system

Abstract

We provide the system. [Solution] A sensor device that acquires data, A computing device that analyzes information obtained from the aforementioned sensor device and generates optimal management commands, A control device that controls the equipment based on commands from the aforementioned computing device, Based on the aforementioned analysis results, a means to optimize energy usage and implement efficient security management, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern home environment, there is a problem that it is difficult to achieve efficient management of household appliances, security, and rapid notification in an emergency. The inconvenience caused by each function existing individually and not being able to be managed centrally is significant, and many users feel the associated labor and psychological uneasiness. The present invention aims to overcome these problems and realize a safer and more comfortable home environment.

Means for Solving the Problems

[0005] This invention is a system that uses a detection device to acquire data, collects information from home appliances and sensors, and generates optimal control commands by analyzing this data with a processing device. The operating device of this system automatically controls the equipment, enabling efficient management of home appliances. Furthermore, in terms of security, it detects suspected intrusions and issues warning signals, and in emergencies, it quickly contacts multiple emergency services, thereby improving the safety and convenience of the home.

[0006] A "detection device that acquires data" is a device used to collect information from home appliances and sensors, and provides the initial data necessary for the operation of the entire system.

[0007] A "processing device" is a device that analyzes data obtained from detection devices and generates control commands according to the situation, playing a central role in the entire system.

[0008] An "operating device" is a device that actually controls equipment based on commands from a processing unit, and it functions autonomously without user intervention.

[0009] A "simulated intrusion" refers to an event in a security system that detects a suspicious person or unidentified movement to alert the user.

[0010] A "warning signal" is a signal emitted when a system detects an abnormality, and it is a means of informing the user and those around them of danger.

[0011] An "emergency" refers to a sudden event that requires a rapid response, and is a situation that triggers the system to initiate special actions such as automatic notification.

[0012] "Recipient" refers to the recipient or organization that is scheduled to receive communications from the system in the event of an emergency. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a smart home system that works in conjunction with various household appliances to ensure efficient operation and safety. The program outline and specific examples are shown below.

[0035] First, the system involves installing various sensors and home appliances, which act as "terminals," in living spaces. Each terminal functions as a detection device that acquires data and transmits usage status and environmental information to a "server."

[0036] The server acts as a "processing device" that processes the received data, using machine learning algorithms to set optimal operating schedules for home appliances and criteria for security warnings. Based on these processing results, the server sends control commands to terminals, which act as "operating devices," to turn appliances on and off or adjust their power consumption.

[0037] Users can access the system through smart devices. They can not only check the current status of home appliances via the application, but also manually change settings. Furthermore, users can directly operate the system when real-time security warnings or emergency calls are needed.

[0038] For example, if a security camera detects motion while the user is away, the server analyzes the data and immediately sends a warning to the user's smart device if there is a high probability of an intruder. Furthermore, if the emergency button is pressed, the server automatically notifies pre-configured contacts.

[0039] This system aims to provide users with a safe and comfortable living environment by improving the efficiency and security of equipment management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal acquires data from various sensors, including temperature, humidity, and power consumption. The terminal then transmits this data to the server.

[0043] Step 2:

[0044] The server begins analysis based on the received data. Using machine learning algorithms, it generates optimal appliance usage patterns and schedules to reduce energy consumption.

[0045] Step 3:

[0046] The server sends the generated schedule to the terminal. The terminal automatically turns appliances on and off and switches their operating modes according to this schedule.

[0047] Step 4:

[0048] If the camera installed on the device detects suspicious activity, it immediately sends the video data to the server.

[0049] Step 5:

[0050] The server uses a facial recognition system to compare video data with a registered facial database, and if it determines the person is suspicious, it sends a warning to the user.

[0051] Step 6:

[0052] Users can receive alerts on their smart devices and remotely view live video from the scene. They can also manually control home appliances and make emergency calls as needed.

[0053] Step 7:

[0054] When the emergency button is pressed, the terminal sends a signal to the server, which simultaneously notifies multiple registered recipients. The user can then check the status and provide additional information if necessary.

[0055] (Example 1)

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

[0057] Until now, existing technologies have not adequately provided a system that can achieve both efficient operation and safety for various household appliances. Furthermore, with the need for intruder detection and rapid response in emergencies, there is a need for a method that allows users to easily manage and operate the system remotely.

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

[0059] In this invention, the server includes a plurality of sensor devices that function as detection means, processing means for aggregating and analyzing environmental information and usage status obtained from the sensor devices, and optimization means for formulating an optimal operation schedule by applying a machine learning algorithm. This makes it possible to achieve efficient control of household appliances and security measures.

[0060] "Detection means" refers to multiple sensor devices installed to acquire environmental information and usage status in real time.

[0061] "Processing means" refers to a function within a server that collects and analyzes data obtained from sensor devices.

[0062] "Optimization methods" refer to techniques that use machine learning algorithms to analyze data and optimize the operating schedule of home appliances.

[0063] "Operation means" refers to the function of a server that issues control commands for home appliances based on the results of the optimization means.

[0064] "Interface means" refers to the means by which users can access the system through a dedicated application to check the status of home appliances and change settings.

[0065] "Communication means" refers to a function that allows simultaneous contact with pre-set recipients in the event of an emergency.

[0066] This invention is a system for efficiently operating various household appliances and ensuring safety. An embodiment of this system is shown below.

[0067] The server plays a central role in the system, receiving and processing data sent from terminals. Specifically, the server uses machine learning algorithms to formulate optimal operating schedules for home appliances. Therefore, high-performance computer hardware with data aggregation and analysis capabilities, as well as machine learning libraries, are required.

[0068] The terminal is installed in the room as a machine with sensors and control functions. Various sensors detect environmental information such as temperature, illuminance, and human movement, and transmit it to the server. For example, a temperature sensor constantly monitors the room temperature and instructs the server to turn the heating on or off if it exceeds a specified range.

[0069] Users can access the system using their smart devices to monitor and control the status of various home appliances. Remote operation and scheduling are possible via a dedicated application. Furthermore, if the terminal detects a security anomaly, the server sends an alert to the user's smart device. For example, if suspicious activity is detected, the user can view the footage through the application and issue security commands as needed.

[0070] As a concrete example, consider a scenario where a sensor detects an anomaly while the user is away. In this case, the sensor detects movement and sends that information to the server. The server analyzes the data, and if it determines that there is a high probability of an intruder, it immediately sends a notification to the user. This notification is delivered to the user via a smartphone app, allowing the user to check the safety of their home no matter where they are.

[0071] Examples of prompts include, "Please explain how the smart home system enhances security when I'm away," and "Please tell me how to optimize energy consumption in my home."

[0072] This type of system is expected to significantly improve the safety and comfort of the home.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The terminal uses sensor devices to detect environmental information and acquire data such as indoor temperature, illuminance, and human movement. It receives real-time data from these sensors as input. If a sensor measures the temperature and detects a value below 20 degrees Celsius, it sends that information to the server. The environmental information is then transferred to the server as output.

[0076] Step 2:

[0077] The server receives data transmitted from the terminal, aggregates it, and analyzes it. It receives data from the terminal regarding temperature and operating status as input. The server then uses machine learning algorithms to compare the current situation with past data to determine if it deviates from normal operating patterns. As output, it generates appropriate operational commands.

[0078] Step 3:

[0079] The server sends commands to the terminal to control the equipment based on the analysis results. The input is a control command based on the analysis results. For example, if the room temperature is low, the server instructs the terminal to turn on the heating. The output is a command that specifies how the appliance should operate.

[0080] Step 4:

[0081] The terminal receives commands from the server and actually controls the equipment. As input, it receives control commands from the server. For example, a terminal that receives a command to turn on the heater will actually activate the heater. As output, the appliance starts operating according to the command.

[0082] Step 5:

[0083] The user receives notifications from the application using a smart device. As input, the user receives status and warning information about home appliances distributed from the server. Based on this, they check if the appliances are functioning correctly and perform manual settings as needed. As output, the user takes action to check the situation in their home and take action accordingly.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] In modern urban environments, the depletion of energy resources and the rise in crime are serious problems. However, conventional technologies make it difficult to efficiently manage energy resources across a wide area and implement immediate crime prevention measures. Therefore, there is a need to build an efficient system to ensure the safety of citizens and a comfortable living environment.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means including a sensor device for acquiring data, a computing means for analyzing information obtained from the sensor device and generating optimal management commands, a control means for controlling equipment based on commands from the computing device, and means for optimizing energy usage based on the analysis results and performing efficient security management. This enables efficient optimization of energy consumption throughout the urban environment and rapid security response.

[0089] A "data acquisition sensor device" is a device that uses various sensor devices placed within a city to acquire environmental information and signs of abnormalities in real time.

[0090] A "computer that analyzes information and generates optimal management commands" is a device that includes a processor that generates commands for efficient energy management and security measures based on data collected from sensor devices.

[0091] A "control device for controlling equipment" is a device that controls various facilities within a city, such as streetlights and security cameras, according to commands transmitted from a computing device, in order to achieve optimal operation.

[0092] "Means for optimizing energy usage and implementing efficient security management" refer to methods and technologies for systematically operating facilities to reduce overall city energy consumption, mitigate security risks, and ensure the safety of citizens.

[0093] The system to realize this application consists of various sensor devices, servers, computing devices, control devices, and user terminals deployed throughout the city. The sensor devices acquire environmental and security information in real time and transmit it to the server. The sensor devices used include IoT sensors and security cameras.

[0094] The server receives data transmitted from the sensor devices and performs analysis using a computing device. This analysis utilizes machine learning algorithms using Python, employing analysis tools such as TENSORFLOW® and Scikit-learn for optimizing energy consumption and detecting suspicious individuals. Based on the analysis results, the computing device generates optimal management commands and transmits them to the control device.

[0095] The control unit optimizes the operation of various facilities within the city, such as streetlights and security cameras, according to the commands it receives. Analysis results and warning information are also sent to user terminals, allowing for remote intervention as needed. Smartphones and smart glasses are used as user terminals.

[0096] As a concrete example, if unusual activity is detected in a specific area at night, the server analyzes the information through a computing device, and if it is determined to be a suspicious person, it immediately sends a notification to the user's terminal. An example of a prompt message in this case could be input to the generative AI model as, "Please tell me about the design of a smart system that optimizes the energy of the entire city while detecting and notifying users of suspicious individuals in real time."

[0097] This system aims to improve the efficiency of urban energy management and enhance the safety of citizens.

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The server acquires environmental and motion information in real time from sensor devices placed throughout the city. This input data includes temperature, illuminance, and the presence or absence of motion. The server aggregates this data and prepares it for analysis.

[0101] Step 2:

[0102] The server sends the acquired data to the computing device and begins data analysis using machine learning algorithms. TensorFlow and Scikit-learn are used here to perform anomaly detection and optimize energy consumption. The computing device outputs the analysis results, including whether or not suspicious individuals were detected and an optimal energy usage plan.

[0103] Step 3:

[0104] The server generates control commands for each piece of equipment based on the output of the computing device. These commands include specific actions such as turning lights on and off or instructing cameras to operate. By sending these commands to the control unit, the actual equipment is controlled on-site.

[0105] Step 4:

[0106] The control unit receives commands from the server and controls target equipment within the city. For example, it may perform specific actions such as turning on streetlights in areas where motion is detected or instructing cameras to zoom.

[0107] Step 5:

[0108] The server retrieves the results of the control command execution from the sensor device again and receives new data as feedback. This information is used for subsequent data analysis and control command generation.

[0109] Step 6:

[0110] The user's terminal receives notifications of analysis results and important warning information. Users can receive this information in real time via their smartphones or smart glasses and take remote action as needed.

[0111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0112] This invention aims to recognize the user's emotional state and optimize the environment accordingly by incorporating an emotion engine into a smart home system within the home. The program's operation overview and specific examples are shown below.

[0113] The system first acquires the user's facial expressions and voice tone in real time through "terminals" such as facial recognition cameras and voice sensors. These terminals then transmit this emotion-related data to a "server."

[0114] The server analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it determines that the user is fatigued, the emotion engine processes that state and generates control commands to promote relaxation.

[0115] Based on these control commands, the server issues instructions to various "operating devices," adjusting the operating modes of home appliances, lighting, and sound systems. This allows for the creation of a home environment that adapts to the user's emotional state.

[0116] For example, when a user returns home tired, the system detects this from their facial expression, and the server instructs them to enter relaxation mode. In this case, the terminal softens the lighting and plays their preferred relaxing music. Conversely, if the user has an energetic expression, the system provides an uplifting environment by playing energetic music.

[0117] Furthermore, if the server detects that a user is experiencing stress, it can suggest appropriate content and programs based on the user's hobbies and preferences.

[0118] Thus, in this embodiment of the present invention, the aim is to enrich home life by providing an environment that matches the user's mental state through the incorporation of an emotion engine.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The device uses a facial recognition camera and voice sensor to detect the user's facial expressions and tone of voice, and collects this data. The device then sends this emotion-related data to a server.

[0122] Step 2:

[0123] The server analyzes the transmitted data using an emotion engine to evaluate the user's emotional state. For example, if the voice tone is low and the facial expression is subdued, the server may determine that the user is tired.

[0124] Step 3:

[0125] The server generates control commands based on the analysis of the user's emotional state. These commands include necessary changes to the settings of home appliances to optimize the user's emotions.

[0126] Step 4:

[0127] The server sends the generated control commands to the terminal, which then adjusts home appliances, lighting, and sound systems. For example, it might instruct the terminal to change the lighting to a softer color tone and play relaxing music.

[0128] Step 5:

[0129] Users can operate within this optimized environment. Furthermore, users can use their smart devices to change system settings as needed, customizing the environment to their preferences and needs.

[0130] Step 6:

[0131] The server uses the user's past preference data to further optimize the environment and suggest additional content and entertainment if the user desires it.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] In modern homes, the operation of various devices often remains fixed and does not take into account the user's deep emotional state. Therefore, environmental optimization to reduce user stress and fatigue is insufficient. Furthermore, the inability to respond flexibly to user preferences and emotions is a problem.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes sensing means for acquiring facial expression and voice data, processing means for analyzing emotion-related data acquired from the sensing means and generating optimal environmental control commands based on the emotional state, and operating means for adjusting a plurality of household appliances based on the control commands from the processing means. This makes it possible to provide an optimal home environment according to the user's emotional state and to make home life more comfortable and fulfilling.

[0137] "Sensing means" refers to a device or apparatus for acquiring the user's facial expressions and voice data.

[0138] "Processing means" refers to a system or device for analyzing acquired emotion-related data and generating optimal environmental control commands based on the user's emotional state.

[0139] "Operating means" refers to a mechanism or device for adjusting and controlling multiple household appliances based on generated control commands.

[0140] An "environmental control command" is a set of specific instructions for changing or adjusting the operating mode of household appliances according to the user's emotional state.

[0141] "Household appliances" refer to electrical appliances, lighting, audio systems, and other devices used in a home environment.

[0142] This invention is a system that reflects the user's emotional state in the home environment and improves comfort within the home by working in conjunction with a smart home system. Specifically, it uses a facial recognition camera and a voice sensor as sensing means. These hardware devices acquire the user's facial expressions and voice tone in real time and transmit the data to a processing unit.

[0143] The server uses software called an emotion engine to analyze this acquired data in detail. Based on machine learning algorithms, the emotion engine can classify the user's emotional state into categories such as joy, anger, sadness, and pleasure, and determine the degree of stress and fatigue. Based on the analysis results, the server generates environmental control commands to optimize the home environment. These commands are sent to control devices such as lighting, audio systems, and home appliances, automatically adjusting the operation of the equipment.

[0144] As a concrete example, consider a scenario where a user returns home tired from work. When the device detects the user's tired facial expression and calm tone of voice, the detected data is sent to the server, where the emotion engine determines it to be "fatigue." Based on this, the server instructs the control device to enter relaxation mode. The lighting is softened, and relaxing music plays from the speaker. Conversely, if the user appears energetic, the device is instructed to brighten the lighting and play upbeat music.

[0145] Furthermore, when a user is in a specific emotional state, the server can refer to the user's preferences and past history to suggest additional content and activities. This goes beyond mere emotion recognition, enabling the provision of a user-centered home environment.

[0146] Examples of prompts include, "Please provide specific steps for integrating an emotion engine into a smart home system and implementing environmental changes that respond to the user's emotions," and "Please generate effective usage scenarios for an emotion-recognition-based home appliance control system." Through these, the system can always operate in a way that is sensitive to the user's emotions.

[0147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0148] Step 1:

[0149] The device acquires user input using the smart home system's sensing devices. Specifically, it captures the user's facial expressions with a facial recognition camera and records their voice tone with an audio sensor. This data is collected as basic information indicating the user's emotional state. The input here is real-time video and audio data, and the output is preparatory data for emotion analysis.

[0150] Step 2:

[0151] The device sends the acquired facial expression and audio data to the server. This transmission is encrypted to ensure that the data reaches the server securely. The input is emotion-related data collected by the device, and the output is data for analysis passed to the server. This step includes specific actions to prevent data loss or tampering.

[0152] Step 3:

[0153] The server analyzes input data using an emotion engine. It performs machine learning-based data processing to infer emotions from the user's facial expressions and tone of voice. This process allows the server to quantify emotional states such as "fatigue" or "joy." The input is data sent from the terminal, and the output is the result of evaluating the user's emotional state. This operation includes comparison with an existing emotion database.

[0154] Step 4:

[0155] The server generates environmental control commands based on the analysis results. For example, if it determines that the user needs to "relax," it will create commands to soften the lighting and play calming music. The input is the analysis results from the emotion engine, and the output is expressed as specific commands. This step utilizes an algorithm for setting commands that are appropriate to the user's current situation.

[0156] Step 5:

[0157] The server sends the generated control commands to the operating device. The operating device adjusts household appliances and lighting according to the commands and performs the specified actions. The input is the control commands from the server, and the output is the physical environmental changes within the home. These specific actions include starting up devices and changing modes.

[0158] (Application Example 2)

[0159] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0160] In modern society, individuals often experience stress and frustration in their daily lives, resulting in a decline in their quality of life. Especially in urban areas, the environment in public facilities is often designed without considering individual emotional states, leading to discomfort for users. This, in turn, reduces user satisfaction and hinders efficient living.

[0161] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0162] In this invention, the server includes a recognition means for detecting the emotional state of a user, an analysis means for analyzing emotional data obtained from the recognition means to generate appropriate environmental control commands, and an adjustment means for controlling equipment based on the environmental control commands generated by the analysis means. This enables the environment in individual and public spaces to be automatically adjusted according to the emotional state of the user, improving the quality of life for the user and making comfortable living in urban areas possible.

[0163] "User" refers to an individual who uses the system or facility, and whose emotional state is detected and analyzed by the system.

[0164] "Emotional state" refers to an individual's emotional state, specifically their mood or mental tendencies at a particular time.

[0165] "Recognition means" refers to a technical device or algorithm used to detect the emotional state of a user.

[0166] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, tone of voice, etc., and is the subject of analysis by the system.

[0167] An "environmental control command" refers to a specific operational command used to adjust the operation of equipment based on analyzed emotional data.

[0168] "Analysis means" refers to a technical method or program that processes emotional data obtained from recognition means and generates environmental control commands.

[0169] "Adjustment means" refers to a technical system that operates physical or digital equipment based on generated environmental control commands to change environmental settings.

[0170] "Public facilities" refer to buildings or places used by the general public in urban areas, and their environment is subject to regulation.

[0171] This invention is a system that detects the emotional state of users in real time and optimizes the environment of homes and public spaces based on that state. The system mainly consists of recognition means, analysis means, and adjustment means.

[0172] The server uses "recognition means" to detect the user's emotional state through a camera that recognizes the user's face and sensors that pick up sound. This recognition means may be implemented in the customer's smart device, such as a smartphone or smart glasses. The data obtained by the recognition means is recorded as "emotional data."

[0173] The server processes the acquired emotional data using "analysis tools" and generates purposeful "environmental control commands" utilizing an AI model. This analysis process utilizes libraries such as OpenCV and Librosa to analyze facial expression patterns and voice tone.

[0174] The server then uses "adjustment means" to operate physical or digital devices based on the generated environmental control commands. This adjustment includes lighting, temperature control equipment, and sound systems present in homes and public buildings.

[0175] For example, when a server analyzes that a user is tired, it can send a command to soften the lighting in the living space and play relaxing music. Furthermore, in a co-working space within a smart city, the entire facility's environment can be dynamically changed based on user emotional data.

[0176] An example of a prompt to be input to the generating AI model is, "Please suggest optimal adjustments to the home and public space environment when the user is analyzed to be fatigued within the smart city." This prompt is expected to enable the AI ​​to generate appropriate instructions, enriching the user experience.

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The device activates its camera to recognize the user's face and acquires facial image data. Simultaneously, it uses the microphone to record the user's voice and collects audio data. The input consists of facial image and audio data, which is then sent to the next processing step.

[0180] Step 2:

[0181] The server utilizes an AI model to analyze acquired facial images and audio data. Specifically, it estimates emotions based on facial expressions from the facial images and analyzes voice tone and speed from the audio data. This data processing clarifies the user's emotional state. The output is the analyzed emotional state data.

[0182] Step 3:

[0183] The server generates environmental control commands using the analyzed emotional state data. For example, if it determines that the user is tired, it might generate commands to soften the lighting and play calming music. The input is emotional state data, and the output is environmental control commands.

[0184] Step 4:

[0185] The server transmits generated environmental control commands to the adjustment unit, operating household appliances and public facility equipment. Specifically, it may change the lighting in a smart home to a predetermined brightness and instruct the sound system to play a specified piece of music. The input is the environmental control command, and the output is the physical or digital change in the environment caused by the adjustment unit.

[0186] Step 5:

[0187] Users can feel comfortable in a well-adjusted environment. This comfortable environment is the result of the server dynamically adapting based on the user's emotional state, and the environment is readjusted as needed by continuously monitoring the user's condition.

[0188] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0189] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0190] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0194] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0195] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0196] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0197] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0198] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0199] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0200] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0202] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0203] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0204] This invention provides a smart home system that works in conjunction with various household appliances to ensure efficient operation and safety. The program outline and specific examples are shown below.

[0205] First, the system involves installing various sensors and home appliances, which act as "terminals," in living spaces. Each terminal functions as a detection device that acquires data and transmits usage status and environmental information to a "server."

[0206] The server acts as a "processing device" that processes the received data, using machine learning algorithms to set optimal operating schedules for home appliances and criteria for security warnings. Based on these processing results, the server sends control commands to terminals, which act as "operating devices," to turn appliances on and off or adjust their power consumption.

[0207] Users can access the system through smart devices. They can not only check the current status of home appliances via the application, but also manually change settings. Furthermore, users can directly operate the system when real-time security warnings or emergency calls are needed.

[0208] For example, if a security camera detects motion while the user is away, the server analyzes the data and immediately sends a warning to the user's smart device if there is a high probability of an intruder. Furthermore, if the emergency button is pressed, the server automatically notifies pre-configured contacts.

[0209] This system aims to provide users with a safe and comfortable living environment by improving the efficiency and security of equipment management.

[0210] The following describes the processing flow.

[0211] Step 1:

[0212] The terminal acquires data from various sensors, including temperature, humidity, and power consumption. The terminal then transmits this data to the server.

[0213] Step 2:

[0214] The server begins analysis based on the received data. Using machine learning algorithms, it generates optimal appliance usage patterns and schedules to reduce energy consumption.

[0215] Step 3:

[0216] The server sends the generated schedule to the terminal. The terminal automatically turns appliances on and off and switches their operating modes according to this schedule.

[0217] Step 4:

[0218] If the camera installed on the device detects suspicious activity, it immediately sends the video data to the server.

[0219] Step 5:

[0220] The server uses a facial recognition system to compare video data with a registered facial database, and if it determines the person is suspicious, it sends a warning to the user.

[0221] Step 6:

[0222] Users can receive alerts on their smart devices and remotely view live video from the scene. They can also manually control home appliances and make emergency calls as needed.

[0223] Step 7:

[0224] When the emergency button is pressed, the terminal sends a signal to the server, which simultaneously notifies multiple registered recipients. The user can then check the status and provide additional information if necessary.

[0225] (Example 1)

[0226] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] Until now, existing technologies have not adequately provided a system that can achieve both efficient operation and safety for various household appliances. Furthermore, with the need for intruder detection and rapid response in emergencies, there is a need for a method that allows users to easily manage and operate the system remotely.

[0228] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0229] In this invention, the server includes a plurality of sensor devices that function as detection means, processing means for aggregating and analyzing environmental information and usage status obtained from the sensor devices, and optimization means for formulating an optimal operation schedule by applying a machine learning algorithm. This makes it possible to achieve efficient control of household appliances and security measures.

[0230] "Detection means" refers to multiple sensor devices installed to acquire environmental information and usage status in real time.

[0231] "Processing means" refers to a function within a server that collects and analyzes data obtained from sensor devices.

[0232] "Optimization methods" refer to techniques that use machine learning algorithms to analyze data and optimize the operating schedule of home appliances.

[0233] "Operation means" refers to the function of a server that issues control commands for home appliances based on the results of the optimization means.

[0234] "Interface means" refers to the means by which users can access the system through a dedicated application to check the status of home appliances and change settings.

[0235] "Communication means" refers to a function that allows simultaneous contact with pre-set recipients in the event of an emergency.

[0236] This invention is a system for efficiently operating various household appliances and ensuring safety. An embodiment of this system is shown below.

[0237] The server plays a central role in the system, receiving and processing data sent from terminals. Specifically, the server uses machine learning algorithms to formulate optimal operating schedules for home appliances. Therefore, high-performance computer hardware with data aggregation and analysis capabilities, as well as machine learning libraries, are required.

[0238] The terminal is installed in the room as a machine with sensors and control functions. Various sensors detect environmental information such as temperature, illuminance, and human movement, and transmit it to the server. For example, a temperature sensor constantly monitors the room temperature and instructs the server to turn the heating on or off if it exceeds a specified range.

[0239] Users can access the system using their smart devices to monitor and control the status of various home appliances. Remote operation and scheduling are possible via a dedicated application. Furthermore, if the terminal detects a security anomaly, the server sends an alert to the user's smart device. For example, if suspicious activity is detected, the user can view the footage through the application and issue security commands as needed.

[0240] As a concrete example, consider a scenario where a sensor detects an anomaly while the user is away. In this case, the sensor detects movement and sends that information to the server. The server analyzes the data, and if it determines that there is a high probability of an intruder, it immediately sends a notification to the user. This notification is delivered to the user via a smartphone app, allowing the user to check the safety of their home no matter where they are.

[0241] Examples of prompts include, "Please explain how the smart home system enhances security when I'm away," and "Please tell me how to optimize energy consumption in my home."

[0242] This type of system is expected to significantly improve the safety and comfort of the home.

[0243] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0244] Step 1:

[0245] The terminal uses sensor devices to detect environmental information and acquire data such as indoor temperature, illuminance, and human movement. It receives real-time data from these sensors as input. If a sensor measures the temperature and detects a value below 20 degrees Celsius, it sends that information to the server. The environmental information is then transferred to the server as output.

[0246] Step 2:

[0247] The server receives data transmitted from the terminal, aggregates it, and analyzes it. It receives data from the terminal regarding temperature and operating status as input. The server then uses machine learning algorithms to compare the current situation with past data to determine if it deviates from normal operating patterns. As output, it generates appropriate operational commands.

[0248] Step 3:

[0249] The server sends commands to the terminal to control the equipment based on the analysis results. The input is a control command based on the analysis results. For example, if the room temperature is low, the server instructs the terminal to turn on the heating. The output is a command that specifies how the appliance should operate.

[0250] Step 4:

[0251] The terminal receives commands from the server and actually controls the equipment. As input, it receives control commands from the server. For example, a terminal that receives a command to turn on the heater will actually activate the heater. As output, the appliance starts operating according to the command.

[0252] Step 5:

[0253] The user receives notifications from the application using a smart device. As input, the user receives status and warning information about home appliances distributed from the server. Based on this, they check if the appliances are functioning correctly and perform manual settings as needed. As output, the user takes action to check the situation in their home and take action accordingly.

[0254] (Application Example 1)

[0255] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] In modern urban environments, the depletion of energy resources and the rise in crime are serious problems. However, conventional technologies make it difficult to efficiently manage energy resources across a wide area and implement immediate crime prevention measures. Therefore, there is a need to build an efficient system to ensure the safety of citizens and a comfortable living environment.

[0257] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0258] In this invention, the server includes means including a sensor device for acquiring data, a computing means for analyzing information obtained from the sensor device and generating optimal management commands, a control means for controlling equipment based on commands from the computing device, and means for optimizing energy usage based on the analysis results and performing efficient security management. This enables efficient optimization of energy consumption throughout the urban environment and rapid security response.

[0259] A "data acquisition sensor device" is a device that uses various sensor devices placed within a city to acquire environmental information and signs of abnormalities in real time.

[0260] A "computer that analyzes information and generates optimal management commands" is a device that includes a processor that generates commands for efficient energy management and security measures based on data collected from sensor devices.

[0261] A "control device for controlling equipment" is a device that controls various facilities within a city, such as streetlights and security cameras, according to commands transmitted from a computing device, in order to achieve optimal operation.

[0262] "Means for optimizing energy usage and implementing efficient security management" refer to methods and technologies for systematically operating facilities to reduce overall city energy consumption, mitigate security risks, and ensure the safety of citizens.

[0263] The system to realize this application consists of various sensor devices, servers, computing devices, control devices, and user terminals deployed throughout the city. The sensor devices acquire environmental and security information in real time and transmit it to the server. The sensor devices used include IoT sensors and security cameras.

[0264] The server receives data transmitted from the sensor devices and performs analysis using a computing device. This analysis utilizes machine learning algorithms based on Python, and analytical tools such as TensorFlow and Scikit-learn are used for optimizing energy consumption and detecting suspicious individuals. Based on the analysis results, the computing device generates optimal management commands and sends them to the control device.

[0265] The control unit optimizes the operation of various facilities within the city, such as streetlights and security cameras, according to the commands it receives. Analysis results and warning information are also sent to user terminals, allowing for remote intervention as needed. Smartphones and smart glasses are used as user terminals.

[0266] As a concrete example, if unusual activity is detected in a specific area at night, the server analyzes the information through a computing device, and if it is determined to be a suspicious person, it immediately sends a notification to the user's terminal. An example of a prompt message in this case could be input to the generative AI model as, "Please tell me about the design of a smart system that optimizes the energy of the entire city while detecting and notifying users of suspicious individuals in real time."

[0267] This system aims to improve the efficiency of urban energy management and enhance the safety of citizens.

[0268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0269] Step 1:

[0270] The server acquires environmental and motion information in real time from sensor devices placed throughout the city. This input data includes temperature, illuminance, and the presence or absence of motion. The server aggregates this data and prepares it for analysis.

[0271] Step 2:

[0272] The server sends the acquired data to the computing device and begins data analysis using machine learning algorithms. TensorFlow and Scikit-learn are used here to perform anomaly detection and optimize energy consumption. The computing device outputs the analysis results, including whether or not suspicious individuals were detected and an optimal energy usage plan.

[0273] Step 3:

[0274] The server generates control commands for each piece of equipment based on the output of the computing device. These commands include specific actions such as turning lights on and off or instructing cameras to operate. By sending these commands to the control unit, the actual equipment is controlled on-site.

[0275] Step 4:

[0276] The control device receives the commands sent from the server and controls the target facilities within the city. For example, it performs specific actions such as turning on the streetlights in the area where a moving object is detected and instructing the camera to zoom in.

[0277] Step 5:

[0278] The server obtains the results of the executed control commands from the sensor device again and receives new data as feedback. This information is utilized for the next data analysis and control command generation.

[0279] Step 6:

[0280] The user terminal is notified of the analysis results and important warning information. The user can receive this information in real time through a smartphone or smart glasses and can respond with remote operations if necessary.

[0281] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0282] In the present invention, by incorporating an emotion engine into the smart home system within the home, the aim is to recognize the user's emotional state and optimize the environment accordingly. The following shows the outline and specific examples of the program operation.

[0283] The system first obtains the user's facial expressions and voice tones in real time through "terminals" such as a face recognition camera and a voice sensor. The terminal transmits these emotion-related data to the "server".

[0284] The server analyzes the received data by the emotion engine and recognizes the user's emotional state. For example, when it is determined that the user is tired, the emotion engine processes that state and generates a control command to promote relaxation.

[0285] Based on this control instruction, the server issues instructions to various "operating devices" to adjust the operating modes of household appliances, lighting, and audio systems. Thereby, a home environment adapted to the user's emotional state can be created.

[0286] As a specific example, when the user returns home tired, the system senses this from the expression and the server instructs the relaxation mode. In this case, the terminal softens the lighting and plays the user's favorite relaxation music. Conversely, when the user has a lively expression, an energetic environment is provided by playing active music.

[0287] Furthermore, when it is determined that the user is feeling stressed, it is also possible for the server to propose corresponding content and programs based on the user's hobbies and preferences.

[0288] Thus, in the form of the present invention, by providing an environment suitable for the user's mental state through the incorporation of an emotion engine, it aims to enrich family life.

[0289] The following describes the processing flow.

[0290] Step 1:

[0291] The terminal uses a face recognition camera and a voice sensor to sense the user's expression and voice tone, and collects these data. The terminal transmits these emotion-related data to the server.

[0292] Step 2:

[0293] The server analyzes the transmitted data with the emotion engine and evaluates the user's emotional state. For example, when the voice tone is low and the expression is lacking, it is determined that the user is tired.

[0294] Step 3:

[0295] The server generates control commands based on the analysis of the user's emotional state. These commands include necessary changes to the settings of home appliances to optimize the user's emotions.

[0296] Step 4:

[0297] The server sends the generated control commands to the terminal, which then adjusts home appliances, lighting, and sound systems. For example, it might instruct the terminal to change the lighting to a softer color tone and play relaxing music.

[0298] Step 5:

[0299] Users can operate within this optimized environment. Furthermore, users can use their smart devices to change system settings as needed, customizing the environment to their preferences and needs.

[0300] Step 6:

[0301] The server uses the user's past preference data to further optimize the environment and suggest additional content and entertainment if the user desires it.

[0302] (Example 2)

[0303] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0304] In modern homes, the operation of various devices often remains fixed and does not take into account the user's deep emotional state. Therefore, environmental optimization to reduce user stress and fatigue is insufficient. Furthermore, the inability to respond flexibly to user preferences and emotions is a problem.

[0305] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0306] In this invention, the server includes a sensing means for acquiring facial expressions and voice data, a processing means for analyzing the emotion-related data acquired from the sensing means and generating an optimal environmental control command based on the emotional state, and an operating means for adjusting a plurality of household devices based on the control command from the processing means. Thereby, it is possible to provide an optimal home environment according to the user's emotional state and make family life more comfortable and rich.

[0307] The "sensing means" is a device or apparatus for acquiring the facial expressions and voice data of the user.

[0308] The "processing means" is a system or apparatus for analyzing the acquired emotion-related data and generating an optimal environmental control command based on the user's emotional state.

[0309] The "operating means" is a mechanism or apparatus for adjusting and controlling a plurality of household devices based on the generated control command.

[0310] The "environmental control command" is specific instruction content for changing and adjusting the operation mode of household devices according to the user's emotional state.

[0311] The "household devices" refer to electrical appliances, lighting, audio systems, and other devices used in the home environment.

[0312] This invention is a system that reflects the user's emotional state in the home environment and improves the comfort in the home by cooperating with the smart home system. Specifically, a face recognition camera and a voice sensor are used as the sensing means. These hardware devices acquire the user's facial expressions and voice tones in real time and transmit the data to the processing means.

[0313] The server uses software called an emotion engine to analyze this acquired data in detail. Based on machine learning algorithms, the emotion engine can classify the user's emotional state into categories such as joy, anger, sadness, and pleasure, and determine the degree of stress and fatigue. Based on the analysis results, the server generates environmental control commands to optimize the home environment. These commands are sent to control devices such as lighting, audio systems, and home appliances, automatically adjusting the operation of the equipment.

[0314] As a concrete example, consider a scenario where a user returns home tired from work. When the device detects the user's tired facial expression and calm tone of voice, the detected data is sent to the server, where the emotion engine determines it to be "fatigue." Based on this, the server instructs the control device to enter relaxation mode. The lighting is softened, and relaxing music plays from the speaker. Conversely, if the user appears energetic, the device is instructed to brighten the lighting and play upbeat music.

[0315] Furthermore, when a user is in a specific emotional state, the server can refer to the user's preferences and past history to suggest additional content and activities. This goes beyond mere emotion recognition, enabling the provision of a user-centered home environment.

[0316] Examples of prompts include, "Please provide specific steps for integrating an emotion engine into a smart home system and implementing environmental changes that respond to the user's emotions," and "Please generate effective usage scenarios for an emotion-recognition-based home appliance control system." Through these, the system can always operate in a way that is sensitive to the user's emotions.

[0317] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0318] Step 1:

[0319] The device acquires user input using the smart home system's sensing devices. Specifically, it captures the user's facial expressions with a facial recognition camera and records their voice tone with an audio sensor. This data is collected as basic information indicating the user's emotional state. The input here is real-time video and audio data, and the output is preparatory data for emotion analysis.

[0320] Step 2:

[0321] The device sends the acquired facial expression and audio data to the server. This transmission is encrypted to ensure that the data reaches the server securely. The input is emotion-related data collected by the device, and the output is data for analysis passed to the server. This step includes specific actions to prevent data loss or tampering.

[0322] Step 3:

[0323] The server analyzes input data using an emotion engine. It performs machine learning-based data processing to infer emotions from the user's facial expressions and tone of voice. This process allows the server to quantify emotional states such as "fatigue" or "joy." The input is data sent from the terminal, and the output is the result of evaluating the user's emotional state. This operation includes comparison with an existing emotion database.

[0324] Step 4:

[0325] The server generates environmental control commands based on the analysis results. For example, if it determines that the user needs to "relax," it will create commands to soften the lighting and play calming music. The input is the analysis results from the emotion engine, and the output is expressed as specific commands. This step utilizes an algorithm for setting commands that are appropriate to the user's current situation.

[0326] Step 5:

[0327] The server sends the generated control commands to the operating device. The operating device adjusts household appliances and lighting according to the commands and performs the specified actions. The input is the control commands from the server, and the output is the physical environmental changes within the home. These specific actions include starting up devices and changing modes.

[0328] (Application Example 2)

[0329] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0330] In modern society, individuals often experience stress and frustration in their daily lives, resulting in a decline in their quality of life. Especially in urban areas, the environment in public facilities is often designed without considering individual emotional states, leading to discomfort for users. This, in turn, reduces user satisfaction and hinders efficient living.

[0331] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0332] In this invention, the server includes a recognition means for detecting the emotional state of a user, an analysis means for analyzing emotional data obtained from the recognition means to generate appropriate environmental control commands, and an adjustment means for controlling equipment based on the environmental control commands generated by the analysis means. This enables the environment in individual and public spaces to be automatically adjusted according to the emotional state of the user, improving the quality of life for the user and making comfortable living in urban areas possible.

[0333] "User" refers to an individual who uses the system or facility, and whose emotional state is detected and analyzed by the system.

[0334] "Emotional state" refers to an individual's emotional state, specifically their mood or mental tendencies at a particular time.

[0335] "Recognition means" refers to a technical device or algorithm used to detect the emotional state of a user.

[0336] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, tone of voice, etc., and is the subject of analysis by the system.

[0337] An "environmental control command" refers to a specific operational command used to adjust the operation of equipment based on analyzed emotional data.

[0338] "Analysis means" refers to a technical method or program that processes emotional data obtained from recognition means and generates environmental control commands.

[0339] "Adjustment means" refers to a technical system that operates physical or digital equipment based on generated environmental control commands to change environmental settings.

[0340] "Public facilities" refer to buildings or places used by the general public in urban areas, and their environment is subject to regulation.

[0341] This invention is a system that detects the emotional state of users in real time and optimizes the environment of homes and public spaces based on that state. The system mainly consists of recognition means, analysis means, and adjustment means.

[0342] The server uses "recognition means" to detect the user's emotional state through a camera that recognizes the user's face and sensors that pick up sound. This recognition means may be implemented in the customer's smart device, such as a smartphone or smart glasses. The data obtained by the recognition means is recorded as "emotional data."

[0343] The server processes the acquired emotional data using "analysis tools" and generates purposeful "environmental control commands" utilizing an AI model. This analysis process utilizes libraries such as OpenCV and Librosa to analyze facial expression patterns and voice tone.

[0344] The server then uses "adjustment means" to operate physical or digital devices based on the generated environmental control commands. This adjustment includes lighting, temperature control equipment, and sound systems present in homes and public buildings.

[0345] For example, when a server analyzes that a user is tired, it can send a command to soften the lighting in the living space and play relaxing music. Furthermore, in a co-working space within a smart city, the entire facility's environment can be dynamically changed based on user emotional data.

[0346] An example of a prompt to be input to the generating AI model is, "Please suggest optimal adjustments to the home and public space environment when the user is analyzed to be fatigued within the smart city." This prompt is expected to enable the AI ​​to generate appropriate instructions, enriching the user experience.

[0347] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0348] Step 1:

[0349] The device activates its camera to recognize the user's face and acquires facial image data. Simultaneously, it uses the microphone to record the user's voice and collects audio data. The input consists of facial image and audio data, which is then sent to the next processing step.

[0350] Step 2:

[0351] The server utilizes an AI model to analyze acquired facial images and audio data. Specifically, it estimates emotions based on facial expressions from the facial images and analyzes voice tone and speed from the audio data. This data processing clarifies the user's emotional state. The output is the analyzed emotional state data.

[0352] Step 3:

[0353] The server generates environmental control commands using the analyzed emotional state data. For example, if it determines that the user is tired, it might generate commands to soften the lighting and play calming music. The input is emotional state data, and the output is environmental control commands.

[0354] Step 4:

[0355] The server transmits generated environmental control commands to the adjustment unit, operating household appliances and public facility equipment. Specifically, it may change the lighting in a smart home to a predetermined brightness and instruct the sound system to play a specified piece of music. The input is the environmental control command, and the output is the physical or digital change in the environment caused by the adjustment unit.

[0356] Step 5:

[0357] Users can feel comfortable in a well-adjusted environment. This comfortable environment is the result of the server dynamically adapting based on the user's emotional state, and the environment is readjusted as needed by continuously monitoring the user's condition.

[0358] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0359] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0360] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0361] [Third Embodiment]

[0362] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0363] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0364] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0365] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0366] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0367] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0368] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0369] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0370] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0371] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0372] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0373] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0374] This invention provides a smart home system that works in conjunction with various household appliances to ensure efficient operation and safety. The program outline and specific examples are shown below.

[0375] First, the system involves installing various sensors and home appliances, which act as "terminals," in living spaces. Each terminal functions as a detection device that acquires data and transmits usage status and environmental information to a "server."

[0376] The server acts as a "processing device" that processes the received data, using machine learning algorithms to set optimal operating schedules for home appliances and criteria for security warnings. Based on these processing results, the server sends control commands to terminals, which act as "operating devices," to turn appliances on and off or adjust their power consumption.

[0377] Users can access the system through smart devices. They can not only check the current status of home appliances via the application, but also manually change settings. Furthermore, users can directly operate the system when real-time security warnings or emergency calls are needed.

[0378] For example, if a security camera detects motion while the user is away, the server analyzes the data and immediately sends a warning to the user's smart device if there is a high probability of an intruder. Furthermore, if the emergency button is pressed, the server automatically notifies pre-configured contacts.

[0379] This system aims to provide users with a safe and comfortable living environment by improving the efficiency and security of equipment management.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The terminal acquires data from various sensors, including temperature, humidity, and power consumption. The terminal then transmits this data to the server.

[0383] Step 2:

[0384] The server begins analysis based on the received data. Using machine learning algorithms, it generates optimal appliance usage patterns and schedules to reduce energy consumption.

[0385] Step 3:

[0386] The server sends the generated schedule to the terminal. The terminal automatically turns appliances on and off and switches their operating modes according to this schedule.

[0387] Step 4:

[0388] If the camera installed on the device detects suspicious activity, it immediately sends the video data to the server.

[0389] Step 5:

[0390] The server uses a facial recognition system to compare video data with a registered facial database, and if it determines the person is suspicious, it sends a warning to the user.

[0391] Step 6:

[0392] Users can receive alerts on their smart devices and remotely view live video from the scene. They can also manually control home appliances and make emergency calls as needed.

[0393] Step 7:

[0394] When the emergency button is pressed, the terminal sends a signal to the server, which simultaneously notifies multiple registered recipients. The user can then check the status and provide additional information if necessary.

[0395] (Example 1)

[0396] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0397] Until now, existing technologies have not adequately provided a system that can achieve both efficient operation and safety for various household appliances. Furthermore, with the need for intruder detection and rapid response in emergencies, there is a need for a method that allows users to easily manage and operate the system remotely.

[0398] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0399] In this invention, the server includes a plurality of sensor devices that function as detection means, processing means for aggregating and analyzing environmental information and usage status obtained from the sensor devices, and optimization means for formulating an optimal operation schedule by applying a machine learning algorithm. This makes it possible to achieve efficient control of household appliances and security measures.

[0400] "Detection means" refers to multiple sensor devices installed to acquire environmental information and usage status in real time.

[0401] "Processing means" refers to a function within a server that collects and analyzes data obtained from sensor devices.

[0402] "Optimization methods" refer to techniques that use machine learning algorithms to analyze data and optimize the operating schedule of home appliances.

[0403] "Operation means" refers to the function of a server that issues control commands for home appliances based on the results of the optimization means.

[0404] "Interface means" refers to the means by which users can access the system through a dedicated application to check the status of home appliances and change settings.

[0405] "Communication means" refers to a function that allows simultaneous contact with pre-set recipients in the event of an emergency.

[0406] This invention is a system for efficiently operating various household appliances and ensuring safety. An embodiment of this system is shown below.

[0407] The server plays a central role in the system, receiving and processing data sent from terminals. Specifically, the server uses machine learning algorithms to formulate optimal operating schedules for home appliances. Therefore, high-performance computer hardware with data aggregation and analysis capabilities, as well as machine learning libraries, are required.

[0408] The terminal is installed in the room as a machine with sensors and control functions. Various sensors detect environmental information such as temperature, illuminance, and human movement, and transmit it to the server. For example, a temperature sensor constantly monitors the room temperature and instructs the server to turn the heating on or off if it exceeds a specified range.

[0409] Users can access the system using their smart devices to monitor and control the status of various home appliances. Remote operation and scheduling are possible via a dedicated application. Furthermore, if the terminal detects a security anomaly, the server sends an alert to the user's smart device. For example, if suspicious activity is detected, the user can view the footage through the application and issue security commands as needed.

[0410] As a concrete example, consider a scenario where a sensor detects an anomaly while the user is away. In this case, the sensor detects movement and sends that information to the server. The server analyzes the data, and if it determines that there is a high probability of an intruder, it immediately sends a notification to the user. This notification is delivered to the user via a smartphone app, allowing the user to check the safety of their home no matter where they are.

[0411] Examples of prompts include, "Please explain how the smart home system enhances security when I'm away," and "Please tell me how to optimize energy consumption in my home."

[0412] This type of system is expected to significantly improve the safety and comfort of the home.

[0413] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0414] Step 1:

[0415] The terminal uses sensor devices to detect environmental information and acquire data such as indoor temperature, illuminance, and human movement. It receives real-time data from these sensors as input. If a sensor measures the temperature and detects a value below 20 degrees Celsius, it sends that information to the server. The environmental information is then transferred to the server as output.

[0416] Step 2:

[0417] The server receives data transmitted from the terminal, aggregates it, and analyzes it. It receives data from the terminal regarding temperature and operating status as input. The server then uses machine learning algorithms to compare the current situation with past data to determine if it deviates from normal operating patterns. As output, it generates appropriate operational commands.

[0418] Step 3:

[0419] The server sends commands to the terminal to control the equipment based on the analysis results. The input is a control command based on the analysis results. For example, if the room temperature is low, the server instructs the terminal to turn on the heating. The output is a command that specifies how the appliance should operate.

[0420] Step 4:

[0421] The terminal receives commands from the server and actually controls the equipment. As input, it receives control commands from the server. For example, a terminal that receives a command to turn on the heater will actually activate the heater. As output, the appliance starts operating according to the command.

[0422] Step 5:

[0423] The user receives notifications from the application using a smart device. As input, the user receives status and warning information about home appliances distributed from the server. Based on this, they check if the appliances are functioning correctly and perform manual settings as needed. As output, the user takes action to check the situation in their home and take action accordingly.

[0424] (Application Example 1)

[0425] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0426] In modern urban environments, the depletion of energy resources and the rise in crime are serious problems. However, conventional technologies make it difficult to efficiently manage energy resources across a wide area and implement immediate crime prevention measures. Therefore, there is a need to build an efficient system to ensure the safety of citizens and a comfortable living environment.

[0427] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0428] In this invention, the server includes means including a sensor device for acquiring data, a computing means for analyzing information obtained from the sensor device and generating optimal management commands, a control means for controlling equipment based on commands from the computing device, and means for optimizing energy usage based on the analysis results and performing efficient security management. This enables efficient optimization of energy consumption throughout the urban environment and rapid security response.

[0429] A "data acquisition sensor device" is a device that uses various sensor devices placed within a city to acquire environmental information and signs of abnormalities in real time.

[0430] A "computer that analyzes information and generates optimal management commands" is a device that includes a processor that generates commands for efficient energy management and security measures based on data collected from sensor devices.

[0431] A "control device for controlling equipment" is a device that controls various facilities within a city, such as streetlights and security cameras, according to commands transmitted from a computing device, in order to achieve optimal operation.

[0432] "Means for optimizing energy usage and implementing efficient security management" refer to methods and technologies for systematically operating facilities to reduce overall city energy consumption, mitigate security risks, and ensure the safety of citizens.

[0433] The system to realize this application consists of various sensor devices, servers, computing devices, control devices, and user terminals deployed throughout the city. The sensor devices acquire environmental and security information in real time and transmit it to the server. The sensor devices used include IoT sensors and security cameras.

[0434] The server receives data transmitted from the sensor devices and performs analysis using a computing device. This analysis utilizes machine learning algorithms based on Python, and analytical tools such as TensorFlow and Scikit-learn are used for optimizing energy consumption and detecting suspicious individuals. Based on the analysis results, the computing device generates optimal management commands and sends them to the control device.

[0435] The control unit optimizes the operation of various facilities within the city, such as streetlights and security cameras, according to the commands it receives. Analysis results and warning information are also sent to user terminals, allowing for remote intervention as needed. Smartphones and smart glasses are used as user terminals.

[0436] As a concrete example, if unusual activity is detected in a specific area at night, the server analyzes the information through a computing device, and if it is determined to be a suspicious person, it immediately sends a notification to the user's terminal. An example of a prompt message in this case could be input to the generative AI model as, "Please tell me about the design of a smart system that optimizes the energy of the entire city while detecting and notifying users of suspicious individuals in real time."

[0437] This system aims to improve the efficiency of urban energy management and enhance the safety of citizens.

[0438] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0439] Step 1:

[0440] The server acquires environmental and motion information in real time from sensor devices placed throughout the city. This input data includes temperature, illuminance, and the presence or absence of motion. The server aggregates this data and prepares it for analysis.

[0441] Step 2:

[0442] The server sends the acquired data to the computing device and begins data analysis using machine learning algorithms. TensorFlow and Scikit-learn are used here to perform anomaly detection and optimize energy consumption. The computing device outputs the analysis results, including whether or not suspicious individuals were detected and an optimal energy usage plan.

[0443] Step 3:

[0444] The server generates control commands for each piece of equipment based on the output of the computing device. These commands include specific actions such as turning lights on and off or instructing cameras to operate. By sending these commands to the control unit, the actual equipment is controlled on-site.

[0445] Step 4:

[0446] The control unit receives commands from the server and controls target equipment within the city. For example, it may perform specific actions such as turning on streetlights in areas where motion is detected or instructing cameras to zoom.

[0447] Step 5:

[0448] The server retrieves the results of the control command execution from the sensor device again and receives new data as feedback. This information is used for subsequent data analysis and control command generation.

[0449] Step 6:

[0450] The user's terminal receives notifications of analysis results and important warning information. Users can receive this information in real time via their smartphones or smart glasses and take remote action as needed.

[0451] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0452] This invention aims to recognize the user's emotional state and optimize the environment accordingly by incorporating an emotion engine into a smart home system within the home. The program's operation overview and specific examples are shown below.

[0453] The system first acquires the user's facial expressions and voice tone in real time through "terminals" such as facial recognition cameras and voice sensors. These terminals then transmit this emotion-related data to a "server."

[0454] The server analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it determines that the user is fatigued, the emotion engine processes that state and generates control commands to promote relaxation.

[0455] Based on these control commands, the server issues instructions to various "operating devices," adjusting the operating modes of home appliances, lighting, and sound systems. This allows for the creation of a home environment that adapts to the user's emotional state.

[0456] For example, when a user returns home tired, the system detects this from their facial expression, and the server instructs them to enter relaxation mode. In this case, the terminal softens the lighting and plays their preferred relaxing music. Conversely, if the user has an energetic expression, the system provides an uplifting environment by playing energetic music.

[0457] Furthermore, if the server detects that a user is experiencing stress, it can suggest appropriate content and programs based on the user's hobbies and preferences.

[0458] Thus, in this embodiment of the present invention, the aim is to enrich home life by providing an environment that matches the user's mental state through the incorporation of an emotion engine.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The device uses a facial recognition camera and voice sensor to detect the user's facial expressions and tone of voice, and collects this data. The device then sends this emotion-related data to a server.

[0462] Step 2:

[0463] The server analyzes the transmitted data using an emotion engine to evaluate the user's emotional state. For example, if the voice tone is low and the facial expression is subdued, the server may determine that the user is tired.

[0464] Step 3:

[0465] The server generates control commands based on the analysis of the user's emotional state. These commands include necessary changes to the settings of home appliances to optimize the user's emotions.

[0466] Step 4:

[0467] The server sends the generated control commands to the terminal, which then adjusts home appliances, lighting, and sound systems. For example, it might instruct the terminal to change the lighting to a softer color tone and play relaxing music.

[0468] Step 5:

[0469] Users can operate within this optimized environment. Furthermore, users can use their smart devices to change system settings as needed, customizing the environment to their preferences and needs.

[0470] Step 6:

[0471] The server uses the user's past preference data to further optimize the environment and suggest additional content and entertainment if the user desires it.

[0472] (Example 2)

[0473] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In modern homes, the operation of various devices often remains fixed and does not take into account the user's deep emotional state. Therefore, environmental optimization to reduce user stress and fatigue is insufficient. Furthermore, the inability to respond flexibly to user preferences and emotions is a problem.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0476] In this invention, the server includes sensing means for acquiring facial expression and voice data, processing means for analyzing emotion-related data acquired from the sensing means and generating optimal environmental control commands based on the emotional state, and operating means for adjusting a plurality of household appliances based on the control commands from the processing means. This makes it possible to provide an optimal home environment according to the user's emotional state and to make home life more comfortable and fulfilling.

[0477] "Sensing means" refers to a device or apparatus for acquiring the user's facial expressions and voice data.

[0478] "Processing means" refers to a system or device for analyzing acquired emotion-related data and generating optimal environmental control commands based on the user's emotional state.

[0479] "Operating means" refers to a mechanism or device for adjusting and controlling multiple household appliances based on generated control commands.

[0480] An "environmental control command" is a set of specific instructions for changing or adjusting the operating mode of household appliances according to the user's emotional state.

[0481] "Household appliances" refer to electrical appliances, lighting, audio systems, and other devices used in a home environment.

[0482] This invention is a system that reflects the user's emotional state in the home environment and improves comfort within the home by working in conjunction with a smart home system. Specifically, it uses a facial recognition camera and a voice sensor as sensing means. These hardware devices acquire the user's facial expressions and voice tone in real time and transmit the data to a processing unit.

[0483] The server uses software called an emotion engine to analyze this acquired data in detail. Based on machine learning algorithms, the emotion engine can classify the user's emotional state into categories such as joy, anger, sadness, and pleasure, and determine the degree of stress and fatigue. Based on the analysis results, the server generates environmental control commands to optimize the home environment. These commands are sent to control devices such as lighting, audio systems, and home appliances, automatically adjusting the operation of the equipment.

[0484] As a concrete example, consider a scenario where a user returns home tired from work. When the device detects the user's tired facial expression and calm tone of voice, the detected data is sent to the server, where the emotion engine determines it to be "fatigue." Based on this, the server instructs the control device to enter relaxation mode. The lighting is softened, and relaxing music plays from the speaker. Conversely, if the user appears energetic, the device is instructed to brighten the lighting and play upbeat music.

[0485] Furthermore, when a user is in a specific emotional state, the server can refer to the user's preferences and past history to suggest additional content and activities. This goes beyond mere emotion recognition, enabling the provision of a user-centered home environment.

[0486] Examples of prompts include, "Please provide specific steps for integrating an emotion engine into a smart home system and implementing environmental changes that respond to the user's emotions," and "Please generate effective usage scenarios for an emotion-recognition-based home appliance control system." Through these, the system can always operate in a way that is sensitive to the user's emotions.

[0487] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0488] Step 1:

[0489] The device acquires user input using the smart home system's sensing devices. Specifically, it captures the user's facial expressions with a facial recognition camera and records their voice tone with an audio sensor. This data is collected as basic information indicating the user's emotional state. The input here is real-time video and audio data, and the output is preparatory data for emotion analysis.

[0490] Step 2:

[0491] The device sends the acquired facial expression and audio data to the server. This transmission is encrypted to ensure that the data reaches the server securely. The input is emotion-related data collected by the device, and the output is data for analysis passed to the server. This step includes specific actions to prevent data loss or tampering.

[0492] Step 3:

[0493] The server analyzes input data using an emotion engine. It performs machine learning-based data processing to infer emotions from the user's facial expressions and tone of voice. This process allows the server to quantify emotional states such as "fatigue" or "joy." The input is data sent from the terminal, and the output is the result of evaluating the user's emotional state. This operation includes comparison with an existing emotion database.

[0494] Step 4:

[0495] The server generates environmental control commands based on the analysis results. For example, if it determines that the user needs to "relax," it will create commands to soften the lighting and play calming music. The input is the analysis results from the emotion engine, and the output is expressed as specific commands. This step utilizes an algorithm for setting commands that are appropriate to the user's current situation.

[0496] Step 5:

[0497] The server sends the generated control commands to the operating device. The operating device adjusts household appliances and lighting according to the commands and performs the specified actions. The input is the control commands from the server, and the output is the physical environmental changes within the home. These specific actions include starting up devices and changing modes.

[0498] (Application Example 2)

[0499] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] In modern society, individuals often experience stress and frustration in their daily lives, resulting in a decline in their quality of life. Especially in urban areas, the environment in public facilities is often designed without considering individual emotional states, leading to discomfort for users. This, in turn, reduces user satisfaction and hinders efficient living.

[0501] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0502] In this invention, the server includes a recognition means for detecting the emotional state of a user, an analysis means for analyzing emotional data obtained from the recognition means to generate appropriate environmental control commands, and an adjustment means for controlling equipment based on the environmental control commands generated by the analysis means. This enables the environment in individual and public spaces to be automatically adjusted according to the emotional state of the user, improving the quality of life for the user and making comfortable living in urban areas possible.

[0503] "User" refers to an individual who uses the system or facility, and whose emotional state is detected and analyzed by the system.

[0504] "Emotional state" refers to an individual's emotional state, specifically their mood or mental tendencies at a particular time.

[0505] "Recognition means" refers to a technical device or algorithm used to detect the emotional state of a user.

[0506] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, tone of voice, etc., and is the subject of analysis by the system.

[0507] An "environmental control command" refers to a specific operational command used to adjust the operation of equipment based on analyzed emotional data.

[0508] "Analysis means" refers to a technical method or program that processes emotional data obtained from recognition means and generates environmental control commands.

[0509] "Adjustment means" refers to a technical system that operates physical or digital equipment based on generated environmental control commands to change environmental settings.

[0510] "Public facilities" refer to buildings or places used by the general public in urban areas, and their environment is subject to regulation.

[0511] This invention is a system that detects the emotional state of users in real time and optimizes the environment of homes and public spaces based on that state. The system mainly consists of recognition means, analysis means, and adjustment means.

[0512] The server uses "recognition means" to detect the user's emotional state through a camera that recognizes the user's face and sensors that pick up sound. This recognition means may be implemented in the customer's smart device, such as a smartphone or smart glasses. The data obtained by the recognition means is recorded as "emotional data."

[0513] The server processes the acquired emotional data using "analysis tools" and generates purposeful "environmental control commands" utilizing an AI model. This analysis process utilizes libraries such as OpenCV and Librosa to analyze facial expression patterns and voice tone.

[0514] The server then uses "adjustment means" to operate physical or digital devices based on the generated environmental control commands. This adjustment includes lighting, temperature control equipment, and sound systems present in homes and public buildings.

[0515] For example, when a server analyzes that a user is tired, it can send a command to soften the lighting in the living space and play relaxing music. Furthermore, in a co-working space within a smart city, the entire facility's environment can be dynamically changed based on user emotional data.

[0516] An example of a prompt to be input to the generating AI model is, "Please suggest optimal adjustments to the home and public space environment when the user is analyzed to be fatigued within the smart city." This prompt is expected to enable the AI ​​to generate appropriate instructions, enriching the user experience.

[0517] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0518] Step 1:

[0519] The device activates its camera to recognize the user's face and acquires facial image data. Simultaneously, it uses the microphone to record the user's voice and collects audio data. The input consists of facial image and audio data, which is then sent to the next processing step.

[0520] Step 2:

[0521] The server utilizes an AI model to analyze acquired facial images and audio data. Specifically, it estimates emotions based on facial expressions from the facial images and analyzes voice tone and speed from the audio data. This data processing clarifies the user's emotional state. The output is the analyzed emotional state data.

[0522] Step 3:

[0523] The server generates environmental control commands using the analyzed emotional state data. For example, if it determines that the user is tired, it might generate commands to soften the lighting and play calming music. The input is emotional state data, and the output is environmental control commands.

[0524] Step 4:

[0525] The server transmits generated environmental control commands to the adjustment unit, operating household appliances and public facility equipment. Specifically, it may change the lighting in a smart home to a predetermined brightness and instruct the sound system to play a specified piece of music. The input is the environmental control command, and the output is the physical or digital change in the environment caused by the adjustment unit.

[0526] Step 5:

[0527] Users can feel comfortable in a well-adjusted environment. This comfortable environment is the result of the server dynamically adapting based on the user's emotional state, and the environment is readjusted as needed by continuously monitoring the user's condition.

[0528] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0529] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0530] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0531] [Fourth Embodiment]

[0532] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0533] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0534] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0535] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0536] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0537] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0538] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0539] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0540] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0541] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0542] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0543] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0544] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0545] This invention provides a smart home system that works in conjunction with various household appliances to ensure efficient operation and safety. The program outline and specific examples are shown below.

[0546] First, the system involves installing various sensors and home appliances, which act as "terminals," in living spaces. Each terminal functions as a detection device that acquires data and transmits usage status and environmental information to a "server."

[0547] The server acts as a "processing device" that processes the received data, using machine learning algorithms to set optimal operating schedules for home appliances and criteria for security warnings. Based on these processing results, the server sends control commands to terminals, which act as "operating devices," to turn appliances on and off or adjust their power consumption.

[0548] Users can access the system through smart devices. They can not only check the current status of home appliances via the application, but also manually change settings. Furthermore, users can directly operate the system when real-time security warnings or emergency calls are needed.

[0549] For example, if a security camera detects motion while the user is away, the server analyzes the data and immediately sends a warning to the user's smart device if there is a high probability of an intruder. Furthermore, if the emergency button is pressed, the server automatically notifies pre-configured contacts.

[0550] This system aims to provide users with a safe and comfortable living environment by improving the efficiency and security of equipment management.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The terminal acquires data from various sensors, including temperature, humidity, and power consumption. The terminal then transmits this data to the server.

[0554] Step 2:

[0555] The server begins analysis based on the received data. Using machine learning algorithms, it generates optimal appliance usage patterns and schedules to reduce energy consumption.

[0556] Step 3:

[0557] The server sends the generated schedule to the terminal. The terminal automatically turns appliances on and off and switches their operating modes according to this schedule.

[0558] Step 4:

[0559] If the camera installed on the device detects suspicious activity, it immediately sends the video data to the server.

[0560] Step 5:

[0561] The server uses a facial recognition system to compare video data with a registered facial database, and if it determines the person is suspicious, it sends a warning to the user.

[0562] Step 6:

[0563] Users can receive alerts on their smart devices and remotely view live video from the scene. They can also manually control home appliances and make emergency calls as needed.

[0564] Step 7:

[0565] When the emergency button is pressed, the terminal sends a signal to the server, which simultaneously notifies multiple registered recipients. The user can then check the status and provide additional information if necessary.

[0566] (Example 1)

[0567] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0568] Until now, existing technologies have not adequately provided a system that can achieve both efficient operation and safety for various household appliances. Furthermore, with the need for intruder detection and rapid response in emergencies, there is a need for a method that allows users to easily manage and operate the system remotely.

[0569] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0570] In this invention, the server includes a plurality of sensor devices that function as detection means, processing means for aggregating and analyzing environmental information and usage status obtained from the sensor devices, and optimization means for formulating an optimal operation schedule by applying a machine learning algorithm. This makes it possible to achieve efficient control of household appliances and security measures.

[0571] "Detection means" refers to multiple sensor devices installed to acquire environmental information and usage status in real time.

[0572] "Processing means" refers to a function within a server that collects and analyzes data obtained from sensor devices.

[0573] "Optimization methods" refer to techniques that use machine learning algorithms to analyze data and optimize the operating schedule of home appliances.

[0574] "Operation means" refers to the function of a server that issues control commands for home appliances based on the results of the optimization means.

[0575] "Interface means" refers to the means by which users can access the system through a dedicated application to check the status of home appliances and change settings.

[0576] "Communication means" refers to a function that allows simultaneous contact with pre-set recipients in the event of an emergency.

[0577] This invention is a system for efficiently operating various household appliances and ensuring safety. An embodiment of this system is shown below.

[0578] The server plays a central role in the system, receiving and processing data sent from terminals. Specifically, the server uses machine learning algorithms to formulate optimal operating schedules for home appliances. Therefore, high-performance computer hardware with data aggregation and analysis capabilities, as well as machine learning libraries, are required.

[0579] The terminal is installed in the room as a machine with sensors and control functions. Various sensors detect environmental information such as temperature, illuminance, and human movement, and transmit it to the server. For example, a temperature sensor constantly monitors the room temperature and instructs the server to turn the heating on or off if it exceeds a specified range.

[0580] Users can access the system using their smart devices to monitor and control the status of various home appliances. Remote operation and scheduling are possible via a dedicated application. Furthermore, if the terminal detects a security anomaly, the server sends an alert to the user's smart device. For example, if suspicious activity is detected, the user can view the footage through the application and issue security commands as needed.

[0581] As a concrete example, consider a scenario where a sensor detects an anomaly while the user is away. In this case, the sensor detects movement and sends that information to the server. The server analyzes the data, and if it determines that there is a high probability of an intruder, it immediately sends a notification to the user. This notification is delivered to the user via a smartphone app, allowing the user to check the safety of their home no matter where they are.

[0582] Examples of prompts include, "Please explain how the smart home system enhances security when I'm away," and "Please tell me how to optimize energy consumption in my home."

[0583] This type of system is expected to significantly improve the safety and comfort of the home.

[0584] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0585] Step 1:

[0586] The terminal uses sensor devices to detect environmental information and acquire data such as indoor temperature, illuminance, and human movement. It receives real-time data from these sensors as input. If a sensor measures the temperature and detects a value below 20 degrees Celsius, it sends that information to the server. The environmental information is then transferred to the server as output.

[0587] Step 2:

[0588] The server receives data transmitted from the terminal, aggregates it, and analyzes it. It receives data from the terminal regarding temperature and operating status as input. The server then uses machine learning algorithms to compare the current situation with past data to determine if it deviates from normal operating patterns. As output, it generates appropriate operational commands.

[0589] Step 3:

[0590] The server sends commands to the terminal to control the equipment based on the analysis results. The input is a control command based on the analysis results. For example, if the room temperature is low, the server instructs the terminal to turn on the heating. The output is a command that specifies how the appliance should operate.

[0591] Step 4:

[0592] The terminal receives commands from the server and actually controls the equipment. As input, it receives control commands from the server. For example, a terminal that receives a command to turn on the heater will actually activate the heater. As output, the appliance starts operating according to the command.

[0593] Step 5:

[0594] The user receives notifications from the application using a smart device. As input, the user receives status and warning information about home appliances distributed from the server. Based on this, they check if the appliances are functioning correctly and perform manual settings as needed. As output, the user takes action to check the situation in their home and take action accordingly.

[0595] (Application Example 1)

[0596] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] In modern urban environments, the depletion of energy resources and the rise in crime are serious problems. However, conventional technologies make it difficult to efficiently manage energy resources across a wide area and implement immediate crime prevention measures. Therefore, there is a need to build an efficient system to ensure the safety of citizens and a comfortable living environment.

[0598] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0599] In this invention, the server includes means including a sensor device for acquiring data, a computing means for analyzing information obtained from the sensor device and generating optimal management commands, a control means for controlling equipment based on commands from the computing device, and means for optimizing energy usage based on the analysis results and performing efficient security management. This enables efficient optimization of energy consumption throughout the urban environment and rapid security response.

[0600] A "data acquisition sensor device" is a device that uses various sensor devices placed within a city to acquire environmental information and signs of abnormalities in real time.

[0601] A "computer that analyzes information and generates optimal management commands" is a device that includes a processor that generates commands for efficient energy management and security measures based on data collected from sensor devices.

[0602] A "control device for controlling equipment" is a device that controls various facilities within a city, such as streetlights and security cameras, according to commands transmitted from a computing device, in order to achieve optimal operation.

[0603] "Means for optimizing energy usage and implementing efficient security management" refer to methods and technologies for systematically operating facilities to reduce overall city energy consumption, mitigate security risks, and ensure the safety of citizens.

[0604] The system to realize this application consists of various sensor devices, servers, computing devices, control devices, and user terminals deployed throughout the city. The sensor devices acquire environmental and security information in real time and transmit it to the server. The sensor devices used include IoT sensors and security cameras.

[0605] The server receives data transmitted from the sensor devices and performs analysis using a computing device. This analysis utilizes machine learning algorithms based on Python, and analytical tools such as TensorFlow and Scikit-learn are used for optimizing energy consumption and detecting suspicious individuals. Based on the analysis results, the computing device generates optimal management commands and sends them to the control device.

[0606] The control unit optimizes the operation of various facilities within the city, such as streetlights and security cameras, according to the commands it receives. Analysis results and warning information are also sent to user terminals, allowing for remote intervention as needed. Smartphones and smart glasses are used as user terminals.

[0607] As a concrete example, if unusual activity is detected in a specific area at night, the server analyzes the information through a computing device, and if it is determined to be a suspicious person, it immediately sends a notification to the user's terminal. An example of a prompt message in this case could be input to the generative AI model as, "Please tell me about the design of a smart system that optimizes the energy of the entire city while detecting and notifying users of suspicious individuals in real time."

[0608] This system aims to improve the efficiency of urban energy management and enhance the safety of citizens.

[0609] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0610] Step 1:

[0611] The server acquires environmental and motion information in real time from sensor devices placed throughout the city. This input data includes temperature, illuminance, and the presence or absence of motion. The server aggregates this data and prepares it for analysis.

[0612] Step 2:

[0613] The server sends the acquired data to the computing device and begins data analysis using machine learning algorithms. TensorFlow and Scikit-learn are used here to perform anomaly detection and optimize energy consumption. The computing device outputs the analysis results, including whether or not suspicious individuals were detected and an optimal energy usage plan.

[0614] Step 3:

[0615] The server generates control commands for each piece of equipment based on the output of the computing device. These commands include specific actions such as turning lights on and off or instructing cameras to operate. By sending these commands to the control unit, the actual equipment is controlled on-site.

[0616] Step 4:

[0617] The control unit receives commands from the server and controls target equipment within the city. For example, it may perform specific actions such as turning on streetlights in areas where motion is detected or instructing cameras to zoom.

[0618] Step 5:

[0619] The server retrieves the results of the control command execution from the sensor device again and receives new data as feedback. This information is used for subsequent data analysis and control command generation.

[0620] Step 6:

[0621] The user's terminal receives notifications of analysis results and important warning information. Users can receive this information in real time via their smartphones or smart glasses and take remote action as needed.

[0622] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0623] This invention aims to recognize the user's emotional state and optimize the environment accordingly by incorporating an emotion engine into a smart home system within the home. The program's operation overview and specific examples are shown below.

[0624] The system first acquires the user's facial expressions and voice tone in real time through "terminals" such as facial recognition cameras and voice sensors. These terminals then transmit this emotion-related data to a "server."

[0625] The server analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it determines that the user is fatigued, the emotion engine processes that state and generates control commands to promote relaxation.

[0626] Based on these control commands, the server issues instructions to various "operating devices," adjusting the operating modes of home appliances, lighting, and sound systems. This allows for the creation of a home environment that adapts to the user's emotional state.

[0627] For example, when a user returns home tired, the system detects this from their facial expression, and the server instructs them to enter relaxation mode. In this case, the terminal softens the lighting and plays their preferred relaxing music. Conversely, if the user has an energetic expression, the system provides an uplifting environment by playing energetic music.

[0628] Furthermore, if the server detects that a user is experiencing stress, it can suggest appropriate content and programs based on the user's hobbies and preferences.

[0629] Thus, in this embodiment of the present invention, the aim is to enrich home life by providing an environment that matches the user's mental state through the incorporation of an emotion engine.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The device uses a facial recognition camera and voice sensor to detect the user's facial expressions and tone of voice, and collects this data. The device then sends this emotion-related data to a server.

[0633] Step 2:

[0634] The server analyzes the transmitted data using an emotion engine to evaluate the user's emotional state. For example, if the voice tone is low and the facial expression is subdued, the server may determine that the user is tired.

[0635] Step 3:

[0636] The server generates control commands based on the analysis of the user's emotional state. These commands include necessary changes to the settings of home appliances to optimize the user's emotions.

[0637] Step 4:

[0638] The server sends the generated control commands to the terminal, which then adjusts home appliances, lighting, and sound systems. For example, it might instruct the terminal to change the lighting to a softer color tone and play relaxing music.

[0639] Step 5:

[0640] Users can operate within this optimized environment. Furthermore, users can use their smart devices to change system settings as needed, customizing the environment to their preferences and needs.

[0641] Step 6:

[0642] The server uses the user's past preference data to further optimize the environment and suggest additional content and entertainment if the user desires it.

[0643] (Example 2)

[0644] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0645] In modern homes, the operation of various devices often remains fixed and does not take into account the user's deep emotional state. Therefore, environmental optimization to reduce user stress and fatigue is insufficient. Furthermore, the inability to respond flexibly to user preferences and emotions is a problem.

[0646] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0647] In this invention, the server includes sensing means for acquiring facial expression and voice data, processing means for analyzing emotion-related data acquired from the sensing means and generating optimal environmental control commands based on the emotional state, and operating means for adjusting a plurality of household appliances based on the control commands from the processing means. This makes it possible to provide an optimal home environment according to the user's emotional state and to make home life more comfortable and fulfilling.

[0648] "Sensing means" refers to a device or apparatus for acquiring the user's facial expressions and voice data.

[0649] "Processing means" refers to a system or device for analyzing acquired emotion-related data and generating optimal environmental control commands based on the user's emotional state.

[0650] "Operating means" refers to a mechanism or device for adjusting and controlling multiple household appliances based on generated control commands.

[0651] An "environmental control command" is a set of specific instructions for changing or adjusting the operating mode of household appliances according to the user's emotional state.

[0652] "Household appliances" refer to electrical appliances, lighting, audio systems, and other devices used in a home environment.

[0653] This invention is a system that reflects the user's emotional state in the home environment and improves comfort within the home by working in conjunction with a smart home system. Specifically, it uses a facial recognition camera and a voice sensor as sensing means. These hardware devices acquire the user's facial expressions and voice tone in real time and transmit the data to a processing unit.

[0654] The server uses software called an emotion engine to analyze this acquired data in detail. Based on machine learning algorithms, the emotion engine can classify the user's emotional state into categories such as joy, anger, sadness, and pleasure, and determine the degree of stress and fatigue. Based on the analysis results, the server generates environmental control commands to optimize the home environment. These commands are sent to control devices such as lighting, audio systems, and home appliances, automatically adjusting the operation of the equipment.

[0655] As a concrete example, consider a scenario where a user returns home tired from work. When the device detects the user's tired facial expression and calm tone of voice, the detected data is sent to the server, where the emotion engine determines it to be "fatigue." Based on this, the server instructs the control device to enter relaxation mode. The lighting is softened, and relaxing music plays from the speaker. Conversely, if the user appears energetic, the device is instructed to brighten the lighting and play upbeat music.

[0656] Furthermore, when a user is in a specific emotional state, the server can refer to the user's preferences and past history to suggest additional content and activities. This goes beyond mere emotion recognition, enabling the provision of a user-centered home environment.

[0657] Examples of prompts include, "Please provide specific steps for integrating an emotion engine into a smart home system and implementing environmental changes that respond to the user's emotions," and "Please generate effective usage scenarios for an emotion-recognition-based home appliance control system." Through these, the system can always operate in a way that is sensitive to the user's emotions.

[0658] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0659] Step 1:

[0660] The device acquires user input using the smart home system's sensing devices. Specifically, it captures the user's facial expressions with a facial recognition camera and records their voice tone with an audio sensor. This data is collected as basic information indicating the user's emotional state. The input here is real-time video and audio data, and the output is preparatory data for emotion analysis.

[0661] Step 2:

[0662] The device sends the acquired facial expression and audio data to the server. This transmission is encrypted to ensure that the data reaches the server securely. The input is emotion-related data collected by the device, and the output is data for analysis passed to the server. This step includes specific actions to prevent data loss or tampering.

[0663] Step 3:

[0664] The server analyzes input data using an emotion engine. It performs machine learning-based data processing to infer emotions from the user's facial expressions and tone of voice. This process allows the server to quantify emotional states such as "fatigue" or "joy." The input is data sent from the terminal, and the output is the result of evaluating the user's emotional state. This operation includes comparison with an existing emotion database.

[0665] Step 4:

[0666] The server generates environmental control commands based on the analysis results. For example, if it determines that the user needs to "relax," it will create commands to soften the lighting and play calming music. The input is the analysis results from the emotion engine, and the output is expressed as specific commands. This step utilizes an algorithm for setting commands that are appropriate to the user's current situation.

[0667] Step 5:

[0668] The server sends the generated control commands to the operating device. The operating device adjusts household appliances and lighting according to the commands and performs the specified actions. The input is the control commands from the server, and the output is the physical environmental changes within the home. These specific actions include starting up devices and changing modes.

[0669] (Application Example 2)

[0670] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] In modern society, individuals often experience stress and frustration in their daily lives, resulting in a decline in their quality of life. Especially in urban areas, the environment in public facilities is often designed without considering individual emotional states, leading to discomfort for users. This, in turn, reduces user satisfaction and hinders efficient living.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0673] In this invention, the server includes a recognition means for detecting the emotional state of a user, an analysis means for analyzing emotional data obtained from the recognition means to generate appropriate environmental control commands, and an adjustment means for controlling equipment based on the environmental control commands generated by the analysis means. This enables the environment in individual and public spaces to be automatically adjusted according to the emotional state of the user, improving the quality of life for the user and making comfortable living in urban areas possible.

[0674] "User" refers to an individual who uses the system or facility, and whose emotional state is detected and analyzed by the system.

[0675] "Emotional state" refers to an individual's emotional state, specifically their mood or mental tendencies at a particular time.

[0676] "Recognition means" refers to a technical device or algorithm used to detect the emotional state of a user.

[0677] "Emotional data" refers to information about a user's emotions, obtained from their facial expressions, tone of voice, etc., and is the subject of analysis by the system.

[0678] An "environmental control command" refers to a specific operational command used to adjust the operation of equipment based on analyzed emotional data.

[0679] "Analysis means" refers to a technical method or program that processes emotional data obtained from recognition means and generates environmental control commands.

[0680] "Adjustment means" refers to a technical system that operates physical or digital equipment based on generated environmental control commands to change environmental settings.

[0681] "Public facilities" refer to buildings or places used by the general public in urban areas, and their environment is subject to regulation.

[0682] This invention is a system that detects the emotional state of users in real time and optimizes the environment of homes and public spaces based on that state. The system mainly consists of recognition means, analysis means, and adjustment means.

[0683] The server uses "recognition means" to detect the user's emotional state through a camera that recognizes the user's face and sensors that pick up sound. This recognition means may be implemented in the customer's smart device, such as a smartphone or smart glasses. The data obtained by the recognition means is recorded as "emotional data."

[0684] The server processes the acquired emotional data using "analysis tools" and generates purposeful "environmental control commands" utilizing an AI model. This analysis process utilizes libraries such as OpenCV and Librosa to analyze facial expression patterns and voice tone.

[0685] The server then uses "adjustment means" to operate physical or digital devices based on the generated environmental control commands. This adjustment includes lighting, temperature control equipment, and sound systems present in homes and public buildings.

[0686] For example, when a server analyzes that a user is tired, it can send a command to soften the lighting in the living space and play relaxing music. Furthermore, in a co-working space within a smart city, the entire facility's environment can be dynamically changed based on user emotional data.

[0687] An example of a prompt to be input to the generating AI model is, "Please suggest optimal adjustments to the home and public space environment when the user is analyzed to be fatigued within the smart city." This prompt is expected to enable the AI ​​to generate appropriate instructions, enriching the user experience.

[0688] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0689] Step 1:

[0690] The device activates its camera to recognize the user's face and acquires facial image data. Simultaneously, it uses the microphone to record the user's voice and collects audio data. The input consists of facial image and audio data, which is then sent to the next processing step.

[0691] Step 2:

[0692] The server utilizes an AI model to analyze acquired facial images and audio data. Specifically, it estimates emotions based on facial expressions from the facial images and analyzes voice tone and speed from the audio data. This data processing clarifies the user's emotional state. The output is the analyzed emotional state data.

[0693] Step 3:

[0694] The server generates environmental control commands using the analyzed emotional state data. For example, if it determines that the user is tired, it might generate commands to soften the lighting and play calming music. The input is emotional state data, and the output is environmental control commands.

[0695] Step 4:

[0696] The server transmits generated environmental control commands to the adjustment unit, operating household appliances and public facility equipment. Specifically, it may change the lighting in a smart home to a predetermined brightness and instruct the sound system to play a specified piece of music. The input is the environmental control command, and the output is the physical or digital change in the environment caused by the adjustment unit.

[0697] Step 5:

[0698] Users can feel comfortable in a well-adjusted environment. This comfortable environment is the result of the server dynamically adapting based on the user's emotional state, and the environment is readjusted as needed by continuously monitoring the user's condition.

[0699] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0700] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0701] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0702] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0703] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0704] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0705] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0706] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0707] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0708] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0709] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0710] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0711] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0712] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0713] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0714] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0715] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0716] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0717] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0718] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0719] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0720] The following is further disclosed regarding the embodiments described above.

[0721] (Claim 1)

[0722] A detection device that acquires data,

[0723] A processing device that analyzes data obtained from the detection device and generates an optimal control command,

[0724] An operating device that controls the equipment based on commands from the aforementioned processing device,

[0725] A system that includes this.

[0726] (Claim 2)

[0727] The system according to claim 1, comprising a function to detect a suspected intrusion and issue a warning signal.

[0728] (Claim 3)

[0729] The system according to claim 1, which has a function to simultaneously contact multiple recipients in an emergency.

[0730] "Example 1"

[0731] (Claim 1)

[0732] Multiple sensor devices that function as detection means,

[0733] Processing means for aggregating and analyzing environmental information and usage status obtained from the aforementioned sensor device,

[0734] An optimization method that applies machine learning algorithms to formulate the optimal operational schedule,

[0735] An operating means that issues a command to control the equipment based on the processing result by the optimization means,

[0736] An interface means for users to access and check the status and change settings through a dedicated application,

[0737] A system that includes this.

[0738] (Claim 2)

[0739] The system according to claim 1, comprising means for detecting a suspected intrusion and transmitting a warning signal to the user's communication device.

[0740] (Claim 3)

[0741] The system according to claim 1, further comprising a communication means for simultaneously contacting multiple pre-configured recipients in the event of an emergency.

[0742] "Application Example 1"

[0743] (Claim 1)

[0744] A sensor device that acquires data,

[0745] A computing device that analyzes information obtained from the aforementioned sensor device and generates optimal management commands,

[0746] A control device that controls the equipment based on commands from the aforementioned computing device,

[0747] Based on the aforementioned analysis results, a means to optimize energy usage and implement efficient security management,

[0748] A system that includes this.

[0749] (Claim 2)

[0750] The system according to claim 1, comprising the function of detecting a suspected intrusion within an urban area and transmitting alarm information via a communication means.

[0751] (Claim 3)

[0752] The system according to claim 1, which has a function to simultaneously contact relevant organizations in the event of an emergency, thereby optimizing the operation of public facilities.

[0753] "Example 2 of combining an emotion engine"

[0754] (Claim 1)

[0755] A sensing means for acquiring facial expression and voice data,

[0756] A processing means that analyzes emotion-related data acquired by the sensing means and generates an optimal environmental control command based on the emotional state,

[0757] An operating means for adjusting multiple household appliances based on control commands from the processing means,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, comprising the function of recognizing emotional states and adjusting the environment.

[0761] (Claim 3)

[0762] The system according to claim 1, comprising a function that suggests content based on user preferences.

[0763] "Application example 2 when combining with an emotional engine"

[0764] (Claim 1)

[0765] A recognition means for detecting the emotional state of the user,

[0766] An analysis means that analyzes emotional data obtained from the aforementioned recognition means to generate appropriate environmental control commands,

[0767] Adjustment means for controlling equipment based on environmental control commands generated by the analysis means,

[0768] A system that includes this.

[0769] (Claim 2)

[0770] The system according to claim 1, comprising a function for automatically adjusting environmental settings in a public facility according to the emotional state of the user.

[0771] (Claim 3)

[0772] The system according to claim 1, which has a function that enables individual and public spatial environment adjustments in response to the emotions of users in urban areas. [Explanation of symbols]

[0773] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor device that acquires data, A computing device that analyzes information obtained from the aforementioned sensor device and generates optimal management commands, A control device that controls the equipment based on commands from the aforementioned computing device, Based on the aforementioned analysis results, a means to optimize energy usage and implement efficient security management, A system that includes this.

2. The system according to claim 1, comprising a function to detect a suspected intrusion within an urban area and transmit alarm information via a communication means.

3. The system according to claim 1, which has a function to simultaneously contact relevant organizations in the event of an emergency, thereby optimizing the operation of public facilities.

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