system

Abstract

The system according to this embodiment aims to efficiently control smart home devices and home appliances. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a control unit, and a customization unit. The data collection unit collects sensor data. The analysis unit analyzes the data collected by the data collection unit. The control unit controls the device based on the analysis results obtained by the analysis unit. The customization unit provides functions for the user to customize the system.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 the conventional technology, the control of smart home devices and home appliances is not performed efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently control smart home devices and home appliances.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a customization unit. The collection unit collects sensor data. The analysis unit analyzes the data collected by the collection unit. The control unit controls the device based on the analysis result obtained by the analysis unit. The customization unit provides a function for the user to customize the system. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently control smart home devices and home appliances. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The smart home control system according to an embodiment of the present invention is a platform that efficiently controls smart home devices and home appliances using AI. This smart home control system collects sensor data from each smart home device and home appliance, analyzes the collected sensor data using AI, and learns the user's behavior patterns and environmental changes to derive the optimal control method. Furthermore, it automatically controls each smart home device and home appliance based on the analysis results. For example, it automatically adjusts the air conditioner when the room temperature rises, and issues an alert when the security system detects an anomaly. This platform is designed to allow users to balance comfort and energy efficiency, and the system can be configured and customized using a smartphone or tablet. For example, it is possible to set a schedule to control a specific device at a specific time, or to create rules to control devices based on specific conditions. As a result, the AI-powered smart home platform can make the user's life more comfortable and efficient. For example, it can maintain a comfortable indoor environment while minimizing energy consumption. In addition, the automation of the security system improves user safety. As a result, the smart home control system can learn the user's behavior patterns and environmental changes to derive the optimal control method.

[0029] The smart home control system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, and a customization unit. The data collection unit collects sensor data. The data collection unit collects sensor data such as temperature sensors, illuminance sensors, and motion sensors. For example, the data collection unit measures the room temperature using a temperature sensor and collects the data. The data collection unit can also measure the brightness of the room using an illuminance sensor and collect the data. Furthermore, the data collection unit can detect the user's movements using a motion sensor and collect the data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the collected sensor data and learn the user's behavior patterns and environmental changes. For example, the analysis unit uses machine learning algorithms to analyze the data and identify the user's behavior patterns. Furthermore, the analysis unit can use deep learning technology to analyze the data and detect environmental changes. The control unit controls devices based on the analysis results obtained by the analysis unit. For example, the control unit controls the air conditioner to adjust the room temperature. The control unit can also control the lighting to adjust the brightness of the room. Furthermore, the control unit can also control the curtains to open and close them. The customization section provides an interface for users to customize the system. For example, the customization section provides an application for configuring and customizing the system using a smartphone or tablet. The customization section also provides an interface for setting a schedule to control specific devices during specific time periods. Furthermore, the customization section can also provide an interface for creating rules to control devices based on specific conditions. This allows the smart home control system according to the embodiment to efficiently collect, analyze, control, and customize sensor data.

[0030] The data collection unit can collect sensor data from temperature sensors, illuminance sensors, and motion sensors. For example, the data collection unit can measure the room temperature using a temperature sensor and collect the data. Examples of temperature sensors include digital and analog temperature sensors. A digital temperature sensor outputs the temperature as a digital signal, and the data collection unit receives this signal and collects the data. An analog temperature sensor outputs the temperature as an analog signal, and the data collection unit converts this signal from analog to digital and collects the data. The data collection unit can also measure the brightness of a room using an illuminance sensor and collect the data. Examples of illuminance sensors include photodiodes and phototransistors. A photodiode outputs a current according to the intensity of light, and the data collection unit measures this current and collects the data. A phototransistor outputs a voltage according to the intensity of light, and the data collection unit measures this voltage and collects the data. Furthermore, the data collection unit can also detect user movement using a motion sensor and collect the data. Examples of motion sensors include infrared sensors and ultrasonic sensors. An infrared sensor emits infrared light and detects movement by detecting its reflection. Ultrasonic sensors emit ultrasonic waves and detect motion by detecting their reflection. This allows for the collection of diverse sensor data, enabling the acquisition of more detailed environmental information.

[0031] The analysis unit can analyze collected sensor data using AI to learn user behavior patterns and environmental changes. For example, the analysis unit can analyze collected sensor data using machine learning algorithms. Machine learning algorithms such as regression analysis and clustering are used. Regression analysis is used to model data trends and predict future data. Clustering is used to divide data into groups and group similar data together. The analysis unit can also analyze collected sensor data using deep learning technology. Deep learning technology, for example, uses neural networks to analyze data and learn complex patterns. A neural network consists of multiple layers of nodes that process input data and generate output. Furthermore, the analysis unit can also analyze collected sensor data using reinforcement learning algorithms. Reinforcement learning algorithms allow agents to interact with the environment and learn actions to maximize rewards. This enables the AI ​​to learn user behavior patterns and environmental changes with high accuracy.

[0032] The control unit can automatically control devices such as air conditioners, lighting, curtains, and music playback devices based on the analysis results. For example, the control unit can control the air conditioner to adjust the room temperature. Air conditioner control includes, for example, changing the temperature setting and operating mode. For example, the control unit can switch the air conditioner to cooling mode to lower the room temperature if it becomes too high. The control unit can also control the lighting to adjust the brightness of the room. Lighting control includes, for example, adjusting the brightness and changing the timing of turning the lights on and off. For example, the control unit can turn on the lights when the room becomes dark to ensure sufficient brightness. Furthermore, the control unit can also control the opening and closing of curtains. Curtain control includes, for example, changing the timing and degree of opening and closing. For example, the control unit can close the curtains when the sunlight becomes too strong to prevent the room temperature from rising. The control unit can also control music playback devices to play music. Music playback control includes, for example, selecting the music to play and adjusting the volume. For example, the control unit can play relaxing music when the user wants to relax, providing a comfortable environment. This allows for the automatic control of devices based on analysis results, thereby providing a comfortable environment.

[0033] The control unit can issue an alert when the security system detects an anomaly. For example, the control unit issues an alert when the security system detects an anomaly. The security system consists of, for example, surveillance cameras and intrusion detection sensors. Surveillance cameras capture images and detect abnormal movements. Intrusion detection sensors detect the opening and closing of doors and windows and detect unauthorized intrusions. For example, the control unit issues an alert and notifies the user when a surveillance camera detects abnormal movement. The control unit can also issue an alert and notify the user when an intrusion detection sensor detects an unauthorized intrusion. Issuing alerts includes, for example, notifications to smartphones and tablets, issuing audio alarms, and sending emails. For example, the control unit can send a push notification to a smartphone to inform of the anomaly. The control unit can also issue an audio alarm to inform of the anomaly. Furthermore, the control unit can also send an email to inform of the anomaly. This allows for a quick response when the security system detects an anomaly.

[0034] The customization section can provide an interface for users to configure and customize the system using smartphones or tablets. For example, the customization section can provide an application for configuring and customizing the system using smartphones or tablets. The application is compatible with platforms such as iOS and Android. The customization section can also provide an interface for setting schedules to control specific devices during specific time periods. Users can use the application to set a schedule to automatically turn on the air conditioner during specific time periods. Furthermore, the customization section can provide an interface for creating rules to control devices based on specific conditions. Users can use the application to create a rule to automatically turn on the air conditioner when the room temperature exceeds a certain temperature. In addition, the customization section provides an interface for users to change system settings. Users can use the application to change the air conditioner temperature settings and lighting brightness settings. This allows users to configure and customize the system using smartphones or tablets.

[0035] The data collection unit can analyze the user's past behavior patterns and select the optimal sensor placement. For example, the data collection unit can concentrate sensors in rooms frequently used by the user to efficiently collect data. For example, the data collection unit can dynamically change the sensor placement based on the user's behavior patterns to achieve optimal data collection. The data collection unit can also reduce unnecessary data collection by analyzing the user's past behavior patterns and optimizing the sensor placement. This enables efficient data collection by optimizing the sensor placement based on the user's behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into a generating AI and have the generating AI select the optimal sensor placement.

[0036] The data collection unit can filter sensor data based on the user's current activity status and environmental conditions. For example, if the user is sleeping, the data collection unit can filter illuminance sensor data to avoid collecting unnecessary data. If the user is out, for example, the data collection unit can filter motion sensor data to reduce energy consumption. The data collection unit can also filter temperature sensor data when the user is relaxed to maintain a comfortable environment. By filtering data according to the user's activity status and environmental conditions, unnecessary data collection can be avoided, enabling efficient data management. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting sensor data. For example, if the user is at home, the data collection unit will prioritize the collection of data from the indoor temperature sensor. If the user is in the garden, the data collection unit will prioritize the collection of data from the outdoor illuminance sensor. Furthermore, if the user is in a car, the data collection unit can prioritize the collection of data from the in-car motion sensor. This enables efficient data management by prioritizing the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI select highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data when collecting sensor data. For example, if a user posts "It's cold" on social media, the data collection unit will prioritize collecting data from the temperature sensor. For example, if a user posts "It's dark" on social media, the data collection unit will prioritize collecting data from the illuminance sensor. The data collection unit can also prioritize collecting data from the motion sensor if a user posts "There's not much movement" on social media. This allows for more appropriate data management by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI select the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the sensor data during the analysis. For example, the analysis unit performs a detailed analysis on important sensor data to improve accuracy. For example, the analysis unit performs a concise analysis on less important sensor data to prioritize efficiency. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the sensor data to provide optimal results. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the sensor data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0040] The analysis unit can apply different analysis algorithms depending on the category of sensor data during analysis. For example, the analysis unit can apply an algorithm that analyzes temperature fluctuation patterns to temperature sensor data. For example, the analysis unit can apply an algorithm that analyzes the relationship between light intensity and time of day to illuminance sensor data. The analysis unit can also apply an algorithm that analyzes the frequency and pattern of motion to motion sensor data. By applying an appropriate analysis algorithm according to the category of sensor data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0041] The analysis unit can determine the priority of analysis based on the timing of sensor data collection during the analysis process. For example, the analysis unit can prioritize the analysis of the latest sensor data to enable real-time responses. For example, the analysis unit can analyze long-term trends by referring to past sensor data. The analysis unit can also dynamically adjust the priority of analysis according to the timing of sensor data collection to provide optimal results. This enables real-time responses by determining the priority of analysis based on the timing of sensor data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI perform the determination of analysis priorities based on the collection timing.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the sensor data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant sensor data to improve accuracy. For example, the analysis unit can prioritize efficiency by postponing the analysis of less relevant sensor data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the sensor data to provide optimal results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the sensor data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0043] The control unit can analyze the user's past behavior patterns during control and select an appropriate control method. For example, the control unit can provide optimal lighting based on lighting settings the user has previously preferred. For example, the control unit can automatically adjust air conditioner settings based on the user's past behavior patterns. The control unit can also analyze the user's past behavior patterns and provide optimal music playback settings. This enables efficient device control by selecting the optimal control method based on the user's past behavior patterns. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user behavior data into a generating AI and have the generating AI select the optimal control method.

[0044] The control unit can customize the device's control means based on the user's current living situation during control. For example, if the user is at home, the control unit can adjust the lighting and air conditioning to optimal settings. If the user is away from home, the control unit can enhance the security system and reduce energy consumption. The control unit can also stop music playback devices and provide a quiet environment if the user is sleeping. This allows for more appropriate device control by customizing the device's control means according to the user's current living situation. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user living situation data into a generating AI and have the generating AI perform the customization of the control means.

[0045] The control unit can select an appropriate control method during control, taking into account the user's geographical location information. For example, if the user is at home, the control unit can optimally control indoor devices. For example, if the user is in the garden, the control unit can optimally control outdoor devices. Furthermore, if the user is in a car, the control unit can also optimally control devices inside the car. This enables efficient device control by selecting the optimal control method based on the user's geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's location data into a generating AI and have the generating AI select the optimal control method.

[0046] The control unit can analyze the user's social media activity during control and propose control measures. For example, if the user posts "It's cold" on social media, the control unit can adjust the air conditioner settings. For example, if the user posts "It's dark" on social media, the control unit can adjust the lighting settings. The control unit can also adjust the volume of the music playback device if the user posts "It's noisy" on social media. This enables more appropriate device control by proposing control measures based on the user's social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of control measures.

[0047] The customization unit can provide the optimal customization method by referring to the user's past setting history during customization. For example, the customization unit can suggest the optimal lighting settings based on the brightness and color of the lighting previously set by the user. For example, the customization unit can suggest the optimal temperature settings by referring to the user's past air conditioner setting history. The customization unit can also suggest the optimal music playback option based on the user's past music playback settings. This enables efficient customization by providing the optimal customization method based on the user's past setting history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's setting history data into a generating AI and have the generating AI perform the task of providing the optimal customization method.

[0048] The customization unit can customize customization options based on the user's current living situation during the customization process. For example, if the user is at home, the customization unit will prioritize customizing the settings of indoor devices. If the user is away from home, the customization unit will prioritize customizing the settings of the security system. Furthermore, if the user is sleeping, the customization unit can prioritize customizing the settings of lighting and music playback devices. This allows for more appropriate customization by customizing the options according to the user's current living situation. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input user living situation data into a generating AI and have the generating AI select the customization options.

[0049] The customization unit can provide an appropriate customization method during customization, taking into account the user's device information. For example, if the user is using a smartphone, the customization unit can provide a customization interface that matches the screen size. For example, if the user is using a tablet, the customization unit can provide a customization interface optimized for a larger screen. Furthermore, if the user is using a smartwatch, the customization unit can also provide a concise and highly visible customization interface. This enables efficient customization by providing the optimal customization method based on the user's device information. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal customization method.

[0050] The customization unit can analyze the user's social media activity during customization and suggest customization options. For example, if the user posts "It's cold" on social media, the customization unit can suggest air conditioner setting options. For example, if the user posts "It's dark" on social media, the customization unit can suggest lighting setting options. The customization unit can also suggest music playback device volume setting options if the user posts "It's noisy" on social media. This allows for more appropriate customization by suggesting options based on the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the user's social media data into a generating AI and have the generating AI suggest customization options.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can collect user health data and adjust device control based on the user's health status. For example, if the user's heart rate is high, the unit can lower the air conditioner temperature to provide a comfortable environment. It can also collect the user's sleep data and, if the sleep quality is poor, adjust the lighting brightness to create a relaxing environment. Furthermore, it can collect the user's exercise data and play relaxing music on the music playback device after exercise. This enables optimal device control tailored to the user's health status.

[0053] The control unit can analyze the user's past behavior patterns and select the optimal control method. For example, it can provide optimal lighting based on the lighting settings the user has previously preferred. It can also automatically adjust air conditioning settings based on the user's past behavior patterns. Furthermore, it can analyze the user's past behavior patterns and provide optimal music playback settings. This enables optimal control based on the user's past behavior patterns.

[0054] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is at home, it can prioritize the collection of data from the indoor temperature sensor. Similarly, if the user is in the garden, it can prioritize the collection of data from the outdoor illuminance sensor. Furthermore, if the user is in a car, it can prioritize the collection of data from the car's motion sensor. This enables efficient data collection based on the user's geographical location.

[0055] The analysis unit can prioritize analysis based on the timing of sensor data collection. For example, it can prioritize the analysis of the latest sensor data to enable real-time responses. It can also analyze long-term trends by referring to past sensor data. Furthermore, it can dynamically adjust the analysis priority according to the timing of sensor data collection to provide optimal results. This enables efficient analysis based on the timing of sensor data collection.

[0056] The control unit can analyze the user's social media activity and propose control measures. For example, if the user posts "It's cold" on social media, it can adjust the air conditioner settings. Similarly, if the user posts "It's dark" on social media, it can adjust the lighting settings. Furthermore, if the user posts "It's noisy" on social media, it can adjust the volume of the music playback device. This enables appropriate device control based on the user's social media activity.

[0057] The data collection unit can analyze the user's past behavior patterns and select the optimal sensor placement. For example, it can concentrate sensors in rooms frequently used by the user to efficiently collect data. It can also dynamically change the sensor placement based on the user's behavior patterns to achieve optimal data collection. Furthermore, by analyzing the user's past behavior patterns and optimizing the sensor placement, it is possible to reduce unnecessary data collection. This enables efficient data collection based on the user's behavior patterns.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects sensor data. The data collection unit collects sensor data such as temperature sensors, illuminance sensors, and motion sensors. The data collection unit measures the room temperature using a temperature sensor and collects the data. It can also measure the brightness of the room using an illuminance sensor and collect the data. Furthermore, it can detect user movement using a motion sensor and collect the data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the collected sensor data and learn user behavior patterns and environmental changes. For example, it can use machine learning algorithms to analyze the data and identify user behavior patterns. It can also use deep learning technology to analyze the data and detect environmental changes. Step 3: The control unit controls the device based on the analysis results obtained by the analysis unit. The control unit controls the air conditioner to adjust the room temperature. It can also control the lighting to adjust the brightness of the room. Furthermore, it can control the curtains to open and close them. Step 4: The customization section provides an interface for users to customize the system. The customization section provides an application for configuring and customizing the system using a smartphone or tablet. For example, it can provide an interface for setting a schedule to control a specific device during a specific time period. It can also provide an interface for creating rules to control devices based on specific conditions.

[0060] (Example of form 2) The smart home control system according to an embodiment of the present invention is a platform that efficiently controls smart home devices and home appliances using AI. This smart home control system collects sensor data from each smart home device and home appliance, analyzes the collected sensor data using AI, and learns the user's behavior patterns and environmental changes to derive the optimal control method. Furthermore, it automatically controls each smart home device and home appliance based on the analysis results. For example, it automatically adjusts the air conditioner when the room temperature rises, and issues an alert when the security system detects an anomaly. This platform is designed to allow users to balance comfort and energy efficiency, and the system can be configured and customized using a smartphone or tablet. For example, it is possible to set a schedule to control a specific device at a specific time, or to create rules to control devices based on specific conditions. As a result, the AI-powered smart home platform can make the user's life more comfortable and efficient. For example, it can maintain a comfortable indoor environment while minimizing energy consumption. In addition, the automation of the security system improves user safety. As a result, the smart home control system can learn the user's behavior patterns and environmental changes to derive the optimal control method.

[0061] The smart home control system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, and a customization unit. The data collection unit collects sensor data. The data collection unit collects sensor data such as temperature sensors, illuminance sensors, and motion sensors. For example, the data collection unit measures the room temperature using a temperature sensor and collects the data. The data collection unit can also measure the brightness of the room using an illuminance sensor and collect the data. Furthermore, the data collection unit can detect the user's movements using a motion sensor and collect the data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the collected sensor data and learn the user's behavior patterns and environmental changes. For example, the analysis unit uses machine learning algorithms to analyze the data and identify the user's behavior patterns. Furthermore, the analysis unit can use deep learning technology to analyze the data and detect environmental changes. The control unit controls devices based on the analysis results obtained by the analysis unit. For example, the control unit controls the air conditioner to adjust the room temperature. The control unit can also control the lighting to adjust the brightness of the room. Furthermore, the control unit can also control the curtains to open and close them. The customization section provides an interface for users to customize the system. For example, the customization section provides an application for configuring and customizing the system using a smartphone or tablet. The customization section also provides an interface for setting a schedule to control specific devices during specific time periods. Furthermore, the customization section can also provide an interface for creating rules to control devices based on specific conditions. This allows the smart home control system according to the embodiment to efficiently collect, analyze, control, and customize sensor data.

[0062] The data collection unit can collect sensor data from temperature sensors, illuminance sensors, and motion sensors. For example, the data collection unit can measure the room temperature using a temperature sensor and collect the data. Examples of temperature sensors include digital and analog temperature sensors. A digital temperature sensor outputs the temperature as a digital signal, and the data collection unit receives this signal and collects the data. An analog temperature sensor outputs the temperature as an analog signal, and the data collection unit converts this signal from analog to digital and collects the data. The data collection unit can also measure the brightness of a room using an illuminance sensor and collect the data. Examples of illuminance sensors include photodiodes and phototransistors. A photodiode outputs a current according to the intensity of light, and the data collection unit measures this current and collects the data. A phototransistor outputs a voltage according to the intensity of light, and the data collection unit measures this voltage and collects the data. Furthermore, the data collection unit can also detect user movement using a motion sensor and collect the data. Examples of motion sensors include infrared sensors and ultrasonic sensors. An infrared sensor emits infrared light and detects movement by detecting its reflection. Ultrasonic sensors emit ultrasonic waves and detect motion by detecting their reflection. This allows for the collection of diverse sensor data, enabling the acquisition of more detailed environmental information.

[0063] The analysis unit can analyze collected sensor data using AI to learn user behavior patterns and environmental changes. For example, the analysis unit can analyze collected sensor data using machine learning algorithms. Machine learning algorithms such as regression analysis and clustering are used. Regression analysis is used to model data trends and predict future data. Clustering is used to divide data into groups and group similar data together. The analysis unit can also analyze collected sensor data using deep learning technology. Deep learning technology, for example, uses neural networks to analyze data and learn complex patterns. A neural network consists of multiple layers of nodes that process input data and generate output. Furthermore, the analysis unit can also analyze collected sensor data using reinforcement learning algorithms. Reinforcement learning algorithms allow agents to interact with the environment and learn actions to maximize rewards. This enables the AI ​​to learn user behavior patterns and environmental changes with high accuracy.

[0064] The control unit can automatically control devices such as air conditioners, lighting, curtains, and music playback devices based on the analysis results. For example, the control unit can control the air conditioner to adjust the room temperature. Air conditioner control includes, for example, changing the temperature setting and operating mode. For example, the control unit can switch the air conditioner to cooling mode to lower the room temperature if it becomes too high. The control unit can also control the lighting to adjust the brightness of the room. Lighting control includes, for example, adjusting the brightness and changing the timing of turning the lights on and off. For example, the control unit can turn on the lights when the room becomes dark to ensure sufficient brightness. Furthermore, the control unit can also control the opening and closing of curtains. Curtain control includes, for example, changing the timing and degree of opening and closing. For example, the control unit can close the curtains when the sunlight becomes too strong to prevent the room temperature from rising. The control unit can also control music playback devices to play music. Music playback control includes, for example, selecting the music to play and adjusting the volume. For example, the control unit can play relaxing music when the user wants to relax, providing a comfortable environment. This allows for the automatic control of devices based on analysis results, thereby providing a comfortable environment.

[0065] The control unit can issue an alert when the security system detects an anomaly. For example, the control unit issues an alert when the security system detects an anomaly. The security system consists of, for example, surveillance cameras and intrusion detection sensors. Surveillance cameras capture images and detect abnormal movements. Intrusion detection sensors detect the opening and closing of doors and windows and detect unauthorized intrusions. For example, the control unit issues an alert and notifies the user when a surveillance camera detects abnormal movement. The control unit can also issue an alert and notify the user when an intrusion detection sensor detects an unauthorized intrusion. Issuing alerts includes, for example, notifications to smartphones and tablets, issuing audio alarms, and sending emails. For example, the control unit can send a push notification to a smartphone to inform of the anomaly. The control unit can also issue an audio alarm to inform of the anomaly. Furthermore, the control unit can also send an email to inform of the anomaly. This allows for a quick response when the security system detects an anomaly.

[0066] The customization section can provide an interface for users to configure and customize the system using smartphones or tablets. For example, the customization section can provide an application for configuring and customizing the system using smartphones or tablets. The application is compatible with platforms such as iOS and Android. The customization section can also provide an interface for setting schedules to control specific devices during specific time periods. Users can use the application to set a schedule to automatically turn on the air conditioner during specific time periods. Furthermore, the customization section can provide an interface for creating rules to control devices based on specific conditions. Users can use the application to create a rule to automatically turn on the air conditioner when the room temperature exceeds a certain temperature. In addition, the customization section provides an interface for users to change system settings. Users can use the application to change the air conditioner temperature settings and lighting brightness settings. This allows users to configure and customize the system using smartphones or tablets.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of sensor data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can set a lower frequency of sensor data collection to conserve energy. If the user is stressed, the data collection unit can set a higher frequency of sensor data collection to respond quickly to environmental changes. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only important sensor data to enable rapid control. This allows for rapid response while conserving energy by adjusting the timing of sensor data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can analyze the user's past behavior patterns and select the optimal sensor placement. For example, the data collection unit can concentrate sensors in rooms frequently used by the user to efficiently collect data. For example, the data collection unit can dynamically change the sensor placement based on the user's behavior patterns to achieve optimal data collection. The data collection unit can also reduce unnecessary data collection by analyzing the user's past behavior patterns and optimizing the sensor placement. This enables efficient data collection by optimizing the sensor placement based on the user's behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into a generating AI and have the generating AI select the optimal sensor placement.

[0069] The data collection unit can filter sensor data based on the user's current activity status and environmental conditions. For example, if the user is sleeping, the data collection unit can filter illuminance sensor data to avoid collecting unnecessary data. If the user is out, for example, the data collection unit can filter motion sensor data to reduce energy consumption. The data collection unit can also filter temperature sensor data when the user is relaxed to maintain a comfortable environment. By filtering data according to the user's activity status and environmental conditions, unnecessary data collection can be avoided, enabling efficient data management. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data into a generating AI and have the generating AI perform data filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of sensor data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting temperature sensor data to maintain a comfortable environment. If the user is stressed, the data collection unit may prioritize collecting illuminance sensor data to respond quickly to environmental changes. The data collection unit may also prioritize collecting motion sensor data if the user is in a hurry to enable rapid control. By prioritizing sensor data according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting sensor data. For example, if the user is at home, the data collection unit will prioritize the collection of data from the indoor temperature sensor. If the user is in the garden, the data collection unit will prioritize the collection of data from the outdoor illuminance sensor. Furthermore, if the user is in a car, the data collection unit can prioritize the collection of data from the in-car motion sensor. This enables efficient data management by prioritizing the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI select highly relevant data.

[0072] The data collection unit can analyze the user's social media activity and collect relevant data when collecting sensor data. For example, if a user posts "It's cold" on social media, the data collection unit will prioritize collecting data from the temperature sensor. For example, if a user posts "It's dark" on social media, the data collection unit will prioritize collecting data from the illuminance sensor. The data collection unit can also prioritize collecting data from the motion sensor if a user posts "There's not much movement" on social media. This allows for more appropriate data management by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI select the relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. If the user is stressed, for example, the analysis unit can provide concise analysis results to encourage quick action. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results to support quick decision-making. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the sensor data during the analysis. For example, the analysis unit performs a detailed analysis on important sensor data to improve accuracy. For example, the analysis unit performs a concise analysis on less important sensor data to prioritize efficiency. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the sensor data to provide optimal results. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the sensor data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0075] The analysis unit can apply different analysis algorithms depending on the category of sensor data during analysis. For example, the analysis unit can apply an algorithm that analyzes temperature fluctuation patterns to temperature sensor data. For example, the analysis unit can apply an algorithm that analyzes the relationship between light intensity and time of day to illuminance sensor data. The analysis unit can also apply an algorithm that analyzes the frequency and pattern of motion to motion sensor data. By applying an appropriate analysis algorithm according to the category of sensor data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. For example, if the user is stressed, the analysis unit can provide concise analysis results to encourage quick action. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results to support quick decision-making. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can determine the priority of analysis based on the timing of sensor data collection during the analysis process. For example, the analysis unit can prioritize the analysis of the latest sensor data to enable real-time responses. For example, the analysis unit can analyze long-term trends by referring to past sensor data. The analysis unit can also dynamically adjust the priority of analysis according to the timing of sensor data collection to provide optimal results. This enables real-time responses by determining the priority of analysis based on the timing of sensor data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI perform the determination of analysis priorities based on the collection timing.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the sensor data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant sensor data to improve accuracy. For example, the analysis unit can prioritize efficiency by postponing the analysis of less relevant sensor data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the sensor data to provide optimal results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the sensor data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0079] The control unit can estimate the user's emotions and adjust the device's control methods based on the estimated emotions. For example, if the user is relaxed, the control unit can adjust the lighting to a soft light to provide a comfortable environment. If the user is stressed, the control unit can use a music playback device to play relaxing music. The control unit can also quickly adjust the air conditioning to provide a comfortable room temperature if the user is in a hurry. In this way, a more comfortable environment can be provided by adjusting the device's control methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The control unit can analyze the user's past behavior patterns during control and select an appropriate control method. For example, the control unit can provide optimal lighting based on lighting settings the user has previously preferred. For example, the control unit can automatically adjust air conditioner settings based on the user's past behavior patterns. The control unit can also analyze the user's past behavior patterns and provide optimal music playback settings. This enables efficient device control by selecting the optimal control method based on the user's past behavior patterns. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user behavior data into a generating AI and have the generating AI select the optimal control method.

[0081] The control unit can customize the device's control means based on the user's current living situation during control. For example, if the user is at home, the control unit can adjust the lighting and air conditioning to optimal settings. If the user is away from home, the control unit can enhance the security system and reduce energy consumption. The control unit can also stop music playback devices and provide a quiet environment if the user is sleeping. This allows for more appropriate device control by customizing the device's control means according to the user's current living situation. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user living situation data into a generating AI and have the generating AI perform the customization of the control means.

[0082] The control unit can estimate the user's emotions and determine device control priorities based on the estimated emotions. For example, if the user is relaxed, the control unit may prioritize adjusting the lighting to provide a comfortable environment. If the user is stressed, the control unit may prioritize controlling the music playback device to play relaxing music. The control unit may also prioritize adjusting the air conditioner to provide a comfortable room temperature if the user is in a hurry. This allows for more appropriate device control by determining device control priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The control unit can select an appropriate control method during control, taking into account the user's geographical location information. For example, if the user is at home, the control unit can optimally control indoor devices. For example, if the user is in the garden, the control unit can optimally control outdoor devices. Furthermore, if the user is in a car, the control unit can also optimally control devices inside the car. This enables efficient device control by selecting the optimal control method based on the user's geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's location data into a generating AI and have the generating AI select the optimal control method.

[0084] The control unit can analyze the user's social media activity during control and propose control measures. For example, if the user posts "It's cold" on social media, the control unit can adjust the air conditioner settings. For example, if the user posts "It's dark" on social media, the control unit can adjust the lighting settings. The control unit can also adjust the volume of the music playback device if the user posts "It's noisy" on social media. This enables more appropriate device control by proposing control measures based on the user's social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of control measures.

[0085] The customization unit can estimate the user's emotions and adjust the customization interface based on the estimated emotions. For example, if the user is relaxed, the customization unit can provide detailed customization options, allowing the user to freely change settings. If the user is stressed, for example, the customization unit can provide a simple interface to simplify the customization process. Furthermore, if the user is in a hurry, the customization unit can prioritize voice input, enabling quick customization settings. This allows for more appropriate customization by adjusting the customization interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The customization unit can provide the optimal customization method by referring to the user's past setting history during customization. For example, the customization unit can suggest the optimal lighting settings based on the brightness and color of the lighting previously set by the user. For example, the customization unit can suggest the optimal temperature settings by referring to the user's past air conditioner setting history. The customization unit can also suggest the optimal music playback option based on the user's past music playback settings. This enables efficient customization by providing the optimal customization method based on the user's past setting history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's setting history data into a generating AI and have the generating AI perform the task of providing the optimal customization method.

[0087] The customization unit can customize customization options based on the user's current living situation during the customization process. For example, if the user is at home, the customization unit will prioritize customizing the settings of indoor devices. If the user is away from home, the customization unit will prioritize customizing the settings of the security system. Furthermore, if the user is sleeping, the customization unit can prioritize customizing the settings of lighting and music playback devices. This allows for more appropriate customization by customizing the options according to the user's current living situation. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input user living situation data into a generating AI and have the generating AI select the customization options.

[0088] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated emotions. For example, if the user is relaxed, the customization unit might prioritize lighting customization to provide a comfortable environment. If the user is stressed, the customization unit might prioritize music playback device customization to provide relaxing music. Furthermore, if the user is in a hurry, the customization unit might prioritize air conditioning customization to quickly provide a comfortable room temperature. This allows for more appropriate customization by determining customization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The customization unit can provide an appropriate customization method during customization, taking into account the user's device information. For example, if the user is using a smartphone, the customization unit can provide a customization interface that matches the screen size. For example, if the user is using a tablet, the customization unit can provide a customization interface optimized for a larger screen. Furthermore, if the user is using a smartwatch, the customization unit can also provide a concise and highly visible customization interface. This enables efficient customization by providing the optimal customization method based on the user's device information. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal customization method.

[0090] The customization unit can analyze the user's social media activity during customization and suggest customization options. For example, if the user posts "It's cold" on social media, the customization unit can suggest air conditioner setting options. For example, if the user posts "It's dark" on social media, the customization unit can suggest lighting setting options. The customization unit can also suggest music playback device volume setting options if the user posts "It's noisy" on social media. This allows for more appropriate customization by suggesting options based on the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the user's social media data into a generating AI and have the generating AI suggest customization options. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as temperature, illuminance, and operation using the sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses AI to analyze the collected data and learn the user's behavior patterns and environmental changes. The control unit is implemented in the control unit 46A of the smart device 14, and controls air conditioners, lighting, curtains, etc., based on the analysis results. The customization unit provides an interface for the user to customize the system through an application on the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, and customization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as temperature, illuminance, and operation using the sensors of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and learns the user's behavior patterns and environmental changes. The control unit is implemented, for example, by the control unit 46A of the smart glasses 214, which controls air conditioners, lighting, curtains, etc., based on the analysis results. The customization unit provides, for example, an interface for the user to customize the system through the application of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, and customization unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data such as temperature, illuminance, and operation using the sensors of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and learns the user's behavior patterns and environmental changes. The control unit is implemented, for example, by the control unit 46A of the headset terminal 314, which controls air conditioners, lighting, curtains, etc., based on the analysis results. The customization unit provides, for example, an interface for the user to customize the system through an application on the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, and customization unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as temperature, illuminance, and movement using the sensors of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and learns the user's behavior patterns and environmental changes. The control unit is implemented, for example, by the control unit 46A of the robot 414, which controls air conditioners, lighting, curtains, etc., based on the analysis results. The customization unit provides, for example, an interface for the user to customize the system through the robot 414's application.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The data collection unit can collect user health data and adjust device control based on the user's health status. For example, if the user's heart rate is high, the unit can lower the air conditioner temperature to provide a comfortable environment. It can also collect the user's sleep data and, if the sleep quality is poor, adjust the lighting brightness to create a relaxing environment. Furthermore, it can collect the user's exercise data and play relaxing music on the music playback device after exercise. This enables optimal device control tailored to the user's health status.

[0093] The analysis unit can estimate the user's emotions and adjust the device's control methods based on those estimates. For example, if the user is relaxed, it can adjust the lighting to a softer glow to provide a comfortable environment. If the user is stressed, it can play relaxing music using a music playback device. Furthermore, if the user is in a hurry, it can quickly adjust the air conditioning to provide a comfortable room temperature. This enables device control that responds to the user's emotions.

[0094] The control unit can analyze the user's past behavior patterns and select the optimal control method. For example, it can provide optimal lighting based on the lighting settings the user has previously preferred. It can also automatically adjust air conditioning settings based on the user's past behavior patterns. Furthermore, it can analyze the user's past behavior patterns and provide optimal music playback settings. This enables optimal control based on the user's past behavior patterns.

[0095] The customization section can estimate the user's emotions and adjust the customization interface based on those emotions. For example, if the user is relaxed, it can provide detailed customization options and allow the user to freely change settings. If the user is stressed, it can provide a simple interface to simplify the customization process. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick customization settings. This enables customization that responds to the user's emotions.

[0096] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is at home, it can prioritize the collection of data from the indoor temperature sensor. Similarly, if the user is in the garden, it can prioritize the collection of data from the outdoor illuminance sensor. Furthermore, if the user is in a car, it can prioritize the collection of data from the car's motion sensor. This enables efficient data collection based on the user's geographical location.

[0097] The analysis unit can prioritize analysis based on the timing of sensor data collection. For example, it can prioritize the analysis of the latest sensor data to enable real-time responses. It can also analyze long-term trends by referring to past sensor data. Furthermore, it can dynamically adjust the analysis priority according to the timing of sensor data collection to provide optimal results. This enables efficient analysis based on the timing of sensor data collection.

[0098] The control unit can analyze the user's social media activity and propose control measures. For example, if the user posts "It's cold" on social media, it can adjust the air conditioner settings. Similarly, if the user posts "It's dark" on social media, it can adjust the lighting settings. Furthermore, if the user posts "It's noisy" on social media, it can adjust the volume of the music playback device. This enables appropriate device control based on the user's social media activity.

[0099] The customization function can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is relaxed, it can prioritize lighting customization to provide a comfortable environment. If the user is stressed, it can prioritize music playback device customization to provide relaxing music. Furthermore, if the user is in a hurry, it can prioritize air conditioning customization to quickly provide a comfortable room temperature. This enables customization that responds to the user's emotions.

[0100] The data collection unit can analyze the user's past behavior patterns and select the optimal sensor placement. For example, it can concentrate sensors in rooms frequently used by the user to efficiently collect data. It can also dynamically change the sensor placement based on the user's behavior patterns to achieve optimal data collection. Furthermore, by analyzing the user's past behavior patterns and optimizing the sensor placement, it is possible to reduce unnecessary data collection. This enables efficient data collection based on the user's behavior patterns.

[0101] The control unit can estimate the user's emotions and determine device control priorities based on those emotions. For example, if the user is relaxed, it can prioritize adjusting the lighting to provide a comfortable environment. If the user is stressed, it can prioritize controlling the music playback device and play relaxing music. Furthermore, if the user is in a hurry, it can prioritize adjusting the air conditioning to provide a comfortable room temperature. This enables device control that responds to the user's emotions.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The data collection unit collects sensor data. The data collection unit collects sensor data such as temperature sensors, illuminance sensors, and motion sensors. The data collection unit measures the room temperature using a temperature sensor and collects the data. It can also measure the brightness of the room using an illuminance sensor and collect the data. Furthermore, it can detect user movement using a motion sensor and collect the data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the collected sensor data and learn user behavior patterns and environmental changes. For example, it can use machine learning algorithms to analyze the data and identify user behavior patterns. It can also use deep learning technology to analyze the data and detect environmental changes. Step 3: The control unit controls the device based on the analysis results obtained by the analysis unit. The control unit controls the air conditioner to adjust the room temperature. It can also control the lighting to adjust the brightness of the room. Furthermore, it can control the curtains to open and close them. Step 4: The customization section provides an interface for users to customize the system. The customization section provides an application for configuring and customizing the system using a smartphone or tablet. For example, it can provide an interface for setting a schedule to control a specific device during a specific time period. It can also provide an interface for creating rules to control devices based on specific conditions.

[0104] 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.

[0105] Data generation model 58 is a form of 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> 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 inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0111] 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.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0113] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] 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.

[0115] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0127] 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.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0129] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] 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.

[0131] 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.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0143] 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.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0145] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] 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.

[0147] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] 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.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] 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.

[0158] Figure 9 shows the 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.

[0159] 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.

[0160] 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.

[0161] 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, and motorcycles, 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 based, for example, 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.

[0162] 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."

[0163] 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.

[0164] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] 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 other things 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.

[0174] 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.

[0175] [Explanation of Symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects sensor data, An analysis unit analyzes the data collected by the aforementioned collection unit, A control unit that controls the device based on the analysis results obtained by the analysis unit, It includes a customization section for users to customize the system. A system characterized by the following features.

2. The aforementioned collection unit is Collect sensor data from temperature sensors, illuminance sensors, and motion sensors. The system according to feature 1.

3. The aforementioned analysis unit is The collected sensor data is analyzed using AI to learn user behavior patterns and environmental changes. The system according to feature 1.

4. The control unit, Based on the analysis results, it automatically controls air conditioners, lighting, curtains, and music playback devices. The system according to feature 1.

5. The control unit, The security system will issue an alert if it detects an anomaly. The system according to feature 1.

6. The aforementioned customization unit is It provides an interface for users to configure and customize the system using their smartphones or tablets. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sensor data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior patterns and select the appropriate sensor placement. The system according to feature 1.

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