System

Abstract

A system is provided.SOLUTION: A system, comprising: means for managing security settings using a generative AI model; means for recording energy usage and calculating carbon footprint based thereon; and means for providing energy savings tips.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a lack of solutions that integrate the management of socially important security and environmental protection. Many users handle security systems and energy conservation measures separately, making it difficult to optimize them simultaneously. In addition, there is a lack of systems that allow users to effectively manage their home energy consumption and CO2 emissions. [Means for solving the problem]

[0005] The present invention provides a system that uses a generative AI model to manage security settings, record energy usage, and calculate carbon dioxide emissions. The system includes a means for providing energy-saving tips to a user. The system also includes a means for monitoring carbon dioxide emissions in the home and presenting improvement suggestions to the user. Furthermore, the system includes a troubleshooting means for providing appropriate solutions to problems reported by the user. This allows users to optimize both security and energy efficiency and achieve a sustainable lifestyle.

[0006] A "generative AI model" is an algorithm that uses machine learning technology to generate new data and information.

[0007] "Security settings" are settings that manage the operation of sensors and devices installed to ensure security within the home.

[0008] "Energy usage" is the amount of energy, such as electricity or gas, consumed within a specific period of time.

[0009] "Carbon dioxide emissions" is the amount of carbon dioxide released into the atmosphere through energy consumption and other activities.

[0010] "Energy saving tips" are specific advice and techniques for reducing energy consumption.

[0011] "Domestic carbon dioxide emissions" refers to carbon dioxide emissions generated by everyday activities and energy consumption at home.

[0012] "Troubleshooting methods" are methods or processes for correcting or resolving system malfunctions or problems.

[0013] "Trouble" refers to a failure or problem in the operation of a system or device.

[0014] "Improvement proposals" are suggestions or advice for improving the efficiency or effectiveness of existing conditions or systems.

[0015] A "sustainable lifestyle" is one that uses current and future resources efficiently and minimizes environmental impact.

[0016] A "system" is a set of devices or programs in which multiple elements are interrelated and configured to achieve a specific purpose. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[0039] Overall system overview

[0040] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server. Users access the system through their devices and input and manage security settings and energy usage information. The server processes this data and provides appropriate advice and suggestions to users.

[0041] Program processing

[0042] Security Settings Management

[0043] The user enters security settings. The device sends this setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, and the user confirms that the security settings have been applied.

[0044] Record energy usage and calculate CO2 emissions

[0045] The user inputs the amount of energy used. The device sends this usage data to the server. The server stores the received data and calculates the amount of CO2 emissions based on it. The calculation results are notified to the user, and the amount of energy used and CO2 emissions are clearly displayed.

[0046] Providing energy-saving tips

[0047] The user requests energy-saving tips. The device sends the request to the server. The server generates appropriate advice from a pre-defined list of energy-saving tips and sends it to the device. The user can use this advice to reduce energy consumption.

[0048] troubleshooting

[0049] The user reports a problem. The device sends the report to the server. The server analyzes the received problem and selects an appropriate solution from a pre-defined list of solutions. The selected solution is sent to the user's device, providing information to help solve the problem.

[0050] Specific examples

[0051] Update security settings

[0052] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which then updates the system's security settings. The user then sees a message on their screen saying "Security settings updated."

[0053] Energy usage record

[0054] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[0055] Providing energy-saving tips

[0056] The user requests specific advice on energy conservation and sends a request on their device. The server provides tips such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree" and displays them to the user. The user can use these tips to reduce energy consumption.

[0057] troubleshooting

[0058] The user reports a sensor malfunction on their device and sends it to the server. The server then suggests a solution, "Please restart the sensor," and displays it on the user's screen. The user can follow the suggestion to resolve the problem.

[0059] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[0060] The processing flow will be explained below.

[0061] Security Settings Management

[0062] Step 1:

[0063] The user enters security settings (e.g., front door lock, window sensor on / off) using a smartphone or tablet.

[0064] Step 2:

[0065] The device sends the security setting data entered by the user to the server.

[0066] Step 3:

[0067] The server analyzes the received security setting data and updates the security setting in the system.

[0068] Step 4:

[0069] The server sends the updated security settings to the user's device.

[0070] Step 5:

[0071] The user's device will display a message indicating that the security settings have been successfully updated.

[0072] Record energy usage and calculate CO2 emissions

[0073] Step 1:

[0074] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[0075] Step 2:

[0076] The device sends energy usage data to the server.

[0077] Step 3:

[0078] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[0079] Step 4:

[0080] The server sends the calculated CO2 emissions to the user's terminal.

[0081] Step 5:

[0082] The user's device displays energy usage and CO2 emissions.

[0083] Providing energy-saving tips

[0084] Step 1:

[0085] Users submit requests for energy-saving tips from their smartphones or tablets.

[0086] Step 2:

[0087] The device sends an energy saving hint request to the server.

[0088] Step 3:

[0089] The server generates appropriate advice from a pre-configured list of energy saving tips.

[0090] Step 4:

[0091] The server sends the generated energy saving hints to the user's device.

[0092] Step 5:

[0093] The user's device displays energy saving tips.

[0094] troubleshooting

[0095] Step 1:

[0096] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[0097] Step 2:

[0098] The device sends the user's problem report to the server.

[0099] Step 3:

[0100] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[0101] Step 4:

[0102] The server sends the selected solution to the user's device.

[0103] Step 5:

[0104] The user's device will display recommended solutions to resolve the issue.

[0105] By executing the processing steps for each function, the system enables management of security settings, efficient energy use, and quick troubleshooting.The system provides a clear and easy-to-use interface for users, supporting a sustainable lifestyle.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] In today's smart home environment, systems that balance security and energy efficiency are not yet fully established. In particular, there is a need for systems that allow users to easily manage security settings, accurately calculate greenhouse gas emissions based on energy usage, and receive appropriate energy-saving advice. There is also a lack of systems that can quickly provide solutions to problems that arise in the home.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes: means for managing information settings using a generative computer system; means for recording energy consumption and calculating greenhouse gas emissions based on the recorded energy consumption; means for providing energy-saving advice; means for collecting setting information entered by users through an information device consisting of multiple terminals and sending it to a central processing unit; means for the central processing unit to analyze the received information and respond to the user with appropriate settings or advice; and means for storing energy consumption and related information in a database and retrieving and recalculating it as needed. This allows users to efficiently manage security settings, understand energy consumption and greenhouse gas emissions, and take specific energy-saving measures. It also provides quick solutions to problems that arise in the home, improving the sustainability of lifestyles.

[0111] A "generative computer system" is a system that has the technology to automatically generate, manipulate, and manage specifications based on user input information.

[0112] "Information Settings" refers to various data and settings related to security and energy usage that users can manage and operate.

[0113] "Greenhouse gas emissions" refers to the total amount of greenhouse gases, including carbon dioxide, emitted from households and facilities over a certain period of time.

[0114] "Energy saving advice" means specific advice or suggestions for reducing energy consumption.

[0115] "Information device" refers to a terminal (e.g., smartphone, tablet, sensor, etc.) used to facilitate the input, processing, and output of data.

[0116] A "central processing unit" is a computer system that receives input data, analyzes and processes it, and provides appropriate control.

[0117] "Database" means a system for efficiently storing, managing, and retrieving energy usage and related information.

[0118] "Energy consumption" refers to the total amount of energy, such as electricity and gas, consumed in a home or facility over a certain period of time.

[0119] A "problem-solving tool" is a system that provides quick and appropriate solutions to problems or troubles reported by users.

[0120] "Appropriate settings or advice" refers to the best settings or suggestions provided by the generative computer system based on the user's input information.

[0121] This invention relates to an eco-friendly smart home security system with a generative computing system at its core. This system optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[0122] Overall system overview

[0123] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a central processing unit. Users access the system through their devices and input and manage security settings and energy usage information. The central processing unit processes this data and provides appropriate advice and suggestions to users.

[0124] Hardware and software used

[0125] The hardware used is a smartphone, tablet, sensors, and a central processing unit, while the software includes a generative computing system that manages security settings, records energy usage, calculates greenhouse gas emissions, provides energy conservation advice, and troubleshoots.

[0126] Security Settings Management

[0127] A user opens an application on a smartphone or tablet and enters settings for the front door lock or window sensor. The device sends this setting information to the central processing unit. The central processing unit analyzes the received information and updates the system's security settings. It then sends a message to the device saying "Security settings have been updated."

[0128] Record energy usage and calculate greenhouse gas emissions

[0129] The user inputs the amount of energy used that day through the application. For example, if the energy usage is input as 5.5 kWh, the device will send this data to the central processing unit. The central processing unit stores the received data in a database and calculates the greenhouse gas emissions (e.g., 2.75 kg) based on the data using the production calculation system. The calculation result will be displayed on the user's device.

[0130] Providing advice on energy conservation

[0131] When a user requests energy-saving advice, they send a request through the application, for example, using the prompt "Request specific energy-saving advice." The central processing unit selects the most appropriate advice from the internal hint list and sends it to the user's device. The user can receive advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[0132] troubleshooting

[0133] When a user reports a system problem, they use an application to input the specific problem. The terminal sends this report to the central processing unit, which analyzes the problem reported by the generative computer system and suggests an appropriate solution. For example, in response to a report of a "sensor malfunction," the central processing unit suggests the solution "restart the sensor." The user can solve the problem by following this instruction.

[0134] Prompt Sentence Examples

[0135] Below are some specific examples of prompt sentences to input into the generative AI model.

[0136] 1. "Turn on security settings for your front doors and windows and receive a confirmation message."

[0137] 2. "Enter your energy usage as 5.5 kWh and calculate and display your greenhouse gas emissions."

[0138] 3. "Submit a request for energy saving advice and view tips."

[0139] 4. "Report sensor malfunctions and provide appropriate solutions."

[0140] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Security Settings Management

[0143] Step 1:

[0144] The user opens the application and enters the security settings.

[0145] Input: On / off setting for front door lock and window sensor

[0146] How it works: A user operates an app on their smartphone or tablet and enters security settings.

[0147] Step 2:

[0148] The terminal sends the setting information to the server.

[0149] Input: Security setting information entered by the user

[0150] Output: Communication data via HTTPS protocol, including configuration information

[0151] How it works: The device encrypts the configuration information in real time and sends it to the server.

[0152] Step 3:

[0153] The server analyzes the configuration information and updates the security settings.

[0154] Input: Security setting information received by the server

[0155] Data processing: Generative AI models analyze information and convert it into appropriate settings

[0156] Output: Updated security settings

[0157] How it works: The server uses the generative AI model to analyze the configuration information and update the system's security settings.

[0158] Step 4:

[0159] The server sends a confirmation message to the terminal.

[0160] Enter: Updated security settings

[0161] Output: "Security settings updated" message

[0162] Operation: The server generates a message with the updated settings and sends it to the device.

[0163] Step 5:

[0164] The user confirms that the settings should be applied.

[0165] Input: "Security settings updated" message

[0166] Output: Visual confirmation

[0167] Action: The user checks the device screen to confirm that the settings have been applied.

[0168] Record energy usage and calculate greenhouse gas emissions

[0169] Step 1:

[0170] The user inputs energy usage through the application.

[0171] Input: Energy usage data (e.g. 5.5 kWh)

[0172] How it works: A user enters their energy usage using a smartphone or tablet.

[0173] Step 2:

[0174] The device sends energy usage data to the server.

[0175] Input: Energy usage data

[0176] Output: JSON format data containing energy usage

[0177] How it works: The device converts energy usage data into JSON format and sends it to the server.

[0178] Step 3:

[0179] The server stores the data and calculates greenhouse gas emissions.

[0180] Input: Energy usage data

[0181] Data processing: Calculating emissions with generative AI models

[0182] Output: Calculated greenhouse gas emissions (e.g. 2.75 kg)

[0183] How it works: A server stores energy usage data in a database and applies an algorithm to calculate emissions.

[0184] Step 4:

[0185] The server notifies the user of the calculation results.

[0186] Input: Calculated greenhouse gas emissions

[0187] Output: Notification message containing emissions data

[0188] Operation: The server generates the calculation result as a message and sends it to the terminal.

[0189] Providing advice on energy conservation

[0190] Step 1:

[0191] A user asks for energy conservation advice.

[0192] Input: Energy conservation advice request

[0193] Action: The user taps the energy saving advice request button in the application.

[0194] Step 2:

[0195] The terminal sends a request for advice to the server.

[0196] Input: Energy conservation advice request

[0197] Output: Communication data including request data

[0198] Operation: The device sends a request to the server.

[0199] Step 3:

[0200] The server generates appropriate advice and sends it to the terminal.

[0201] Input: Advice request

[0202] Data processing: Advice generation using generative AI models

[0203] Output: Advice message (e.g., "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree")

[0204] Operation: The server selects the best advice from the advice list, generates it as a message, and sends it to the terminal.

[0205] Step 4:

[0206] The user receives the advice.

[0207] Input: Advice message

[0208] Output: Visual confirmation and actionable advice

[0209] Action: The user checks the advice displayed on the device screen and follows it.

[0210] troubleshooting

[0211] Step 1:

[0212] A user reports a system problem.

[0213] Input: Specific problem report (e.g. sensor malfunction)

[0214] Action: A user fills in details on a problem report form in an application.

[0215] Step 2:

[0216] The device sends the report to the server.

[0217] Input: Trouble report data

[0218] Output: Communication data including report data

[0219] Operation: The terminal sends report data to the server.

[0220] Step 3:

[0221] The server analyzes the problem and chooses a solution.

[0222] Input: Trouble report data

[0223] Data processing: problem analysis and solution selection using generative AI models

[0224] Output: Solution message (e.g. "Please restart the sensor")

[0225] How it works: The server analyzes the reported problem and chooses the best solution.

[0226] Step 4:

[0227] The server sends the solution to the user's terminal.

[0228] Input: Selected Solution

[0229] Output: Solution message

[0230] Operation: The server generates a solution as a message and sends it to the terminal.

[0231] Step 5:

[0232] The user reviews the solution and resolves the issue.

[0233] Input: Solution message

[0234] Output: Visual confirmation and actionable solutions

[0235] Action: The user sees the solution displayed on the device screen and follows the instructions to resolve the issue.

[0236] (Application example 1)

[0237] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0238] Conventional smart home security systems manage security and energy efficiency separately, making it difficult to optimize each. Energy-saving advice and troubleshooting are standardized, lacking the ability to address individual user needs. Furthermore, real-time security monitoring is not possible, leading to delayed detection of abnormalities and making it difficult to ensure user safety.

[0239] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0240] In this invention, the server includes a means for managing security settings using a generative AI model, a means for recording energy usage and calculating carbon dioxide emissions based on the recorded energy usage, a means for providing energy-saving tips, and a means for performing real-time security monitoring and notifying the user when an abnormality is detected. This makes it possible to comprehensively optimize both security and energy efficiency and provide energy-saving advice and troubleshooting customized for each user. Furthermore, real-time security monitoring allows for immediate detection of abnormalities, ensuring user safety.

[0241] A "generative AI model" is an algorithm that generates new data and information based on input data, and is a type of artificial intelligence that performs pattern recognition and prediction.

[0242] "Security settings" refers to setting information for managing access control and the operation of security devices within a home or a specific area.

[0243] "Energy usage" refers to the amount of energy consumed per unit time, and measures the amount of electricity, gas, etc. consumed in the home and elsewhere.

[0244] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted through energy consumption, and is a measurement indicator of environmental impact.

[0245] "Energy saving tips" refer to specific advice and suggestions for reducing energy consumption, which serve as a guide for users to improve their energy usage efficiency.

[0246] "Real-time security monitoring" refers to processes and systems that instantly monitor the current status and immediately notify you if an abnormality is detected.

[0247] "Troubleshooting" refers to the process of analyzing problems that occur in systems or devices and providing appropriate solutions.

[0248] "Notifications" are a mechanism for sending messages and alerts to users to immediately convey important information.

[0249] This invention is an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency and helps realize a sustainable lifestyle. The system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server.

[0250] Hardware and software used

[0251] Smartphone: A handheld device used for user input and display of information.

[0252] Security camera: A device used to monitor the home and detect abnormalities.

[0253] Cloud server: A server responsible for data processing and running generative AI models.

[0254] Generative AI models: Algorithms that manage configuration, analyze, and generate recommendations.

[0255] Overall system configuration

[0256] The system has the following main features:

[0257] 1. Security Settings Management

[0258] Users input security settings such as door locks and window sensors via their smartphone or tablet. The device then sends this information to the server, where a generative AI model analyzes and applies the settings. Once the settings are updated, a confirmation message is sent to the user.

[0259] 2. Record your energy usage and calculate your CO2 emissions

[0260] The user inputs their energy usage data into their smartphone. The device then sends this data to a cloud server, which calculates the amount of CO2 emissions. The calculation results are then instantly fed back to the user's smartphone.

[0261] 3. Providing energy-saving tips

[0262] When a user requests energy-saving advice, the server uses a generative AI model to generate optimal energy-saving tips for the user and sends them to their smartphone.

[0263] 4. Troubleshooting

[0264] When a user reports a malfunction in a sensor or other device, a cloud server receives it, and a generative AI model proposes an appropriate solution and sends it to the smartphone.

[0265] 5. Real-time security monitoring

[0266] Security cameras constantly monitor the home, and if they detect any abnormalities, the data is sent to a cloud server and a real-time notification is sent to the user's smartphone.

[0267] Specific examples

[0268] 1. Update your security settings

[0269] The user sets the front door lock and window sensor on their smartphone. The cloud server receives this information and updates the settings. The user is then notified with a message that "security settings have been updated."

[0270] 2. Record your energy usage

[0271] The user enters their energy usage for the day as 5.5 kWh on their smartphone. The cloud server receives this data, and the generative AI model calculates the CO2 emissions as 2.75 kg. The calculation result is fed back to the smartphone.

[0272] 3. Providing energy-saving tips

[0273] A user types "Please give me some energy-saving advice" into their smartphone. The cloud server uses a generative AI model to generate suggestions to the user, such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[0274] 4. Troubleshooting

[0275] When a user reports that "the sensor is not working," the cloud server uses a generative AI model to suggest a solution, such as "restart the sensor," and sends it to the smartphone.

[0276] 5. Real-time security monitoring

[0277] Security cameras transmit data in real time, and if an abnormality (such as an unauthorized door opening or closing) is detected, the cloud server immediately sends a notification to the user's smartphone.

[0278] Prompt Sentence Examples

[0279] "Please give me some energy saving advice based on my energy usage."

[0280] "Check the footage from your home security cameras in real time and let us know if there are any abnormalities."

[0281] "Please turn on the front door lock and window sensors."

[0282] "Sensor not working properly"

[0283] Based on these prompts, the cloud server and generative AI model work together to provide optimal information processing and suggestions, thereby building a system that supports the user's lifestyle.

[0284] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0285] Step 1:

[0286] Users use their smartphones or tablets to input security settings, energy usage, energy saving tips, or troubleshooting requests.

[0287] (Input): User requests (e.g., changing security settings, entering energy usage data)

[0288] (Output): The requested data is sent from the terminal to the server.

[0289] Step 2:

[0290] The terminal transmits the received user request data to the cloud server.

[0291] (Input): User request data

[0292] (Output): The request data is sent to the cloud server.

[0293] Step 3:

[0294] The server analyzes the received request data and determines the appropriate processing method.

[0295] (Input): Request data

[0296] (Output): Analysis results (e.g., security setting changes, energy usage records, anomaly detection start instructions)

[0297] Step 4:

[0298] It uses generative AI models to perform processing based on user requests.

[0299] (Input): Analysis results

[0300] (Output): Processing results (e.g., updated security settings, energy saving advice, troubleshooting solutions)

[0301] Step 5:

[0302] Based on the processing results, the cloud server performs specific data processing and calculations, such as calculating CO2 emissions based on energy consumption.

[0303] (Input): Processing result

[0304] (Output): Data processing or calculation results (e.g., CO2 emissions, specific energy-saving advice)

[0305] Step 6:

[0306] The server generates the final results and feeds them back to the user's smartphone or tablet.

[0307] (Input): Data processing or calculation results

[0308] (Output): Feedback to the user (e.g., security setting confirmation message, CO2 emission notification, energy saving advice display)

[0309] Specific operation example

[0310] Changing security settings

[0311] Step 1: The user types "turn on front door lock" into their smartphone.

[0312] (Input): "Lock front door on."

[0313] (Output): The requested data is sent from the terminal to the server.

[0314] Step 2: The device sends a request to change the security settings to the cloud server.

[0315] (Input): Security setting change request

[0316] (Output): The request data is sent to the cloud server.

[0317] Step 3: The server analyzes the request data and determines the setting changes for the front door lock.

[0318] (Input): Security setting change request data

[0319] (Output): Analysis result (instruction to turn on door lock)

[0320] Step 4: The generative AI model applies the door lock configuration changes and updates the system.

[0321] (Input): Analysis result (instruction to turn on door lock)

[0322] (Output): Processing result (Door lock on setting)

[0323] Step 5: The server makes the configuration changes and displays "Security settings updated" on the user's screen.

[0324] (Input): Processing result (door lock on setting)

[0325] (Output): Feedback to the user (security settings update message)

[0326] Prompt Sentence Examples

[0327] "Please set the front door lock to on"

[0328] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0329] This invention relates to an environmentally friendly smart home security system that combines a generative AI model and an emotion engine. The system assists users in optimizing security settings and energy efficiency, and provides personalized services based on the user's emotions. Specific embodiments of the invention are described in detail below.

[0330] Overall system overview

[0331] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[0332] Device: A device operated by a user, such as a smartphone or tablet.

[0333] Server: A computer system that analyzes and processes data.

[0334] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[0335] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[0336] Program processing

[0337] Security Settings Management

[0338] The user enters security settings on the device. The device sends the setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which the user confirms.

[0339] Record energy usage and calculate CO2 emissions

[0340] The user inputs their energy usage into the device. The device then sends this data to the server, which stores the data and uses an AI model to calculate CO2 emissions. The results of the calculation are then sent to the user, who is then shown the energy usage and CO2 emissions.

[0341] Providing energy-saving tips

[0342] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[0343] troubleshooting

[0344] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[0345] Emotion engine processing

[0346] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[0347] Specific examples

[0348] Update security settings

[0349] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[0350] Energy usage record

[0351] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[0352] Providing energy-saving tips

[0353] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[0354] troubleshooting

[0355] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[0356] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[0357] The processing flow will be explained below.

[0358] Security Settings Management

[0359] Step 1:

[0360] The user uses a smartphone or tablet to enter security settings (e.g., front door lock, window sensor on / off).

[0361] Step 2:

[0362] The device sends the security setting data entered by the user to the server.

[0363] Step 3:

[0364] The server analyzes the received security setting data and updates the security setting in the system.

[0365] Step 4:

[0366] The server sends the updated security settings to the user's device.

[0367] Step 5:

[0368] The user's device will display a message indicating that the security settings have been successfully updated.

[0369] Record energy usage and calculate CO2 emissions

[0370] Step 1:

[0371] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[0372] Step 2:

[0373] The device sends energy usage data to the server.

[0374] Step 3:

[0375] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[0376] Step 4:

[0377] The server sends the calculated CO2 emissions to the user's terminal.

[0378] Step 5:

[0379] The user's device displays energy usage and CO2 emissions.

[0380] Providing energy-saving tips

[0381] Step 1:

[0382] Users submit requests for energy-saving tips from their smartphones or tablets.

[0383] Step 2:

[0384] The device sends an energy saving hint request to the server.

[0385] Step 3:

[0386] The server generates appropriate advice from a pre-configured list of energy saving tips.

[0387] Step 4:

[0388] The server sends the generated energy saving hints to the user's device.

[0389] Step 5:

[0390] The user's device displays energy saving tips.

[0391] troubleshooting

[0392] Step 1:

[0393] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[0394] Step 2:

[0395] The device sends the user's problem report to the server.

[0396] Step 3:

[0397] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[0398] Step 4:

[0399] The server sends the selected solution to the user's device.

[0400] Step 5:

[0401] The user's device will display recommended solutions to resolve the issue.

[0402] Emotion engine processing

[0403] Step 1:

[0404] Users use their smartphones or tablets to provide emotional data through voice, facial expressions, or text input.

[0405] Step 2:

[0406] The device sends the user's emotional data to the server.

[0407] Step 3:

[0408] The server uses an emotion engine to analyze the user's emotion data.

[0409] Step 4:

[0410] Based on sentiment analysis, the server optimizes security settings and energy-saving tips to improve user safety.

[0411] Step 5:

[0412] The server sends optimized security settings and energy-saving tips to the device.

[0413] Step 6:

[0414] Your device will display personalized settings and advice based on your emotions.

[0415] Specific examples

[0416] Update security settings

[0417] Step 1:

[0418] The user turns on the front door lock and window sensor on the device.

[0419] Step 2:

[0420] The terminal sends this setting information to the server.

[0421] Step 3:

[0422] The server updates the system security settings.

[0423] Step 4:

[0424] The server sends a confirmation message to the terminal.

[0425] Step 5:

[0426] The user will see the message "Security settings have been updated."

[0427] Energy usage record

[0428] Step 1:

[0429] A user enters their energy usage for the day as 5.5 kWh on their device.

[0430] Step 2:

[0431] The terminal transmits this data to the server.

[0432] Step 3:

[0433] The server calculates the CO2 emissions to be 2.75 kg.

[0434] Step 4:

[0435] The server sends the calculation results to the terminal.

[0436] Step 5:

[0437] Energy usage and CO2 emissions are displayed on the user's screen.

[0438] Providing energy-saving tips

[0439] Step 1:

[0440] The user requests specific energy saving advice and submits a request on the device.

[0441] Step 2:

[0442] The device sends a request to the server.

[0443] Step 3:

[0444] The server generates hints using a generative AI model and an emotion engine.

[0445] Step 4:

[0446] The server sends the hint to the device.

[0447] Step 5:

[0448] Tips such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" will be displayed on the user's screen.

[0449] troubleshooting

[0450] Step 1:

[0451] The user reports a sensor malfunction on the device.

[0452] Step 2:

[0453] The terminal sends a report to the server.

[0454] Step 3:

[0455] The server generates the solution "Please restart the sensor."

[0456] Step 4:

[0457] The server sends the solution to the device.

[0458] Step 5:

[0459] The user will see a message on their screen saying "Please restart the sensor" and the user will be able to resolve the issue.

[0460] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[0461] Example 2

[0462] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0463] Modern smart home systems are required to manage security, improve energy efficiency, and provide advice on energy conservation, but few systems can comprehensively perform all of these functions. Furthermore, there is a need for systems that can provide services that take into account the user's emotions and psychological state. A comprehensive and personalized system that can solve these issues is desired.

[0464] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0465] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, and means for having an emotion engine for analyzing user emotions and providing services based on the emotions. This enables security management, improved energy efficiency, energy saving, and the provision of personalized services according to the user's emotions.

[0466] A "generative AI model" is a machine learning algorithm used to manage security settings and analyze energy usage.

[0467] "Security settings" are settings to ensure the safety of your home, and include turning on and off front door locks and window sensors.

[0468] "Energy usage" refers to energy consumption, such as the amount of electricity used within a household.

[0469] "Carbon dioxide emissions" refers to the amount of CO2 emitted as a result of energy consumption.

[0470] "Energy saving tips" refers to specific advice on how to reduce energy consumption.

[0471] An "emotion engine" is an algorithm that analyzes emotions from a user's voice, facial expressions, text input, etc., and provides services based on that analysis.

[0472] This invention relates to an eco-friendly smart home security system that combines a generative AI model and an emotion engine. The system helps users optimize security settings and energy efficiency, and provides personalized services based on the user's emotions.

[0473] Overall system overview

[0474] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[0475] Device: A device operated by a user, such as a smartphone or tablet.

[0476] Server: A computer system that analyzes and processes data.

[0477] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[0478] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[0479] Program processing

[0480] Security Settings Management

[0481] The user enters security settings on the device. The device sends the settings information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which is displayed to the user.

[0482] Record energy usage and calculate CO2 emissions

[0483] The user inputs their energy consumption data into the device. The device then sends this data to the server, which stores the data and uses a generative AI model to calculate CO2 emissions. The calculation results are then sent to the device and displayed to the user.

[0484] Providing energy-saving tips

[0485] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[0486] troubleshooting

[0487] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[0488] Emotion engine processing

[0489] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[0490] Specific examples

[0491] Update security settings

[0492] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[0493] Energy usage record

[0494] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[0495] Providing energy-saving tips

[0496] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[0497] troubleshooting

[0498] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[0499] Prompt Sentence Examples

[0500] A prompt based on the security configuration management example is as follows:

[0501] "After a user turns on their front door lock and window sensor on their smartphone and the system sends the settings to the server, explain how the security settings are updated."

[0502] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0503] Security settings management process flow

[0504] Step 1:

[0505] The user enters security settings (e.g., front door lock, window sensor on) on the device.

[0506] Input: User security setting information (e.g. front door lock ON, window sensor ON).

[0507] Output: The configuration information is saved to the device.

[0508] Step 2:

[0509] The terminal assembles the input security setting information into a packet.

[0510] Input: User security settings information.

[0511] Output: Security configuration information in packet format.

[0512] Step 3:

[0513] The terminal sends this packet to the server.

[0514] Input: Security configuration information in packet format.

[0515] Output: The packet is sent to the server.

[0516] Step 4:

[0517] The server analyzes the received configuration information.

[0518] Input: Security configuration information in received packet format.

[0519] Output: Parsed security configuration information.

[0520] Step 5:

[0521] The server updates the security settings within the system based on the analysis results.

[0522] Input: Parsed security configuration information.

[0523] Output: The updated security settings.

[0524] Step 6:

[0525] The server notifies the terminal that the update is complete.

[0526] Input: Completion notification.

[0527] Output: A notification message is sent to the terminal.

[0528] Step 7:

[0529] The terminal displays to the user a confirmation message received from the server.

[0530] Input: Notification message from the server.

[0531] Output: The user will see the message "Security settings have been updated."

[0532] Record energy usage and calculate CO2 emissions

[0533] Step 1:

[0534] The user enters the amount of energy usage for that day into the device (e.g., 5.5 kWh).

[0535] Input: User energy usage data.

[0536] Output: Energy usage data is saved on the device.

[0537] Step 2:

[0538] The terminal assembles the input energy usage data into packets.

[0539] Input: Energy usage data.

[0540] Output: Energy usage data in packet format.

[0541] Step 3:

[0542] The terminal sends this packet to the server.

[0543] Input: Energy usage data in packet format.

[0544] Output: The packet is sent to the server.

[0545] Step 4:

[0546] The server stores the received energy usage data.

[0547] Input: Received energy usage data in packet format.

[0548] Output: Energy usage data is stored on the server.

[0549] Step 5:

[0550] The server uses a generative AI model to calculate CO2 emissions (e.g., 2.75 kg).

[0551] Input: Energy usage data.

[0552] Output: Calculated CO2 emissions data.

[0553] Step 6:

[0554] The server notifies the user of the calculation results.

[0555] Input: Calculated CO2 emissions data.

[0556] Output: A notification message is sent to the terminal.

[0557] Step 7:

[0558] The terminal displays the calculated CO2 emissions received from the server to the user.

[0559] Input: Notification message from the server.

[0560] Output: The message "Today's CO2 emissions are 2.75 kg" is displayed to the user.

[0561] Providing energy-saving tips

[0562] Step 1:

[0563] The user requests energy saving tips from the device.

[0564] Input: The user's request.

[0565] Output: The request is saved to the device.

[0566] Step 2:

[0567] The terminal collects the request contents into packets and sends them to the server.

[0568] Input: The user's request.

[0569] Output: The request in the form of a packet is sent to the server.

[0570] Step 3:

[0571] The server passes the received request to the generative AI model and emotion engine.

[0572] Input: The request in packet format.

[0573] Output: Input data to the generative AI model and emotion engine.

[0574] Step 4:

[0575] The server generates energy-saving tips based on the user's emotional data and energy usage.

[0576] Input: User emotion data and energy usage data.

[0577] Output: Generated energy saving tips.

[0578] Step 5:

[0579] The server notifies the user of the generated hint.

[0580] Input: Generated energy saving tips.

[0581] Output: A notification message is sent to the terminal.

[0582] Step 6:

[0583] The device displays the energy saving tips received from the server to the user.

[0584] Input: Notification message from the server.

[0585] Output: Advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" is displayed to the user.

[0586] troubleshooting

[0587] Step 1:

[0588] Users report system malfunctions from their devices.

[0589] Input: User's bug report.

[0590] Output: Report data is saved to the device.

[0591] Step 2:

[0592] The terminal assembles the report contents into packets and sends them to the server.

[0593] Input: User's bug report.

[0594] Output: The problem report in the form of a packet is sent to the server.

[0595] Step 3:

[0596] The server analyzes the received problem.

[0597] Input: A defect report in packet format.

[0598] Output: Parsed problem data.

[0599] Step 4:

[0600] The server selects an appropriate solution from a pre-configured list of solutions.

[0601] Input: Parsed problem data.

[0602] Output: A good solution.

[0603] Step 5:

[0604] The server will notify the user of the solution, for example by sending a message saying "Please restart the sensor."

[0605] Input: The correct solution.

[0606] Output: A notification message is sent to the terminal.

[0607] Step 6:

[0608] The device displays a message that takes into account the user's emotional state along with the solution received from the server.

[0609] Input: Notification message from the server.

[0610] Output: "Please restart your sensor" along with "We apologize for the inconvenience."

[0611] Emotion engine processing

[0612] Step 1:

[0613] The device captures voice, facial expressions, text input, and more while the user is using the system.

[0614] Input: User voice, facial expression, and text input data.

[0615] Output: The captured data.

[0616] Step 2:

[0617] The device collects the captured data and sends it to the server in packets.

[0618] Input: The captured data.

[0619] Output: Data in the form of packets is sent to the server.

[0620] Step 3:

[0621] The server analyzes the received data using the emotion engine.

[0622] Input: Data in packet format.

[0623] Output: Sentiment data parsed by the sentiment engine.

[0624] Step 4:

[0625] The server reflects the analysis results of the emotion engine in various functions within the system.

[0626] Input: Parsed emotion data.

[0627] Output: Personalized service content.

[0628] Step 5:

[0629] The device displays messages and suggestions to the user that reflect the emotion analysis results received from the server.

[0630] Input: Sentiment analysis results and suggestions based on them.

[0631] Output: A customized message or suggestion is displayed to the user.

[0632] (Application example 2)

[0633] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0634] Security and energy management are becoming increasingly important in modern brick-and-mortar stores, but it is difficult to efficiently manage these while improving customer satisfaction. It is also necessary to reduce the burden on staff and achieve sustainable operations. Conventional systems often address individual issues, lacking a centralized means of resolving them. While there is a demand for personalized service based on customer emotions, achieving this remains a challenge.

[0635] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0636] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, means for analyzing user emotions and providing personalized services based on the recorded energy usage, means for managing security cameras and sensors in a physical store, detecting abnormalities and notifying store staff, means for monitoring energy usage in the physical store and proposing energy-saving measures, and means for analyzing customer emotions in the store and providing services according to customer satisfaction. This integrates security and energy management in a physical store, improving customer satisfaction while reducing the burden on staff and enabling sustainable operations.

[0637] A "generative AI model" is a system that uses machine learning algorithms to analyze data and make decisions, managing user security settings and analyzing and making suggestions about energy usage.

[0638] "Means to manage security settings" refers to the ability to check the settings of security devices, including security cameras and sensors, and update them as necessary.

[0639] "Means for recording energy usage" refers to a function that monitors the amount of energy consumed within a store or home and stores that data.

[0640] The "means for calculating carbon dioxide emissions" is a function for calculating the amount of carbon dioxide emitted based on the recorded energy usage.

[0641] "Means for providing energy saving tips" is a function that analyzes the user's energy usage and makes specific suggestions for improving energy efficiency.

[0642] "Means for analyzing emotions" refers to a function that estimates the emotional state of a user or customer based on their facial expressions, voice, and text input, and responds accordingly.

[0643] "Means for providing personalized services" refers to the ability to analyze user emotions and behavioral data to provide individually optimized services.

[0644] "Means for managing security cameras and sensors" refers to a function that monitors data from security cameras and various sensors within physical stores in real time and notifies customers when an abnormality is detected.

[0645] "Means of notifying store staff" refers to a function that immediately notifies store staff of any abnormalities detected by security cameras or sensors.

[0646] "Means for monitoring energy usage" refers to a function that monitors energy consumption in physical stores in real time and enables efficient energy management.

[0647] "Means for proposing energy-saving measures" is a function that proposes effective energy-saving measures based on data on energy usage in physical stores.

[0648] "Means for analyzing customer emotions" is a function for monitoring the facial expressions and behavior of customers in the store and understanding their emotional state.

[0649] "Means for providing services according to customer satisfaction" refers to a function that utilizes the results of customer sentiment analysis to provide customized services to increase customer satisfaction.

[0650] This invention provides a smart security and energy management system for brick-and-mortar stores that combines a generative AI model and an emotion engine to manage security, optimize energy efficiency, and improve customer service.

[0651] Overall system overview

[0652] The system consists of the following main components:

[0653] Terminal: A device (smartphone or tablet) operated by store staff.

[0654] Server: A computer system that analyzes and processes data and utilizes generative AI models and emotion engines.

[0655] Generative AI model: A machine learning algorithm that analyzes energy usage, manages security settings, and suggests energy-saving measures.

[0656] Emotion engine: An algorithm that analyzes the emotions of users and customers and provides personalized services based on that.

[0657] Security cameras and sensors: These are devices that are responsible for security in physical stores and detect abnormalities.

[0658] Security Settings Management

[0659] The device manages the settings of security cameras and sensors. Once the settings are changed, the information is sent from the device to the server, where it is analyzed and updated. If an abnormality is detected, the server sends a notification to the store staff's device, prompting them to take appropriate action.

[0660] Record energy usage and calculate CO2 emissions

[0661] The device records the store's energy usage and sends the data to a server, which uses a generative AI model to calculate the carbon footprint based on the energy usage. The results are then sent back to the device and communicated to the store staff.

[0662] Providing energy-saving tips

[0663] The terminal requests energy-saving tips based on energy usage. The server uses a generative AI model and emotion engine to generate customized energy-saving suggestions based on staff emotions and the store's energy usage, providing appropriate energy-saving measures to the store.

[0664] Improved customer service

[0665] The server uses cameras installed in the store and an emotion engine to analyze customer emotions in real time. For example, if it determines that a customer is tired, it will send a suggestion to guide them to a relaxation zone to their device. This allows store staff to provide attentive service according to the customer's emotions.

[0666] troubleshooting

[0667] Any system malfunctions or abnormal behavior reported by the device are sent to the server, which analyzes the problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device using an emotion engine, taking into account the emotional state of the staff member, thereby supporting fast and effective problem solving.

[0668] Specific examples

[0669] Example of energy usage record:

[0670] Store staff enter the amount of energy used that day into the terminal. For example, if "energy usage: 5.5 kWh" is entered into the terminal, this data is sent to the server, which then uses a generative AI model to calculate "CO2 emissions: 2.75 kg" and notify the terminal of the result.

[0671] Example prompts for energy saving tips:

[0672] Analysis of store energy consumption

[0673] Store energy use: 5.5 kWh

[0674] CO2 emissions: 2.75 kg

[0675] Generate energy saving tips

[0676] Current staff sentiment: happy

[0677] Energy saving tips:

[0678] Change the lighting to LED.

[0679] Please increase the air conditioner temperature setting by 1 degree.

[0680] This system will enable physical stores to manage security and optimize energy efficiency, and provide personalized services that take customer emotions into consideration.

[0681] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0682] Step 1:

[0683] Users manage security settings on their devices. Users enter security camera and sensor settings on their devices and send the settings information to the server. The server analyzes the received data and updates the security settings for the entire system. After updating, the server sends a confirmation message to the device, which the user confirms.

[0684] Input: Security settings information from the user

[0685] Output: Security setting confirmation message to the terminal

[0686] Step 2:

[0687] The user inputs their energy usage into the device. The device then sends this data to the server. The server stores the received energy usage data and calculates carbon dioxide emissions using a generative AI model. The calculation results are then sent to the device and notified to the user.

[0688] Input: Energy usage data from users

[0689] Output: Notification of carbon dioxide emissions calculation results

[0690] Step 3:

[0691] The user requests energy-saving tips from their device. The device then sends the request to the server. The server uses a generative AI model and an emotion engine to analyze the user's emotional data and energy usage status, and generates energy-saving tips. The generated tips are then sent to the device for the user to review.

[0692] Input: Energy saving hint request from user

[0693] Output: Customized energy saving tips notification

[0694] Step 4:

[0695] The server monitors data from security cameras and sensors in real time, and if an abnormality is detected, it sends a notification to the store staff's device. The store staff receives the notification and takes appropriate action. The emotion engine analyzes the staff's emotional data and provides alerts with different levels of urgency as necessary.

[0696] Input: Data from security cameras and sensors

[0697] Output: Anomaly detection notification and alerts according to urgency

[0698] Step 5:

[0699] The server monitors the energy usage of the physical store, analyzes the data, and proposes energy-saving measures. For example, it proposes cutting unnecessary electricity when the store is closed. The analysis results and proposals are sent to the terminal and notified to the store staff.

[0700] Input: Energy usage data for physical stores

[0701] Output: Notification of energy saving measures

[0702] Step 6:

[0703] The server uses cameras installed in the store and an emotion engine to analyze the customer's emotions and propose services based on their level of satisfaction. For example, if it determines that the customer is tired, it will send a suggestion to the terminal to guide them to a relaxation zone. Store staff will then use this suggestion to assist the customer.

[0704] Input: Camera footage from inside the store

[0705] Output: Notification of service proposals based on customer satisfaction

[0706] Step 7:

[0707] A user reports a system malfunction on their device and sends the information to the server. The server analyzes the malfunction information, selects an appropriate solution from a pre-defined list of solutions, and sends the solution to the device along with a message that takes into account the user's emotional state using an emotion engine. The user then uses this information to solve the problem.

[0708] Input: User bug report

[0709] Output: Defect resolution and mitigation message notification

[0710] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0711] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0712] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0713] [Second embodiment]

[0714] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0715] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0716] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0717] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0718] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0719] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0720] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0721] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0722] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0723] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0724] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0725] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0726] This invention relates to an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[0727] Overall system overview

[0728] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server. Users access the system through their devices and input and manage security settings and energy usage information. The server processes this data and provides appropriate advice and suggestions to users.

[0729] Program processing

[0730] Security Settings Management

[0731] The user enters security settings. The device sends this setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, and the user confirms that the security settings have been applied.

[0732] Record energy usage and calculate CO2 emissions

[0733] The user inputs the amount of energy used. The device sends this usage data to the server. The server stores the received data and calculates the amount of CO2 emissions based on it. The calculation results are notified to the user, and the amount of energy used and CO2 emissions are clearly displayed.

[0734] Providing energy-saving tips

[0735] The user requests energy-saving tips. The device sends the request to the server. The server generates appropriate advice from a pre-defined list of energy-saving tips and sends it to the device. The user can use this advice to reduce energy consumption.

[0736] troubleshooting

[0737] The user reports a problem. The device sends the report to the server. The server analyzes the received problem and selects an appropriate solution from a pre-defined list of solutions. The selected solution is sent to the user's device, providing information to help solve the problem.

[0738] Specific examples

[0739] Update security settings

[0740] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which then updates the system's security settings. The user then sees a message on their screen saying "Security settings updated."

[0741] Energy usage record

[0742] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[0743] Providing energy-saving tips

[0744] The user requests specific advice on energy conservation and sends a request on their device. The server provides tips such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree" and displays them to the user. The user can use these tips to reduce energy consumption.

[0745] troubleshooting

[0746] The user reports a sensor malfunction on their device and sends it to the server. The server then suggests a solution, "Please restart the sensor," and displays it on the user's screen. The user can follow the suggestion to resolve the problem.

[0747] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[0748] The processing flow will be explained below.

[0749] Security Settings Management

[0750] Step 1:

[0751] The user enters security settings (e.g., front door lock, window sensor on / off) using a smartphone or tablet.

[0752] Step 2:

[0753] The device sends the security setting data entered by the user to the server.

[0754] Step 3:

[0755] The server analyzes the received security setting data and updates the security setting in the system.

[0756] Step 4:

[0757] The server sends the updated security settings to the user's device.

[0758] Step 5:

[0759] The user's device will display a message indicating that the security settings have been successfully updated.

[0760] Record energy usage and calculate CO2 emissions

[0761] Step 1:

[0762] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[0763] Step 2:

[0764] The device sends energy usage data to the server.

[0765] Step 3:

[0766] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[0767] Step 4:

[0768] The server sends the calculated CO2 emissions to the user's terminal.

[0769] Step 5:

[0770] The user's device displays energy usage and CO2 emissions.

[0771] Providing energy-saving tips

[0772] Step 1:

[0773] Users submit requests for energy-saving tips from their smartphones or tablets.

[0774] Step 2:

[0775] The device sends an energy saving hint request to the server.

[0776] Step 3:

[0777] The server generates appropriate advice from a pre-configured list of energy saving tips.

[0778] Step 4:

[0779] The server sends the generated energy saving hints to the user's device.

[0780] Step 5:

[0781] The user's device displays energy saving tips.

[0782] troubleshooting

[0783] Step 1:

[0784] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[0785] Step 2:

[0786] The device sends the user's problem report to the server.

[0787] Step 3:

[0788] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[0789] Step 4:

[0790] The server sends the selected solution to the user's device.

[0791] Step 5:

[0792] The user's device will display recommended solutions to resolve the issue.

[0793] By executing the processing steps for each function, the system enables management of security settings, efficient energy use, and quick troubleshooting.The system provides a clear and easy-to-use interface for users, supporting a sustainable lifestyle.

[0794] Example 1

[0795] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0796] In today's smart home environment, systems that balance security and energy efficiency are not yet fully established. In particular, there is a need for systems that allow users to easily manage security settings, accurately calculate greenhouse gas emissions based on energy usage, and receive appropriate energy-saving advice. There is also a lack of systems that can quickly provide solutions to problems that arise in the home.

[0797] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0798] In this invention, the server includes: means for managing information settings using a generative computer system; means for recording energy consumption and calculating greenhouse gas emissions based on the recorded energy consumption; means for providing energy-saving advice; means for collecting setting information entered by users through an information device consisting of multiple terminals and sending it to a central processing unit; means for the central processing unit to analyze the received information and respond to the user with appropriate settings or advice; and means for storing energy consumption and related information in a database and retrieving and recalculating it as needed. This allows users to efficiently manage security settings, understand energy consumption and greenhouse gas emissions, and take specific energy-saving measures. It also provides quick solutions to problems that arise in the home, improving the sustainability of lifestyles.

[0799] A "generative computer system" is a system that has the technology to automatically generate, manipulate, and manage specifications based on user input information.

[0800] "Information Settings" refers to various data and settings related to security and energy usage that users can manage and operate.

[0801] "Greenhouse gas emissions" refers to the total amount of greenhouse gases, including carbon dioxide, emitted from households and facilities over a certain period of time.

[0802] "Energy saving advice" means specific advice or suggestions for reducing energy consumption.

[0803] "Information device" refers to a terminal (e.g., smartphone, tablet, sensor, etc.) used to facilitate the input, processing, and output of data.

[0804] A "central processing unit" is a computer system that receives input data, analyzes and processes it, and provides appropriate control.

[0805] "Database" means a system for efficiently storing, managing, and retrieving energy usage and related information.

[0806] "Energy consumption" refers to the total amount of energy, such as electricity and gas, consumed in a home or facility over a certain period of time.

[0807] A "problem-solving tool" is a system that provides quick and appropriate solutions to problems or troubles reported by users.

[0808] "Appropriate settings or advice" refers to the best settings or suggestions provided by the generative computer system based on the user's input information.

[0809] This invention relates to an eco-friendly smart home security system with a generative computing system at its core. This system optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[0810] Overall system overview

[0811] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a central processing unit. Users access the system through their devices and input and manage security settings and energy usage information. The central processing unit processes this data and provides appropriate advice and suggestions to users.

[0812] Hardware and software used

[0813] The hardware used is a smartphone, tablet, sensors, and a central processing unit, while the software includes a generative computing system that manages security settings, records energy usage, calculates greenhouse gas emissions, provides energy conservation advice, and troubleshoots.

[0814] Security Settings Management

[0815] A user opens an application on a smartphone or tablet and enters settings for the front door lock or window sensor. The device sends this setting information to the central processing unit. The central processing unit analyzes the received information and updates the system's security settings. It then sends a message to the device saying "Security settings have been updated."

[0816] Record energy usage and calculate greenhouse gas emissions

[0817] The user inputs the amount of energy used that day through the application. For example, if the energy usage is input as 5.5 kWh, the device will send this data to the central processing unit. The central processing unit stores the received data in a database and calculates the greenhouse gas emissions (e.g., 2.75 kg) based on the data using the production calculation system. The calculation result will be displayed on the user's device.

[0818] Providing advice on energy conservation

[0819] When a user requests energy-saving advice, they send a request through the application, for example, using the prompt "Request specific energy-saving advice." The central processing unit selects the most appropriate advice from the internal hint list and sends it to the user's device. The user can receive advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[0820] troubleshooting

[0821] When a user reports a system problem, they use an application to input the specific problem. The terminal sends this report to the central processing unit, which analyzes the problem reported by the generative computer system and suggests an appropriate solution. For example, in response to a report of a "sensor malfunction," the central processing unit suggests the solution "restart the sensor." The user can solve the problem by following this instruction.

[0822] Prompt Sentence Examples

[0823] Below are some specific examples of prompt sentences to input into the generative AI model.

[0824] 1. "Turn on security settings for your front doors and windows and receive a confirmation message."

[0825] 2. "Enter your energy usage as 5.5 kWh and calculate and display your greenhouse gas emissions."

[0826] 3. "Submit a request for energy saving advice and view tips."

[0827] 4. "Report sensor malfunctions and provide appropriate solutions."

[0828] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[0829] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0830] Security Settings Management

[0831] Step 1:

[0832] The user opens the application and enters the security settings.

[0833] Input: On / off setting for front door lock and window sensor

[0834] How it works: A user operates an app on their smartphone or tablet and enters security settings.

[0835] Step 2:

[0836] The terminal sends the setting information to the server.

[0837] Input: Security setting information entered by the user

[0838] Output: Communication data via HTTPS protocol, including configuration information

[0839] How it works: The device encrypts the configuration information in real time and sends it to the server.

[0840] Step 3:

[0841] The server analyzes the configuration information and updates the security settings.

[0842] Input: Security setting information received by the server

[0843] Data processing: Generative AI models analyze information and convert it into appropriate settings

[0844] Output: Updated security settings

[0845] How it works: The server uses the generative AI model to analyze the configuration information and update the system's security settings.

[0846] Step 4:

[0847] The server sends a confirmation message to the terminal.

[0848] Enter: Updated security settings

[0849] Output: "Security settings updated" message

[0850] Operation: The server generates a message with the updated settings and sends it to the device.

[0851] Step 5:

[0852] The user confirms that the settings should be applied.

[0853] Input: "Security settings updated" message

[0854] Output: Visual confirmation

[0855] Action: The user checks the device screen to confirm that the settings have been applied.

[0856] Record energy usage and calculate greenhouse gas emissions

[0857] Step 1:

[0858] The user inputs energy usage through the application.

[0859] Input: Energy usage data (e.g. 5.5 kWh)

[0860] How it works: A user enters their energy usage using a smartphone or tablet.

[0861] Step 2:

[0862] The device sends energy usage data to the server.

[0863] Input: Energy usage data

[0864] Output: JSON format data containing energy usage

[0865] How it works: The device converts energy usage data into JSON format and sends it to the server.

[0866] Step 3:

[0867] The server stores the data and calculates greenhouse gas emissions.

[0868] Input: Energy usage data

[0869] Data processing: Calculating emissions with generative AI models

[0870] Output: Calculated greenhouse gas emissions (e.g. 2.75 kg)

[0871] How it works: A server stores energy usage data in a database and applies an algorithm to calculate emissions.

[0872] Step 4:

[0873] The server notifies the user of the calculation results.

[0874] Input: Calculated greenhouse gas emissions

[0875] Output: Notification message containing emissions data

[0876] Operation: The server generates the calculation result as a message and sends it to the terminal.

[0877] Providing advice on energy conservation

[0878] Step 1:

[0879] A user asks for energy conservation advice.

[0880] Input: Energy conservation advice request

[0881] Action: The user taps the energy saving advice request button in the application.

[0882] Step 2:

[0883] The terminal sends a request for advice to the server.

[0884] Input: Energy conservation advice request

[0885] Output: Communication data including request data

[0886] Operation: The device sends a request to the server.

[0887] Step 3:

[0888] The server generates appropriate advice and sends it to the terminal.

[0889] Input: Advice request

[0890] Data processing: Advice generation using generative AI models

[0891] Output: Advice message (e.g., "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree")

[0892] Operation: The server selects the best advice from the advice list, generates it as a message, and sends it to the terminal.

[0893] Step 4:

[0894] The user receives the advice.

[0895] Input: Advice message

[0896] Output: Visual confirmation and actionable advice

[0897] Action: The user checks the advice displayed on the device screen and follows it.

[0898] troubleshooting

[0899] Step 1:

[0900] A user reports a system problem.

[0901] Input: Specific problem report (e.g. sensor malfunction)

[0902] Action: A user fills in details on a problem report form in an application.

[0903] Step 2:

[0904] The device sends the report to the server.

[0905] Input: Trouble report data

[0906] Output: Communication data including report data

[0907] Operation: The terminal sends report data to the server.

[0908] Step 3:

[0909] The server analyzes the problem and chooses a solution.

[0910] Input: Trouble report data

[0911] Data processing: problem analysis and solution selection using generative AI models

[0912] Output: Solution message (e.g. "Please restart the sensor")

[0913] How it works: The server analyzes the reported problem and chooses the best solution.

[0914] Step 4:

[0915] The server sends the solution to the user's terminal.

[0916] Input: Selected Solution

[0917] Output: Solution message

[0918] Operation: The server generates a solution as a message and sends it to the terminal.

[0919] Step 5:

[0920] The user reviews the solution and resolves the issue.

[0921] Input: Solution message

[0922] Output: Visual confirmation and actionable solutions

[0923] Action: The user sees the solution displayed on the device screen and follows the instructions to resolve the issue.

[0924] (Application example 1)

[0925] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0926] Conventional smart home security systems manage security and energy efficiency separately, making it difficult to optimize each. Energy-saving advice and troubleshooting are standardized, lacking the ability to address individual user needs. Furthermore, real-time security monitoring is not possible, leading to delayed detection of abnormalities and making it difficult to ensure user safety.

[0927] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0928] In this invention, the server includes a means for managing security settings using a generative AI model, a means for recording energy usage and calculating carbon dioxide emissions based on the recorded energy usage, a means for providing energy-saving tips, and a means for performing real-time security monitoring and notifying the user when an abnormality is detected. This makes it possible to comprehensively optimize both security and energy efficiency and provide energy-saving advice and troubleshooting customized for each user. Furthermore, real-time security monitoring allows for immediate detection of abnormalities, ensuring user safety.

[0929] A "generative AI model" is an algorithm that generates new data and information based on input data, and is a type of artificial intelligence that performs pattern recognition and prediction.

[0930] "Security settings" refers to setting information for managing access control and the operation of security devices within a home or a specific area.

[0931] "Energy usage" refers to the amount of energy consumed per unit time, and measures the amount of electricity, gas, etc. consumed in the home and elsewhere.

[0932] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted through energy consumption, and is a measurement indicator of environmental impact.

[0933] "Energy saving tips" refer to specific advice and suggestions for reducing energy consumption, which serve as a guide for users to improve their energy usage efficiency.

[0934] "Real-time security monitoring" refers to processes and systems that instantly monitor the current status and immediately notify you if an abnormality is detected.

[0935] "Troubleshooting" refers to the process of analyzing problems that occur in systems or devices and providing appropriate solutions.

[0936] "Notifications" are a mechanism for sending messages and alerts to users to immediately convey important information.

[0937] This invention is an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency and helps realize a sustainable lifestyle. The system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server.

[0938] Hardware and software used

[0939] Smartphone: A handheld device used for user input and display of information.

[0940] Security camera: A device used to monitor the home and detect abnormalities.

[0941] Cloud server: A server responsible for data processing and running generative AI models.

[0942] Generative AI models: Algorithms that manage configuration, analyze, and generate recommendations.

[0943] Overall system configuration

[0944] The system has the following main features:

[0945] 1. Security Settings Management

[0946] Users input security settings such as door locks and window sensors via their smartphone or tablet. The device then sends this information to the server, where a generative AI model analyzes and applies the settings. Once the settings are updated, a confirmation message is sent to the user.

[0947] 2. Record your energy usage and calculate your CO2 emissions

[0948] The user inputs their energy usage data into their smartphone. The device then sends this data to a cloud server, which calculates the amount of CO2 emissions. The calculation results are then instantly fed back to the user's smartphone.

[0949] 3. Providing energy-saving tips

[0950] When a user requests energy-saving advice, the server uses a generative AI model to generate optimal energy-saving tips for the user and sends them to their smartphone.

[0951] 4. Troubleshooting

[0952] When a user reports a malfunction in a sensor or other device, a cloud server receives it, and a generative AI model proposes an appropriate solution and sends it to the smartphone.

[0953] 5. Real-time security monitoring

[0954] Security cameras constantly monitor the home, and if they detect any abnormalities, the data is sent to a cloud server and a real-time notification is sent to the user's smartphone.

[0955] Specific examples

[0956] 1. Update your security settings

[0957] The user sets the front door lock and window sensor on their smartphone. The cloud server receives this information and updates the settings. The user is then notified with a message that "security settings have been updated."

[0958] 2. Record your energy usage

[0959] The user enters their energy usage for the day as 5.5 kWh on their smartphone. The cloud server receives this data, and the generative AI model calculates the CO2 emissions as 2.75 kg. The calculation result is fed back to the smartphone.

[0960] 3. Providing energy-saving tips

[0961] A user types "Please give me some energy-saving advice" into their smartphone. The cloud server uses a generative AI model to generate suggestions to the user, such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[0962] 4. Troubleshooting

[0963] When a user reports that "the sensor is not working," the cloud server uses a generative AI model to suggest a solution, such as "restart the sensor," and sends it to the smartphone.

[0964] 5. Real-time security monitoring

[0965] Security cameras transmit data in real time, and if an abnormality (such as an unauthorized door opening or closing) is detected, the cloud server immediately sends a notification to the user's smartphone.

[0966] Prompt Sentence Examples

[0967] "Please give me some energy saving advice based on my energy usage."

[0968] "Check the footage from your home security cameras in real time and let us know if there are any abnormalities."

[0969] "Please turn on the front door lock and window sensors."

[0970] "Sensor not working properly"

[0971] Based on these prompts, the cloud server and generative AI model work together to provide optimal information processing and suggestions, thereby building a system that supports the user's lifestyle.

[0972] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0973] Step 1:

[0974] Users use their smartphones or tablets to input security settings, energy usage, energy saving tips, or troubleshooting requests.

[0975] (Input): User requests (e.g., changing security settings, entering energy usage data)

[0976] (Output): The requested data is sent from the terminal to the server.

[0977] Step 2:

[0978] The terminal transmits the received user request data to the cloud server.

[0979] (Input): User request data

[0980] (Output): The request data is sent to the cloud server.

[0981] Step 3:

[0982] The server analyzes the received request data and determines the appropriate processing method.

[0983] (Input): Request data

[0984] (Output): Analysis results (e.g., security setting changes, energy usage records, anomaly detection start instructions)

[0985] Step 4:

[0986] It uses generative AI models to perform processing based on user requests.

[0987] (Input): Analysis results

[0988] (Output): Processing results (e.g., updated security settings, energy saving advice, troubleshooting solutions)

[0989] Step 5:

[0990] Based on the processing results, the cloud server performs specific data processing and calculations, such as calculating CO2 emissions based on energy consumption.

[0991] (Input): Processing result

[0992] (Output): Data processing or calculation results (e.g., CO2 emissions, specific energy-saving advice)

[0993] Step 6:

[0994] The server generates the final results and feeds them back to the user's smartphone or tablet.

[0995] (Input): Data processing or calculation results

[0996] (Output): Feedback to the user (e.g., security setting confirmation message, CO2 emission notification, energy saving advice display)

[0997] Specific operation example

[0998] Changing security settings

[0999] Step 1: The user types "turn on front door lock" into their smartphone.

[1000] (Input): "Lock front door on."

[1001] (Output): The requested data is sent from the terminal to the server.

[1002] Step 2: The device sends a request to change the security settings to the cloud server.

[1003] (Input): Security setting change request

[1004] (Output): The request data is sent to the cloud server.

[1005] Step 3: The server analyzes the request data and determines the setting changes for the front door lock.

[1006] (Input): Security setting change request data

[1007] (Output): Analysis result (instruction to turn on door lock)

[1008] Step 4: The generative AI model applies the door lock configuration changes and updates the system.

[1009] (Input): Analysis result (instruction to turn on door lock)

[1010] (Output): Processing result (Door lock on setting)

[1011] Step 5: The server makes the configuration changes and displays "Security settings updated" on the user's screen.

[1012] (Input): Processing result (door lock on setting)

[1013] (Output): Feedback to the user (security settings update message)

[1014] Prompt Sentence Examples

[1015] "Please set the front door lock to on"

[1016] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1017] This invention relates to an environmentally friendly smart home security system that combines a generative AI model and an emotion engine. The system assists users in optimizing security settings and energy efficiency, and provides personalized services based on the user's emotions. Specific embodiments of the invention are described in detail below.

[1018] Overall system overview

[1019] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[1020] Device: A device operated by a user, such as a smartphone or tablet.

[1021] Server: A computer system that analyzes and processes data.

[1022] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[1023] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[1024] Program processing

[1025] Security Settings Management

[1026] The user enters security settings on the device. The device sends the setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which the user confirms.

[1027] Record energy usage and calculate CO2 emissions

[1028] The user inputs their energy usage into the device. The device then sends this data to the server, which stores the data and uses an AI model to calculate CO2 emissions. The results of the calculation are then sent to the user, who is then shown the energy usage and CO2 emissions.

[1029] Providing energy-saving tips

[1030] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[1031] troubleshooting

[1032] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[1033] Emotion engine processing

[1034] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[1035] Specific examples

[1036] Update security settings

[1037] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[1038] Energy usage record

[1039] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[1040] Providing energy-saving tips

[1041] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[1042] troubleshooting

[1043] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[1044] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[1045] The processing flow will be explained below.

[1046] Security Settings Management

[1047] Step 1:

[1048] The user uses a smartphone or tablet to enter security settings (e.g., front door lock, window sensor on / off).

[1049] Step 2:

[1050] The device sends the security setting data entered by the user to the server.

[1051] Step 3:

[1052] The server analyzes the received security setting data and updates the security setting in the system.

[1053] Step 4:

[1054] The server sends the updated security settings to the user's device.

[1055] Step 5:

[1056] The user's device will display a message indicating that the security settings have been successfully updated.

[1057] Record energy usage and calculate CO2 emissions

[1058] Step 1:

[1059] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[1060] Step 2:

[1061] The device sends energy usage data to the server.

[1062] Step 3:

[1063] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[1064] Step 4:

[1065] The server sends the calculated CO2 emissions to the user's terminal.

[1066] Step 5:

[1067] The user's device displays energy usage and CO2 emissions.

[1068] Providing energy-saving tips

[1069] Step 1:

[1070] Users submit requests for energy-saving tips from their smartphones or tablets.

[1071] Step 2:

[1072] The device sends an energy saving hint request to the server.

[1073] Step 3:

[1074] The server generates appropriate advice from a pre-configured list of energy saving tips.

[1075] Step 4:

[1076] The server sends the generated energy saving hints to the user's device.

[1077] Step 5:

[1078] The user's device displays energy saving tips.

[1079] troubleshooting

[1080] Step 1:

[1081] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[1082] Step 2:

[1083] The device sends the user's problem report to the server.

[1084] Step 3:

[1085] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[1086] Step 4:

[1087] The server sends the selected solution to the user's device.

[1088] Step 5:

[1089] The user's device will display recommended solutions to resolve the issue.

[1090] Emotion engine processing

[1091] Step 1:

[1092] Users use their smartphones or tablets to provide emotional data through voice, facial expressions, or text input.

[1093] Step 2:

[1094] The device sends the user's emotional data to the server.

[1095] Step 3:

[1096] The server uses an emotion engine to analyze the user's emotion data.

[1097] Step 4:

[1098] Based on sentiment analysis, the server optimizes security settings and energy-saving tips to improve user safety.

[1099] Step 5:

[1100] The server sends optimized security settings and energy-saving tips to the device.

[1101] Step 6:

[1102] Your device will display personalized settings and advice based on your emotions.

[1103] Specific examples

[1104] Update security settings

[1105] Step 1:

[1106] The user turns on the front door lock and window sensor on the device.

[1107] Step 2:

[1108] The terminal sends this setting information to the server.

[1109] Step 3:

[1110] The server updates the system security settings.

[1111] Step 4:

[1112] The server sends a confirmation message to the terminal.

[1113] Step 5:

[1114] The user will see the message "Security settings have been updated."

[1115] Energy usage record

[1116] Step 1:

[1117] A user enters their energy usage for the day as 5.5 kWh on their device.

[1118] Step 2:

[1119] The terminal transmits this data to the server.

[1120] Step 3:

[1121] The server calculates the CO2 emissions to be 2.75 kg.

[1122] Step 4:

[1123] The server sends the calculation results to the terminal.

[1124] Step 5:

[1125] Energy usage and CO2 emissions are displayed on the user's screen.

[1126] Providing energy-saving tips

[1127] Step 1:

[1128] The user requests specific energy saving advice and submits a request on the device.

[1129] Step 2:

[1130] The device sends a request to the server.

[1131] Step 3:

[1132] The server generates hints using a generative AI model and an emotion engine.

[1133] Step 4:

[1134] The server sends the hint to the device.

[1135] Step 5:

[1136] Tips such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" will be displayed on the user's screen.

[1137] troubleshooting

[1138] Step 1:

[1139] The user reports a sensor malfunction on the device.

[1140] Step 2:

[1141] The terminal sends a report to the server.

[1142] Step 3:

[1143] The server generates the solution "Please restart the sensor."

[1144] Step 4:

[1145] The server sends the solution to the device.

[1146] Step 5:

[1147] The user will see a message on their screen saying "Please restart the sensor" and the user will be able to resolve the issue.

[1148] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[1149] Example 2

[1150] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1151] Modern smart home systems are required to manage security, improve energy efficiency, and provide advice on energy conservation, but few systems can comprehensively perform all of these functions. Furthermore, there is a need for systems that can provide services that take into account the user's emotions and psychological state. A comprehensive and personalized system that can solve these issues is desired.

[1152] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1153] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, and means for having an emotion engine for analyzing user emotions and providing services based on the emotions. This enables security management, improved energy efficiency, energy saving, and the provision of personalized services according to the user's emotions.

[1154] A "generative AI model" is a machine learning algorithm used to manage security settings and analyze energy usage.

[1155] "Security settings" are settings to ensure the safety of your home, and include turning on and off front door locks and window sensors.

[1156] "Energy usage" refers to energy consumption, such as the amount of electricity used within a household.

[1157] "Carbon dioxide emissions" refers to the amount of CO2 emitted as a result of energy consumption.

[1158] "Energy saving tips" refers to specific advice on how to reduce energy consumption.

[1159] An "emotion engine" is an algorithm that analyzes emotions from a user's voice, facial expressions, text input, etc., and provides services based on that analysis.

[1160] This invention relates to an eco-friendly smart home security system that combines a generative AI model and an emotion engine. The system helps users optimize security settings and energy efficiency, and provides personalized services based on the user's emotions.

[1161] Overall system overview

[1162] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[1163] Device: A device operated by a user, such as a smartphone or tablet.

[1164] Server: A computer system that analyzes and processes data.

[1165] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[1166] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[1167] Program processing

[1168] Security Settings Management

[1169] The user enters security settings on the device. The device sends the settings information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which is displayed to the user.

[1170] Record energy usage and calculate CO2 emissions

[1171] The user inputs their energy consumption data into the device. The device then sends this data to the server, which stores the data and uses a generative AI model to calculate CO2 emissions. The calculation results are then sent to the device and displayed to the user.

[1172] Providing energy-saving tips

[1173] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[1174] troubleshooting

[1175] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[1176] Emotion engine processing

[1177] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[1178] Specific examples

[1179] Update security settings

[1180] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[1181] Energy usage record

[1182] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[1183] Providing energy-saving tips

[1184] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[1185] troubleshooting

[1186] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[1187] Prompt Sentence Examples

[1188] A prompt based on the security configuration management example is as follows:

[1189] "After a user turns on their front door lock and window sensor on their smartphone and the system sends the settings to the server, explain how the security settings are updated."

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Security settings management process flow

[1192] Step 1:

[1193] The user enters security settings (e.g., front door lock, window sensor on) on the device.

[1194] Input: User security setting information (e.g. front door lock ON, window sensor ON).

[1195] Output: The configuration information is saved to the device.

[1196] Step 2:

[1197] The terminal assembles the input security setting information into a packet.

[1198] Input: User security settings information.

[1199] Output: Security configuration information in packet format.

[1200] Step 3:

[1201] The terminal sends this packet to the server.

[1202] Input: Security configuration information in packet format.

[1203] Output: The packet is sent to the server.

[1204] Step 4:

[1205] The server analyzes the received configuration information.

[1206] Input: Security configuration information in received packet format.

[1207] Output: Parsed security configuration information.

[1208] Step 5:

[1209] The server updates the security settings within the system based on the analysis results.

[1210] Input: Parsed security configuration information.

[1211] Output: The updated security settings.

[1212] Step 6:

[1213] The server notifies the terminal that the update is complete.

[1214] Input: Completion notification.

[1215] Output: A notification message is sent to the terminal.

[1216] Step 7:

[1217] The terminal displays to the user a confirmation message received from the server.

[1218] Input: Notification message from the server.

[1219] Output: The user will see the message "Security settings have been updated."

[1220] Record energy usage and calculate CO2 emissions

[1221] Step 1:

[1222] The user enters the amount of energy usage for that day into the device (e.g., 5.5 kWh).

[1223] Input: User energy usage data.

[1224] Output: Energy usage data is saved on the device.

[1225] Step 2:

[1226] The terminal assembles the input energy usage data into packets.

[1227] Input: Energy usage data.

[1228] Output: Energy usage data in packet format.

[1229] Step 3:

[1230] The terminal sends this packet to the server.

[1231] Input: Energy usage data in packet format.

[1232] Output: The packet is sent to the server.

[1233] Step 4:

[1234] The server stores the received energy usage data.

[1235] Input: Received energy usage data in packet format.

[1236] Output: Energy usage data is stored on the server.

[1237] Step 5:

[1238] The server uses a generative AI model to calculate CO2 emissions (e.g., 2.75 kg).

[1239] Input: Energy usage data.

[1240] Output: Calculated CO2 emissions data.

[1241] Step 6:

[1242] The server notifies the user of the calculation results.

[1243] Input: Calculated CO2 emissions data.

[1244] Output: A notification message is sent to the terminal.

[1245] Step 7:

[1246] The terminal displays the calculated CO2 emissions received from the server to the user.

[1247] Input: Notification message from the server.

[1248] Output: The message "Today's CO2 emissions are 2.75 kg" is displayed to the user.

[1249] Providing energy-saving tips

[1250] Step 1:

[1251] The user requests energy saving tips from the device.

[1252] Input: The user's request.

[1253] Output: The request is saved to the device.

[1254] Step 2:

[1255] The terminal collects the request contents into packets and sends them to the server.

[1256] Input: The user's request.

[1257] Output: The request in the form of a packet is sent to the server.

[1258] Step 3:

[1259] The server passes the received request to the generative AI model and emotion engine.

[1260] Input: The request in packet format.

[1261] Output: Input data to the generative AI model and emotion engine.

[1262] Step 4:

[1263] The server generates energy-saving tips based on the user's emotional data and energy usage.

[1264] Input: User emotion data and energy usage data.

[1265] Output: Generated energy saving tips.

[1266] Step 5:

[1267] The server notifies the user of the generated hint.

[1268] Input: Generated energy saving tips.

[1269] Output: A notification message is sent to the terminal.

[1270] Step 6:

[1271] The device displays the energy saving tips received from the server to the user.

[1272] Input: Notification message from the server.

[1273] Output: Advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" is displayed to the user.

[1274] troubleshooting

[1275] Step 1:

[1276] Users report system malfunctions from their devices.

[1277] Input: User's bug report.

[1278] Output: Report data is saved to the device.

[1279] Step 2:

[1280] The terminal assembles the report contents into packets and sends them to the server.

[1281] Input: User's bug report.

[1282] Output: The problem report in the form of a packet is sent to the server.

[1283] Step 3:

[1284] The server analyzes the received problem.

[1285] Input: A defect report in packet format.

[1286] Output: Parsed problem data.

[1287] Step 4:

[1288] The server selects an appropriate solution from a pre-configured list of solutions.

[1289] Input: Parsed problem data.

[1290] Output: A good solution.

[1291] Step 5:

[1292] The server will notify the user of the solution, for example by sending a message saying "Please restart the sensor."

[1293] Input: The correct solution.

[1294] Output: A notification message is sent to the terminal.

[1295] Step 6:

[1296] The device displays a message that takes into account the user's emotional state along with the solution received from the server.

[1297] Input: Notification message from the server.

[1298] Output: "Please restart your sensor" along with "We apologize for the inconvenience."

[1299] Emotion engine processing

[1300] Step 1:

[1301] The device captures voice, facial expressions, text input, and more while the user is using the system.

[1302] Input: User voice, facial expression, and text input data.

[1303] Output: The captured data.

[1304] Step 2:

[1305] The device collects the captured data and sends it to the server in packets.

[1306] Input: The captured data.

[1307] Output: Data in the form of packets is sent to the server.

[1308] Step 3:

[1309] The server analyzes the received data using the emotion engine.

[1310] Input: Data in packet format.

[1311] Output: Sentiment data parsed by the sentiment engine.

[1312] Step 4:

[1313] The server reflects the analysis results of the emotion engine in various functions within the system.

[1314] Input: Parsed emotion data.

[1315] Output: Personalized service content.

[1316] Step 5:

[1317] The device displays messages and suggestions to the user that reflect the emotion analysis results received from the server.

[1318] Input: Sentiment analysis results and suggestions based on them.

[1319] Output: A customized message or suggestion is displayed to the user.

[1320] (Application example 2)

[1321] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1322] Security and energy management are becoming increasingly important in modern brick-and-mortar stores, but it is difficult to efficiently manage these while improving customer satisfaction. It is also necessary to reduce the burden on staff and achieve sustainable operations. Conventional systems often address individual issues, lacking a centralized means of resolving them. While there is a demand for personalized service based on customer emotions, achieving this remains a challenge.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1324] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, means for analyzing user emotions and providing personalized services based on the recorded energy usage, means for managing security cameras and sensors in a physical store, detecting abnormalities and notifying store staff, means for monitoring energy usage in the physical store and proposing energy-saving measures, and means for analyzing customer emotions in the store and providing services according to customer satisfaction. This integrates security and energy management in a physical store, improving customer satisfaction while reducing the burden on staff and enabling sustainable operations.

[1325] A "generative AI model" is a system that uses machine learning algorithms to analyze data and make decisions, managing user security settings and analyzing and making suggestions about energy usage.

[1326] "Means to manage security settings" refers to the ability to check the settings of security devices, including security cameras and sensors, and update them as necessary.

[1327] "Means for recording energy usage" refers to a function that monitors the amount of energy consumed within a store or home and stores that data.

[1328] The "means for calculating carbon dioxide emissions" is a function for calculating the amount of carbon dioxide emitted based on the recorded energy usage.

[1329] "Means for providing energy saving tips" is a function that analyzes the user's energy usage and makes specific suggestions for improving energy efficiency.

[1330] "Means for analyzing emotions" refers to a function that estimates the emotional state of a user or customer based on their facial expressions, voice, and text input, and responds accordingly.

[1331] "Means for providing personalized services" refers to the ability to analyze user emotions and behavioral data to provide individually optimized services.

[1332] "Means for managing security cameras and sensors" refers to a function that monitors data from security cameras and various sensors within physical stores in real time and notifies customers when an abnormality is detected.

[1333] "Means of notifying store staff" refers to a function that immediately notifies store staff of any abnormalities detected by security cameras or sensors.

[1334] "Means for monitoring energy usage" refers to a function that monitors energy consumption in physical stores in real time and enables efficient energy management.

[1335] "Means for proposing energy-saving measures" is a function that proposes effective energy-saving measures based on data on energy usage in physical stores.

[1336] "Means for analyzing customer emotions" is a function for monitoring the facial expressions and behavior of customers in the store and understanding their emotional state.

[1337] "Means for providing services according to customer satisfaction" refers to a function that utilizes the results of customer sentiment analysis to provide customized services to increase customer satisfaction.

[1338] This invention provides a smart security and energy management system for brick-and-mortar stores that combines a generative AI model and an emotion engine to manage security, optimize energy efficiency, and improve customer service.

[1339] Overall system overview

[1340] The system consists of the following main components:

[1341] Terminal: A device (smartphone or tablet) operated by store staff.

[1342] Server: A computer system that analyzes and processes data and utilizes generative AI models and emotion engines.

[1343] Generative AI model: A machine learning algorithm that analyzes energy usage, manages security settings, and suggests energy-saving measures.

[1344] Emotion engine: An algorithm that analyzes the emotions of users and customers and provides personalized services based on that.

[1345] Security cameras and sensors: These are devices that are responsible for security in physical stores and detect abnormalities.

[1346] Security Settings Management

[1347] The device manages the settings of security cameras and sensors. Once the settings are changed, the information is sent from the device to the server, where it is analyzed and updated. If an abnormality is detected, the server sends a notification to the store staff's device, prompting them to take appropriate action.

[1348] Record energy usage and calculate CO2 emissions

[1349] The device records the store's energy usage and sends the data to a server, which uses a generative AI model to calculate the carbon footprint based on the energy usage. The results are then sent back to the device and communicated to the store staff.

[1350] Providing energy-saving tips

[1351] The terminal requests energy-saving tips based on energy usage. The server uses a generative AI model and emotion engine to generate customized energy-saving suggestions based on staff emotions and the store's energy usage, providing appropriate energy-saving measures to the store.

[1352] Improved customer service

[1353] The server uses cameras installed in the store and an emotion engine to analyze customer emotions in real time. For example, if it determines that a customer is tired, it will send a suggestion to guide them to a relaxation zone to their device. This allows store staff to provide attentive service according to the customer's emotions.

[1354] troubleshooting

[1355] Any system malfunctions or abnormal behavior reported by the device are sent to the server, which analyzes the problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device using an emotion engine, taking into account the emotional state of the staff member, thereby supporting fast and effective problem solving.

[1356] Specific examples

[1357] Example of energy usage record:

[1358] Store staff enter the amount of energy used that day into the terminal. For example, if "energy usage: 5.5 kWh" is entered into the terminal, this data is sent to the server, which then uses a generative AI model to calculate "CO2 emissions: 2.75 kg" and notify the terminal of the result.

[1359] Example prompts for energy saving tips:

[1360] Analysis of store energy consumption

[1361] Store energy use: 5.5 kWh

[1362] CO2 emissions: 2.75 kg

[1363] Generate energy saving tips

[1364] Current staff sentiment: happy

[1365] Energy saving tips:

[1366] Change the lighting to LED.

[1367] Please increase the air conditioner temperature setting by 1 degree.

[1368] This system will enable physical stores to manage security and optimize energy efficiency, and provide personalized services that take customer emotions into consideration.

[1369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1370] Step 1:

[1371] Users manage security settings on their devices. Users enter security camera and sensor settings on their devices and send the settings information to the server. The server analyzes the received data and updates the security settings for the entire system. After updating, the server sends a confirmation message to the device, which the user confirms.

[1372] Input: Security settings information from the user

[1373] Output: Security setting confirmation message to the terminal

[1374] Step 2:

[1375] The user inputs their energy usage into the device. The device then sends this data to the server. The server stores the received energy usage data and calculates carbon dioxide emissions using a generative AI model. The calculation results are then sent to the device and notified to the user.

[1376] Input: Energy usage data from users

[1377] Output: Notification of carbon dioxide emissions calculation results

[1378] Step 3:

[1379] The user requests energy-saving tips from their device. The device then sends the request to the server. The server uses a generative AI model and an emotion engine to analyze the user's emotional data and energy usage status, and generates energy-saving tips. The generated tips are then sent to the device for the user to review.

[1380] Input: Energy saving hint request from user

[1381] Output: Customized energy saving tips notification

[1382] Step 4:

[1383] The server monitors data from security cameras and sensors in real time, and if an abnormality is detected, it sends a notification to the store staff's device. The store staff receives the notification and takes appropriate action. The emotion engine analyzes the staff's emotional data and provides alerts with different levels of urgency as necessary.

[1384] Input: Data from security cameras and sensors

[1385] Output: Anomaly detection notification and alerts according to urgency

[1386] Step 5:

[1387] The server monitors the energy usage of the physical store, analyzes the data, and proposes energy-saving measures. For example, it proposes cutting unnecessary electricity when the store is closed. The analysis results and proposals are sent to the terminal and notified to the store staff.

[1388] Input: Energy usage data for physical stores

[1389] Output: Notification of energy saving measures

[1390] Step 6:

[1391] The server uses cameras installed in the store and an emotion engine to analyze the customer's emotions and propose services based on their level of satisfaction. For example, if it determines that the customer is tired, it will send a suggestion to the terminal to guide them to a relaxation zone. Store staff will then use this suggestion to assist the customer.

[1392] Input: Camera footage from inside the store

[1393] Output: Notification of service proposals based on customer satisfaction

[1394] Step 7:

[1395] A user reports a system malfunction on their device and sends the information to the server. The server analyzes the malfunction information, selects an appropriate solution from a pre-defined list of solutions, and sends the solution to the device along with a message that takes into account the user's emotional state using an emotion engine. The user then uses this information to solve the problem.

[1396] Input: User bug report

[1397] Output: Defect resolution and mitigation message notification

[1398] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1399] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1400] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1401] [Third embodiment]

[1402] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1403] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1404] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1405] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1406] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1407] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1408] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1409] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1410] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1411] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1412] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1413] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1414] This invention relates to an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[1415] Overall system overview

[1416] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server. Users access the system through their devices and input and manage security settings and energy usage information. The server processes this data and provides appropriate advice and suggestions to users.

[1417] Program processing

[1418] Security Settings Management

[1419] The user enters security settings. The device sends this setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, and the user confirms that the security settings have been applied.

[1420] Record energy usage and calculate CO2 emissions

[1421] The user inputs the amount of energy used. The device sends this usage data to the server. The server stores the received data and calculates the amount of CO2 emissions based on it. The calculation results are notified to the user, and the amount of energy used and CO2 emissions are clearly displayed.

[1422] Providing energy-saving tips

[1423] The user requests energy-saving tips. The device sends the request to the server. The server generates appropriate advice from a pre-defined list of energy-saving tips and sends it to the device. The user can use this advice to reduce energy consumption.

[1424] troubleshooting

[1425] The user reports a problem. The device sends the report to the server. The server analyzes the received problem and selects an appropriate solution from a pre-defined list of solutions. The selected solution is sent to the user's device, providing information to help solve the problem.

[1426] Specific examples

[1427] Update security settings

[1428] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which then updates the system's security settings. The user then sees a message on their screen saying "Security settings updated."

[1429] Energy usage record

[1430] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[1431] Providing energy-saving tips

[1432] The user requests specific advice on energy conservation and sends a request on their device. The server provides tips such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree" and displays them to the user. The user can use these tips to reduce energy consumption.

[1433] troubleshooting

[1434] The user reports a sensor malfunction on their device and sends it to the server. The server then suggests a solution, "Please restart the sensor," and displays it on the user's screen. The user can follow the suggestion to resolve the problem.

[1435] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[1436] The processing flow will be explained below.

[1437] Security Settings Management

[1438] Step 1:

[1439] The user enters security settings (e.g., front door lock, window sensor on / off) using a smartphone or tablet.

[1440] Step 2:

[1441] The device sends the security setting data entered by the user to the server.

[1442] Step 3:

[1443] The server analyzes the received security setting data and updates the security setting in the system.

[1444] Step 4:

[1445] The server sends the updated security settings to the user's device.

[1446] Step 5:

[1447] The user's device will display a message indicating that the security settings have been successfully updated.

[1448] Record energy usage and calculate CO2 emissions

[1449] Step 1:

[1450] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[1451] Step 2:

[1452] The device sends energy usage data to the server.

[1453] Step 3:

[1454] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[1455] Step 4:

[1456] The server sends the calculated CO2 emissions to the user's terminal.

[1457] Step 5:

[1458] The user's device displays energy usage and CO2 emissions.

[1459] Providing energy-saving tips

[1460] Step 1:

[1461] Users submit requests for energy-saving tips from their smartphones or tablets.

[1462] Step 2:

[1463] The device sends an energy saving hint request to the server.

[1464] Step 3:

[1465] The server generates appropriate advice from a pre-configured list of energy saving tips.

[1466] Step 4:

[1467] The server sends the generated energy saving hints to the user's device.

[1468] Step 5:

[1469] The user's device displays energy saving tips.

[1470] troubleshooting

[1471] Step 1:

[1472] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[1473] Step 2:

[1474] The device sends the user's problem report to the server.

[1475] Step 3:

[1476] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[1477] Step 4:

[1478] The server sends the selected solution to the user's device.

[1479] Step 5:

[1480] The user's device will display recommended solutions to resolve the issue.

[1481] By executing the processing steps for each function, the system enables management of security settings, efficient energy use, and quick troubleshooting.The system provides a clear and easy-to-use interface for users, supporting a sustainable lifestyle.

[1482] Example 1

[1483] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1484] In today's smart home environment, systems that balance security and energy efficiency are not yet fully established. In particular, there is a need for systems that allow users to easily manage security settings, accurately calculate greenhouse gas emissions based on energy usage, and receive appropriate energy-saving advice. There is also a lack of systems that can quickly provide solutions to problems that arise in the home.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1486] In this invention, the server includes: means for managing information settings using a generative computer system; means for recording energy consumption and calculating greenhouse gas emissions based on the recorded energy consumption; means for providing energy-saving advice; means for collecting setting information entered by users through an information device consisting of multiple terminals and sending it to a central processing unit; means for the central processing unit to analyze the received information and respond to the user with appropriate settings or advice; and means for storing energy consumption and related information in a database and retrieving and recalculating it as needed. This allows users to efficiently manage security settings, understand energy consumption and greenhouse gas emissions, and take specific energy-saving measures. It also provides quick solutions to problems that arise in the home, improving the sustainability of lifestyles.

[1487] A "generative computer system" is a system that has the technology to automatically generate, manipulate, and manage specifications based on user input information.

[1488] "Information Settings" refers to various data and settings related to security and energy usage that users can manage and operate.

[1489] "Greenhouse gas emissions" refers to the total amount of greenhouse gases, including carbon dioxide, emitted from households and facilities over a certain period of time.

[1490] "Energy saving advice" means specific advice or suggestions for reducing energy consumption.

[1491] "Information device" refers to a terminal (e.g., smartphone, tablet, sensor, etc.) used to facilitate the input, processing, and output of data.

[1492] A "central processing unit" is a computer system that receives input data, analyzes and processes it, and provides appropriate control.

[1493] "Database" means a system for efficiently storing, managing, and retrieving energy usage and related information.

[1494] "Energy consumption" refers to the total amount of energy, such as electricity and gas, consumed in a home or facility over a certain period of time.

[1495] A "problem-solving tool" is a system that provides quick and appropriate solutions to problems or troubles reported by users.

[1496] "Appropriate settings or advice" refers to the best settings or suggestions provided by the generative computer system based on the user's input information.

[1497] This invention relates to an eco-friendly smart home security system with a generative computing system at its core. This system optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[1498] Overall system overview

[1499] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a central processing unit. Users access the system through their devices and input and manage security settings and energy usage information. The central processing unit processes this data and provides appropriate advice and suggestions to users.

[1500] Hardware and software used

[1501] The hardware used is a smartphone, tablet, sensors, and a central processing unit, while the software includes a generative computing system that manages security settings, records energy usage, calculates greenhouse gas emissions, provides energy conservation advice, and troubleshoots.

[1502] Security Settings Management

[1503] A user opens an application on a smartphone or tablet and enters settings for the front door lock or window sensor. The device sends this setting information to the central processing unit. The central processing unit analyzes the received information and updates the system's security settings. It then sends a message to the device saying "Security settings have been updated."

[1504] Record energy usage and calculate greenhouse gas emissions

[1505] The user inputs the amount of energy used that day through the application. For example, if the energy usage is input as 5.5 kWh, the device will send this data to the central processing unit. The central processing unit stores the received data in a database and calculates the greenhouse gas emissions (e.g., 2.75 kg) based on the data using the production calculation system. The calculation result will be displayed on the user's device.

[1506] Providing advice on energy conservation

[1507] When a user requests energy-saving advice, they send a request through the application, for example, using the prompt "Request specific energy-saving advice." The central processing unit selects the most appropriate advice from the internal hint list and sends it to the user's device. The user can receive advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[1508] troubleshooting

[1509] When a user reports a system problem, they use an application to input the specific problem. The terminal sends this report to the central processing unit, which analyzes the problem reported by the generative computer system and suggests an appropriate solution. For example, in response to a report of a "sensor malfunction," the central processing unit suggests the solution "restart the sensor." The user can solve the problem by following this instruction.

[1510] Prompt Sentence Examples

[1511] Below are some specific examples of prompt sentences to input into the generative AI model.

[1512] 1. "Turn on security settings for your front doors and windows and receive a confirmation message."

[1513] 2. "Enter your energy usage as 5.5 kWh and calculate and display your greenhouse gas emissions."

[1514] 3. "Submit a request for energy saving advice and view tips."

[1515] 4. "Report sensor malfunctions and provide appropriate solutions."

[1516] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[1517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1518] Security Settings Management

[1519] Step 1:

[1520] The user opens the application and enters the security settings.

[1521] Input: On / off setting for front door lock and window sensor

[1522] How it works: A user operates an app on their smartphone or tablet and enters security settings.

[1523] Step 2:

[1524] The terminal sends the setting information to the server.

[1525] Input: Security setting information entered by the user

[1526] Output: Communication data via HTTPS protocol, including configuration information

[1527] How it works: The device encrypts the configuration information in real time and sends it to the server.

[1528] Step 3:

[1529] The server analyzes the configuration information and updates the security settings.

[1530] Input: Security setting information received by the server

[1531] Data processing: Generative AI models analyze information and convert it into appropriate settings

[1532] Output: Updated security settings

[1533] How it works: The server uses the generative AI model to analyze the configuration information and update the system's security settings.

[1534] Step 4:

[1535] The server sends a confirmation message to the terminal.

[1536] Enter: Updated security settings

[1537] Output: "Security settings updated" message

[1538] Operation: The server generates a message with the updated settings and sends it to the device.

[1539] Step 5:

[1540] The user confirms that the settings should be applied.

[1541] Input: "Security settings updated" message

[1542] Output: Visual confirmation

[1543] Action: The user checks the device screen to confirm that the settings have been applied.

[1544] Record energy usage and calculate greenhouse gas emissions

[1545] Step 1:

[1546] The user inputs energy usage through the application.

[1547] Input: Energy usage data (e.g. 5.5 kWh)

[1548] How it works: A user enters their energy usage using a smartphone or tablet.

[1549] Step 2:

[1550] The device sends energy usage data to the server.

[1551] Input: Energy usage data

[1552] Output: JSON format data containing energy usage

[1553] How it works: The device converts energy usage data into JSON format and sends it to the server.

[1554] Step 3:

[1555] The server stores the data and calculates greenhouse gas emissions.

[1556] Input: Energy usage data

[1557] Data processing: Calculating emissions with generative AI models

[1558] Output: Calculated greenhouse gas emissions (e.g. 2.75 kg)

[1559] How it works: A server stores energy usage data in a database and applies an algorithm to calculate emissions.

[1560] Step 4:

[1561] The server notifies the user of the calculation results.

[1562] Input: Calculated greenhouse gas emissions

[1563] Output: Notification message containing emissions data

[1564] Operation: The server generates the calculation result as a message and sends it to the terminal.

[1565] Providing advice on energy conservation

[1566] Step 1:

[1567] A user asks for energy conservation advice.

[1568] Input: Energy conservation advice request

[1569] Action: The user taps the energy saving advice request button in the application.

[1570] Step 2:

[1571] The terminal sends a request for advice to the server.

[1572] Input: Energy conservation advice request

[1573] Output: Communication data including request data

[1574] Operation: The device sends a request to the server.

[1575] Step 3:

[1576] The server generates appropriate advice and sends it to the terminal.

[1577] Input: Advice request

[1578] Data processing: Advice generation using generative AI models

[1579] Output: Advice message (e.g., "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree")

[1580] Operation: The server selects the best advice from the advice list, generates it as a message, and sends it to the terminal.

[1581] Step 4:

[1582] The user receives the advice.

[1583] Input: Advice message

[1584] Output: Visual confirmation and actionable advice

[1585] Action: The user checks the advice displayed on the device screen and follows it.

[1586] troubleshooting

[1587] Step 1:

[1588] A user reports a system problem.

[1589] Input: Specific problem report (e.g. sensor malfunction)

[1590] Action: A user fills in details on a problem report form in an application.

[1591] Step 2:

[1592] The device sends the report to the server.

[1593] Input: Trouble report data

[1594] Output: Communication data including report data

[1595] Operation: The terminal sends report data to the server.

[1596] Step 3:

[1597] The server analyzes the problem and chooses a solution.

[1598] Input: Trouble report data

[1599] Data processing: problem analysis and solution selection using generative AI models

[1600] Output: Solution message (e.g. "Please restart the sensor")

[1601] How it works: The server analyzes the reported problem and chooses the best solution.

[1602] Step 4:

[1603] The server sends the solution to the user's terminal.

[1604] Input: Selected Solution

[1605] Output: Solution message

[1606] Operation: The server generates a solution as a message and sends it to the terminal.

[1607] Step 5:

[1608] The user reviews the solution and resolves the issue.

[1609] Input: Solution message

[1610] Output: Visual confirmation and actionable solutions

[1611] Action: The user sees the solution displayed on the device screen and follows the instructions to resolve the issue.

[1612] (Application example 1)

[1613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1614] Conventional smart home security systems manage security and energy efficiency separately, making it difficult to optimize each. Energy-saving advice and troubleshooting are standardized, lacking the ability to address individual user needs. Furthermore, real-time security monitoring is not possible, leading to delayed detection of abnormalities and making it difficult to ensure user safety.

[1615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1616] In this invention, the server includes a means for managing security settings using a generative AI model, a means for recording energy usage and calculating carbon dioxide emissions based on the recorded energy usage, a means for providing energy-saving tips, and a means for performing real-time security monitoring and notifying the user when an abnormality is detected. This makes it possible to comprehensively optimize both security and energy efficiency and provide energy-saving advice and troubleshooting customized for each user. Furthermore, real-time security monitoring allows for immediate detection of abnormalities, ensuring user safety.

[1617] A "generative AI model" is an algorithm that generates new data and information based on input data, and is a type of artificial intelligence that performs pattern recognition and prediction.

[1618] "Security settings" refers to setting information for managing access control and the operation of security devices within a home or a specific area.

[1619] "Energy usage" refers to the amount of energy consumed per unit time, and measures the amount of electricity, gas, etc. consumed in the home and elsewhere.

[1620] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted through energy consumption, and is a measurement indicator of environmental impact.

[1621] "Energy saving tips" refer to specific advice and suggestions for reducing energy consumption, which serve as a guide for users to improve their energy usage efficiency.

[1622] "Real-time security monitoring" refers to processes and systems that instantly monitor the current status and immediately notify you if an abnormality is detected.

[1623] "Troubleshooting" refers to the process of analyzing problems that occur in systems or devices and providing appropriate solutions.

[1624] "Notifications" are a mechanism for sending messages and alerts to users to immediately convey important information.

[1625] This invention is an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency and helps realize a sustainable lifestyle. The system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server.

[1626] Hardware and software used

[1627] Smartphone: A handheld device used for user input and display of information.

[1628] Security camera: A device used to monitor the home and detect abnormalities.

[1629] Cloud server: A server responsible for data processing and running generative AI models.

[1630] Generative AI models: Algorithms that manage configuration, analyze, and generate recommendations.

[1631] Overall system configuration

[1632] The system has the following main features:

[1633] 1. Security Settings Management

[1634] Users input security settings such as door locks and window sensors via their smartphone or tablet. The device then sends this information to the server, where a generative AI model analyzes and applies the settings. Once the settings are updated, a confirmation message is sent to the user.

[1635] 2. Record your energy usage and calculate your CO2 emissions

[1636] The user inputs their energy usage data into their smartphone. The device then sends this data to a cloud server, which calculates the amount of CO2 emissions. The calculation results are then instantly fed back to the user's smartphone.

[1637] 3. Providing energy-saving tips

[1638] When a user requests energy-saving advice, the server uses a generative AI model to generate optimal energy-saving tips for the user and sends them to their smartphone.

[1639] 4. Troubleshooting

[1640] When a user reports a malfunction in a sensor or other device, a cloud server receives it, and a generative AI model proposes an appropriate solution and sends it to the smartphone.

[1641] 5. Real-time security monitoring

[1642] Security cameras constantly monitor the home, and if they detect any abnormalities, the data is sent to a cloud server and a real-time notification is sent to the user's smartphone.

[1643] Specific examples

[1644] 1. Update your security settings

[1645] The user sets the front door lock and window sensor on their smartphone. The cloud server receives this information and updates the settings. The user is then notified with a message that "security settings have been updated."

[1646] 2. Record your energy usage

[1647] The user enters their energy usage for the day as 5.5 kWh on their smartphone. The cloud server receives this data, and the generative AI model calculates the CO2 emissions as 2.75 kg. The calculation result is fed back to the smartphone.

[1648] 3. Providing energy-saving tips

[1649] A user types "Please give me some energy-saving advice" into their smartphone. The cloud server uses a generative AI model to generate suggestions to the user, such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[1650] 4. Troubleshooting

[1651] When a user reports that "the sensor is not working," the cloud server uses a generative AI model to suggest a solution, such as "restart the sensor," and sends it to the smartphone.

[1652] 5. Real-time security monitoring

[1653] Security cameras transmit data in real time, and if an abnormality (such as an unauthorized door opening or closing) is detected, the cloud server immediately sends a notification to the user's smartphone.

[1654] Prompt Sentence Examples

[1655] "Please give me some energy saving advice based on my energy usage."

[1656] "Check the footage from your home security cameras in real time and let us know if there are any abnormalities."

[1657] "Please turn on the front door lock and window sensors."

[1658] "Sensor not working properly"

[1659] Based on these prompts, the cloud server and generative AI model work together to provide optimal information processing and suggestions, thereby building a system that supports the user's lifestyle.

[1660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1661] Step 1:

[1662] Users use their smartphones or tablets to input security settings, energy usage, energy saving tips, or troubleshooting requests.

[1663] (Input): User requests (e.g., changing security settings, entering energy usage data)

[1664] (Output): The requested data is sent from the terminal to the server.

[1665] Step 2:

[1666] The terminal transmits the received user request data to the cloud server.

[1667] (Input): User request data

[1668] (Output): The request data is sent to the cloud server.

[1669] Step 3:

[1670] The server analyzes the received request data and determines the appropriate processing method.

[1671] (Input): Request data

[1672] (Output): Analysis results (e.g., security setting changes, energy usage records, anomaly detection start instructions)

[1673] Step 4:

[1674] It uses generative AI models to perform processing based on user requests.

[1675] (Input): Analysis results

[1676] (Output): Processing results (e.g., updated security settings, energy saving advice, troubleshooting solutions)

[1677] Step 5:

[1678] Based on the processing results, the cloud server performs specific data processing and calculations, such as calculating CO2 emissions based on energy consumption.

[1679] (Input): Processing result

[1680] (Output): Data processing or calculation results (e.g., CO2 emissions, specific energy-saving advice)

[1681] Step 6:

[1682] The server generates the final results and feeds them back to the user's smartphone or tablet.

[1683] (Input): Data processing or calculation results

[1684] (Output): Feedback to the user (e.g., security setting confirmation message, CO2 emission notification, energy saving advice display)

[1685] Specific operation example

[1686] Changing security settings

[1687] Step 1: The user types "turn on front door lock" into their smartphone.

[1688] (Input): "Lock front door on."

[1689] (Output): The requested data is sent from the terminal to the server.

[1690] Step 2: The device sends a request to change the security settings to the cloud server.

[1691] (Input): Security setting change request

[1692] (Output): The request data is sent to the cloud server.

[1693] Step 3: The server analyzes the request data and determines the setting changes for the front door lock.

[1694] (Input): Security setting change request data

[1695] (Output): Analysis result (instruction to turn on door lock)

[1696] Step 4: The generative AI model applies the door lock configuration changes and updates the system.

[1697] (Input): Analysis result (instruction to turn on door lock)

[1698] (Output): Processing result (Door lock on setting)

[1699] Step 5: The server makes the configuration changes and displays "Security settings updated" on the user's screen.

[1700] (Input): Processing result (door lock on setting)

[1701] (Output): Feedback to the user (security settings update message)

[1702] Prompt Sentence Examples

[1703] "Please set the front door lock to on"

[1704] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1705] This invention relates to an environmentally friendly smart home security system that combines a generative AI model and an emotion engine. The system assists users in optimizing security settings and energy efficiency, and provides personalized services based on the user's emotions. Specific embodiments of the invention are described in detail below.

[1706] Overall system overview

[1707] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[1708] Device: A device operated by a user, such as a smartphone or tablet.

[1709] Server: A computer system that analyzes and processes data.

[1710] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[1711] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[1712] Program processing

[1713] Security Settings Management

[1714] The user enters security settings on the device. The device sends the setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which the user confirms.

[1715] Record energy usage and calculate CO2 emissions

[1716] The user inputs their energy usage into the device. The device then sends this data to the server, which stores the data and uses an AI model to calculate CO2 emissions. The results of the calculation are then sent to the user, who is then shown the energy usage and CO2 emissions.

[1717] Providing energy-saving tips

[1718] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[1719] troubleshooting

[1720] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[1721] Emotion engine processing

[1722] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[1723] Specific examples

[1724] Update security settings

[1725] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[1726] Energy usage record

[1727] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[1728] Providing energy-saving tips

[1729] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[1730] troubleshooting

[1731] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[1732] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[1733] The processing flow will be explained below.

[1734] Security Settings Management

[1735] Step 1:

[1736] The user uses a smartphone or tablet to enter security settings (e.g., front door lock, window sensor on / off).

[1737] Step 2:

[1738] The device sends the security setting data entered by the user to the server.

[1739] Step 3:

[1740] The server analyzes the received security setting data and updates the security setting in the system.

[1741] Step 4:

[1742] The server sends the updated security settings to the user's device.

[1743] Step 5:

[1744] The user's device will display a message indicating that the security settings have been successfully updated.

[1745] Record energy usage and calculate CO2 emissions

[1746] Step 1:

[1747] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[1748] Step 2:

[1749] The device sends energy usage data to the server.

[1750] Step 3:

[1751] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[1752] Step 4:

[1753] The server sends the calculated CO2 emissions to the user's terminal.

[1754] Step 5:

[1755] The user's device displays energy usage and CO2 emissions.

[1756] Providing energy-saving tips

[1757] Step 1:

[1758] Users submit requests for energy-saving tips from their smartphones or tablets.

[1759] Step 2:

[1760] The device sends an energy saving hint request to the server.

[1761] Step 3:

[1762] The server generates appropriate advice from a pre-configured list of energy saving tips.

[1763] Step 4:

[1764] The server sends the generated energy saving hints to the user's device.

[1765] Step 5:

[1766] The user's device displays energy saving tips.

[1767] troubleshooting

[1768] Step 1:

[1769] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[1770] Step 2:

[1771] The device sends the user's problem report to the server.

[1772] Step 3:

[1773] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[1774] Step 4:

[1775] The server sends the selected solution to the user's device.

[1776] Step 5:

[1777] The user's device will display recommended solutions to resolve the issue.

[1778] Emotion engine processing

[1779] Step 1:

[1780] Users use their smartphones or tablets to provide emotional data through voice, facial expressions, or text input.

[1781] Step 2:

[1782] The device sends the user's emotional data to the server.

[1783] Step 3:

[1784] The server uses an emotion engine to analyze the user's emotion data.

[1785] Step 4:

[1786] Based on sentiment analysis, the server optimizes security settings and energy-saving tips to improve user safety.

[1787] Step 5:

[1788] The server sends optimized security settings and energy-saving tips to the device.

[1789] Step 6:

[1790] Your device will display personalized settings and advice based on your emotions.

[1791] Specific examples

[1792] Update security settings

[1793] Step 1:

[1794] The user turns on the front door lock and window sensor on the device.

[1795] Step 2:

[1796] The terminal sends this setting information to the server.

[1797] Step 3:

[1798] The server updates the system security settings.

[1799] Step 4:

[1800] The server sends a confirmation message to the terminal.

[1801] Step 5:

[1802] The user will see the message "Security settings have been updated."

[1803] Energy usage record

[1804] Step 1:

[1805] A user enters their energy usage for the day as 5.5 kWh on their device.

[1806] Step 2:

[1807] The terminal transmits this data to the server.

[1808] Step 3:

[1809] The server calculates the CO2 emissions to be 2.75 kg.

[1810] Step 4:

[1811] The server sends the calculation results to the terminal.

[1812] Step 5:

[1813] Energy usage and CO2 emissions are displayed on the user's screen.

[1814] Providing energy-saving tips

[1815] Step 1:

[1816] The user requests specific energy saving advice and submits a request on the device.

[1817] Step 2:

[1818] The device sends a request to the server.

[1819] Step 3:

[1820] The server generates hints using a generative AI model and an emotion engine.

[1821] Step 4:

[1822] The server sends the hint to the device.

[1823] Step 5:

[1824] Tips such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" will be displayed on the user's screen.

[1825] troubleshooting

[1826] Step 1:

[1827] The user reports a sensor malfunction on the device.

[1828] Step 2:

[1829] The terminal sends a report to the server.

[1830] Step 3:

[1831] The server generates the solution "Please restart the sensor."

[1832] Step 4:

[1833] The server sends the solution to the device.

[1834] Step 5:

[1835] The user will see a message on their screen saying "Please restart the sensor" and the user will be able to resolve the issue.

[1836] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[1837] Example 2

[1838] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1839] Modern smart home systems are required to manage security, improve energy efficiency, and provide advice on energy conservation, but few systems can comprehensively perform all of these functions. Furthermore, there is a need for systems that can provide services that take into account the user's emotions and psychological state. A comprehensive and personalized system that can solve these issues is desired.

[1840] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1841] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, and means for having an emotion engine for analyzing user emotions and providing services based on the emotions. This enables security management, improved energy efficiency, energy saving, and the provision of personalized services according to the user's emotions.

[1842] A "generative AI model" is a machine learning algorithm used to manage security settings and analyze energy usage.

[1843] "Security settings" are settings to ensure the safety of your home, and include turning on and off front door locks and window sensors.

[1844] "Energy usage" refers to energy consumption, such as the amount of electricity used within a household.

[1845] "Carbon dioxide emissions" refers to the amount of CO2 emitted as a result of energy consumption.

[1846] "Energy saving tips" refers to specific advice on how to reduce energy consumption.

[1847] An "emotion engine" is an algorithm that analyzes emotions from a user's voice, facial expressions, text input, etc., and provides services based on that analysis.

[1848] This invention relates to an eco-friendly smart home security system that combines a generative AI model and an emotion engine. The system helps users optimize security settings and energy efficiency, and provides personalized services based on the user's emotions.

[1849] Overall system overview

[1850] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[1851] Device: A device operated by a user, such as a smartphone or tablet.

[1852] Server: A computer system that analyzes and processes data.

[1853] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[1854] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[1855] Program processing

[1856] Security Settings Management

[1857] The user enters security settings on the device. The device sends the settings information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which is displayed to the user.

[1858] Record energy usage and calculate CO2 emissions

[1859] The user inputs their energy consumption data into the device. The device then sends this data to the server, which stores the data and uses a generative AI model to calculate CO2 emissions. The calculation results are then sent to the device and displayed to the user.

[1860] Providing energy-saving tips

[1861] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[1862] troubleshooting

[1863] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[1864] Emotion engine processing

[1865] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[1866] Specific examples

[1867] Update security settings

[1868] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[1869] Energy usage record

[1870] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[1871] Providing energy-saving tips

[1872] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[1873] troubleshooting

[1874] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[1875] Prompt Sentence Examples

[1876] A prompt based on the security configuration management example is as follows:

[1877] "After a user turns on their front door lock and window sensor on their smartphone and the system sends the settings to the server, explain how the security settings are updated."

[1878] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1879] Security settings management process flow

[1880] Step 1:

[1881] The user enters security settings (e.g., front door lock, window sensor on) on the device.

[1882] Input: User security setting information (e.g. front door lock ON, window sensor ON).

[1883] Output: The configuration information is saved to the device.

[1884] Step 2:

[1885] The terminal assembles the input security setting information into a packet.

[1886] Input: User security settings information.

[1887] Output: Security configuration information in packet format.

[1888] Step 3:

[1889] The terminal sends this packet to the server.

[1890] Input: Security configuration information in packet format.

[1891] Output: The packet is sent to the server.

[1892] Step 4:

[1893] The server analyzes the received configuration information.

[1894] Input: Security configuration information in received packet format.

[1895] Output: Parsed security configuration information.

[1896] Step 5:

[1897] The server updates the security settings within the system based on the analysis results.

[1898] Input: Parsed security configuration information.

[1899] Output: The updated security settings.

[1900] Step 6:

[1901] The server notifies the terminal that the update is complete.

[1902] Input: Completion notification.

[1903] Output: A notification message is sent to the terminal.

[1904] Step 7:

[1905] The terminal displays to the user a confirmation message received from the server.

[1906] Input: Notification message from the server.

[1907] Output: The user will see the message "Security settings have been updated."

[1908] Record energy usage and calculate CO2 emissions

[1909] Step 1:

[1910] The user enters the amount of energy usage for that day into the device (e.g., 5.5 kWh).

[1911] Input: User energy usage data.

[1912] Output: Energy usage data is saved on the device.

[1913] Step 2:

[1914] The terminal assembles the input energy usage data into packets.

[1915] Input: Energy usage data.

[1916] Output: Energy usage data in packet format.

[1917] Step 3:

[1918] The terminal sends this packet to the server.

[1919] Input: Energy usage data in packet format.

[1920] Output: The packet is sent to the server.

[1921] Step 4:

[1922] The server stores the received energy usage data.

[1923] Input: Received energy usage data in packet format.

[1924] Output: Energy usage data is stored on the server.

[1925] Step 5:

[1926] The server uses a generative AI model to calculate CO2 emissions (e.g., 2.75 kg).

[1927] Input: Energy usage data.

[1928] Output: Calculated CO2 emissions data.

[1929] Step 6:

[1930] The server notifies the user of the calculation results.

[1931] Input: Calculated CO2 emissions data.

[1932] Output: A notification message is sent to the terminal.

[1933] Step 7:

[1934] The terminal displays the calculated CO2 emissions received from the server to the user.

[1935] Input: Notification message from the server.

[1936] Output: The message "Today's CO2 emissions are 2.75 kg" is displayed to the user.

[1937] Providing energy-saving tips

[1938] Step 1:

[1939] The user requests energy saving tips from the device.

[1940] Input: The user's request.

[1941] Output: The request is saved to the device.

[1942] Step 2:

[1943] The terminal collects the request contents into packets and sends them to the server.

[1944] Input: The user's request.

[1945] Output: The request in the form of a packet is sent to the server.

[1946] Step 3:

[1947] The server passes the received request to the generative AI model and emotion engine.

[1948] Input: The request in packet format.

[1949] Output: Input data to the generative AI model and emotion engine.

[1950] Step 4:

[1951] The server generates energy-saving tips based on the user's emotional data and energy usage.

[1952] Input: User emotion data and energy usage data.

[1953] Output: Generated energy saving tips.

[1954] Step 5:

[1955] The server notifies the user of the generated hint.

[1956] Input: Generated energy saving tips.

[1957] Output: A notification message is sent to the terminal.

[1958] Step 6:

[1959] The device displays the energy saving tips received from the server to the user.

[1960] Input: Notification message from the server.

[1961] Output: Advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" is displayed to the user.

[1962] troubleshooting

[1963] Step 1:

[1964] Users report system malfunctions from their devices.

[1965] Input: User's bug report.

[1966] Output: Report data is saved to the device.

[1967] Step 2:

[1968] The terminal assembles the report contents into packets and sends them to the server.

[1969] Input: User's bug report.

[1970] Output: The problem report in the form of a packet is sent to the server.

[1971] Step 3:

[1972] The server analyzes the received problem.

[1973] Input: A defect report in packet format.

[1974] Output: Parsed problem data.

[1975] Step 4:

[1976] The server selects an appropriate solution from a pre-configured list of solutions.

[1977] Input: Parsed problem data.

[1978] Output: A good solution.

[1979] Step 5:

[1980] The server will notify the user of the solution, for example by sending a message saying "Please restart the sensor."

[1981] Input: The correct solution.

[1982] Output: A notification message is sent to the terminal.

[1983] Step 6:

[1984] The device displays a message that takes into account the user's emotional state along with the solution received from the server.

[1985] Input: Notification message from the server.

[1986] Output: "Please restart your sensor" along with "We apologize for the inconvenience."

[1987] Emotion engine processing

[1988] Step 1:

[1989] The device captures voice, facial expressions, text input, and more while the user is using the system.

[1990] Input: User voice, facial expression, and text input data.

[1991] Output: The captured data.

[1992] Step 2:

[1993] The device collects the captured data and sends it to the server in packets.

[1994] Input: The captured data.

[1995] Output: Data in the form of packets is sent to the server.

[1996] Step 3:

[1997] The server analyzes the received data using the emotion engine.

[1998] Input: Data in packet format.

[1999] Output: Sentiment data parsed by the sentiment engine.

[2000] Step 4:

[2001] The server reflects the analysis results of the emotion engine in various functions within the system.

[2002] Input: Parsed emotion data.

[2003] Output: Personalized service content.

[2004] Step 5:

[2005] The device displays messages and suggestions to the user that reflect the emotion analysis results received from the server.

[2006] Input: Sentiment analysis results and suggestions based on them.

[2007] Output: A customized message or suggestion is displayed to the user.

[2008] (Application example 2)

[2009] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2010] Security and energy management are becoming increasingly important in modern brick-and-mortar stores, but it is difficult to efficiently manage these while improving customer satisfaction. It is also necessary to reduce the burden on staff and achieve sustainable operations. Conventional systems often address individual issues, lacking a centralized means of resolving them. While there is a demand for personalized service based on customer emotions, achieving this remains a challenge.

[2011] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2012] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, means for analyzing user emotions and providing personalized services based on the recorded energy usage, means for managing security cameras and sensors in a physical store, detecting abnormalities and notifying store staff, means for monitoring energy usage in the physical store and proposing energy-saving measures, and means for analyzing customer emotions in the store and providing services according to customer satisfaction. This integrates security and energy management in a physical store, improving customer satisfaction while reducing the burden on staff and enabling sustainable operations.

[2013] A "generative AI model" is a system that uses machine learning algorithms to analyze data and make decisions, managing user security settings and analyzing and making suggestions about energy usage.

[2014] "Means to manage security settings" refers to the ability to check the settings of security devices, including security cameras and sensors, and update them as necessary.

[2015] "Means for recording energy usage" refers to a function that monitors the amount of energy consumed within a store or home and stores that data.

[2016] The "means for calculating carbon dioxide emissions" is a function for calculating the amount of carbon dioxide emitted based on the recorded energy usage.

[2017] "Means for providing energy saving tips" is a function that analyzes the user's energy usage and makes specific suggestions for improving energy efficiency.

[2018] "Means for analyzing emotions" refers to a function that estimates the emotional state of a user or customer based on their facial expressions, voice, and text input, and responds accordingly.

[2019] "Means for providing personalized services" refers to the ability to analyze user emotions and behavioral data to provide individually optimized services.

[2020] "Means for managing security cameras and sensors" refers to a function that monitors data from security cameras and various sensors within physical stores in real time and notifies customers when an abnormality is detected.

[2021] "Means of notifying store staff" refers to a function that immediately notifies store staff of any abnormalities detected by security cameras or sensors.

[2022] "Means for monitoring energy usage" refers to a function that monitors energy consumption in physical stores in real time and enables efficient energy management.

[2023] "Means for proposing energy-saving measures" is a function that proposes effective energy-saving measures based on data on energy usage in physical stores.

[2024] "Means for analyzing customer emotions" is a function for monitoring the facial expressions and behavior of customers in the store and understanding their emotional state.

[2025] "Means for providing services according to customer satisfaction" refers to a function that utilizes the results of customer sentiment analysis to provide customized services to increase customer satisfaction.

[2026] This invention provides a smart security and energy management system for brick-and-mortar stores that combines a generative AI model and an emotion engine to manage security, optimize energy efficiency, and improve customer service.

[2027] Overall system overview

[2028] The system consists of the following main components:

[2029] Terminal: A device (smartphone or tablet) operated by store staff.

[2030] Server: A computer system that analyzes and processes data and utilizes generative AI models and emotion engines.

[2031] Generative AI model: A machine learning algorithm that analyzes energy usage, manages security settings, and suggests energy-saving measures.

[2032] Emotion engine: An algorithm that analyzes the emotions of users and customers and provides personalized services based on that.

[2033] Security cameras and sensors: These are devices that are responsible for security in physical stores and detect abnormalities.

[2034] Security Settings Management

[2035] The device manages the settings of security cameras and sensors. Once the settings are changed, the information is sent from the device to the server, where it is analyzed and updated. If an abnormality is detected, the server sends a notification to the store staff's device, prompting them to take appropriate action.

[2036] Record energy usage and calculate CO2 emissions

[2037] The device records the store's energy usage and sends the data to a server, which uses a generative AI model to calculate the carbon footprint based on the energy usage. The results are then sent back to the device and communicated to the store staff.

[2038] Providing energy-saving tips

[2039] The terminal requests energy-saving tips based on energy usage. The server uses a generative AI model and emotion engine to generate customized energy-saving suggestions based on staff emotions and the store's energy usage, providing appropriate energy-saving measures to the store.

[2040] Improved customer service

[2041] The server uses cameras installed in the store and an emotion engine to analyze customer emotions in real time. For example, if it determines that a customer is tired, it will send a suggestion to guide them to a relaxation zone to their device. This allows store staff to provide attentive service according to the customer's emotions.

[2042] troubleshooting

[2043] Any system malfunctions or abnormal behavior reported by the device are sent to the server, which analyzes the problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device using an emotion engine, taking into account the emotional state of the staff member, thereby supporting fast and effective problem solving.

[2044] Specific examples

[2045] Example of energy usage record:

[2046] Store staff enter the amount of energy used that day into the terminal. For example, if "energy usage: 5.5 kWh" is entered into the terminal, this data is sent to the server, which then uses a generative AI model to calculate "CO2 emissions: 2.75 kg" and notify the terminal of the result.

[2047] Example prompts for energy saving tips:

[2048] Analysis of store energy consumption

[2049] Store energy use: 5.5 kWh

[2050] CO2 emissions: 2.75 kg

[2051] Generate energy saving tips

[2052] Current staff sentiment: happy

[2053] Energy saving tips:

[2054] Change the lighting to LED.

[2055] Please increase the air conditioner temperature setting by 1 degree.

[2056] This system will enable physical stores to manage security and optimize energy efficiency, and provide personalized services that take customer emotions into consideration.

[2057] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2058] Step 1:

[2059] Users manage security settings on their devices. Users enter security camera and sensor settings on their devices and send the settings information to the server. The server analyzes the received data and updates the security settings for the entire system. After updating, the server sends a confirmation message to the device, which the user confirms.

[2060] Input: Security settings information from the user

[2061] Output: Security setting confirmation message to the terminal

[2062] Step 2:

[2063] The user inputs their energy usage into the device. The device then sends this data to the server. The server stores the received energy usage data and calculates carbon dioxide emissions using a generative AI model. The calculation results are then sent to the device and notified to the user.

[2064] Input: Energy usage data from users

[2065] Output: Notification of carbon dioxide emissions calculation results

[2066] Step 3:

[2067] The user requests energy-saving tips from their device. The device then sends the request to the server. The server uses a generative AI model and an emotion engine to analyze the user's emotional data and energy usage status, and generates energy-saving tips. The generated tips are then sent to the device for the user to review.

[2068] Input: Energy saving hint request from user

[2069] Output: Customized energy saving tips notification

[2070] Step 4:

[2071] The server monitors data from security cameras and sensors in real time, and if an abnormality is detected, it sends a notification to the store staff's device. The store staff receives the notification and takes appropriate action. The emotion engine analyzes the staff's emotional data and provides alerts with different levels of urgency as necessary.

[2072] Input: Data from security cameras and sensors

[2073] Output: Anomaly detection notification and alerts according to urgency

[2074] Step 5:

[2075] The server monitors the energy usage of the physical store, analyzes the data, and proposes energy-saving measures. For example, it proposes cutting unnecessary electricity when the store is closed. The analysis results and proposals are sent to the terminal and notified to the store staff.

[2076] Input: Energy usage data for physical stores

[2077] Output: Notification of energy saving measures

[2078] Step 6:

[2079] The server uses cameras installed in the store and an emotion engine to analyze the customer's emotions and propose services based on their level of satisfaction. For example, if it determines that the customer is tired, it will send a suggestion to the terminal to guide them to a relaxation zone. Store staff will then use this suggestion to assist the customer.

[2080] Input: Camera footage from inside the store

[2081] Output: Notification of service proposals based on customer satisfaction

[2082] Step 7:

[2083] A user reports a system malfunction on their device and sends the information to the server. The server analyzes the malfunction information, selects an appropriate solution from a pre-defined list of solutions, and sends the solution to the device along with a message that takes into account the user's emotional state using an emotion engine. The user then uses this information to solve the problem.

[2084] Input: User bug report

[2085] Output: Defect resolution and mitigation message notification

[2086] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2088] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2089] [Fourth embodiment]

[2090] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2091] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2093] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2094] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2097] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2098] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2099] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2101] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2102] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2103] This invention relates to an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[2104] Overall system overview

[2105] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server. Users access the system through their devices and input and manage security settings and energy usage information. The server processes this data and provides appropriate advice and suggestions to users.

[2106] Program processing

[2107] Security Settings Management

[2108] The user enters security settings. The device sends this setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, and the user confirms that the security settings have been applied.

[2109] Record energy usage and calculate CO2 emissions

[2110] The user inputs the amount of energy used. The device sends this usage data to the server. The server stores the received data and calculates the amount of CO2 emissions based on it. The calculation results are notified to the user, and the amount of energy used and CO2 emissions are clearly displayed.

[2111] Providing energy-saving tips

[2112] The user requests energy-saving tips. The device sends the request to the server. The server generates appropriate advice from a pre-defined list of energy-saving tips and sends it to the device. The user can use this advice to reduce energy consumption.

[2113] troubleshooting

[2114] The user reports a problem. The device sends the report to the server. The server analyzes the received problem and selects an appropriate solution from a pre-defined list of solutions. The selected solution is sent to the user's device, providing information to help solve the problem.

[2115] Specific examples

[2116] Update security settings

[2117] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which then updates the system's security settings. The user then sees a message on their screen saying "Security settings updated."

[2118] Energy usage record

[2119] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[2120] Providing energy-saving tips

[2121] The user requests specific advice on energy conservation and sends a request on their device. The server provides tips such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree" and displays them to the user. The user can use these tips to reduce energy consumption.

[2122] troubleshooting

[2123] The user reports a sensor malfunction on their device and sends it to the server. The server then suggests a solution, "Please restart the sensor," and displays it on the user's screen. The user can follow the suggestion to resolve the problem.

[2124] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[2125] The processing flow will be explained below.

[2126] Security Settings Management

[2127] Step 1:

[2128] The user enters security settings (e.g., front door lock, window sensor on / off) using a smartphone or tablet.

[2129] Step 2:

[2130] The device sends the security setting data entered by the user to the server.

[2131] Step 3:

[2132] The server analyzes the received security setting data and updates the security setting in the system.

[2133] Step 4:

[2134] The server sends the updated security settings to the user's device.

[2135] Step 5:

[2136] The user's device will display a message indicating that the security settings have been successfully updated.

[2137] Record energy usage and calculate CO2 emissions

[2138] Step 1:

[2139] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[2140] Step 2:

[2141] The device sends energy usage data to the server.

[2142] Step 3:

[2143] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[2144] Step 4:

[2145] The server sends the calculated CO2 emissions to the user's terminal.

[2146] Step 5:

[2147] The user's device displays energy usage and CO2 emissions.

[2148] Providing energy-saving tips

[2149] Step 1:

[2150] Users submit requests for energy-saving tips from their smartphones or tablets.

[2151] Step 2:

[2152] The device sends an energy saving hint request to the server.

[2153] Step 3:

[2154] The server generates appropriate advice from a pre-configured list of energy saving tips.

[2155] Step 4:

[2156] The server sends the generated energy saving hints to the user's device.

[2157] Step 5:

[2158] The user's device displays energy saving tips.

[2159] troubleshooting

[2160] Step 1:

[2161] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[2162] Step 2:

[2163] The device sends the user's problem report to the server.

[2164] Step 3:

[2165] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[2166] Step 4:

[2167] The server sends the selected solution to the user's device.

[2168] Step 5:

[2169] The user's device will display recommended solutions to resolve the issue.

[2170] By executing the processing steps for each function, the system enables management of security settings, efficient energy use, and quick troubleshooting.The system provides a clear and easy-to-use interface for users, supporting a sustainable lifestyle.

[2171] Example 1

[2172] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2173] In today's smart home environment, systems that balance security and energy efficiency are not yet fully established. In particular, there is a need for systems that allow users to easily manage security settings, accurately calculate greenhouse gas emissions based on energy usage, and receive appropriate energy-saving advice. There is also a lack of systems that can quickly provide solutions to problems that arise in the home.

[2174] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2175] In this invention, the server includes: means for managing information settings using a generative computer system; means for recording energy consumption and calculating greenhouse gas emissions based on the recorded energy consumption; means for providing energy-saving advice; means for collecting setting information entered by users through an information device consisting of multiple terminals and sending it to a central processing unit; means for the central processing unit to analyze the received information and respond to the user with appropriate settings or advice; and means for storing energy consumption and related information in a database and retrieving and recalculating it as needed. This allows users to efficiently manage security settings, understand energy consumption and greenhouse gas emissions, and take specific energy-saving measures. It also provides quick solutions to problems that arise in the home, improving the sustainability of lifestyles.

[2176] A "generative computer system" is a system that has the technology to automatically generate, manipulate, and manage specifications based on user input information.

[2177] "Information Settings" refers to various data and settings related to security and energy usage that users can manage and operate.

[2178] "Greenhouse gas emissions" refers to the total amount of greenhouse gases, including carbon dioxide, emitted from households and facilities over a certain period of time.

[2179] "Energy saving advice" means specific advice or suggestions for reducing energy consumption.

[2180] "Information device" refers to a terminal (e.g., smartphone, tablet, sensor, etc.) used to facilitate the input, processing, and output of data.

[2181] A "central processing unit" is a computer system that receives input data, analyzes and processes it, and provides appropriate control.

[2182] "Database" means a system for efficiently storing, managing, and retrieving energy usage and related information.

[2183] "Energy consumption" refers to the total amount of energy, such as electricity and gas, consumed in a home or facility over a certain period of time.

[2184] A "problem-solving tool" is a system that provides quick and appropriate solutions to problems or troubles reported by users.

[2185] "Appropriate settings or advice" refers to the best settings or suggestions provided by the generative computer system based on the user's input information.

[2186] This invention relates to an eco-friendly smart home security system with a generative computing system at its core. This system optimizes security and energy efficiency, helping to realize a sustainable lifestyle.

[2187] Overall system overview

[2188] This system consists of multiple devices (smartphones, tablets, sensors, etc.) and a central processing unit. Users access the system through their devices and input and manage security settings and energy usage information. The central processing unit processes this data and provides appropriate advice and suggestions to users.

[2189] Hardware and software used

[2190] The hardware used is a smartphone, tablet, sensors, and a central processing unit, while the software includes a generative computing system that manages security settings, records energy usage, calculates greenhouse gas emissions, provides energy conservation advice, and troubleshoots.

[2191] Security Settings Management

[2192] A user opens an application on a smartphone or tablet and enters settings for the front door lock or window sensor. The device sends this setting information to the central processing unit. The central processing unit analyzes the received information and updates the system's security settings. It then sends a message to the device saying "Security settings have been updated."

[2193] Record energy usage and calculate greenhouse gas emissions

[2194] The user inputs the amount of energy used that day through the application. For example, if the energy usage is input as 5.5 kWh, the device will send this data to the central processing unit. The central processing unit stores the received data in a database and calculates the greenhouse gas emissions (e.g., 2.75 kg) based on the data using the production calculation system. The calculation result will be displayed on the user's device.

[2195] Providing advice on energy conservation

[2196] When a user requests energy-saving advice, they send a request through the application, for example, using the prompt "Request specific energy-saving advice." The central processing unit selects the most appropriate advice from the internal hint list and sends it to the user's device. The user can receive advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[2197] troubleshooting

[2198] When a user reports a system problem, they use an application to input the specific problem. The terminal sends this report to the central processing unit, which analyzes the problem reported by the generative computer system and suggests an appropriate solution. For example, in response to a report of a "sensor malfunction," the central processing unit suggests the solution "restart the sensor." The user can solve the problem by following this instruction.

[2199] Prompt Sentence Examples

[2200] Below are some specific examples of prompt sentences to input into the generative AI model.

[2201] 1. "Turn on security settings for your front doors and windows and receive a confirmation message."

[2202] 2. "Enter your energy usage as 5.5 kWh and calculate and display your greenhouse gas emissions."

[2203] 3. "Submit a request for energy saving advice and view tips."

[2204] 4. "Report sensor malfunctions and provide appropriate solutions."

[2205] As described above, the present invention achieves both security and energy efficiency, helping users achieve a sustainable lifestyle.

[2206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2207] Security Settings Management

[2208] Step 1:

[2209] The user opens the application and enters the security settings.

[2210] Input: On / off setting for front door lock and window sensor

[2211] How it works: A user operates an app on their smartphone or tablet and enters security settings.

[2212] Step 2:

[2213] The terminal sends the setting information to the server.

[2214] Input: Security setting information entered by the user

[2215] Output: Communication data via HTTPS protocol, including configuration information

[2216] How it works: The device encrypts the configuration information in real time and sends it to the server.

[2217] Step 3:

[2218] The server analyzes the configuration information and updates the security settings.

[2219] Input: Security setting information received by the server

[2220] Data processing: Generative AI models analyze information and convert it into appropriate settings

[2221] Output: Updated security settings

[2222] How it works: The server uses the generative AI model to analyze the configuration information and update the system's security settings.

[2223] Step 4:

[2224] The server sends a confirmation message to the terminal.

[2225] Enter: Updated security settings

[2226] Output: "Security settings updated" message

[2227] Operation: The server generates a message with the updated settings and sends it to the device.

[2228] Step 5:

[2229] The user confirms that the settings should be applied.

[2230] Input: "Security settings updated" message

[2231] Output: Visual confirmation

[2232] Action: The user checks the device screen to confirm that the settings have been applied.

[2233] Record energy usage and calculate greenhouse gas emissions

[2234] Step 1:

[2235] The user inputs energy usage through the application.

[2236] Input: Energy usage data (e.g. 5.5 kWh)

[2237] How it works: A user enters their energy usage using a smartphone or tablet.

[2238] Step 2:

[2239] The device sends energy usage data to the server.

[2240] Input: Energy usage data

[2241] Output: JSON format data containing energy usage

[2242] How it works: The device converts energy usage data into JSON format and sends it to the server.

[2243] Step 3:

[2244] The server stores the data and calculates greenhouse gas emissions.

[2245] Input: Energy usage data

[2246] Data processing: Calculating emissions with generative AI models

[2247] Output: Calculated greenhouse gas emissions (e.g. 2.75 kg)

[2248] How it works: A server stores energy usage data in a database and applies an algorithm to calculate emissions.

[2249] Step 4:

[2250] The server notifies the user of the calculation results.

[2251] Input: Calculated greenhouse gas emissions

[2252] Output: Notification message containing emissions data

[2253] Operation: The server generates the calculation result as a message and sends it to the terminal.

[2254] Providing advice on energy conservation

[2255] Step 1:

[2256] A user asks for energy conservation advice.

[2257] Input: Energy conservation advice request

[2258] Action: The user taps the energy saving advice request button in the application.

[2259] Step 2:

[2260] The terminal sends a request for advice to the server.

[2261] Input: Energy conservation advice request

[2262] Output: Communication data including request data

[2263] Operation: The device sends a request to the server.

[2264] Step 3:

[2265] The server generates appropriate advice and sends it to the terminal.

[2266] Input: Advice request

[2267] Data processing: Advice generation using generative AI models

[2268] Output: Advice message (e.g., "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree")

[2269] Operation: The server selects the best advice from the advice list, generates it as a message, and sends it to the terminal.

[2270] Step 4:

[2271] The user receives the advice.

[2272] Input: Advice message

[2273] Output: Visual confirmation and actionable advice

[2274] Action: The user checks the advice displayed on the device screen and follows it.

[2275] troubleshooting

[2276] Step 1:

[2277] A user reports a system problem.

[2278] Input: Specific problem report (e.g. sensor malfunction)

[2279] Action: A user fills in details on a problem report form in an application.

[2280] Step 2:

[2281] The device sends the report to the server.

[2282] Input: Trouble report data

[2283] Output: Communication data including report data

[2284] Operation: The terminal sends report data to the server.

[2285] Step 3:

[2286] The server analyzes the problem and chooses a solution.

[2287] Input: Trouble report data

[2288] Data processing: problem analysis and solution selection using generative AI models

[2289] Output: Solution message (e.g. "Please restart the sensor")

[2290] How it works: The server analyzes the reported problem and chooses the best solution.

[2291] Step 4:

[2292] The server sends the solution to the user's terminal.

[2293] Input: Selected Solution

[2294] Output: Solution message

[2295] Operation: The server generates a solution as a message and sends it to the terminal.

[2296] Step 5:

[2297] The user reviews the solution and resolves the issue.

[2298] Input: Solution message

[2299] Output: Visual confirmation and actionable solutions

[2300] Action: The user sees the solution displayed on the device screen and follows the instructions to resolve the issue.

[2301] (Application example 1)

[2302] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2303] Conventional smart home security systems manage security and energy efficiency separately, making it difficult to optimize each. Energy-saving advice and troubleshooting are standardized, lacking the ability to address individual user needs. Furthermore, real-time security monitoring is not possible, leading to delayed detection of abnormalities and making it difficult to ensure user safety.

[2304] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2305] In this invention, the server includes a means for managing security settings using a generative AI model, a means for recording energy usage and calculating carbon dioxide emissions based on the recorded energy usage, a means for providing energy-saving tips, and a means for performing real-time security monitoring and notifying the user when an abnormality is detected. This makes it possible to comprehensively optimize both security and energy efficiency and provide energy-saving advice and troubleshooting customized for each user. Furthermore, real-time security monitoring allows for immediate detection of abnormalities, ensuring user safety.

[2306] A "generative AI model" is an algorithm that generates new data and information based on input data, and is a type of artificial intelligence that performs pattern recognition and prediction.

[2307] "Security settings" refers to setting information for managing access control and the operation of security devices within a home or a specific area.

[2308] "Energy usage" refers to the amount of energy consumed per unit time, and measures the amount of electricity, gas, etc. consumed in the home and elsewhere.

[2309] "Carbon dioxide emissions" refers to the total amount of carbon dioxide emitted through energy consumption, and is a measurement indicator of environmental impact.

[2310] "Energy saving tips" refer to specific advice and suggestions for reducing energy consumption, which serve as a guide for users to improve their energy usage efficiency.

[2311] "Real-time security monitoring" refers to processes and systems that instantly monitor the current status and immediately notify you if an abnormality is detected.

[2312] "Troubleshooting" refers to the process of analyzing problems that occur in systems or devices and providing appropriate solutions.

[2313] "Notifications" are a mechanism for sending messages and alerts to users to immediately convey important information.

[2314] This invention is an eco-friendly smart home security system with a generative AI model at its core, which optimizes security and energy efficiency and helps realize a sustainable lifestyle. The system consists of multiple devices (smartphones, tablets, sensors, etc.) and a server.

[2315] Hardware and software used

[2316] Smartphone: A handheld device used for user input and display of information.

[2317] Security camera: A device used to monitor the home and detect abnormalities.

[2318] Cloud server: A server responsible for data processing and running generative AI models.

[2319] Generative AI models: Algorithms that manage configuration, analyze, and generate recommendations.

[2320] Overall system configuration

[2321] The system has the following main features:

[2322] 1. Security Settings Management

[2323] Users input security settings such as door locks and window sensors via their smartphone or tablet. The device then sends this information to the server, where a generative AI model analyzes and applies the settings. Once the settings are updated, a confirmation message is sent to the user.

[2324] 2. Record your energy usage and calculate your CO2 emissions

[2325] The user inputs their energy usage data into their smartphone. The device then sends this data to a cloud server, which calculates the amount of CO2 emissions. The calculation results are then instantly fed back to the user's smartphone.

[2326] 3. Providing energy-saving tips

[2327] When a user requests energy-saving advice, the server uses a generative AI model to generate optimal energy-saving tips for the user and sends them to their smartphone.

[2328] 4. Troubleshooting

[2329] When a user reports a malfunction in a sensor or other device, a cloud server receives it, and a generative AI model proposes an appropriate solution and sends it to the smartphone.

[2330] 5. Real-time security monitoring

[2331] Security cameras constantly monitor the home, and if they detect any abnormalities, the data is sent to a cloud server and a real-time notification is sent to the user's smartphone.

[2332] Specific examples

[2333] 1. Update your security settings

[2334] The user sets the front door lock and window sensor on their smartphone. The cloud server receives this information and updates the settings. The user is then notified with a message that "security settings have been updated."

[2335] 2. Record your energy usage

[2336] The user enters their energy usage for the day as 5.5 kWh on their smartphone. The cloud server receives this data, and the generative AI model calculates the CO2 emissions as 2.75 kg. The calculation result is fed back to the smartphone.

[2337] 3. Providing energy-saving tips

[2338] A user types "Please give me some energy-saving advice" into their smartphone. The cloud server uses a generative AI model to generate suggestions to the user, such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree."

[2339] 4. Troubleshooting

[2340] When a user reports that "the sensor is not working," the cloud server uses a generative AI model to suggest a solution, such as "restart the sensor," and sends it to the smartphone.

[2341] 5. Real-time security monitoring

[2342] Security cameras transmit data in real time, and if an abnormality (such as an unauthorized door opening or closing) is detected, the cloud server immediately sends a notification to the user's smartphone.

[2343] Prompt Sentence Examples

[2344] "Please give me some energy saving advice based on my energy usage."

[2345] "Check the footage from your home security cameras in real time and let us know if there are any abnormalities."

[2346] "Please turn on the front door lock and window sensors."

[2347] "Sensor not working properly"

[2348] Based on these prompts, the cloud server and generative AI model work together to provide optimal information processing and suggestions, thereby building a system that supports the user's lifestyle.

[2349] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2350] Step 1:

[2351] Users use their smartphones or tablets to input security settings, energy usage, energy saving tips, or troubleshooting requests.

[2352] (Input): User requests (e.g., changing security settings, entering energy usage data)

[2353] (Output): The requested data is sent from the terminal to the server.

[2354] Step 2:

[2355] The terminal transmits the received user request data to the cloud server.

[2356] (Input): User request data

[2357] (Output): The request data is sent to the cloud server.

[2358] Step 3:

[2359] The server analyzes the received request data and determines the appropriate processing method.

[2360] (Input): Request data

[2361] (Output): Analysis results (e.g., security setting changes, energy usage records, anomaly detection start instructions)

[2362] Step 4:

[2363] It uses generative AI models to perform processing based on user requests.

[2364] (Input): Analysis results

[2365] (Output): Processing results (e.g., updated security settings, energy saving advice, troubleshooting solutions)

[2366] Step 5:

[2367] Based on the processing results, the cloud server performs specific data processing and calculations, such as calculating CO2 emissions based on energy consumption.

[2368] (Input): Processing result

[2369] (Output): Data processing or calculation results (e.g., CO2 emissions, specific energy-saving advice)

[2370] Step 6:

[2371] The server generates the final results and feeds them back to the user's smartphone or tablet.

[2372] (Input): Data processing or calculation results

[2373] (Output): Feedback to the user (e.g., security setting confirmation message, CO2 emission notification, energy saving advice display)

[2374] Specific operation example

[2375] Changing security settings

[2376] Step 1: The user types "turn on front door lock" into their smartphone.

[2377] (Input): "Lock front door on."

[2378] (Output): The requested data is sent from the terminal to the server.

[2379] Step 2: The device sends a request to change the security settings to the cloud server.

[2380] (Input): Security setting change request

[2381] (Output): The request data is sent to the cloud server.

[2382] Step 3: The server analyzes the request data and determines the setting changes for the front door lock.

[2383] (Input): Security setting change request data

[2384] (Output): Analysis result (instruction to turn on door lock)

[2385] Step 4: The generative AI model applies the door lock configuration changes and updates the system.

[2386] (Input): Analysis result (instruction to turn on door lock)

[2387] (Output): Processing result (Door lock on setting)

[2388] Step 5: The server makes the configuration changes and displays "Security settings updated" on the user's screen.

[2389] (Input): Processing result (door lock on setting)

[2390] (Output): Feedback to the user (security settings update message)

[2391] Prompt Sentence Examples

[2392] "Please set the front door lock to on"

[2393] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2394] This invention relates to an environmentally friendly smart home security system that combines a generative AI model and an emotion engine. The system assists users in optimizing security settings and energy efficiency, and provides personalized services based on the user's emotions. Specific embodiments of the invention are described in detail below.

[2395] Overall system overview

[2396] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[2397] Device: A device operated by a user, such as a smartphone or tablet.

[2398] Server: A computer system that analyzes and processes data.

[2399] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[2400] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[2401] Program processing

[2402] Security Settings Management

[2403] The user enters security settings on the device. The device sends the setting information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which the user confirms.

[2404] Record energy usage and calculate CO2 emissions

[2405] The user inputs their energy usage into the device. The device then sends this data to the server, which stores the data and uses an AI model to calculate CO2 emissions. The results of the calculation are then sent to the user, who is then shown the energy usage and CO2 emissions.

[2406] Providing energy-saving tips

[2407] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[2408] troubleshooting

[2409] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[2410] Emotion engine processing

[2411] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[2412] Specific examples

[2413] Update security settings

[2414] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[2415] Energy usage record

[2416] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[2417] Providing energy-saving tips

[2418] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[2419] troubleshooting

[2420] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[2421] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[2422] The processing flow will be explained below.

[2423] Security Settings Management

[2424] Step 1:

[2425] The user uses a smartphone or tablet to enter security settings (e.g., front door lock, window sensor on / off).

[2426] Step 2:

[2427] The device sends the security setting data entered by the user to the server.

[2428] Step 3:

[2429] The server analyzes the received security setting data and updates the security setting in the system.

[2430] Step 4:

[2431] The server sends the updated security settings to the user's device.

[2432] Step 5:

[2433] The user's device will display a message indicating that the security settings have been successfully updated.

[2434] Record energy usage and calculate CO2 emissions

[2435] Step 1:

[2436] Users enter their energy usage for the day or a specific period via their smartphone or tablet.

[2437] Step 2:

[2438] The device sends energy usage data to the server.

[2439] Step 3:

[2440] The server stores the received energy usage data and runs an algorithm to calculate CO2 emissions.

[2441] Step 4:

[2442] The server sends the calculated CO2 emissions to the user's terminal.

[2443] Step 5:

[2444] The user's device displays energy usage and CO2 emissions.

[2445] Providing energy-saving tips

[2446] Step 1:

[2447] Users submit requests for energy-saving tips from their smartphones or tablets.

[2448] Step 2:

[2449] The device sends an energy saving hint request to the server.

[2450] Step 3:

[2451] The server generates appropriate advice from a pre-configured list of energy saving tips.

[2452] Step 4:

[2453] The server sends the generated energy saving hints to the user's device.

[2454] Step 5:

[2455] The user's device displays energy saving tips.

[2456] troubleshooting

[2457] Step 1:

[2458] Users report system glitches or problems (e.g., sensor errors) via their smartphone or tablet.

[2459] Step 2:

[2460] The device sends the user's problem report to the server.

[2461] Step 3:

[2462] The server analyzes the received problem and selects the appropriate solution from a pre-defined list of solutions.

[2463] Step 4:

[2464] The server sends the selected solution to the user's device.

[2465] Step 5:

[2466] The user's device will display recommended solutions to resolve the issue.

[2467] Emotion engine processing

[2468] Step 1:

[2469] Users use their smartphones or tablets to provide emotional data through voice, facial expressions, or text input.

[2470] Step 2:

[2471] The device sends the user's emotional data to the server.

[2472] Step 3:

[2473] The server uses an emotion engine to analyze the user's emotion data.

[2474] Step 4:

[2475] Based on sentiment analysis, the server optimizes security settings and energy-saving tips to improve user safety.

[2476] Step 5:

[2477] The server sends optimized security settings and energy-saving tips to the device.

[2478] Step 6:

[2479] Your device will display personalized settings and advice based on your emotions.

[2480] Specific examples

[2481] Update security settings

[2482] Step 1:

[2483] The user turns on the front door lock and window sensor on the device.

[2484] Step 2:

[2485] The terminal sends this setting information to the server.

[2486] Step 3:

[2487] The server updates the system security settings.

[2488] Step 4:

[2489] The server sends a confirmation message to the terminal.

[2490] Step 5:

[2491] The user will see the message "Security settings have been updated."

[2492] Energy usage record

[2493] Step 1:

[2494] A user enters their energy usage for the day as 5.5 kWh on their device.

[2495] Step 2:

[2496] The terminal transmits this data to the server.

[2497] Step 3:

[2498] The server calculates the CO2 emissions to be 2.75 kg.

[2499] Step 4:

[2500] The server sends the calculation results to the terminal.

[2501] Step 5:

[2502] Energy usage and CO2 emissions are displayed on the user's screen.

[2503] Providing energy-saving tips

[2504] Step 1:

[2505] The user requests specific energy saving advice and submits a request on the device.

[2506] Step 2:

[2507] The device sends a request to the server.

[2508] Step 3:

[2509] The server generates hints using a generative AI model and an emotion engine.

[2510] Step 4:

[2511] The server sends the hint to the device.

[2512] Step 5:

[2513] Tips such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" will be displayed on the user's screen.

[2514] troubleshooting

[2515] Step 1:

[2516] The user reports a sensor malfunction on the device.

[2517] Step 2:

[2518] The terminal sends a report to the server.

[2519] Step 3:

[2520] The server generates the solution "Please restart the sensor."

[2521] Step 4:

[2522] The server sends the solution to the device.

[2523] Step 5:

[2524] The user will see a message on their screen saying "Please restart the sensor" and the user will be able to resolve the issue.

[2525] As described above, the present invention supports the realization of a sustainable lifestyle by optimizing both security and energy efficiency, and by providing personalized services that take into account the user's emotions.

[2526] Example 2

[2527] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2528] Modern smart home systems are required to manage security, improve energy efficiency, and provide advice on energy conservation, but few systems can comprehensively perform all of these functions. Furthermore, there is a need for systems that can provide services that take into account the user's emotions and psychological state. A comprehensive and personalized system that can solve these issues is desired.

[2529] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2530] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, and means for having an emotion engine for analyzing user emotions and providing services based on the emotions. This enables security management, improved energy efficiency, energy saving, and the provision of personalized services according to the user's emotions.

[2531] A "generative AI model" is a machine learning algorithm used to manage security settings and analyze energy usage.

[2532] "Security settings" are settings to ensure the safety of your home, and include turning on and off front door locks and window sensors.

[2533] "Energy usage" refers to energy consumption, such as the amount of electricity used within a household.

[2534] "Carbon dioxide emissions" refers to the amount of CO2 emitted as a result of energy consumption.

[2535] "Energy saving tips" refers to specific advice on how to reduce energy consumption.

[2536] An "emotion engine" is an algorithm that analyzes emotions from a user's voice, facial expressions, text input, etc., and provides services based on that analysis.

[2537] This invention relates to an eco-friendly smart home security system that combines a generative AI model and an emotion engine. The system helps users optimize security settings and energy efficiency, and provides personalized services based on the user's emotions.

[2538] Overall system overview

[2539] This system consists of four elements: a terminal, a server, a generative AI model, and an emotion engine.

[2540] Device: A device operated by a user, such as a smartphone or tablet.

[2541] Server: A computer system that analyzes and processes data.

[2542] Generative AI models: Machine learning algorithms for managing security settings and analyzing energy usage.

[2543] Emotion engine: An algorithm that analyzes user emotions and provides services based on them.

[2544] Program processing

[2545] Security Settings Management

[2546] The user enters security settings on the device. The device sends the settings information to the server. The server analyzes the received settings and updates the security settings in the system. After updating, the server sends a confirmation message to the device, which is displayed to the user.

[2547] Record energy usage and calculate CO2 emissions

[2548] The user inputs their energy consumption data into the device. The device then sends this data to the server, which stores the data and uses a generative AI model to calculate CO2 emissions. The calculation results are then sent to the device and displayed to the user.

[2549] Providing energy-saving tips

[2550] The user requests energy-saving tips from their device. The device then sends the request to the server. The server then uses a generative AI model and an emotion engine to generate customized energy-saving tips based on the user's emotions and energy usage, and sends them to the device. The user can use these tips to reduce their energy consumption.

[2551] troubleshooting

[2552] A user reports a system malfunction from their device. The device then sends the report to the server. The server analyzes the received problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device, taking into account the user's emotional state. The user can then use this information to solve the problem.

[2553] Emotion engine processing

[2554] The emotion engine analyzes the user's emotions from their voice, facial expressions, text input, etc. For example, if the user is feeling stressed, it will suggest security settings that prioritize safety. Conversely, if the user is relaxed, it will recommend energy-saving settings that prioritize energy efficiency. The emotion engine also retains the user's emotional data and uses it for future interactions.

[2555] Specific examples

[2556] Update security settings

[2557] The user turns on the front door lock and window sensor on the device. The device sends this setting information to the server, which updates the system's security settings. The server sends a confirmation message to the device, and the user sees "Security settings updated."

[2558] Energy usage record

[2559] The user enters their energy usage for the day as 5.5 kWh. The device sends this data to the server, which calculates the CO2 emissions as 2.75 kg. The calculation result is displayed on the user's screen.

[2560] Providing energy-saving tips

[2561] The user requests specific advice on energy conservation and sends a request via their device. The server uses a generative AI model and an emotion engine to provide tips, such as "switch to LED lighting" or "raise the air conditioner temperature setting by 1 degree," that take emotion data into account and display them to the user. The user can then use these tips to reduce their energy consumption.

[2562] troubleshooting

[2563] The user reports a sensor malfunction on their device and sends it to the server. The server then provides a solution, "Please restart the sensor," along with a message to ease the user's anxiety. The user can then follow the suggestion to resolve the problem.

[2564] Prompt Sentence Examples

[2565] A prompt based on the security configuration management example is as follows:

[2566] "After a user turns on their front door lock and window sensor on their smartphone and the system sends the settings to the server, explain how the security settings are updated."

[2567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2568] Security settings management process flow

[2569] Step 1:

[2570] The user enters security settings (e.g., front door lock, window sensor on) on the device.

[2571] Input: User security setting information (e.g. front door lock ON, window sensor ON).

[2572] Output: The configuration information is saved to the device.

[2573] Step 2:

[2574] The terminal assembles the input security setting information into a packet.

[2575] Input: User security settings information.

[2576] Output: Security configuration information in packet format.

[2577] Step 3:

[2578] The terminal sends this packet to the server.

[2579] Input: Security configuration information in packet format.

[2580] Output: The packet is sent to the server.

[2581] Step 4:

[2582] The server analyzes the received configuration information.

[2583] Input: Security configuration information in received packet format.

[2584] Output: Parsed security configuration information.

[2585] Step 5:

[2586] The server updates the security settings within the system based on the analysis results.

[2587] Input: Parsed security configuration information.

[2588] Output: The updated security settings.

[2589] Step 6:

[2590] The server notifies the terminal that the update is complete.

[2591] Input: Completion notification.

[2592] Output: A notification message is sent to the terminal.

[2593] Step 7:

[2594] The terminal displays to the user a confirmation message received from the server.

[2595] Input: Notification message from the server.

[2596] Output: The user will see the message "Security settings have been updated."

[2597] Record energy usage and calculate CO2 emissions

[2598] Step 1:

[2599] The user enters the amount of energy usage for that day into the device (e.g., 5.5 kWh).

[2600] Input: User energy usage data.

[2601] Output: Energy usage data is saved on the device.

[2602] Step 2:

[2603] The terminal assembles the input energy usage data into packets.

[2604] Input: Energy usage data.

[2605] Output: Energy usage data in packet format.

[2606] Step 3:

[2607] The terminal sends this packet to the server.

[2608] Input: Energy usage data in packet format.

[2609] Output: The packet is sent to the server.

[2610] Step 4:

[2611] The server stores the received energy usage data.

[2612] Input: Received energy usage data in packet format.

[2613] Output: Energy usage data is stored on the server.

[2614] Step 5:

[2615] The server uses a generative AI model to calculate CO2 emissions (e.g., 2.75 kg).

[2616] Input: Energy usage data.

[2617] Output: Calculated CO2 emissions data.

[2618] Step 6:

[2619] The server notifies the user of the calculation results.

[2620] Input: Calculated CO2 emissions data.

[2621] Output: A notification message is sent to the terminal.

[2622] Step 7:

[2623] The terminal displays the calculated CO2 emissions received from the server to the user.

[2624] Input: Notification message from the server.

[2625] Output: The message "Today's CO2 emissions are 2.75 kg" is displayed to the user.

[2626] Providing energy-saving tips

[2627] Step 1:

[2628] The user requests energy saving tips from the device.

[2629] Input: The user's request.

[2630] Output: The request is saved to the device.

[2631] Step 2:

[2632] The terminal collects the request contents into packets and sends them to the server.

[2633] Input: The user's request.

[2634] Output: The request in the form of a packet is sent to the server.

[2635] Step 3:

[2636] The server passes the received request to the generative AI model and emotion engine.

[2637] Input: The request in packet format.

[2638] Output: Input data to the generative AI model and emotion engine.

[2639] Step 4:

[2640] The server generates energy-saving tips based on the user's emotional data and energy usage.

[2641] Input: User emotion data and energy usage data.

[2642] Output: Generated energy saving tips.

[2643] Step 5:

[2644] The server notifies the user of the generated hint.

[2645] Input: Generated energy saving tips.

[2646] Output: A notification message is sent to the terminal.

[2647] Step 6:

[2648] The device displays the energy saving tips received from the server to the user.

[2649] Input: Notification message from the server.

[2650] Output: Advice such as "Change the lighting to LED" or "Raise the air conditioner temperature setting by 1 degree" is displayed to the user.

[2651] troubleshooting

[2652] Step 1:

[2653] Users report system malfunctions from their devices.

[2654] Input: User's bug report.

[2655] Output: Report data is saved to the device.

[2656] Step 2:

[2657] The terminal assembles the report contents into packets and sends them to the server.

[2658] Input: User's bug report.

[2659] Output: The problem report in the form of a packet is sent to the server.

[2660] Step 3:

[2661] The server analyzes the received problem.

[2662] Input: A defect report in packet format.

[2663] Output: Parsed problem data.

[2664] Step 4:

[2665] The server selects an appropriate solution from a pre-configured list of solutions.

[2666] Input: Parsed problem data.

[2667] Output: A good solution.

[2668] Step 5:

[2669] The server will notify the user of the solution, for example by sending a message saying "Please restart the sensor."

[2670] Input: The correct solution.

[2671] Output: A notification message is sent to the terminal.

[2672] Step 6:

[2673] The device displays a message that takes into account the user's emotional state along with the solution received from the server.

[2674] Input: Notification message from the server.

[2675] Output: "Please restart your sensor" along with "We apologize for the inconvenience."

[2676] Emotion engine processing

[2677] Step 1:

[2678] The device captures voice, facial expressions, text input, and more while the user is using the system.

[2679] Input: User voice, facial expression, and text input data.

[2680] Output: The captured data.

[2681] Step 2:

[2682] The device collects the captured data and sends it to the server in packets.

[2683] Input: The captured data.

[2684] Output: Data in the form of packets is sent to the server.

[2685] Step 3:

[2686] The server analyzes the received data using the emotion engine.

[2687] Input: Data in packet format.

[2688] Output: Sentiment data parsed by the sentiment engine.

[2689] Step 4:

[2690] The server reflects the analysis results of the emotion engine in various functions within the system.

[2691] Input: Parsed emotion data.

[2692] Output: Personalized service content.

[2693] Step 5:

[2694] The device displays messages and suggestions to the user that reflect the emotion analysis results received from the server.

[2695] Input: Sentiment analysis results and suggestions based on them.

[2696] Output: A customized message or suggestion is displayed to the user.

[2697] (Application example 2)

[2698] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2699] Security and energy management are becoming increasingly important in modern brick-and-mortar stores, but it is difficult to efficiently manage these while improving customer satisfaction. It is also necessary to reduce the burden on staff and achieve sustainable operations. Conventional systems often address individual issues, lacking a centralized means of resolving them. While there is a demand for personalized service based on customer emotions, achieving this remains a challenge.

[2700] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2701] In this invention, the server includes means for managing security settings using a generative AI model, means for recording energy usage and calculating carbon dioxide emissions based on the recording, means for providing energy-saving tips, means for analyzing user emotions and providing personalized services based on the recorded energy usage, means for managing security cameras and sensors in a physical store, detecting abnormalities and notifying store staff, means for monitoring energy usage in the physical store and proposing energy-saving measures, and means for analyzing customer emotions in the store and providing services according to customer satisfaction. This integrates security and energy management in a physical store, improving customer satisfaction while reducing the burden on staff and enabling sustainable operations.

[2702] A "generative AI model" is a system that uses machine learning algorithms to analyze data and make decisions, managing user security settings and analyzing and making suggestions about energy usage.

[2703] "Means to manage security settings" refers to the ability to check the settings of security devices, including security cameras and sensors, and update them as necessary.

[2704] "Means for recording energy usage" refers to a function that monitors the amount of energy consumed within a store or home and stores that data.

[2705] The "means for calculating carbon dioxide emissions" is a function for calculating the amount of carbon dioxide emitted based on the recorded energy usage.

[2706] "Means for providing energy saving tips" is a function that analyzes the user's energy usage and makes specific suggestions for improving energy efficiency.

[2707] "Means for analyzing emotions" refers to a function that estimates the emotional state of a user or customer based on their facial expressions, voice, and text input, and responds accordingly.

[2708] "Means for providing personalized services" refers to the ability to analyze user emotions and behavioral data to provide individually optimized services.

[2709] "Means for managing security cameras and sensors" refers to a function that monitors data from security cameras and various sensors within physical stores in real time and notifies customers when an abnormality is detected.

[2710] "Means of notifying store staff" refers to a function that immediately notifies store staff of any abnormalities detected by security cameras or sensors.

[2711] "Means for monitoring energy usage" refers to a function that monitors energy consumption in physical stores in real time and enables efficient energy management.

[2712] "Means for proposing energy-saving measures" is a function that proposes effective energy-saving measures based on data on energy usage in physical stores.

[2713] "Means for analyzing customer emotions" is a function for monitoring the facial expressions and behavior of customers in the store and understanding their emotional state.

[2714] "Means for providing services according to customer satisfaction" refers to a function that utilizes the results of customer sentiment analysis to provide customized services to increase customer satisfaction.

[2715] This invention provides a smart security and energy management system for brick-and-mortar stores that combines a generative AI model and an emotion engine to manage security, optimize energy efficiency, and improve customer service.

[2716] Overall system overview

[2717] The system consists of the following main components:

[2718] Terminal: A device (smartphone or tablet) operated by store staff.

[2719] Server: A computer system that analyzes and processes data and utilizes generative AI models and emotion engines.

[2720] Generative AI model: A machine learning algorithm that analyzes energy usage, manages security settings, and suggests energy-saving measures.

[2721] Emotion engine: An algorithm that analyzes the emotions of users and customers and provides personalized services based on that.

[2722] Security cameras and sensors: These are devices that are responsible for security in physical stores and detect abnormalities.

[2723] Security Settings Management

[2724] The device manages the settings of security cameras and sensors. Once the settings are changed, the information is sent from the device to the server, where it is analyzed and updated. If an abnormality is detected, the server sends a notification to the store staff's device, prompting them to take appropriate action.

[2725] Record energy usage and calculate CO2 emissions

[2726] The device records the store's energy usage and sends the data to a server, which uses a generative AI model to calculate the carbon footprint based on the energy usage. The results are then sent back to the device and communicated to the store staff.

[2727] Providing energy-saving tips

[2728] The terminal requests energy-saving tips based on energy usage. The server uses a generative AI model and emotion engine to generate customized energy-saving suggestions based on staff emotions and the store's energy usage, providing appropriate energy-saving measures to the store.

[2729] Improved customer service

[2730] The server uses cameras installed in the store and an emotion engine to analyze customer emotions in real time. For example, if it determines that a customer is tired, it will send a suggestion to guide them to a relaxation zone to their device. This allows store staff to provide attentive service according to the customer's emotions.

[2731] troubleshooting

[2732] Any system malfunctions or abnormal behavior reported by the device are sent to the server, which analyzes the problem, selects an appropriate solution from a pre-defined list of solutions, and sends it to the device using an emotion engine, taking into account the emotional state of the staff member, thereby supporting fast and effective problem solving.

[2733] Specific examples

[2734] Example of energy usage record:

[2735] Store staff enter the amount of energy used that day into the terminal. For example, if "energy usage: 5.5 kWh" is entered into the terminal, this data is sent to the server, which then uses a generative AI model to calculate "CO2 emissions: 2.75 kg" and notify the terminal of the result.

[2736] Example prompts for energy saving tips:

[2737] Analysis of store energy consumption

[2738] Store energy use: 5.5 kWh

[2739] CO2 emissions: 2.75 kg

[2740] Generate energy saving tips

[2741] Current staff sentiment: happy

[2742] Energy saving tips:

[2743] Change the lighting to LED.

[2744] Please increase the air conditioner temperature setting by 1 degree.

[2745] This system will enable physical stores to manage security and optimize energy efficiency, and provide personalized services that take customer emotions into consideration.

[2746] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2747] Step 1:

[2748] Users manage security settings on their devices. Users enter security camera and sensor settings on their devices and send the settings information to the server. The server analyzes the received data and updates the security settings for the entire system. After updating, the server sends a confirmation message to the device, which the user confirms.

[2749] Input: Security settings information from the user

[2750] Output: Security setting confirmation message to the terminal

[2751] Step 2:

[2752] The user inputs their energy usage into the device. The device then sends this data to the server. The server stores the received energy usage data and calculates carbon dioxide emissions using a generative AI model. The calculation results are then sent to the device and notified to the user.

[2753] Input: Energy usage data from users

[2754] Output: Notification of carbon dioxide emissions calculation results

[2755] Step 3:

[2756] The user requests energy-saving tips from their device. The device then sends the request to the server. The server uses a generative AI model and an emotion engine to analyze the user's emotional data and energy usage status, and generates energy-saving tips. The generated tips are then sent to the device for the user to review.

[2757] Input: Energy saving hint request from user

[2758] Output: Customized energy saving tips notification

[2759] Step 4:

[2760] The server monitors data from security cameras and sensors in real time, and if an abnormality is detected, it sends a notification to the store staff's device. The store staff receives the notification and takes appropriate action. The emotion engine analyzes the staff's emotional data and provides alerts with different levels of urgency as necessary.

[2761] Input: Data from security cameras and sensors

[2762] Output: Anomaly detection notification and alerts according to urgency

[2763] Step 5:

[2764] The server monitors the energy usage of the physical store, analyzes the data, and proposes energy-saving measures. For example, it proposes cutting unnecessary electricity when the store is closed. The analysis results and proposals are sent to the terminal and notified to the store staff.

[2765] Input: Energy usage data for physical stores

[2766] Output: Notification of energy saving measures

[2767] Step 6:

[2768] The server uses cameras installed in the store and an emotion engine to analyze the customer's emotions and propose services based on their level of satisfaction. For example, if it determines that the customer is tired, it will send a suggestion to the terminal to guide them to a relaxation zone. Store staff will then use this suggestion to assist the customer.

[2769] Input: Camera footage from inside the store

[2770] Output: Notification of service proposals based on customer satisfaction

[2771] Step 7:

[2772] A user reports a system malfunction on their device and sends the information to the server. The server analyzes the malfunction information, selects an appropriate solution from a pre-defined list of solutions, and sends the solution to the device along with a message that takes into account the user's emotional state using an emotion engine. The user then uses this information to solve the problem.

[2773] Input: User bug report

[2774] Output: Defect resolution and mitigation message notification

[2775] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2776] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2777] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2778] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2779] ...

Claims

1. A means of managing security settings using generative AI models; and a means for recording energy usage and calculating carbon dioxide emissions based thereon; a means of providing energy-saving tips; A system including:

2. The system of claim 1 further comprising means for monitoring carbon dioxide emissions within the home and providing suggestions for improvement to the user.

3. 10. The system of claim 1, further comprising a troubleshooting means for providing appropriate solutions to user-reported problems.

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