data into an appropriate format and prepares it for analysis.

[0113] Step 4:

[0114] The server performs data analysis using a generative AI model. The input is the dataset to be analyzed, and the output is power consumption patterns and energy-saving advice based on them. The server processes the data using specific algorithms to identify abnormal power consumption and potential problems.

[0115] Step 5:

[0116] The server sends the generated advice and warnings to the terminal via a cloud messaging service. The input is the generated advice and warnings, and the output is the message sent to the user. Based on this, the server prepares to inform the user of recommended actions and warnings.

[0117] Step 6:

[0118] The user receives advice and warnings via push notifications on their device. The input is the notification message received on the device, and the output is the user reviewing it and taking appropriate action. This process allows the user to be more mindful of reducing power consumption and enhancing security.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention implements a system for efficiently managing electricity consumption from refrigerator use and promoting energy-saving behavior. The system begins with a measuring device installed in the refrigerator measuring internal environmental data, namely temperature and door opening / closing operations. The measured data is transmitted to a terminal device within the home. This terminal stores the data in a cloud-based database, where it is analyzed by a server. The server analyzes the refrigerator's usage patterns and generates energy-saving advice.

[0121] Furthermore, this system incorporates an emotion engine, adding the ability to recognize the user's emotions. Using the camera and microphone on the device, it analyzes the user's voice and facial expressions in real time to recognize their emotions. Based on the analysis results and emotion data, the server modifies the advice according to the user's emotional state and notifies them at the optimal time and in the most appropriate way.

[0122] For example, when a user is feeling tired, a message with concise instructions and encouragement is sent. Conversely, if the user is interested, more detailed data and specific recommended actions are presented. This allows users to take energy-saving actions in a way that takes their own emotions into consideration.

[0123] Thus, the present invention is a system that supports the efficient use of refrigerators while simultaneously promoting behavioral change in users through an approach that utilizes emotion recognition, thereby enabling long-term reduction in power consumption and improvement of energy conservation awareness.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] A measuring device installed inside the refrigerator senses environmental data such as temperature and door opening / closing status in real time. The data is collected at regular intervals to maintain up-to-date information.

[0127] Step 2:

[0128] The device receives data from the measuring device via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local storage.

[0129] Step 3:

[0130] The device periodically connects to the cloud service and uploads the collected data to a database in the cloud. The data is then permanently stored in the cloud along with time information.

[0131] Step 4:

[0132] The server retrieves new data from the cloud database. The server analyzes this data and gains insights into power consumption by investigating temperature changes and the frequency of door openings and closings.

[0133] Step 5:

[0134] If the analysis reveals any anomalies or areas for improvement regarding power consumption, the server will generate energy-saving advice. This advice will include instructions to change the user's behavior.

[0135] Step 6:

[0136] The device uses its camera and microphone to analyze the user's emotions in order to detect their voice and facial expressions. This emotional data is processed in real time.

[0137] Step 7:

[0138] Based on information provided by the emotion engine, the server adjusts the content of advice and notification methods to match the user's emotional state. For example, if the emotional state is positive, it provides detailed information, while if it is negative, it sends supportive messages.

[0139] Step 8:

[0140] The device provides users with push notifications offering advice tailored to their emotions. Users can review this advice and improve their energy-saving awareness by reflecting on their own behavior.

[0141] Step 9:

[0142] Based on notifications received by the user, the system becomes aware of refrigerator opening and closing behavior and power consumption trends, allowing users to adjust their daily actions and practice energy-saving behaviors.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] The challenge lies in reducing refrigerator energy consumption while promoting energy-saving practices that take into account users' behavior and emotions. Conventional systems provide uniform advice without considering users' emotional states, which hinders behavioral change.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for measuring and acquiring environmental data, means for collecting and recognizing emotional information, and means for generating guidance information based on the analysis results and emotional information. This makes it possible to provide advice that encourages optimal energy-saving behavior while taking into consideration the user's emotions.

[0148] "Acquisition method" refers to a means of measuring the temperature inside the refrigerator and the open / closed state of the door in real time and acquiring that data.

[0149] "Transmission means" refers to means for transmitting acquired data to a data processing device.

[0150] "Storage means" refers to means for saving data acquired by a data processing device to a remote storage device.

[0151] "Analysis means" refers to a method for analyzing refrigerator usage patterns based on data acquired from remote storage devices.

[0152] "Recognition means" refers to means for collecting emotional information through sound and images and recognizing the emotional state of the user.

[0153] "Generation means" refers to means for generating guidance information for energy conservation based on analysis results and emotional information.

[0154] A "notification means" is a means of transmitting generated instructional information to a data processing device and appropriately notifying the user.

[0155] The system of the present invention efficiently manages the energy consumption of refrigerators and promotes energy-saving behavior that takes into consideration the user's feelings. Specifically, the system is composed of terminal devices, a server, and a cloud.

[0156] The terminal device acquires environmental data through a measuring device installed inside the refrigerator. The measuring device includes sensors that monitor the refrigerator's temperature and door open / closed status in real time. This environmental data is securely stored in a cloud-based database via the terminal device. The terminal device is also equipped with a camera and microphone, allowing it to collect emotional information by capturing the user's voice and facial expressions.

[0157] The server retrieves environmental data from a cloud-based database and also receives and analyzes emotional information. The server utilizes analytical algorithms and generative AI models to analyze the user's refrigerator usage patterns and emotional state. Based on the analysis results, it generates specific guidance information to promote energy-saving behaviors. This generated guidance information is personalized, taking into account the user's emotional state.

[0158] The terminal device notifies the user of guidance information received from the server. By receiving this advice, the user can improve their refrigerator usage and manage energy more efficiently.

[0159] For example, if a user is frequently opening and closing the refrigerator door, the server will generate advice such as, "Reducing the opening and closing of the door and taking items out all at once when needed will save energy." If the user looks tired, a gentle message such as, "Take a short break today and try to use the refrigerator more efficiently," will be sent.

[0160] An example of a prompt would be: "Based on refrigerator usage data, please provide specific actions to reduce today's energy consumption by 20%. Also, please give me some concise advice considering my current fatigue level."

[0161] This will enable users to perform energy-saving actions in a way that is emotionally resonant, enjoyable, and efficient.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The terminal acquires environmental data using measuring devices inside the refrigerator. Specifically, it detects the temperature inside the refrigerator with a temperature sensor and checks the door open / closed status with a door open / closed sensor. This input data is processed within the terminal and updated in real time. The output is the latest environmental data, which is ready for use in the next step.

[0165] Step 2:

[0166] The device sends the acquired environmental data to a database in the cloud. Specifically, it securely uploads the data to the cloud using internet protocols. During this process, error checking is performed to confirm that the data has been transmitted correctly. As output, the environmental data stored in the cloud database is obtained and becomes accessible to the server.

[0167] Step 3:

[0168] The server retrieves environmental data stored in the cloud and begins analysis. It receives temperature change patterns and door opening / closing frequency as input, and applies a machine learning algorithm to analyze refrigerator usage patterns. This analysis reveals energy consumption trends. The output is the analysis of consumption trends based on the data.

[0169] Step 4:

[0170] The device uses a camera and microphone to collect user voice and facial expression data. It takes the user's facial expressions and voice information as input and transmits this data to the server in real time. Specific operations include voice recognition and facial expression recognition processes. The output is data indicating the user's emotional state.

[0171] Step 5:

[0172] The server integrates the results of analyzing acquired user emotion data and environmental data. It receives this data as input and uses a generative AI model to generate energy-saving guidance information tailored to the user's emotions. This process, for example, selects a more concise and encouraging message if the user is tired. The output is user-specific energy-saving advice.

[0173] Step 6:

[0174] The terminal notifies the user of energy-saving advice sent from the server. It conveys the advice received as input to the user through screen display and audio output. Specific operations include scheduling to optimize the timing of notifications. The output is specific and practical guidance information received by the user.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] In recent years, efficient operation and energy conservation of refrigeration equipment have become increasingly important, but there is no system in place to promote energy-saving behavior while taking into account the emotional state of employees. When employees are fatigued or stressed, guidance on energy conservation may not be effective, so there is a need to provide appropriate guidance that is tailored to their emotional state.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes an acquisition means for measuring environmental data within the refrigeration equipment and acquiring measurement information, an analysis means for acquiring the measurement information from an information storage device on the cloud and performing analysis, and a recognition means for recognizing a person's emotional state using an emotion recognition means. This makes it possible to monitor the use of the refrigeration equipment while providing energy-saving guidance tailored to the emotional state of employees.

[0180] "Environmental data" refers to information necessary to understand the operating conditions of refrigeration equipment, such as temperature, humidity, and door opening / closing status inside and around the equipment.

[0181] "Measurement information" refers to information obtained by converting environmental data acquired by measuring devices into a specific data format, and is used for energy conservation analysis.

[0182] "Means of acquisition" refers to functions or devices for measuring and collecting environmental data within refrigeration equipment.

[0183] "Transmission means" refers to functions or devices for transferring measurement information obtained by acquisition means to an information processing device or the cloud.

[0184] An "information processing device" is a device used to temporarily store measurement information and transmit it to the cloud as digital data.

[0185] An "information storage device" is a device that is physically or virtually installed on the cloud and has database functionality for long-term storage of measurement information.

[0186] "Analysis means" refers to functions or devices that analyze measurement information acquired from information storage devices on the cloud and generate usage patterns of refrigeration equipment and proposals for energy saving.

[0187] "Generation means" refers to a function or device for generating specific guidance information, such as energy-saving actions, based on the results of the analysis means.

[0188] "Emotion recognition means" refers to technology or devices that detect a person's voice, facial expressions, etc., and identify their emotional state in real time.

[0189] A "notification means" is a function or device that transmits generated guidance information to an information processing device at an appropriate time to inform the user of advice or guidance.

[0190] This invention relates to a system for efficiently operating refrigeration equipment and promoting energy-saving behavior. The system consists of acquisition means, analysis means, generation means, recognition means, and notification means.

[0191] The server uses sensors to monitor temperature, humidity, door opening / closing status, and other environmental data within the refrigeration equipment, and collects the measurement information through acquisition methods. This information is transmitted to a cloud-based storage device via an information processing device. The server then uses analysis methods to examine the data stored in the cloud and analyze the usage status of the refrigeration equipment. This analysis utilizes machine learning algorithms and AI technology to extract patterns such as temperature fluctuations and door opening / closing frequency.

[0192] Based on the analysis results, the server uses a generation mechanism to create guidance information that promotes energy-saving behavior. This information includes, for example, specific suggestions for efficient operation of refrigeration equipment and energy reduction. Furthermore, a recognition mechanism analyzes the user's voice and facial expressions in real time to identify the user's emotional state. For example, a user feeling fatigued can receive a simple message of encouragement along with some advice. Conversely, a user in a positive state can receive detailed analysis results and specific energy-saving behavior suggestions through a notification mechanism.

[0193] As a concrete example, in one supermarket, the introduction of this system reduced energy consumption during business hours by 25% in its refrigerators. This was because it enabled efficient operation tailored to peak times when employees experience stress, thereby optimizing energy consumption.

[0194] An example of a prompt message would be, "Please provide advice on developing a system using data analysis to improve the energy efficiency of refrigerators."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server acquires environmental data such as temperature, humidity, and door opening / closing status inside the refrigeration equipment from sensors. The input is raw data from the sensors, and the output is formatted measurement information. The server converts this information into a digital format and organizes it into a data structure ready for subsequent processing.

[0198] Step 2:

[0199] The server transmits the acquired measurement information to the information processing device via the network. The input is formatted measurement information, and the output is transfer to the cloud environment. The server securely transmits this information to the cloud's information storage device, making it accessible remotely.

[0200] Step 3:

[0201] The server analyzes measurement data stored in a cloud-based data storage device using analytical tools. The input is the measurement data stored in the cloud, and the output is the analysis result. The server processes the data using machine learning algorithms to identify usage patterns of the refrigeration equipment.

[0202] Step 4:

[0203] The server generates energy-saving guidance information based on the analysis results. The input is the analysis results, and the output is the generated guidance information. The server formulates specific energy-saving proposals using conditional logic.

[0204] Step 5:

[0205] The device acquires user voice and facial expression data using a camera and microphone, and processes it with emotion recognition technology. The input is visual and audio data, and the output is the user's current emotional state. The device performs real-time analysis and obtains results from an emotion engine.

[0206] Step 6:

[0207] The server combines the user's emotional state with generated guidance information and sends it to the user at the optimal time using a notification system. The input is the user's emotional state and guidance information, and the output is the notification content. The server adjusts the message content and tone according to the emotional state and presents the information via the terminal.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] This invention is implemented as a system for effectively managing the power consumption of a refrigerator and promoting energy conservation. A measuring device installed inside the refrigerator acquires temperature changes and door open / closed status in real time and periodically transmits this information to a smartphone (terminal). Upon receiving the data, the terminal saves it to a cloud-based database via the internet.

[0225] The server analyzes data collected in the cloud to analyze refrigerator usage. Specifically, it evaluates temperature fluctuations and door opening / closing frequency over time and detects how these affect power consumption.

[0226] If the analysis reveals unnecessary power consumption, the server generates specific energy-saving advice for the user. This advice is sent as a push notification to the user's smartphone, prompting them to review their own habits.

[0227] For example, if the server detects that a user's refrigerator is being opened and closed frequently at night, it will advise them to refrain from doing so. The user receives this advice via a notification on their smartphone and can reduce power consumption by improving their habits.

[0228] Thus, the system of the present invention aims to reduce household electricity costs by providing concrete and continuous support for energy-saving behavior through the linkage of measuring devices, terminals, and the cloud.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] A measuring device installed inside the refrigerator senses the temperature and the status of the door opening and closing in real time. The sensed data is detected and collected as a sample every 5 minutes.

[0232] Step 2:

[0233] The device receives data from the measuring device via Bluetooth or Wi-Fi. The received data is stored in the device's temporary storage.

[0234] Step 3:

[0235] The device periodically connects to the cloud service and uploads the collected data. The data is stored in a cloud database along with the date and time.

[0236] Step 4:

[0237] The server periodically checks the cloud database to retrieve new data. The server uses this data to analyze the refrigerator's temperature changes and opening / closing patterns.

[0238] Step 5:

[0239] The analysis detects energy waste during specific time periods. The server identifies the abnormal usage patterns and pinpoints areas for improvement in user behavior.

[0240] Step 6:

[0241] Based on the analysis results, the server generates specific advice for energy saving. This advice may include suggestions such as "reduce opening and closing the refrigerator at night."

[0242] Step 7:

[0243] The device receives advice sent from the server and notifies the user. The notification is sent via push, making it easy for the user to access.

[0244] Step 8:

[0245] Users check notifications from their devices, take action based on energy-saving advice, and promote energy conservation in their homes.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] There is a need to effectively manage the power consumption of refrigerated storage equipment and promote resource conservation. However, conventional systems have made it difficult to grasp the specific usage status within refrigerated storage equipment in real time and provide energy-saving guidance based on that information. The present invention aims to solve this problem and provide a system that provides clear and actionable resource-saving guidance to users.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes a collection means, a communication means, and a storage means. This makes it possible to grasp the usage status of the refrigerated storage device in real time, generate specific guidance information for resource conservation, and provide it to the user.

[0251] "Collection means" refers to devices or functions used to measure and acquire environmental information within a refrigerated storage device.

[0252] "Communication means" refers to devices or functions used to transmit acquired information to an information processing device.

[0253] "Storage means" refers to devices or functions for saving information acquired by an information processing device to a recording area on a base.

[0254] "Evaluation means" refers to devices or functions that analyze information acquired from the recording area on the substrate to analyze environmental changes and operation frequency.

[0255] "Generation means" refers to devices or functions that generate guidance information for resource conservation based on evaluation results.

[0256] "Notification means" refers to a device or function that transmits generated guidance information to an information processing device and notifies the user.

[0257] This invention is a system for managing the power consumption of refrigerated storage equipment and promoting resource conservation. This system tracks environmental information in real time, generates specific guidance information for resource conservation based on that information, and provides it to the user.

[0258] Measuring devices installed inside refrigerated storage units collect environmental information such as temperature and door open / closed status. This information is transmitted to the user's information processing device via Bluetooth or Wi-Fi. The information processing device stores the received data in a storage area on its infrastructure via the internet. Cloud services such as Amazon Web Services and Google Cloud are used in this process.

[0259] The server retrieves data from the storage area on the infrastructure and performs analysis using data analysis software such as Python or R. The server analyzes hourly temperature changes and the frequency of door opening and closing, and evaluates users' energy consumption behavior based on the results. This evaluation identifies unnecessary power consumption.

[0260] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. This generation process uses the prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" as input and provides appropriate action suggestions.

[0261] For example, if the server detects through analysis that the door of a refrigerated storage unit is frequently opened and closed at night, the AI ​​model generates specific advice such as "It is recommended to reduce the frequency of opening and closing the door at night." This advice is then notified to the user via the information processing device. The user can then review this notification and use it to improve their resource-saving behavior.

[0262] The overall objective of this system is to improve the resource-efficient operation of refrigeration equipment and support users in easily implementing concrete energy-saving actions through the processes of data collection, storage, analysis, guidance generation, and notification.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The terminal acquires environmental information (temperature, door open / closed status) from a measuring device installed inside the refrigerated storage unit. This input information is collected at time intervals, and the terminal receives this data. Specifically, the terminal polls the data using Bluetooth or Wi-Fi and checks for newly acquired data. At this stage, the input is temperature data and door open / closed status from the measuring device, and the output is sensor data received by the terminal.

[0266] Step 2:

[0267] The terminal transmits and stores acquired environmental information in a storage area on a cloud infrastructure via the internet. The terminal then packets the received data and sends it to cloud services such as Amazon Web Services or Google Cloud. The data received as input is stored as output in a database on the cloud. Specifically, the terminal transfers a certain amount of data in batches at regular intervals.

[0268] Step 3:

[0269] The server retrieves environmental information from storage in the cloud and performs data analysis. The server utilizes data analysis libraries in Python and R to analyze environmental information and evaluate temperature changes and door opening / closing frequency. This process receives raw data stored in the cloud as input and generates evaluation results reporting trends and outliers as output. Specifically, the server performs data analysis using scheduled jobs.

[0270] Step 4:

[0271] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. The prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" is used as input. The output consists of actionable advice for the user. Specifically, the server calls the generation AI model and creates optimal guidance information based on the analysis results.

[0272] Step 5:

[0273] The server sends the generated guidance information to the terminal and notifies the user via push notification. The input is the generated guidance information, which is then transferred to the information processing device, and the user receives the notification as output. Specifically, notifications are made using services such as Firebase Cloud Messaging. Users can check the notification on their smartphone and use it to improve their resource-saving behaviors.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Effectively managing energy consumption within cooling systems and minimizing household electricity usage is crucial from the perspective of environmental protection and cost reduction. However, conventional devices merely maintain temperature and lack efficient energy management methods or means of providing users with appropriate warnings for abnormal behavior. This invention aims to solve these problems and achieve improved energy efficiency and enhanced security.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for measuring environmental data in the cooling device and obtaining measurement information, means for transmitting the measurement information to the information processing device, and means for obtaining the measurement information from the storage device on the online and performing analysis. Thereby, by generating guidance information for optimizing the energy consumption of the cooling device and identifying and warning abnormal behaviors, it becomes possible to effectively manage the energy within the household.

[0279] The "cooling device" is a device that uses electricity to store food and articles at a low temperature.

[0280] The "environmental data" is a set of measurable information such as temperature, humidity, and the opening / closing state inside the device.

[0281] The "acquisition means" is a function for collecting physical data using sensors and measuring instruments.

[0282] The "information processing device" is a general machine that refers to computers, smart devices, etc. for receiving and processing data.

[0283] The "online storage device" is a data storage location accessible via the Internet and includes cloud storage.

[0284] The "analysis means" is an algorithm or program for performing analysis based on certain rules using the collected data.

[0285] The "generation means" refers to a function for creating specific information and guidance content based on the analysis results.

[0286] The "notification means" is a technology for notifying the user of the generated information, specifically including push notifications, emails, etc.

[0287] The "monitoring means" is a mechanism or method for continuously observing the situation and collecting data.

[0288] A "warning mechanism" is a system that issues a warning to the user to draw their attention when an abnormality or malfunction is detected.

[0289] The system that realizes this application effectively manages data within the cooling device, aiming to improve energy efficiency and security. Specifically, it is implemented using the following technologies.

[0290] The cooling system is equipped with various sensors (temperature sensors, on / off sensors, etc.) to measure environmental data. A small computer such as a Raspberry Pi is used to acquire data from these sensors and transmit it to an information processing device at regular intervals. For example, it can be equipped with a function to transmit data in real time when the temperature exceeds a set range.

[0291] Smartphones and PCs, which are information processing devices, save received data to the cloud via the internet. In this process, online storage services such as Firebase are used to ensure secure and efficient data storage. The stored data is then used for analysis on the cloud.

[0292] The server uses analytics platforms such as Google Cloud AI to analyze the collected data. Based on the results, specific guidance information for energy conservation is generated. This guidance information may include, for example, advice on how often to open and close cooling system doors.

[0293] The generated guidance information is notified to the information processing device via Firebase Cloud Messaging, etc., and displayed to the user as advice. This allows the user to review how they are using the cooling device. In addition, if abnormal behavior is detected, a warning is issued, prompting appropriate action.

[0294] For example, if the cooling unit door is left open for more than an hour, a warning message will be sent to the user stating, "The door is left open. Please close it immediately." This system not only prevents energy waste but also identifies security issues in daily operations and supports improved user behavior.

[0295] In generative AI models, the following prompt statements are given as examples.

[0296] "Identify wasted energy consumption based on recent usage patterns of cooling equipment and provide specific advice on how to reduce it."

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The terminal acquires data from sensors attached to the cooling system. The input is an analog or digital signal from the sensor, which is processed using a built-in data conversion program to convert it into specific data such as temperature or door open / closed status. The output is periodically generated environmental data.

[0300] Step 2:

[0301] The device transmits the acquired environmental data to a cloud storage device via Wi-Fi or a wired network. The input is the environmental data generated earlier, and the output is this data stored in the cloud. In this process, the device compresses and encrypts the data for secure transmission.

[0302] Step 3:

[0303] The server periodically retrieves the environmental data stored in the cloud. The input is the environmental data stored in the database on the cloud, and the output is the dataset to be analyzed. The server thereby arranges the listed data in an appropriate format and proceeds with the preparation for analysis.

[0304] Step 4:

[0305] The server performs data analysis using the generated AI model. The input is the dataset to be analyzed, and the output is the power consumption pattern and energy-saving advice based on it. The server processes the data using a specific algorithm to identify abnormal power consumption and potential problems.

[0306] Step 5:

[0307] The server sends the generated advice and warnings to the terminal through the cloud messaging service. The input is the generated advice and warnings, and the output is the message to be notified to the user. Based on this, the server prepares to inform the user of recommended actions and precautions.

[0308] Step 6:

[0309] The user receives the advice and warnings pushed to the terminal. The input is the notification message that reaches the terminal, and the output is for the user to confirm this and take appropriate actions. Through this operation, the user can become aware of reducing power consumption and strengthening security.

[0310] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion. [[ID=3o]]

[0311] This invention implements a system for efficiently managing electricity consumption from refrigerator use and promoting energy-saving behavior. The system begins with a measuring device installed in the refrigerator measuring internal environmental data, namely temperature and door opening / closing operations. The measured data is transmitted to a terminal device within the home. This terminal stores the data in a cloud-based database, where it is analyzed by a server. The server analyzes the refrigerator's usage patterns and generates energy-saving advice.

[0312] Furthermore, this system incorporates an emotion engine, adding the ability to recognize the user's emotions. Using the camera and microphone on the device, it analyzes the user's voice and facial expressions in real time to recognize their emotions. Based on the analysis results and emotion data, the server modifies the advice according to the user's emotional state and notifies them at the optimal time and in the most appropriate way.

[0313] For example, when a user is feeling tired, a message with concise instructions and encouragement is sent. Conversely, if the user is interested, more detailed data and specific recommended actions are presented. This allows users to take energy-saving actions in a way that takes their own emotions into consideration.

[0314] Thus, the present invention is a system that supports the efficient use of refrigerators while simultaneously promoting behavioral change in users through an approach that utilizes emotion recognition, thereby enabling long-term reduction in power consumption and improvement of energy conservation awareness.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] A measuring device installed inside the refrigerator senses environmental data such as temperature and door opening / closing status in real time. The data is collected at regular intervals to maintain up-to-date information.

[0318] Step 2:

[0319] The device receives data from the measuring device via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local storage.

[0320] Step 3:

[0321] The device periodically connects to the cloud service and uploads the collected data to a database in the cloud. The data is then permanently stored in the cloud along with time information.

[0322] Step 4:

[0323] The server retrieves new data from the cloud database. The server analyzes this data and gains insights into power consumption by investigating temperature changes and the frequency of door openings and closings.

[0324] Step 5:

[0325] If the analysis reveals any anomalies or areas for improvement regarding power consumption, the server will generate energy-saving advice. This advice will include instructions to change the user's behavior.

[0326] Step 6:

[0327] The device uses its camera and microphone to analyze the user's emotions in order to detect their voice and facial expressions. This emotional data is processed in real time.

[0328] Step 7:

[0329] Based on information provided by the emotion engine, the server adjusts the content of advice and notification methods to match the user's emotional state. For example, if the emotional state is positive, it provides detailed information, while if it is negative, it sends supportive messages.

[0330] Step 8:

[0331] The device provides users with push notifications offering advice tailored to their emotions. Users can review this advice and improve their energy-saving awareness by reflecting on their own behavior.

[0332] Step 9:

[0333] Based on notifications received by the user, the system becomes aware of refrigerator opening and closing behavior and power consumption trends, allowing users to adjust their daily actions and practice energy-saving behaviors.

[0334] (Example 2)

[0335] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0336] The challenge lies in reducing refrigerator energy consumption while promoting energy-saving practices that take into account users' behavior and emotions. Conventional systems provide uniform advice without considering users' emotional states, which hinders behavioral change.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes means for measuring and acquiring environmental data, means for collecting and recognizing emotional information, and means for generating guidance information based on the analysis results and emotional information. This makes it possible to provide advice that encourages optimal energy-saving behavior while taking into consideration the user's emotions.

[0339] "Acquisition method" refers to a means of measuring the temperature inside the refrigerator and the open / closed state of the door in real time and acquiring that data.

[0340] "Transmission means" refers to means for transmitting acquired data to a data processing device.

[0341] "Storage means" refers to means for saving data acquired by a data processing device to a remote storage device.

[0342] "Analysis means" refers to a method for analyzing refrigerator usage patterns based on data acquired from remote storage devices.

[0343] "Recognition means" refers to means for collecting emotional information through sound and images and recognizing the emotional state of the user.

[0344] "Generation means" refers to means for generating guidance information for energy conservation based on analysis results and emotional information.

[0345] A "notification means" is a means of transmitting generated instructional information to a data processing device and appropriately notifying the user.

[0346] The system of the present invention efficiently manages the energy consumption of refrigerators and promotes energy-saving behavior that takes into consideration the user's feelings. Specifically, the system is composed of terminal devices, a server, and a cloud.

[0347] The terminal device acquires environmental data through a measuring device installed inside the refrigerator. The measuring device includes sensors that monitor the refrigerator's temperature and door open / closed status in real time. This environmental data is securely stored in a cloud-based database via the terminal device. The terminal device is also equipped with a camera and microphone, allowing it to collect emotional information by capturing the user's voice and facial expressions.

[0348] The server retrieves environmental data from a cloud-based database and also receives and analyzes emotional information. The server utilizes analytical algorithms and generative AI models to analyze the user's refrigerator usage patterns and emotional state. Based on the analysis results, it generates specific guidance information to promote energy-saving behaviors. This generated guidance information is personalized, taking into account the user's emotional state.

[0349] The terminal device notifies the user of guidance information received from the server. By receiving this advice, the user can improve their refrigerator usage and manage energy more efficiently.

[0350] For example, if a user is frequently opening and closing the refrigerator door, the server will generate advice such as, "Reducing the opening and closing of the door and taking items out all at once when needed will save energy." If the user looks tired, a gentle message such as, "Take a short break today and try to use the refrigerator more efficiently," will be sent.

[0351] An example of a prompt would be: "Based on refrigerator usage data, please provide specific actions to reduce today's energy consumption by 20%. Also, please give me some concise advice considering my current fatigue level."

[0352] This will enable users to perform energy-saving actions in a way that is emotionally resonant, enjoyable, and efficient.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The terminal acquires environmental data using measuring devices inside the refrigerator. Specifically, it detects the temperature inside the refrigerator with a temperature sensor and checks the door open / closed status with a door open / closed sensor. This input data is processed within the terminal and updated in real time. The output is the latest environmental data, which is ready for use in the next step.

[0356] Step 2:

[0357] The device sends the acquired environmental data to a database in the cloud. Specifically, it securely uploads the data to the cloud using internet protocols. During this process, error checking is performed to confirm that the data has been transmitted correctly. As output, the environmental data stored in the cloud database is obtained and becomes accessible to the server.

[0358] Step 3:

[0359] The server retrieves environmental data stored in the cloud and begins analysis. It receives temperature change patterns and door opening / closing frequency as input, and applies a machine learning algorithm to analyze refrigerator usage patterns. This analysis reveals energy consumption trends. The output is the analysis of consumption trends based on the data.

[0360] Step 4:

[0361] The device uses a camera and microphone to collect user voice and facial expression data. It takes the user's facial expressions and voice information as input and transmits this data to the server in real time. Specific operations include voice recognition and facial expression recognition processes. The output is data indicating the user's emotional state.

[0362] Step 5:

[0363] The server integrates the results of analyzing acquired user emotion data and environmental data. It receives this data as input and uses a generative AI model to generate energy-saving guidance information tailored to the user's emotions. This process, for example, selects a more concise and encouraging message if the user is tired. The output is user-specific energy-saving advice.

[0364] Step 6:

[0365] The terminal notifies the user of energy-saving advice sent from the server. It conveys the advice received as input to the user through screen display and audio output. Specific operations include scheduling to optimize the timing of notifications. The output is specific and practical guidance information received by the user.

[0366] (Application Example 2)

[0367] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0368] In recent years, efficient operation and energy conservation of refrigeration equipment have become increasingly important, but there is no system in place to promote energy-saving behavior while taking into account the emotional state of employees. When employees are fatigued or stressed, guidance on energy conservation may not be effective, so there is a need to provide appropriate guidance that is tailored to their emotional state.

[0369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0370] In this invention, the server includes an acquisition means for measuring environmental data within the refrigeration equipment and acquiring measurement information, an analysis means for acquiring the measurement information from an information storage device on the cloud and performing analysis, and a recognition means for recognizing a person's emotional state using an emotion recognition means. This makes it possible to monitor the use of the refrigeration equipment while providing energy-saving guidance tailored to the emotional state of employees.

[0371] "Environmental data" refers to information necessary to understand the operating conditions of refrigeration equipment, such as temperature, humidity, and door opening / closing status inside and around the equipment.

[0372] "Measurement information" refers to information obtained by converting environmental data acquired by measuring devices into a specific data format, and is used for energy conservation analysis.

[0373] "Means of acquisition" refers to functions or devices for measuring and collecting environmental data within refrigeration equipment.

[0374] "Transmission means" refers to functions or devices for transferring measurement information obtained by acquisition means to an information processing device or the cloud.

[0375] An "information processing device" is a device used to temporarily store measurement information and transmit it to the cloud as digital data.

[0376] An "information storage device" is a device that is physically or virtually installed on the cloud and has database functionality for long-term storage of measurement information.

[0377] "Analysis means" refers to functions or devices that analyze measurement information acquired from information storage devices on the cloud and generate usage patterns of refrigeration equipment and proposals for energy saving.

[0378] "Generation means" refers to a function or device for generating specific guidance information, such as energy-saving actions, based on the results of the analysis means.

[0379] "Emotion recognition means" refers to technology or devices that detect a person's voice, facial expressions, etc., and identify their emotional state in real time.

[0380] A "notification means" is a function or device that transmits generated guidance information to an information processing device at an appropriate time to inform the user of advice or guidance.

[0381] This invention relates to a system for efficiently operating refrigeration equipment and promoting energy-saving behavior. The system consists of acquisition means, analysis means, generation means, recognition means, and notification means.

[0382] The server uses sensors to monitor temperature, humidity, door opening / closing status, and other environmental data within the refrigeration equipment, and collects the measurement information through acquisition methods. This information is transmitted to a cloud-based storage device via an information processing device. The server then uses analysis methods to examine the data stored in the cloud and analyze the usage status of the refrigeration equipment. This analysis utilizes machine learning algorithms and AI technology to extract patterns such as temperature fluctuations and door opening / closing frequency.

[0383] Based on the analysis results, the server uses a generation mechanism to create guidance information that promotes energy-saving behavior. This information includes, for example, specific suggestions for efficient operation of refrigeration equipment and energy reduction. Furthermore, a recognition mechanism analyzes the user's voice and facial expressions in real time to identify the user's emotional state. For example, a user feeling fatigued can receive a simple message of encouragement along with some advice. Conversely, a user in a positive state can receive detailed analysis results and specific energy-saving behavior suggestions through a notification mechanism.

[0384] As a concrete example, in one supermarket, the introduction of this system reduced energy consumption during business hours by 25% in its refrigerators. This was because it enabled efficient operation tailored to peak times when employees experience stress, thereby optimizing energy consumption.

[0385] An example of a prompt message would be, "Please provide advice on developing a system using data analysis to improve the energy efficiency of refrigerators."

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server acquires environmental data such as temperature, humidity, and door opening / closing status inside the refrigeration equipment from sensors. The input is raw data from the sensors, and the output is formatted measurement information. The server converts this information into a digital format and organizes it into a data structure ready for subsequent processing.

[0389] Step 2:

[0390] The server transmits the acquired measurement information to the information processing device via the network. The input is formatted measurement information, and the output is transfer to the cloud environment. The server securely transmits this information to the cloud's information storage device, making it accessible remotely.

[0391] Step 3:

[0392] The server analyzes measurement data stored in a cloud-based data storage device using analytical tools. The input is the measurement data stored in the cloud, and the output is the analysis result. The server processes the data using machine learning algorithms to identify usage patterns of the refrigeration equipment.

[0393] Step 4:

[0394] The server generates energy-saving guidance information based on the analysis results. The input is the analysis results, and the output is the generated guidance information. The server formulates specific energy-saving proposals using conditional logic.

[0395] Step 5:

[0396] The device acquires user voice and facial expression data using a camera and microphone, and processes it with emotion recognition technology. The input is visual and audio data, and the output is the user's current emotional state. The device performs real-time analysis and obtains results from an emotion engine.

[0397] Step 6:

[0398] The server combines the user's emotional state with generated guidance information and sends it to the user at the optimal time using a notification system. The input is the user's emotional state and guidance information, and the output is the notification content. The server adjusts the message content and tone according to the emotional state and presents the information via the terminal.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] This invention is implemented as a system for effectively managing the power consumption of a refrigerator and promoting energy conservation. A measuring device installed inside the refrigerator acquires temperature changes and door open / closed status in real time and periodically transmits this information to a smartphone (terminal). Upon receiving the data, the terminal saves it to a cloud-based database via the internet.

[0416] The server analyzes data collected in the cloud to analyze refrigerator usage. Specifically, it evaluates temperature fluctuations and door opening / closing frequency over time and detects how these affect power consumption.

[0417] If the analysis reveals unnecessary power consumption, the server generates specific energy-saving advice for the user. This advice is sent as a push notification to the user's smartphone, prompting them to review their own habits.

[0418] For example, if the server detects that a user's refrigerator is being opened and closed frequently at night, it will advise them to refrain from doing so. The user receives this advice via a notification on their smartphone and can reduce power consumption by improving their habits.

[0419] Thus, the system of the present invention aims to reduce household electricity costs by providing concrete and continuous support for energy-saving behavior through the linkage of measuring devices, terminals, and the cloud.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] A measuring device installed inside the refrigerator senses the temperature and the status of the door opening and closing in real time. The sensed data is detected and collected as a sample every 5 minutes.

[0423] Step 2:

[0424] The device receives data from the measuring device via Bluetooth or Wi-Fi. The received data is stored in the device's temporary storage.

[0425] Step 3:

[0426] The device periodically connects to the cloud service and uploads the collected data. The data is stored in a cloud database along with the date and time.

[0427] Step 4:

[0428] The server periodically checks the cloud database to retrieve new data. The server uses this data to analyze the refrigerator's temperature changes and opening / closing patterns.

[0429] Step 5:

[0430] The analysis detects energy waste during specific time periods. The server identifies the abnormal usage patterns and pinpoints areas for improvement in user behavior.

[0431] Step 6:

[0432] Based on the analysis results, the server generates specific advice for energy saving. This advice may include suggestions such as "reduce opening and closing the refrigerator at night."

[0433] Step 7:

[0434] The device receives advice sent from the server and notifies the user. The notification is sent via push, making it easy for the user to access.

[0435] Step 8:

[0436] Users check notifications from their devices, take action based on energy-saving advice, and promote energy conservation in their homes.

[0437] (Example 1)

[0438] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0439] There is a need to effectively manage the power consumption of refrigerated storage equipment and promote resource conservation. However, conventional systems have made it difficult to grasp the specific usage status within refrigerated storage equipment in real time and provide energy-saving guidance based on that information. The present invention aims to solve this problem and provide a system that provides clear and actionable resource-saving guidance to users.

[0440] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0441] In this invention, the server includes a collection means, a communication means, and a storage means. This makes it possible to grasp the usage status of the refrigerated storage device in real time, generate specific guidance information for resource conservation, and provide it to the user.

[0442] "Collection means" refers to devices or functions used to measure and acquire environmental information within a refrigerated storage device.

[0443] "Communication means" refers to devices or functions used to transmit acquired information to an information processing device.

[0444] "Storage means" refers to devices or functions for saving information acquired by an information processing device to a recording area on a base.

[0445] "Evaluation means" refers to devices or functions that analyze information acquired from the recording area on the substrate to analyze environmental changes and operation frequency.

[0446] "Generation means" refers to devices or functions that generate guidance information for resource conservation based on evaluation results.

[0447] "Notification means" refers to a device or function that transmits generated guidance information to an information processing device and notifies the user.

[0448] This invention is a system for managing the power consumption of refrigerated storage equipment and promoting resource conservation. This system tracks environmental information in real time, generates specific guidance information for resource conservation based on that information, and provides it to the user.

[0449] Measuring devices installed inside refrigerated storage units collect environmental information such as temperature and door open / closed status. This information is transmitted to the user's information processing device via Bluetooth or Wi-Fi. The information processing device stores the received data in a storage area on its infrastructure via the internet. Cloud services such as Amazon Web Services and Google Cloud are used in this process.

[0450] The server retrieves data from the storage area on the infrastructure and performs analysis using data analysis software such as Python or R. The server analyzes hourly temperature changes and the frequency of door opening and closing, and evaluates users' energy consumption behavior based on the results. This evaluation identifies unnecessary power consumption.

[0451] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. This generation process uses the prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" as input and provides appropriate action suggestions.

[0452] For example, if the server detects through analysis that the door of a refrigerated storage unit is frequently opened and closed at night, the AI ​​model generates specific advice such as "It is recommended to reduce the frequency of opening and closing the door at night." This advice is then notified to the user via the information processing device. The user can then review this notification and use it to improve their resource-saving behavior.

[0453] The overall objective of this system is to improve the resource-efficient operation of refrigeration equipment and support users in easily implementing concrete energy-saving actions through the processes of data collection, storage, analysis, guidance generation, and notification.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The terminal acquires environmental information (temperature, door open / closed status) from a measuring device installed inside the refrigerated storage unit. This input information is collected at time intervals, and the terminal receives this data. Specifically, the terminal polls the data using Bluetooth or Wi-Fi and checks for newly acquired data. At this stage, the input is temperature data and door open / closed status from the measuring device, and the output is sensor data received by the terminal.

[0457] Step 2:

[0458] The terminal transmits and stores acquired environmental information in a storage area on a cloud infrastructure via the internet. The terminal then packets the received data and sends it to cloud services such as Amazon Web Services or Google Cloud. The data received as input is stored as output in a database on the cloud. Specifically, the terminal transfers a certain amount of data in batches at regular intervals.

[0459] Step 3:

[0460] The server retrieves environmental information from storage in the cloud and performs data analysis. The server utilizes data analysis libraries in Python and R to analyze environmental information and evaluate temperature changes and door opening / closing frequency. This process receives raw data stored in the cloud as input and generates evaluation results reporting trends and outliers as output. Specifically, the server performs data analysis using scheduled jobs.

[0461] Step 4:

[0462] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. The prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" is used as input. The output consists of actionable advice for the user. Specifically, the server calls the generation AI model and creates optimal guidance information based on the analysis results.

[0463] Step 5:

[0464] The server sends the generated guidance information to the terminal and notifies the user via push notification. The input is the generated guidance information, which is then transferred to the information processing device, and the user receives the notification as output. Specifically, notifications are made using services such as Firebase Cloud Messaging. Users can check the notification on their smartphone and use it to improve their resource-saving behaviors.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] Effectively managing energy consumption within cooling systems and minimizing household electricity usage is crucial from the perspective of environmental protection and cost reduction. However, conventional devices merely maintain temperature and lack efficient energy management methods or means of providing users with appropriate warnings for abnormal behavior. This invention aims to solve these problems and achieve improved energy efficiency and enhanced security.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes means for measuring environmental data within the cooling device and acquiring measurement information, means for transmitting the measurement information to an information processing device, and means for acquiring the measurement information from an online storage device and performing analysis. This makes it possible to effectively manage energy consumption in the home by generating guidance information to optimize the energy consumption of the cooling device and identifying and warning about abnormal behavior.

[0470] A "cooling device" is an electric device used to preserve food and goods at low temperatures.

[0471] "Environmental data" refers to a collection of measurable information such as temperature, humidity, and the open / closed state of devices.

[0472] "Acquisition means" refers to functions for collecting physical data using sensors and measuring instruments.

[0473] An "information processing device" is a general term for a machine such as a computer or smart device used to receive and process data.

[0474] "Online storage devices" refer to data storage locations accessible via the internet, including cloud storage.

[0475] "Analysis tools" refer to algorithms and programs used to perform analysis based on certain rules using collected data.

[0476] "Generative means" refers to a function that creates specific information or instructional content based on analysis results.

[0477] "Notification means" refers to technologies used to inform users of generated information, specifically including push notifications and email.

[0478] "Monitoring measures" refer to mechanisms and methods for continuously observing a situation and collecting data.

[0479] A "warning mechanism" is a system that issues a warning to the user to draw their attention when an abnormality or malfunction is detected.

[0480] The system that realizes this application effectively manages data within the cooling device, aiming to improve energy efficiency and security. Specifically, it is implemented using the following technologies.

[0481] The cooling system is equipped with various sensors (temperature sensors, on / off sensors, etc.) to measure environmental data. A small computer such as a Raspberry Pi is used to acquire data from these sensors and transmit it to an information processing device at regular intervals. For example, it can be equipped with a function to transmit data in real time when the temperature exceeds a set range.

[0482] Smartphones and PCs, which are information processing devices, save received data to the cloud via the internet. In this process, online storage services such as Firebase are used to ensure secure and efficient data storage. The stored data is then used for analysis on the cloud.

[0483] The server uses analytics platforms such as Google Cloud AI to analyze the collected data. Based on the results, specific guidance information for energy conservation is generated. This guidance information may include, for example, advice on how often to open and close cooling system doors.

[0484] The generated guidance information is notified to the information processing device via Firebase Cloud Messaging, etc., and displayed to the user as advice. This allows the user to review how they are using the cooling device. In addition, if abnormal behavior is detected, a warning is issued, prompting appropriate action.

[0485] For example, if the cooling unit door is left open for more than an hour, a warning message will be sent to the user stating, "The door is left open. Please close it immediately." This system not only prevents energy waste but also identifies security issues in daily operations and supports improved user behavior.

[0486] In generative AI models, the following prompt statements are given as examples.

[0487] "Identify wasted energy consumption based on recent usage patterns of cooling equipment and provide specific advice on how to reduce it."

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The terminal acquires data from sensors attached to the cooling system. The input is an analog or digital signal from the sensor, which is processed using a built-in data conversion program to convert it into specific data such as temperature or door open / closed status. The output is periodically generated environmental data.

[0491] Step 2:

[0492] The device transmits the acquired environmental data to a cloud storage device via Wi-Fi or a wired network. The input is the environmental data generated earlier, and the output is this data stored in the cloud. In this process, the device compresses and encrypts the data for secure transmission.

[0493] Step 3:

[0494] The server periodically retrieves environmental data stored in the cloud. The input is environmental data stored in a database on the cloud, and the output is a dataset to be analyzed. The server then organizes the listed data into an appropriate format and prepares it for analysis.

[0495] Step 4:

[0496] The server performs data analysis using a generative AI model. The input is the dataset to be analyzed, and the output is power consumption patterns and energy-saving advice based on them. The server processes the data using specific algorithms to identify abnormal power consumption and potential problems.

[0497] Step 5:

[0498] The server sends the generated advice and warnings to the terminal via a cloud messaging service. The input is the generated advice and warnings, and the output is the message sent to the user. Based on this, the server prepares to inform the user of recommended actions and warnings.

[0499] Step 6:

[0500] The user receives advice and warnings via push notifications on their device. The input is the notification message received on the device, and the output is the user reviewing it and taking appropriate action. This process allows the user to be more mindful of reducing power consumption and enhancing security.

[0501] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0502] This invention implements a system for efficiently managing electricity consumption from refrigerator use and promoting energy-saving behavior. The system begins with a measuring device installed in the refrigerator measuring internal environmental data, namely temperature and door opening / closing operations. The measured data is transmitted to a terminal device within the home. This terminal stores the data in a cloud-based database, where it is analyzed by a server. The server analyzes the refrigerator's usage patterns and generates energy-saving advice.

[0503] Furthermore, this system incorporates an emotion engine, adding the ability to recognize the user's emotions. Using the camera and microphone on the device, it analyzes the user's voice and facial expressions in real time to recognize their emotions. Based on the analysis results and emotion data, the server modifies the advice according to the user's emotional state and notifies them at the optimal time and in the most appropriate way.

[0504] For example, when a user is feeling tired, a message with concise instructions and encouragement is sent. Conversely, if the user is interested, more detailed data and specific recommended actions are presented. This allows users to take energy-saving actions in a way that takes their own emotions into consideration.

[0505] Thus, the present invention is a system that supports the efficient use of refrigerators while simultaneously promoting behavioral change in users through an approach that utilizes emotion recognition, thereby enabling long-term reduction in power consumption and improvement of energy conservation awareness.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] A measuring device installed inside the refrigerator senses environmental data such as temperature and door opening / closing status in real time. The data is collected at regular intervals to maintain up-to-date information.

[0509] Step 2:

[0510] The device receives data from the measuring device via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local storage.

[0511] Step 3:

[0512] The device periodically connects to the cloud service and uploads the collected data to a database in the cloud. The data is then permanently stored in the cloud along with time information.

[0513] Step 4:

[0514] The server retrieves new data from the cloud database. The server analyzes this data and gains insights into power consumption by investigating temperature changes and the frequency of door openings and closings.

[0515] Step 5:

[0516] If the analysis reveals any anomalies or areas for improvement regarding power consumption, the server will generate energy-saving advice. This advice will include instructions to change the user's behavior.

[0517] Step 6:

[0518] The device uses its camera and microphone to analyze the user's emotions in order to detect their voice and facial expressions. This emotional data is processed in real time.

[0519] Step 7:

[0520] Based on information provided by the emotion engine, the server adjusts the content of advice and notification methods to match the user's emotional state. For example, if the emotional state is positive, it provides detailed information, while if it is negative, it sends supportive messages.

[0521] Step 8:

[0522] The device provides users with push notifications offering advice tailored to their emotions. Users can review this advice and improve their energy-saving awareness by reflecting on their own behavior.

[0523] Step 9:

[0524] Based on notifications received by the user, the system becomes aware of refrigerator opening and closing behavior and power consumption trends, allowing users to adjust their daily actions and practice energy-saving behaviors.

[0525] (Example 2)

[0526] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0527] The challenge lies in reducing refrigerator energy consumption while promoting energy-saving practices that take into account users' behavior and emotions. Conventional systems provide uniform advice without considering users' emotional states, which hinders behavioral change.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0529] In this invention, the server includes means for measuring and acquiring environmental data, means for collecting and recognizing emotional information, and means for generating guidance information based on the analysis results and emotional information. This makes it possible to provide advice that encourages optimal energy-saving behavior while taking into consideration the user's emotions.

[0530] "Acquisition method" refers to a means of measuring the temperature inside the refrigerator and the open / closed state of the door in real time and acquiring that data.

[0531] "Transmission means" refers to means for transmitting acquired data to a data processing device.

[0532] "Storage means" refers to means for saving data acquired by a data processing device to a remote storage device.

[0533] "Analysis means" refers to a method for analyzing refrigerator usage patterns based on data acquired from remote storage devices.

[0534] "Recognition means" refers to means for collecting emotional information through sound and images and recognizing the emotional state of the user.

[0535] "Generation means" refers to means for generating guidance information for energy conservation based on analysis results and emotional information.

[0536] A "notification means" is a means of transmitting generated instructional information to a data processing device and appropriately notifying the user.

[0537] The system of the present invention efficiently manages the energy consumption of refrigerators and promotes energy-saving behavior that takes into consideration the user's feelings. Specifically, the system is composed of terminal devices, a server, and a cloud.

[0538] The terminal device acquires environmental data through a measuring device installed inside the refrigerator. The measuring device includes sensors that monitor the refrigerator's temperature and door open / closed status in real time. This environmental data is securely stored in a cloud-based database via the terminal device. The terminal device is also equipped with a camera and microphone, allowing it to collect emotional information by capturing the user's voice and facial expressions.

[0539] The server retrieves environmental data from a cloud-based database and also receives and analyzes emotional information. The server utilizes analytical algorithms and generative AI models to analyze the user's refrigerator usage patterns and emotional state. Based on the analysis results, it generates specific guidance information to promote energy-saving behaviors. This generated guidance information is personalized, taking into account the user's emotional state.

[0540] The terminal device notifies the user of guidance information received from the server. By receiving this advice, the user can improve their refrigerator usage and manage energy more efficiently.

[0541] For example, if a user is frequently opening and closing the refrigerator door, the server will generate advice such as, "Reducing the opening and closing of the door and taking items out all at once when needed will save energy." If the user looks tired, a gentle message such as, "Take a short break today and try to use the refrigerator more efficiently," will be sent.

[0542] An example of a prompt would be: "Based on refrigerator usage data, please provide specific actions to reduce today's energy consumption by 20%. Also, please give me some concise advice considering my current fatigue level."

[0543] This will enable users to perform energy-saving actions in a way that is emotionally resonant, enjoyable, and efficient.

[0544] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0545] Step 1:

[0546] The terminal acquires environmental data using measuring devices inside the refrigerator. Specifically, it detects the temperature inside the refrigerator with a temperature sensor and checks the door open / closed status with a door open / closed sensor. This input data is processed within the terminal and updated in real time. The output is the latest environmental data, which is ready for use in the next step.

[0547] Step 2:

[0548] The device sends the acquired environmental data to a database in the cloud. Specifically, it securely uploads the data to the cloud using internet protocols. During this process, error checking is performed to confirm that the data has been transmitted correctly. As output, the environmental data stored in the cloud database is obtained and becomes accessible to the server.

[0549] Step 3:

[0550] The server retrieves environmental data stored in the cloud and begins analysis. It receives temperature change patterns and door opening / closing frequency as input, and applies a machine learning algorithm to analyze refrigerator usage patterns. This analysis reveals energy consumption trends. The output is the analysis of consumption trends based on the data.

[0551] Step 4:

[0552] The device uses a camera and microphone to collect user voice and facial expression data. It takes the user's facial expressions and voice information as input and transmits this data to the server in real time. Specific operations include voice recognition and facial expression recognition processes. The output is data indicating the user's emotional state.

[0553] Step 5:

[0554] The server integrates the results of analyzing acquired user emotion data and environmental data. It receives this data as input and uses a generative AI model to generate energy-saving guidance information tailored to the user's emotions. This process, for example, selects a more concise and encouraging message if the user is tired. The output is user-specific energy-saving advice.

[0555] Step 6:

[0556] The terminal notifies the user of energy-saving advice sent from the server. It conveys the advice received as input to the user through screen display and audio output. Specific operations include scheduling to optimize the timing of notifications. The output is specific and practical guidance information received by the user.

[0557] (Application Example 2)

[0558] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0559] In recent years, efficient operation and energy conservation of refrigeration equipment have become increasingly important, but there is no system in place to promote energy-saving behavior while taking into account the emotional state of employees. When employees are fatigued or stressed, guidance on energy conservation may not be effective, so there is a need to provide appropriate guidance that is tailored to their emotional state.

[0560] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0561] In this invention, the server includes an acquisition means for measuring environmental data within the refrigeration equipment and acquiring measurement information, an analysis means for acquiring the measurement information from an information storage device on the cloud and performing analysis, and a recognition means for recognizing a person's emotional state using an emotion recognition means. This makes it possible to monitor the use of the refrigeration equipment while providing energy-saving guidance tailored to the emotional state of employees.

[0562] "Environmental data" refers to information necessary to understand the operating conditions of refrigeration equipment, such as temperature, humidity, and door opening / closing status inside and around the equipment.

[0563] "Measurement information" refers to information obtained by converting environmental data acquired by measuring devices into a specific data format, and is used for energy conservation analysis.

[0564] "Means of acquisition" refers to functions or devices for measuring and collecting environmental data within refrigeration equipment.

[0565] "Transmission means" refers to functions or devices for transferring measurement information obtained by acquisition means to an information processing device or the cloud.

[0566] An "information processing device" is a device used to temporarily store measurement information and transmit it to the cloud as digital data.

[0567] An "information storage device" is a device that is physically or virtually installed on the cloud and has database functionality for long-term storage of measurement information.

[0568] "Analysis means" refers to functions or devices that analyze measurement information acquired from information storage devices on the cloud and generate usage patterns of refrigeration equipment and proposals for energy saving.

[0569] "Generation means" refers to a function or device for generating specific guidance information, such as energy-saving actions, based on the results of the analysis means.

[0570] "Emotion recognition means" refers to technology or devices that detect a person's voice, facial expressions, etc., and identify their emotional state in real time.

[0571] A "notification means" is a function or device that transmits generated guidance information to an information processing device at an appropriate time to inform the user of advice or guidance.

[0572] This invention relates to a system for efficiently operating refrigeration equipment and promoting energy-saving behavior. The system consists of acquisition means, analysis means, generation means, recognition means, and notification means.

[0573] The server uses sensors to monitor temperature, humidity, door opening / closing status, and other environmental data within the refrigeration equipment, and collects the measurement information through acquisition methods. This information is transmitted to a cloud-based storage device via an information processing device. The server then uses analysis methods to examine the data stored in the cloud and analyze the usage status of the refrigeration equipment. This analysis utilizes machine learning algorithms and AI technology to extract patterns such as temperature fluctuations and door opening / closing frequency.

[0574] Based on the analysis results, the server uses a generation mechanism to create guidance information that promotes energy-saving behavior. This information includes, for example, specific suggestions for efficient operation of refrigeration equipment and energy reduction. Furthermore, a recognition mechanism analyzes the user's voice and facial expressions in real time to identify the user's emotional state. For example, a user feeling fatigued can receive a simple message of encouragement along with some advice. Conversely, a user in a positive state can receive detailed analysis results and specific energy-saving behavior suggestions through a notification mechanism.

[0575] As a concrete example, in one supermarket, the introduction of this system reduced energy consumption during business hours by 25% in its refrigerators. This was because it enabled efficient operation tailored to peak times when employees experience stress, thereby optimizing energy consumption.

[0576] An example of a prompt message would be, "Please provide advice on developing a system using data analysis to improve the energy efficiency of refrigerators."

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The server acquires environmental data such as temperature, humidity, and door opening / closing status inside the refrigeration equipment from sensors. The input is raw data from the sensors, and the output is formatted measurement information. The server converts this information into a digital format and organizes it into a data structure ready for subsequent processing.

[0580] Step 2:

[0581] The server transmits the acquired measurement information to the information processing device via the network. The input is formatted measurement information, and the output is transfer to the cloud environment. The server securely transmits this information to the cloud's information storage device, making it accessible remotely.

[0582] Step 3:

[0583] The server analyzes measurement data stored in a cloud-based data storage device using analytical tools. The input is the measurement data stored in the cloud, and the output is the analysis result. The server processes the data using machine learning algorithms to identify usage patterns of the refrigeration equipment.

[0584] Step 4:

[0585] The server generates energy-saving guidance information based on the analysis results. The input is the analysis results, and the output is the generated guidance information. The server formulates specific energy-saving proposals using conditional logic.

[0586] Step 5:

[0587] The device acquires user voice and facial expression data using a camera and microphone, and processes it with emotion recognition technology. The input is visual and audio data, and the output is the user's current emotional state. The device performs real-time analysis and obtains results from an emotion engine.

[0588] Step 6:

[0589] The server combines the user's emotional state with generated guidance information and sends it to the user at the optimal time using a notification system. The input is the user's emotional state and guidance information, and the output is the notification content. The server adjusts the message content and tone according to the emotional state and presents the information via the terminal.

[0590] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0591] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0592] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0593] [Fourth Embodiment]

[0594] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0595] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0596] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0597] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0598] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0599] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0600] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0601] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0602] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0603] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0604] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0605] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0606] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] This invention is implemented as a system for effectively managing the power consumption of a refrigerator and promoting energy conservation. A measuring device installed inside the refrigerator acquires temperature changes and door open / closed status in real time and periodically transmits this information to a smartphone (terminal). Upon receiving the data, the terminal saves it to a cloud-based database via the internet.

[0608] The server analyzes data collected in the cloud to analyze refrigerator usage. Specifically, it evaluates temperature fluctuations and door opening / closing frequency over time and detects how these affect power consumption.

[0609] If the analysis reveals unnecessary power consumption, the server generates specific energy-saving advice for the user. This advice is sent as a push notification to the user's smartphone, prompting them to review their own habits.

[0610] For example, if the server detects that a user's refrigerator is being opened and closed frequently at night, it will advise them to refrain from doing so. The user receives this advice via a notification on their smartphone and can reduce power consumption by improving their habits.

[0611] Thus, the system of the present invention aims to reduce household electricity costs by providing concrete and continuous support for energy-saving behavior through the linkage of measuring devices, terminals, and the cloud.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] A measuring device installed inside the refrigerator senses the temperature and the status of the door opening and closing in real time. The sensed data is detected and collected as a sample every 5 minutes.

[0615] Step 2:

[0616] The device receives data from the measuring device via Bluetooth or Wi-Fi. The received data is stored in the device's temporary storage.

[0617] Step 3:

[0618] The device periodically connects to the cloud service and uploads the collected data. The data is stored in a cloud database along with the date and time.

[0619] Step 4:

[0620] The server periodically checks the cloud database to retrieve new data. The server uses this data to analyze the refrigerator's temperature changes and opening / closing patterns.

[0621] Step 5:

[0622] The analysis detects energy waste during specific time periods. The server identifies the abnormal usage patterns and pinpoints areas for improvement in user behavior.

[0623] Step 6:

[0624] Based on the analysis results, the server generates specific advice for energy saving. This advice may include suggestions such as "reduce opening and closing the refrigerator at night."

[0625] Step 7:

[0626] The device receives advice sent from the server and notifies the user. The notification is sent via push, making it easy for the user to access.

[0627] Step 8:

[0628] Users check notifications from their devices, take action based on energy-saving advice, and promote energy conservation in their homes.

[0629] (Example 1)

[0630] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] There is a need to effectively manage the power consumption of refrigerated storage equipment and promote resource conservation. However, conventional systems have made it difficult to grasp the specific usage status within refrigerated storage equipment in real time and provide energy-saving guidance based on that information. The present invention aims to solve this problem and provide a system that provides clear and actionable resource-saving guidance to users.

[0632] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0633] In this invention, the server includes a collection means, a communication means, and a storage means. This makes it possible to grasp the usage status of the refrigerated storage device in real time, generate specific guidance information for resource conservation, and provide it to the user.

[0634] "Collection means" refers to devices or functions used to measure and acquire environmental information within a refrigerated storage device.

[0635] "Communication means" refers to devices or functions used to transmit acquired information to an information processing device.

[0636] "Storage means" refers to devices or functions for saving information acquired by an information processing device to a recording area on a base.

[0637] "Evaluation means" refers to devices or functions that analyze information acquired from the recording area on the substrate to analyze environmental changes and operation frequency.

[0638] "Generation means" refers to devices or functions that generate guidance information for resource conservation based on evaluation results.

[0639] "Notification means" refers to a device or function that transmits generated guidance information to an information processing device and notifies the user.

[0640] This invention is a system for managing the power consumption of refrigerated storage equipment and promoting resource conservation. This system tracks environmental information in real time, generates specific guidance information for resource conservation based on that information, and provides it to the user.

[0641] Measuring devices installed inside refrigerated storage units collect environmental information such as temperature and door open / closed status. This information is transmitted to the user's information processing device via Bluetooth or Wi-Fi. The information processing device stores the received data in a storage area on its infrastructure via the internet. Cloud services such as Amazon Web Services and Google Cloud are used in this process.

[0642] The server retrieves data from the storage area on the infrastructure and performs analysis using data analysis software such as Python or R. The server analyzes hourly temperature changes and the frequency of door opening and closing, and evaluates users' energy consumption behavior based on the results. This evaluation identifies unnecessary power consumption.

[0643] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. This generation process uses the prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" as input and provides appropriate action suggestions.

[0644] For example, if the server detects through analysis that the door of a refrigerated storage unit is frequently opened and closed at night, the AI ​​model generates specific advice such as "It is recommended to reduce the frequency of opening and closing the door at night." This advice is then notified to the user via the information processing device. The user can then review this notification and use it to improve their resource-saving behavior.

[0645] The overall objective of this system is to improve the resource-efficient operation of refrigeration equipment and support users in easily implementing concrete energy-saving actions through the processes of data collection, storage, analysis, guidance generation, and notification.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The terminal acquires environmental information (temperature, door open / closed status) from a measuring device installed inside the refrigerated storage unit. This input information is collected at time intervals, and the terminal receives this data. Specifically, the terminal polls the data using Bluetooth or Wi-Fi and checks for newly acquired data. At this stage, the input is temperature data and door open / closed status from the measuring device, and the output is sensor data received by the terminal.

[0649] Step 2:

[0650] The terminal transmits and stores acquired environmental information in a storage area on a cloud infrastructure via the internet. The terminal then packets the received data and sends it to cloud services such as Amazon Web Services or Google Cloud. The data received as input is stored as output in a database on the cloud. Specifically, the terminal transfers a certain amount of data in batches at regular intervals.

[0651] Step 3:

[0652] The server retrieves environmental information from storage in the cloud and performs data analysis. The server utilizes data analysis libraries in Python and R to analyze environmental information and evaluate temperature changes and door opening / closing frequency. This process receives raw data stored in the cloud as input and generates evaluation results reporting trends and outliers as output. Specifically, the server performs data analysis using scheduled jobs.

[0653] Step 4:

[0654] Based on the analysis results, the server uses a generation AI model to generate specific guidance information for resource conservation. The prompt "Generate energy-saving advice regarding the use of refrigeration equipment at night" is used as input. The output consists of actionable advice for the user. Specifically, the server calls the generation AI model and creates optimal guidance information based on the analysis results.

[0655] Step 5:

[0656] The server sends the generated guidance information to the terminal and notifies the user via push notification. The input is the generated guidance information, which is then transferred to the information processing device, and the user receives the notification as output. Specifically, notifications are made using services such as Firebase Cloud Messaging. Users can check the notification on their smartphone and use it to improve their resource-saving behaviors.

[0657] (Application Example 1)

[0658] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] Effectively managing energy consumption within cooling systems and minimizing household electricity usage is crucial from the perspective of environmental protection and cost reduction. However, conventional devices merely maintain temperature and lack efficient energy management methods or means of providing users with appropriate warnings for abnormal behavior. This invention aims to solve these problems and achieve improved energy efficiency and enhanced security.

[0660] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0661] In this invention, the server includes means for measuring environmental data within the cooling device and acquiring measurement information, means for transmitting the measurement information to an information processing device, and means for acquiring the measurement information from an online storage device and performing analysis. This makes it possible to effectively manage energy consumption in the home by generating guidance information to optimize the energy consumption of the cooling device and identifying and warning about abnormal behavior.

[0662] A "cooling device" is an electric device used to preserve food and goods at low temperatures.

[0663] "Environmental data" refers to a collection of measurable information such as temperature, humidity, and the open / closed state of devices.

[0664] "Acquisition means" refers to functions for collecting physical data using sensors and measuring instruments.

[0665] An "information processing device" is a general term for a machine such as a computer or smart device used to receive and process data.

[0666] "Online storage devices" refer to data storage locations accessible via the internet, including cloud storage.

[0667] "Analysis tools" refer to algorithms and programs used to perform analysis based on certain rules using collected data.

[0668] "Generative means" refers to a function that creates specific information or instructional content based on analysis results.

[0669] "Notification means" refers to technologies used to inform users of generated information, specifically including push notifications and email.

[0670] "Monitoring measures" refer to mechanisms and methods for continuously observing a situation and collecting data.

[0671] A "warning mechanism" is a system that issues a warning to the user to draw their attention when an abnormality or malfunction is detected.

[0672] The system that realizes this application effectively manages data within the cooling device, aiming to improve energy efficiency and security. Specifically, it is implemented using the following technologies.

[0673] The cooling system is equipped with various sensors (temperature sensors, on / off sensors, etc.) to measure environmental data. A small computer such as a Raspberry Pi is used to acquire data from these sensors and transmit it to an information processing device at regular intervals. For example, it can be equipped with a function to transmit data in real time when the temperature exceeds a set range.

[0674] Smartphones and PCs, which are information processing devices, save received data to the cloud via the internet. In this process, online storage services such as Firebase are used to ensure secure and efficient data storage. The stored data is then used for analysis on the cloud.

[0675] The server uses analytics platforms such as Google Cloud AI to analyze the collected data. Based on the results, specific guidance information for energy conservation is generated. This guidance information may include, for example, advice on how often to open and close cooling system doors.

[0676] The generated guidance information is notified to the information processing device via Firebase Cloud Messaging, etc., and displayed to the user as advice. This allows the user to review how they are using the cooling device. In addition, if abnormal behavior is detected, a warning is issued, prompting appropriate action.

[0677] For example, if the cooling unit door is left open for more than an hour, a warning message will be sent to the user stating, "The door is left open. Please close it immediately." This system not only prevents energy waste but also identifies security issues in daily operations and supports improved user behavior.

[0678] In generative AI models, the following prompt statements are given as examples.

[0679] "Identify wasted energy consumption based on recent usage patterns of cooling equipment and provide specific advice on how to reduce it."

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The terminal acquires data from sensors attached to the cooling system. The input is an analog or digital signal from the sensor, which is processed using a built-in data conversion program to convert it into specific data such as temperature or door open / closed status. The output is periodically generated environmental data.

[0683] Step 2:

[0684] The device transmits the acquired environmental data to a cloud storage device via Wi-Fi or a wired network. The input is the environmental data generated earlier, and the output is this data stored in the cloud. In this process, the device compresses and encrypts the data for secure transmission.

[0685] Step 3:

[0686] The server periodically retrieves environmental data stored in the cloud. The input is environmental data stored in a database on the cloud, and the output is a dataset to be analyzed. The server then organizes the listed data into an appropriate format and prepares it for analysis.

[0687] Step 4:

[0688] The server performs data analysis using a generative AI model. The input is the dataset to be analyzed, and the output is power consumption patterns and energy-saving advice based on them. The server processes the data using specific algorithms to identify abnormal power consumption and potential problems.

[0689] Step 5:

[0690] The server sends the generated advice and warnings to the terminal via a cloud messaging service. The input is the generated advice and warnings, and the output is the message sent to the user. Based on this, the server prepares to inform the user of recommended actions and warnings.

[0691] Step 6:

[0692] The user receives advice and warnings via push notifications on their device. The input is the notification message received on the device, and the output is the user reviewing it and taking appropriate action. This process allows the user to be more mindful of reducing power consumption and enhancing security.

[0693] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0694] This invention implements a system for efficiently managing electricity consumption from refrigerator use and promoting energy-saving behavior. The system begins with a measuring device installed in the refrigerator measuring internal environmental data, namely temperature and door opening / closing operations. The measured data is transmitted to a terminal device within the home. This terminal stores the data in a cloud-based database, where it is analyzed by a server. The server analyzes the refrigerator's usage patterns and generates energy-saving advice.

[0695] Furthermore, this system incorporates an emotion engine, adding the ability to recognize the user's emotions. Using the camera and microphone on the device, it analyzes the user's voice and facial expressions in real time to recognize their emotions. Based on the analysis results and emotion data, the server modifies the advice according to the user's emotional state and notifies them at the optimal time and in the most appropriate way.

[0696] For example, when a user is feeling tired, a message with concise instructions and encouragement is sent. Conversely, if the user is interested, more detailed data and specific recommended actions are presented. This allows users to take energy-saving actions in a way that takes their own emotions into consideration.

[0697] Thus, the present invention is a system that supports the efficient use of refrigerators while simultaneously promoting behavioral change in users through an approach that utilizes emotion recognition, thereby enabling long-term reduction in power consumption and improvement of energy conservation awareness.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] A measuring device installed inside the refrigerator senses environmental data such as temperature and door opening / closing status in real time. The data is collected at regular intervals to maintain up-to-date information.

[0701] Step 2:

[0702] The device receives data from the measuring device via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local storage.

[0703] Step 3:

[0704] The device periodically connects to the cloud service and uploads the collected data to a database in the cloud. The data is then permanently stored in the cloud along with time information.

[0705] Step 4:

[0706] The server retrieves new data from the cloud database. The server analyzes this data and gains insights into power consumption by investigating temperature changes and the frequency of door openings and closings.

[0707] Step 5:

[0708] If the analysis reveals any anomalies or areas for improvement regarding power consumption, the server will generate energy-saving advice. This advice will include instructions to change the user's behavior.

[0709] Step 6:

[0710] The device uses its camera and microphone to analyze the user's emotions in order to detect their voice and facial expressions. This emotional data is processed in real time.

[0711] Step 7:

[0712] Based on information provided by the emotion engine, the server adjusts the content of advice and notification methods to match the user's emotional state. For example, if the emotional state is positive, it provides detailed information, while if it is negative, it sends supportive messages.

[0713] Step 8:

[0714] The device provides users with push notifications offering advice tailored to their emotions. Users can review this advice and improve their energy-saving awareness by reflecting on their own behavior.

[0715] Step 9:

[0716] Based on notifications received by the user, the system becomes aware of refrigerator opening and closing behavior and power consumption trends, allowing users to adjust their daily actions and practice energy-saving behaviors.

[0717] (Example 2)

[0718] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] The challenge lies in reducing refrigerator energy consumption while promoting energy-saving practices that take into account users' behavior and emotions. Conventional systems provide uniform advice without considering users' emotional states, which hinders behavioral change.

[0720] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0721] In this invention, the server includes means for measuring and acquiring environmental data, means for collecting and recognizing emotional information, and means for generating guidance information based on the analysis results and emotional information. This makes it possible to provide advice that encourages optimal energy-saving behavior while taking into consideration the user's emotions.

[0722] "Acquisition method" refers to a means of measuring the temperature inside the refrigerator and the open / closed state of the door in real time and acquiring that data.

[0723] "Transmission means" refers to means for transmitting acquired data to a data processing device.

[0724] "Storage means" refers to means for saving data acquired by a data processing device to a remote storage device.

[0725] "Analysis means" refers to a method for analyzing refrigerator usage patterns based on data acquired from remote storage devices.

[0726] "Recognition means" refers to means for collecting emotional information through sound and images and recognizing the emotional state of the user.

[0727] "Generation means" refers to means for generating guidance information for energy conservation based on analysis results and emotional information.

[0728] A "notification means" is a means of transmitting generated instructional information to a data processing device and appropriately notifying the user.

[0729] The system of the present invention efficiently manages the energy consumption of refrigerators and promotes energy-saving behavior that takes into consideration the user's feelings. Specifically, the system is composed of terminal devices, a server, and a cloud.

[0730] The terminal device acquires environmental data through a measuring device installed inside the refrigerator. The measuring device includes sensors that monitor the refrigerator's temperature and door open / closed status in real time. This environmental data is securely stored in a cloud-based database via the terminal device. The terminal device is also equipped with a camera and microphone, allowing it to collect emotional information by capturing the user's voice and facial expressions.

[0731] The server retrieves environmental data from a cloud-based database and also receives and analyzes emotional information. The server utilizes analytical algorithms and generative AI models to analyze the user's refrigerator usage patterns and emotional state. Based on the analysis results, it generates specific guidance information to promote energy-saving behaviors. This generated guidance information is personalized, taking into account the user's emotional state.

[0732] The terminal device notifies the user of guidance information received from the server. By receiving this advice, the user can improve their refrigerator usage and manage energy more efficiently.

[0733] For example, if a user is frequently opening and closing the refrigerator door, the server will generate advice such as, "Reducing the opening and closing of the door and taking items out all at once when needed will save energy." If the user looks tired, a gentle message such as, "Take a short break today and try to use the refrigerator more efficiently," will be sent.

[0734] An example of a prompt would be: "Based on refrigerator usage data, please provide specific actions to reduce today's energy consumption by 20%. Also, please give me some concise advice considering my current fatigue level."

[0735] This will enable users to perform energy-saving actions in a way that is emotionally resonant, enjoyable, and efficient.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The terminal acquires environmental data using measuring devices inside the refrigerator. Specifically, it detects the temperature inside the refrigerator with a temperature sensor and checks the door open / closed status with a door open / closed sensor. This input data is processed within the terminal and updated in real time. The output is the latest environmental data, which is ready for use in the next step.

[0739] Step 2:

[0740] The device sends the acquired environmental data to a database in the cloud. Specifically, it securely uploads the data to the cloud using internet protocols. During this process, error checking is performed to confirm that the data has been transmitted correctly. As output, the environmental data stored in the cloud database is obtained and becomes accessible to the server.

[0741] Step 3:

[0742] The server retrieves environmental data stored in the cloud and begins analysis. It receives temperature change patterns and door opening / closing frequency as input, and applies a machine learning algorithm to analyze refrigerator usage patterns. This analysis reveals energy consumption trends. The output is the analysis of consumption trends based on the data.

[0743] Step 4:

[0744] The device uses a camera and microphone to collect user voice and facial expression data. It takes the user's facial expressions and voice information as input and transmits this data to the server in real time. Specific operations include voice recognition and facial expression recognition processes. The output is data indicating the user's emotional state.

[0745] Step 5:

[0746] The server integrates the results of analyzing acquired user emotion data and environmental data. It receives this data as input and uses a generative AI model to generate energy-saving guidance information tailored to the user's emotions. This process, for example, selects a more concise and encouraging message if the user is tired. The output is user-specific energy-saving advice.

[0747] Step 6:

[0748] The terminal notifies the user of energy-saving advice sent from the server. It conveys the advice received as input to the user through screen display and audio output. Specific operations include scheduling to optimize the timing of notifications. The output is specific and practical guidance information received by the user.

[0749] (Application Example 2)

[0750] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0751] In recent years, efficient operation and energy conservation of refrigeration equipment have become increasingly important, but there is no system in place to promote energy-saving behavior while taking into account the emotional state of employees. When employees are fatigued or stressed, guidance on energy conservation may not be effective, so there is a need to provide appropriate guidance that is tailored to their emotional state.

[0752] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0753] In this invention, the server includes an acquisition means for measuring environmental data within the refrigeration equipment and acquiring measurement information, an analysis means for acquiring the measurement information from an information storage device on the cloud and performing analysis, and a recognition means for recognizing a person's emotional state using an emotion recognition means. This makes it possible to monitor the use of the refrigeration equipment while providing energy-saving guidance tailored to the emotional state of employees.

[0754] "Environmental data" refers to information necessary to understand the operating conditions of refrigeration equipment, such as temperature, humidity, and door opening / closing status inside and around the equipment.

[0755] "Measurement information" refers to information obtained by converting environmental data acquired by measuring devices into a specific data format, and is used for energy conservation analysis.

[0756] "Means of acquisition" refers to functions or devices for measuring and collecting environmental data within refrigeration equipment.

[0757] "Transmission means" refers to functions or devices for transferring measurement information obtained by acquisition means to an information processing device or the cloud.

[0758] An "information processing device" is a device used to temporarily store measurement information and transmit it to the cloud as digital data.

[0759] An "information storage device" is a device that is physically or virtually installed on the cloud and has database functionality for long-term storage of measurement information.

[0760] "Analysis means" refers to functions or devices that analyze measurement information acquired from information storage devices on the cloud and generate usage patterns of refrigeration equipment and proposals for energy saving.

[0761] "Generation means" refers to a function or device for generating specific guidance information, such as energy-saving actions, based on the results of the analysis means.

[0762] "Emotion recognition means" refers to technology or devices that detect a person's voice, facial expressions, etc., and identify their emotional state in real time.

[0763] A "notification means" is a function or device that transmits generated guidance information to an information processing device at an appropriate time to inform the user of advice or guidance.

[0764] This invention relates to a system for efficiently operating refrigeration equipment and promoting energy-saving behavior. The system consists of acquisition means, analysis means, generation means, recognition means, and notification means.

[0765] The server uses sensors to monitor temperature, humidity, door opening / closing status, and other environmental data within the refrigeration equipment, and collects the measurement information through acquisition methods. This information is transmitted to a cloud-based storage device via an information processing device. The server then uses analysis methods to examine the data stored in the cloud and analyze the usage status of the refrigeration equipment. This analysis utilizes machine learning algorithms and AI technology to extract patterns such as temperature fluctuations and door opening / closing frequency.

[0766] Based on the analysis results, the server uses a generation mechanism to create guidance information that promotes energy-saving behavior. This information includes, for example, specific suggestions for efficient operation of refrigeration equipment and energy reduction. Furthermore, a recognition mechanism analyzes the user's voice and facial expressions in real time to identify the user's emotional state. For example, a user feeling fatigued can receive a simple message of encouragement along with some advice. Conversely, a user in a positive state can receive detailed analysis results and specific energy-saving behavior suggestions through a notification mechanism.

[0767] As a concrete example, in one supermarket, the introduction of this system reduced energy consumption during business hours by 25% in its refrigerators. This was because it enabled efficient operation tailored to peak times when employees experience stress, thereby optimizing energy consumption.

[0768] An example of a prompt message would be, "Please provide advice on developing a system using data analysis to improve the energy efficiency of refrigerators."

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The server acquires environmental data such as temperature, humidity, and door opening / closing status inside the refrigeration equipment from sensors. The input is raw data from the sensors, and the output is formatted measurement information. The server converts this information into a digital format and organizes it into a data structure ready for subsequent processing.

[0772] Step 2:

[0773] The server transmits the acquired measurement information to the information processing device via the network. The input is formatted measurement information, and the output is transfer to the cloud environment. The server securely transmits this information to the cloud's information storage device, making it accessible remotely.

[0774] Step 3:

[0775] The server analyzes measurement data stored in a cloud-based data storage device using analytical tools. The input is the measurement data stored in the cloud, and the output is the analysis result. The server processes the data using machine learning algorithms to identify usage patterns of the refrigeration equipment.

[0776] Step 4:

[0777] The server generates energy-saving guidance information based on the analysis results. The input is the analysis results, and the output is the generated guidance information. The server formulates specific energy-saving proposals using conditional logic.

[0778] Step 5:

[0779] The device acquires user voice and facial expression data using a camera and microphone, and processes it with emotion recognition technology. The input is visual and audio data, and the output is the user's current emotional state. The device performs real-time analysis and obtains results from an emotion engine.

[0780] Step 6:

[0781] The server combines the user's emotional state with generated guidance information and sends it to the user at the optimal time using a notification system. The input is the user's emotional state and guidance information, and the output is the notification content. The server adjusts the message content and tone according to the emotional state and presents the information via the terminal.

[0782] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0783] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0784] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0785] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0786] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0787] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0788] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0789] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0790] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0791] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0792] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0793] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0794] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0795] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0796] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0797] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0798] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0799] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0800] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0801] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0802] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0803] The following is further disclosed regarding the embodiments described above.

[0804] (Claim 1)

[0805] A means for measuring environmental data inside a refrigerator and acquiring measurement information,

[0806] A transmission means for transmitting the aforementioned measurement information to a terminal device,

[0807] The terminal device includes a storage means for storing the measurement information in a database on the cloud,

[0808] An analysis means that acquires the measurement information from the aforementioned cloud database and performs analysis,

[0809] A generation means for generating guidance information for energy conservation based on the analysis results,

[0810] A notification means for transmitting the aforementioned instruction information to the terminal device and notifying the user,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, characterized in that the analysis means analyzes temperature changes and opening / closing frequency during a specific time period.

[0814] (Claim 3)

[0815] The system according to claim 1, characterized in that the generation means generates energy-saving guidance information that proposes a specific action.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A means for measuring and acquiring environmental information inside a refrigerated storage device,

[0819] A communication means for transmitting the aforementioned information to an information processing device,

[0820] The information processing device includes storage means for storing the information in a recording area on a substrate,

[0821] An evaluation means that acquires the information from the recording area on the aforementioned substrate and performs analysis,

[0822] A means for generating guidance information for resource conservation based on the evaluation results,

[0823] A notification means for transmitting the aforementioned guidance information to the information processing device and notifying the user,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, characterized in that the evaluation means analyzes environmental changes and operation frequency during a specific time period.

[0827] (Claim 3)

[0828] The system according to claim 1, characterized in that the creation means generates resource-saving guidance information that proposes a specific action.

[0829] "Application Example 1"

[0830] (Claim 1)

[0831] A means for measuring environmental data inside a cooling device and acquiring measurement information,

[0832] A transmission means for transmitting the measurement information to an information processing device,

[0833] The information processing device includes storage means for storing the measurement information in an online storage device,

[0834] An analysis means that acquires the measurement information from the online storage device and performs analysis,

[0835] A generation means for generating guidance information for resource conservation based on the aforementioned analysis results,

[0836] A notification means for transmitting the aforementioned guidance information to the information processing device and notifying the user,

[0837] A monitoring system that provides continuous usage monitoring and security alerts,

[0838] A warning means that identifies abnormal activity using the aforementioned monitoring means and warns the user,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, characterized in that the analysis means analyzes changes in conditions and opening / closing frequency during a specific time period and identifies abnormal behavior.

[0842] (Claim 3)

[0843] The system according to claim 1, characterized in that the generation means generates resource-saving guidance information that proposes specific actions and promotes preventive safety measures.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] A means for measuring environmental data inside a refrigerator and acquiring measurement information,

[0847] A transmission means for transmitting the measurement information to a data processing device,

[0848] The data processing device includes storage means for storing the measurement information in a remote storage device,

[0849] An analysis means that acquires the measurement information from the remote storage device and performs analysis,

[0850] A recognition means that collects emotional information through sound and images and recognizes the emotional state,

[0851] A generation means for generating guidance information for energy saving based on the aforementioned analysis results and emotional information,

[0852] A notification means for transmitting the aforementioned guidance information to the data processing device and notifying the user,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, characterized in that the analysis means analyzes temperature changes and opening / closing frequency during a specific time period and adjusts guidance information according to the user's emotional state.

[0856] (Claim 3)

[0857] The system according to claim 1, characterized in that the generation means proposes a specific action and generates energy-saving guidance information that takes into account the user's feelings.

[0858] "Application example 2 when combining with an emotional engine"

[0859] (Claim 1)

[0860] A means for measuring environmental data inside refrigeration equipment and acquiring measurement information,

[0861] A transmission means for transmitting the measurement information to an information processing device,

[0862] The information processing device includes storage means for storing the measurement information in an information storage device on the cloud,

[0863] An analysis means that acquires the measurement information from the information storage device on the cloud and performs analysis,

[0864] A generation means for generating guidance information for energy conservation based on the analysis results,

[0865] A recognition means that recognizes a person's emotional state using an emotion recognition means,

[0866] A notification means for generating and transmitting guidance information corresponding to the aforementioned emotional state,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, characterized in that the analysis means analyzes temperature changes and opening / closing frequency during a specific time period.

[0870] (Claim 3)

[0871] The system according to claim 1, characterized in that the generation means generates energy-saving guidance information that proposes actions corresponding to a specific emotional state. [Explanation of symbols]

[0872] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for measuring environmental data inside a refrigerator and acquiring measurement information, A transmission means for transmitting the aforementioned measurement information to a terminal device, The terminal device includes a storage means for storing the measurement information in a database on the cloud, An analysis means that acquires the measurement information from the aforementioned cloud database and performs analysis, A generation means for generating guidance information for energy conservation based on the analysis results, A notification means for transmitting the aforementioned instruction information to the terminal device and notifying the user, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means analyzes temperature changes and opening / closing frequency during a specific time period.

3. The system according to claim 1, characterized in that the generation means generates energy-saving guidance information that proposes a specific action.

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