system

Abstract

The system according to this embodiment aims to integrate functions such as health management, energy management, and security within the home. [Solution] The system according to the embodiment comprises an information collection unit, an analysis unit, a proposal unit, and a control unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The control unit performs control based on the content proposed by the proposal unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, it is difficult to integrally perform health management, energy management, security functions, etc. within a home, and there is room for improvement.

[0005] The system according to an embodiment aims to integrally perform health management, energy management, security functions, etc. within a home.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a control unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit. The control unit performs control based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate functions such as home health management, energy management, and security. [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 3, 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 Home Guardian Agent System according to an embodiment of the present invention is a smart device that utilizes an AI agent to integrate health management, energy management, item search assistance, tidying guidance, and security functions. This Home Guardian Agent System works in conjunction with other smart devices to manage health conditions along with the environmental conditions of the entire home. The Home Guardian Agent System provides comprehensive support for the user's life, offering a comfortable and efficient living environment. For example, the Home Guardian Agent System is installed as a stationary device in the home, and the AI ​​agent recognizes camera images and audio to support the resident. Its main applications include an item search support agent, a security agent, a tidying guidance agent, a health management assistant, and an energy efficiency optimization agent. In the item search support agent, the AI ​​agent autonomously scans the room and collects and updates location information of items using object recognition technology. When the user asks what they are looking for with a voice command, it guides them to its location visually and audibly. In the security agent, the AI ​​agent monitors camera footage in real time and autonomously issues a warning if it detects abnormal movement. Furthermore, facial recognition technology is used to distinguish between family members and suspicious individuals, and in the event of an anomaly, it automatically notifies family members and security companies and instructs them to take appropriate action. The tidying guidance agent is an AI agent that automatically analyzes the state of the room and suggests efficient tidying methods and storage locations. It monitors the user's tidying progress in real time and provides advice and messages to maintain motivation according to the progress. The health management assistant is an AI agent that monitors the user's movements and posture in real time and autonomously suggests appropriate stretches and rest times. It also analyzes health data over the long term and automatically generates and provides a customized exercise plan. The energy efficiency optimization agent is an AI agent that learns the energy usage patterns of the entire house and autonomously adjusts the optimal settings for air conditioners, lighting, and appliances based on weather forecast data and lifestyle rhythms. This reduces energy costs and mitigates the environmental impact.This allows the Home Guardian Agent system to support users' lives in all aspects and provide a comfortable and efficient living environment.

[0029] The home guardian agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a control unit. The collection unit collects information. The collection unit can collect, for example, sensor data, user data, environmental data, etc. The collection unit can collect indoor temperature data using a temperature sensor, for example. The collection unit can also collect indoor video data using a camera. Furthermore, the collection unit can also collect audio data using a microphone. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using, for example, data mining, statistical analysis, machine learning algorithms, etc. The analysis unit can, for example, analyze the collected temperature data to understand changes in indoor temperature. Furthermore, the analysis unit can also analyze the collected video data to understand the conditions inside the room. Furthermore, the analysis unit can analyze the collected audio data to understand the sound conditions inside the room. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit can propose, for example, recommended actions, points for improvement, optimization plans, etc. The proposal unit can, for example, propose lowering the air conditioner's set temperature when the indoor temperature is high. Furthermore, the suggestion unit can also suggest adjusting the brightness of the lighting according to the conditions in the room. In addition, the suggestion unit can also suggest adjusting the volume according to the sound conditions in the room. The control unit performs control based on the content suggested by the suggestion unit. The control unit can, for example, operate devices, change system settings, adjust processes, etc. The control unit can, for example, control to lower the set temperature of the air conditioner. The control unit can also control to adjust the brightness of the lighting. In addition, the control unit can also control to adjust the volume. As a result, the home guardian agent system according to the embodiment can support the user's life in all aspects by consistently performing information collection, analysis, suggestion, and control.

[0030] The data collection unit collects information. For example, it can collect sensor data, user data, and environmental data. Specifically, it collects indoor temperature data using temperature sensors. Temperature sensors are installed throughout the room, allowing for real-time detection of temperature changes. This enables the data collection unit to understand in detail how the indoor temperature is fluctuating. The data collection unit can also collect indoor video data using cameras. The cameras use wide-angle lenses to cover the entire room and detect movement and anomalies. Furthermore, the data collection unit can collect audio data using microphones. High-sensitivity microphones are used to record the sound conditions in the room in detail. For example, voices, noises, and ambient sounds can be collected and used for later analysis. This allows the data collection unit to centrally collect diverse data and build an information infrastructure for the entire system. Furthermore, the data collection unit can transmit this data to a cloud server, facilitating data sharing and utilization in collaboration with other departments. For example, the collected data can be made accessible to the analysis unit, enabling real-time data analysis. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it analyzes collected temperature data to understand indoor temperature changes. For example, it can use machine learning algorithms to extract patterns from past temperature data and predict future temperature changes. The analysis unit can also analyze collected video data to understand indoor conditions. For example, it can use image recognition technology to detect movement and anomalies in the room and issue alerts as needed. Furthermore, the analysis unit can analyze collected audio data to understand the sound conditions in the room. For example, it can use speech recognition technology to analyze speech and noises and detect abnormal sounds. This allows the analysis unit to quickly and accurately analyze collected data and understand indoor conditions in real time. Additionally, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict temperature fluctuation trends in specific seasons and time periods based on past temperature data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose recommended actions, areas for improvement, and optimization plans. Specifically, it can suggest lowering the air conditioner's temperature setting when the room temperature is high. For example, if the analysis unit analyzes the room temperature data and the temperature exceeds a certain threshold, the proposal unit will suggest to the user that they lower the air conditioner's temperature setting. The proposal unit can also suggest adjusting the brightness of the lighting according to the room conditions. For example, if the analysis unit analyzes the collected video data and determines that the room is dark, the proposal unit will suggest increasing the brightness of the lighting. Furthermore, the proposal unit can also suggest adjusting the volume according to the sound conditions in the room. For example, if the analysis unit analyzes the collected audio data and determines that the room is noisy, the proposal unit will suggest lowering the volume. In this way, the proposal unit can propose specific actions to optimize the user's living environment and improve user comfort. Furthermore, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the results of users following the suggestions and adjust the suggestion algorithm based on that feedback. Furthermore, the proposal department can combine multiple proposals to suggest more effective actions. This allows the proposal department to provide users with optimal suggestions and support improvements to their living environment.

[0033] The control unit performs control based on the content proposed by the suggestion unit. For example, the control unit can operate devices, change system settings, and adjust processes. Specifically, it can control the setting temperature of an air conditioner. For example, if the suggestion unit proposes lowering the air conditioner's temperature, the control unit will automatically lower it. The control unit can also control the brightness of lighting. For example, if the suggestion unit proposes increasing the brightness of the lighting, the control unit will automatically adjust it. Furthermore, the control unit can also control the volume. For example, if the suggestion unit proposes lowering the volume, the control unit will automatically lower it. This allows the control unit to quickly and accurately execute the content proposed by the suggestion unit, optimizing the user's living environment. Additionally, the control unit can customize the control content according to the user's settings and preferences. For example, if the user prefers a specific temperature or brightness, the control unit will prioritize those settings. The control unit can also coordinate multiple devices for more effective control. For example, it can synchronize the air conditioner and lighting to simultaneously adjust the room temperature and brightness. This allows the control unit to comprehensively support the user's living environment, improving comfort and convenience.

[0034] The collection unit can collect location information of an item. The collection unit can collect location information, for example, by reading an RFID tag attached to the item. The collection unit can also collect location information, for example, by using a beacon attached to the item. The collection unit can also collect location information, for example, by using a GPS device attached to the item. The suggestion unit can guide the user to the location of an item based on its location information. The suggestion unit can, for example, provide voice guidance to the user about the location of the item they are looking for. The suggestion unit can also guide the user by displaying the location of the item they are looking for on a screen. The suggestion unit can also guide the user by displaying the location of the item they are looking for on a map. This allows for efficient support in finding items by collecting location information of an item and guiding the user to its location. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data from an RFID tag attached to an item into a generating AI and have the generating AI collect the location information of the item.

[0035] The collection unit can collect images from cameras. The collection unit can, for example, collect images of a room using a high-resolution camera. The collection unit can also, for example, collect images of a wide area using a wide-angle camera. The collection unit can also, for example, collect images of dark places using a night vision camera. The analysis unit can analyze the camera images to detect abnormal movements. The analysis unit can, for example, detect abnormal movements using a motion detection algorithm. The analysis unit can also, for example, detect suspicious persons using facial recognition technology. The analysis unit can also, for example, detect abnormal behavior using a behavior analysis algorithm. The suggestion unit can issue a warning when abnormal movements are detected. The suggestion unit can, for example, emit a warning sound. The suggestion unit can, for example, display a warning message on the screen. The suggestion unit can, for example, automatically notify a security company. This allows for the provision of a security function by collecting camera images, detecting abnormal movements, and issuing warnings. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input camera video data into a generating AI, which can then perform abnormal motion detection.

[0036] The collection unit can collect information about the state of the room. For example, the collection unit can collect video footage of the room using a camera. The collection unit can also collect temperature and humidity data of the room using a sensor. The collection unit can also collect audio data of the room using a microphone. The analysis unit can analyze the state of the room and propose efficient tidying methods. For example, the analysis unit can analyze the arrangement of objects and propose optimal storage locations. For example, the analysis unit can analyze the cleanliness of the room and propose cleaning timings. For example, the analysis unit can analyze the temperature and humidity data of the room and propose a comfortable environment. The suggestion unit can propose efficient tidying methods. For example, the suggestion unit can propose methods to optimize the arrangement of objects. For example, the suggestion unit can propose storage solutions. For example, the suggestion unit can propose tidying procedures. In this way, by collecting information about the state of the room, analyzing efficient tidying methods, and proposing storage locations, tidying can be supported. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data of a room into a generating AI, which can then generate suggestions for efficient tidying methods.

[0037] The data collection unit can collect user movements and posture. For example, the data collection unit can collect user movement data using a motion sensor. The data collection unit can also collect user posture data using a camera. The data collection unit can also collect user biometric data using a wearable device. The analysis unit can analyze user movements and posture to suggest appropriate stretching and rest times. For example, the analysis unit can analyze user movement data to suggest stretching timing. For example, the analysis unit can analyze user posture data to suggest methods for improving posture. For example, the analysis unit can analyze user biometric data to suggest rest timing. The suggestion unit can suggest exercise plans. For example, the suggestion unit can suggest a customized exercise plan based on user movement data. For example, the suggestion unit can suggest an exercise plan for posture improvement based on user posture data. For example, the suggestion unit can suggest an exercise plan for maintaining health based on user biometric data. This allows for support of health management by collecting user movements and posture, analyzing appropriate stretching and rest times, and suggesting exercise plans. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user movement data into a generating AI and cause the generating AI to suggest appropriate stretches and rest times.

[0038] The data collection unit can collect energy usage patterns. The data collection unit can collect power consumption data, for example, using a smart meter. The data collection unit can also collect data on the time periods of appliance use, for example, using sensors. The data collection unit can also collect energy usage frequency data, for example, using an energy management system. The analysis unit can analyze energy usage patterns and propose optimal settings. The analysis unit can, for example, analyze power consumption data and propose the optimal temperature setting for an air conditioner. The analysis unit can, for example, analyze appliance usage time data and propose the optimal lighting settings. The analysis unit can, for example, analyze energy usage frequency data and propose the optimal operating mode for an appliance. The control unit can adjust the settings of the air conditioner, lighting, and appliances based on the proposed optimal settings. The control unit can, for example, automatically adjust the temperature setting of the air conditioner. The control unit can, for example, automatically adjust the brightness of the lighting. The control unit can, for example, automatically adjust the operating mode of an appliance. This allows for the optimization of energy efficiency by collecting energy usage patterns, analyzing optimal settings, and adjusting the settings of the air conditioner, lighting, and appliances. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input energy usage data into a generating AI and have the AI ​​suggest optimal settings.

[0039] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can collect information from devices and applications that the user has frequently used in the past. For example, the data collection unit can analyze the user's past behavior patterns and collect information at specific time periods. For example, the data collection unit can select an information collection method that corresponds to a specific event or situation based on the user's past behavior history. This enables efficient information collection by analyzing the user's past behavior history and selecting the optimal information collection method. 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 behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0040] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the user is currently working on. The data collection unit can also adjust the content of data collection according to the user's current living situation (e.g., at work, on vacation). The data collection unit can also collect only relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current living situation and areas of interest. 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 living situation data into a generating AI and have the generating AI perform the information filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. The data collection unit can also collect highly relevant information based on the user's travel history. For example, the data collection unit can also collect region-specific information based on the user's current location. This allows for the provision of region-specific information by collecting highly relevant information while considering 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 collect highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also collect information shared by the user's social media followers and friends. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant information. This allows the system to provide information tailored to the user's interests by analyzing the user's social media activity and collecting relevant information. 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 collect relevant information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a health data analysis algorithm to information related to health management. For example, the analysis unit can also apply an energy efficiency analysis algorithm to information related to energy management. For example, the analysis unit can also apply a crime prevention data analysis algorithm to information related to crime prevention. By applying different analysis algorithms depending on the category of information, it becomes possible to perform optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also analyze older information as needed. The analysis unit may also adjust the priority of analysis according to the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. This allows for the prioritization of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal unit can adjust the level of detail of a proposal based on the importance of the information. For example, the proposal unit can provide detailed proposals for highly important information. For example, it can provide simplified proposals for less important information. The proposal unit can also determine the priority of proposals based on the importance of the information. This allows important information to be proposed in detail by adjusting the level of detail of proposals based on the importance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, the proposal unit can apply a health data analysis algorithm to proposals related to health management. For example, the proposal unit can also apply an energy efficiency analysis algorithm to proposals related to energy management. For example, the proposal unit can also apply a crime prevention data analysis algorithm to proposals related to crime prevention. By applying different proposal algorithms depending on the category of information, it becomes possible to make optimal proposals for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input information category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0049] The proposal unit can determine the priority of proposals based on the timing of information collection when making a proposal. For example, the proposal unit will prioritize the most recent information. The proposal unit can also make proposals for older information as needed. The proposal unit can also adjust the priority of proposals according to the timing of information collection. This allows for prioritizing the most recent information by determining the priority of proposals based on the timing of information collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information collection timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal unit may prioritize proposing highly relevant information. For example, the proposal unit may postpone proposing less relevant information. The proposal unit can also adjust the order of proposals according to the relevance of the information. This allows the proposal unit to prioritize proposing highly relevant information by adjusting the order of proposals based on the relevance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0051] The control unit can analyze the user's past behavior history and select the optimal control method during control. For example, the control unit can prioritize providing control methods that the user has frequently used in the past. The control unit can also analyze the user's past behavior patterns and provide the optimal control method for a specific time period. For example, the control unit can select a control method based on the user's past behavior history that corresponds to a specific event or situation. This enables efficient control by analyzing the user's past behavior history and selecting the optimal control method. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user behavior history data into a generating AI and have the generating AI select the optimal control method.

[0052] The control unit can customize the control means based on the user's current living situation during control. For example, the control unit can provide a control method related to a project the user is currently working on. The control unit can also adjust the content of the control according to the user's current living situation (e.g., at work, on vacation). The control unit can also provide a relevant control method based on the user's areas of interest. This allows for optimal control for the user by customizing the control means based on 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.

[0053] The control unit can select the optimal control method during control, taking into account the user's geographical location information. For example, the control unit can provide a control method related to the user's current location. The control unit can also provide a highly relevant control method based on the user's travel history. For example, the control unit can provide a region-specific control method based on the user's current location information. This enables region-specific control by selecting the optimal control method while considering the user's geographical location information. Some or all of the above-described processes 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.

[0054] The control unit can analyze the user's social media activity during control and propose control methods. For example, the control unit can provide control methods related to topics the user has shown interest in on social media. The control unit can also provide control methods based on information shared by the user's social media followers and friends. For example, the control unit can analyze the content of the user's social media posts and provide relevant control methods. This enables control tailored to the user's interests by analyzing the user's social media activity and proposing control methods. 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 suggestions for control methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The Home Guardian Agent system can further analyze the user's past behavior history and suggest the most suitable tidying method. For example, it can prioritize suggesting storage locations that the user has frequently used in the past. It can also analyze the user's past tidying patterns and suggest efficient tidying procedures. Furthermore, based on the user's past behavior history, it can suggest tidying at specific times of day. This makes it possible to suggest tidying methods that take the user's past behavior history into consideration, resulting in more efficient tidying.

[0057] The Home Guardian Agent system can further adjust the suggestions of the energy efficiency optimization agent based on the user's current lifestyle. For example, if the user is at work, it can adjust the lighting to reduce energy consumption. If the user is on vacation, it can adjust the air conditioning settings to maintain a comfortable environment. Furthermore, if the user is away from home, it can suggest turning off unnecessary appliances. This enables energy efficiency optimization tailored to the user's lifestyle, resulting in more effective energy management.

[0058] The Home Guardian Agent system can further adjust the health management assistant's suggestions based on the user's geographical location. For example, if the user is outdoors, it can suggest stretches and exercises that can be done outdoors. If the user is at home, it can suggest exercise plans that can be done indoors. Furthermore, if the user is at a gym, it can suggest exercise plans that utilize the gym's equipment. This enables health management suggestions tailored to the user's geographical location, resulting in more effective health support.

[0059] The Home Guardian Agent system can further analyze users' social media activity and provide relevant crime prevention information. For example, if a user shows interest in crime prevention on social media, it can provide the latest crime prevention information. It can also collect and provide crime prevention information shared by the user's followers and friends. Furthermore, it can analyze the content of users' posts and provide relevant crime prevention information. This makes it possible to provide crime prevention information based on the user's social media activity, leading to more effective crime prevention measures.

[0060] The Home Guardian Agent system can further analyze the user's past activity history and suggest the most optimal way to help find lost items. For example, it can prioritize providing location information for items the user has frequently searched for in the past. It can also analyze the user's past search patterns and suggest efficient search procedures. Furthermore, based on the user's past activity history, it can suggest searching during specific time periods. This enables search support that takes the user's past activity history into account, resulting in more efficient item searching.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects information. The data collection unit can collect, for example, sensor data, user data, and environmental data. For example, the data collection unit can collect indoor temperature data using a temperature sensor. The data collection unit can also collect indoor video data using a camera. Furthermore, the data collection unit can collect audio data using a microphone. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can analyze the collected temperature data to understand the temperature changes in the room. The analysis unit can also analyze the collected video data to understand the conditions in the room. Furthermore, the analysis unit can analyze the collected audio data to understand the sound conditions in the room. Step 3: The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. The proposal unit can, for example, suggest recommended actions, areas for improvement, and optimization plans. For example, the proposal unit may suggest lowering the air conditioner's temperature setting when the room temperature is high. It can also suggest adjusting the brightness of the lighting according to the room conditions. Furthermore, it can suggest adjusting the volume according to the sound conditions in the room. Step 4: The control unit performs control based on the content proposed by the proposal unit. The control unit can, for example, operate devices, change system settings, and adjust processes. For example, the control unit can lower the set temperature of an air conditioner. The control unit can also adjust the brightness of the lighting. Furthermore, the control unit can also adjust the volume.

[0063] (Example of form 2) The Home Guardian Agent System according to an embodiment of the present invention is a smart device that utilizes an AI agent to integrate health management, energy management, item search assistance, tidying guidance, and security functions. This Home Guardian Agent System works in conjunction with other smart devices to manage health conditions along with the environmental conditions of the entire home. The Home Guardian Agent System provides comprehensive support for the user's life, offering a comfortable and efficient living environment. For example, the Home Guardian Agent System is installed as a stationary device in the home, and the AI ​​agent recognizes camera images and audio to support the resident. Its main applications include an item search support agent, a security agent, a tidying guidance agent, a health management assistant, and an energy efficiency optimization agent. In the item search support agent, the AI ​​agent autonomously scans the room and collects and updates location information of items using object recognition technology. When the user asks what they are looking for with a voice command, it guides them to its location visually and audibly. In the security agent, the AI ​​agent monitors camera footage in real time and autonomously issues a warning if it detects abnormal movement. Furthermore, facial recognition technology is used to distinguish between family members and suspicious individuals, and in the event of an anomaly, it automatically notifies family members and security companies and instructs them to take appropriate action. The tidying guidance agent is an AI agent that automatically analyzes the state of the room and suggests efficient tidying methods and storage locations. It monitors the user's tidying progress in real time and provides advice and messages to maintain motivation according to the progress. The health management assistant is an AI agent that monitors the user's movements and posture in real time and autonomously suggests appropriate stretches and rest times. It also analyzes health data over the long term and automatically generates and provides a customized exercise plan. The energy efficiency optimization agent is an AI agent that learns the energy usage patterns of the entire house and autonomously adjusts the optimal settings for air conditioners, lighting, and appliances based on weather forecast data and lifestyle rhythms. This reduces energy costs and mitigates the environmental impact.This allows the Home Guardian Agent system to support users' lives in all aspects and provide a comfortable and efficient living environment.

[0064] The home guardian agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a control unit. The collection unit collects information. The collection unit can collect, for example, sensor data, user data, environmental data, etc. The collection unit can collect indoor temperature data using a temperature sensor, for example. The collection unit can also collect indoor video data using a camera. Furthermore, the collection unit can also collect audio data using a microphone. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using, for example, data mining, statistical analysis, machine learning algorithms, etc. The analysis unit can, for example, analyze the collected temperature data to understand changes in indoor temperature. Furthermore, the analysis unit can also analyze the collected video data to understand the conditions inside the room. Furthermore, the analysis unit can analyze the collected audio data to understand the sound conditions inside the room. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit can propose, for example, recommended actions, points for improvement, optimization plans, etc. The proposal unit can, for example, propose lowering the air conditioner's set temperature when the indoor temperature is high. Furthermore, the suggestion unit can also suggest adjusting the brightness of the lighting according to the conditions in the room. In addition, the suggestion unit can also suggest adjusting the volume according to the sound conditions in the room. The control unit performs control based on the content suggested by the suggestion unit. The control unit can, for example, operate devices, change system settings, adjust processes, etc. The control unit can, for example, control to lower the set temperature of the air conditioner. The control unit can also control to adjust the brightness of the lighting. In addition, the control unit can also control to adjust the volume. As a result, the home guardian agent system according to the embodiment can support the user's life in all aspects by consistently performing information collection, analysis, suggestion, and control.

[0065] The data collection unit collects information. For example, it can collect sensor data, user data, and environmental data. Specifically, it collects indoor temperature data using temperature sensors. Temperature sensors are installed throughout the room, allowing for real-time detection of temperature changes. This enables the data collection unit to understand in detail how the indoor temperature is fluctuating. The data collection unit can also collect indoor video data using cameras. The cameras use wide-angle lenses to cover the entire room and detect movement and anomalies. Furthermore, the data collection unit can collect audio data using microphones. High-sensitivity microphones are used to record the sound conditions in the room in detail. For example, voices, noises, and ambient sounds can be collected and used for later analysis. This allows the data collection unit to centrally collect diverse data and build an information infrastructure for the entire system. Furthermore, the data collection unit can transmit this data to a cloud server, facilitating data sharing and utilization in collaboration with other departments. For example, the collected data can be made accessible to the analysis unit, enabling real-time data analysis. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it analyzes collected temperature data to understand indoor temperature changes. For example, it can use machine learning algorithms to extract patterns from past temperature data and predict future temperature changes. The analysis unit can also analyze collected video data to understand indoor conditions. For example, it can use image recognition technology to detect movement and anomalies in the room and issue alerts as needed. Furthermore, the analysis unit can analyze collected audio data to understand the sound conditions in the room. For example, it can use speech recognition technology to analyze speech and noises and detect abnormal sounds. This allows the analysis unit to quickly and accurately analyze collected data and understand indoor conditions in real time. Additionally, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict temperature fluctuation trends in specific seasons and time periods based on past temperature data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose recommended actions, areas for improvement, and optimization plans. Specifically, it can suggest lowering the air conditioner's temperature setting when the room temperature is high. For example, if the analysis unit analyzes the room temperature data and the temperature exceeds a certain threshold, the proposal unit will suggest to the user that they lower the air conditioner's temperature setting. The proposal unit can also suggest adjusting the brightness of the lighting according to the room conditions. For example, if the analysis unit analyzes the collected video data and determines that the room is dark, the proposal unit will suggest increasing the brightness of the lighting. Furthermore, the proposal unit can also suggest adjusting the volume according to the sound conditions in the room. For example, if the analysis unit analyzes the collected audio data and determines that the room is noisy, the proposal unit will suggest lowering the volume. In this way, the proposal unit can propose specific actions to optimize the user's living environment and improve user comfort. Furthermore, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the results of users following the suggestions and adjust the suggestion algorithm based on that feedback. Furthermore, the proposal department can combine multiple proposals to suggest more effective actions. This allows the proposal department to provide users with optimal suggestions and support improvements to their living environment.

[0068] The control unit performs control based on the content proposed by the suggestion unit. For example, the control unit can operate devices, change system settings, and adjust processes. Specifically, it can control the setting temperature of an air conditioner. For example, if the suggestion unit proposes lowering the air conditioner's temperature, the control unit will automatically lower it. The control unit can also control the brightness of lighting. For example, if the suggestion unit proposes increasing the brightness of the lighting, the control unit will automatically adjust it. Furthermore, the control unit can also control the volume. For example, if the suggestion unit proposes lowering the volume, the control unit will automatically lower it. This allows the control unit to quickly and accurately execute the content proposed by the suggestion unit, optimizing the user's living environment. Additionally, the control unit can customize the control content according to the user's settings and preferences. For example, if the user prefers a specific temperature or brightness, the control unit will prioritize those settings. The control unit can also coordinate multiple devices for more effective control. For example, it can synchronize the air conditioner and lighting to simultaneously adjust the room temperature and brightness. This allows the control unit to comprehensively support the user's living environment, improving comfort and convenience.

[0069] The collection unit can collect location information of an item. The collection unit can collect location information, for example, by reading an RFID tag attached to the item. The collection unit can also collect location information, for example, by using a beacon attached to the item. The collection unit can also collect location information, for example, by using a GPS device attached to the item. The suggestion unit can guide the user to the location of an item based on its location information. The suggestion unit can, for example, provide voice guidance to the user about the location of the item they are looking for. The suggestion unit can also guide the user by displaying the location of the item they are looking for on a screen. The suggestion unit can also guide the user by displaying the location of the item they are looking for on a map. This allows for efficient support in finding items by collecting location information of an item and guiding the user to its location. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data from an RFID tag attached to an item into a generating AI and have the generating AI collect the location information of the item.

[0070] The collection unit can collect images from cameras. The collection unit can, for example, collect images of a room using a high-resolution camera. The collection unit can also, for example, collect images of a wide area using a wide-angle camera. The collection unit can also, for example, collect images of dark places using a night vision camera. The analysis unit can analyze the camera images to detect abnormal movements. The analysis unit can, for example, detect abnormal movements using a motion detection algorithm. The analysis unit can also, for example, detect suspicious persons using facial recognition technology. The analysis unit can also, for example, detect abnormal behavior using a behavior analysis algorithm. The suggestion unit can issue a warning when abnormal movements are detected. The suggestion unit can, for example, emit a warning sound. The suggestion unit can, for example, display a warning message on the screen. The suggestion unit can, for example, automatically notify a security company. This allows for the provision of a security function by collecting camera images, detecting abnormal movements, and issuing warnings. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input camera video data into a generating AI, which can then perform abnormal motion detection.

[0071] The collection unit can collect information about the state of the room. For example, the collection unit can collect video footage of the room using a camera. The collection unit can also collect temperature and humidity data of the room using a sensor. The collection unit can also collect audio data of the room using a microphone. The analysis unit can analyze the state of the room and propose efficient tidying methods. For example, the analysis unit can analyze the arrangement of objects and propose optimal storage locations. For example, the analysis unit can analyze the cleanliness of the room and propose cleaning timings. For example, the analysis unit can analyze the temperature and humidity data of the room and propose a comfortable environment. The suggestion unit can propose efficient tidying methods. For example, the suggestion unit can propose methods to optimize the arrangement of objects. For example, the suggestion unit can propose storage solutions. For example, the suggestion unit can propose tidying procedures. In this way, by collecting information about the state of the room, analyzing efficient tidying methods, and proposing storage locations, tidying can be supported. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data of a room into a generating AI, which can then generate suggestions for efficient tidying methods.

[0072] The data collection unit can collect user movements and posture. For example, the data collection unit can collect user movement data using a motion sensor. The data collection unit can also collect user posture data using a camera. The data collection unit can also collect user biometric data using a wearable device. The analysis unit can analyze user movements and posture to suggest appropriate stretching and rest times. For example, the analysis unit can analyze user movement data to suggest stretching timing. For example, the analysis unit can analyze user posture data to suggest methods for improving posture. For example, the analysis unit can analyze user biometric data to suggest rest timing. The suggestion unit can suggest exercise plans. For example, the suggestion unit can suggest a customized exercise plan based on user movement data. For example, the suggestion unit can suggest an exercise plan for posture improvement based on user posture data. For example, the suggestion unit can suggest an exercise plan for maintaining health based on user biometric data. This allows for support of health management by collecting user movements and posture, analyzing appropriate stretching and rest times, and suggesting exercise plans. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user movement data into a generating AI and cause the generating AI to suggest appropriate stretches and rest times.

[0073] The data collection unit can collect energy usage patterns. The data collection unit can collect power consumption data, for example, using a smart meter. The data collection unit can also collect data on the time periods of appliance use, for example, using sensors. The data collection unit can also collect energy usage frequency data, for example, using an energy management system. The analysis unit can analyze energy usage patterns and propose optimal settings. The analysis unit can, for example, analyze power consumption data and propose the optimal temperature setting for an air conditioner. The analysis unit can, for example, analyze appliance usage time data and propose the optimal lighting settings. The analysis unit can, for example, analyze energy usage frequency data and propose the optimal operating mode for an appliance. The control unit can adjust the settings of the air conditioner, lighting, and appliances based on the proposed optimal settings. The control unit can, for example, automatically adjust the temperature setting of the air conditioner. The control unit can, for example, automatically adjust the brightness of the lighting. The control unit can, for example, automatically adjust the operating mode of an appliance. This allows for the optimization of energy efficiency by collecting energy usage patterns, analyzing optimal settings, and adjusting the settings of the air conditioner, lighting, and appliances. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input energy usage data into a generating AI and have the AI ​​suggest optimal settings.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important information. This reduces the user's burden by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0075] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can collect information from devices and applications that the user has frequently used in the past. For example, the data collection unit can analyze the user's past behavior patterns and collect information at specific time periods. For example, the data collection unit can select an information collection method that corresponds to a specific event or situation based on the user's past behavior history. This enables efficient information collection by analyzing the user's past behavior history and selecting the optimal information collection method. 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 behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0076] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the user is currently working on. The data collection unit can also adjust the content of data collection according to the user's current living situation (e.g., at work, on vacation). The data collection unit can also collect only relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current living situation and areas of interest. 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 living situation data into a generating AI and have the generating AI perform the information filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only high-priority information. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit may prioritize collecting information that can be collected quickly. In this way, important information can be prioritized by determining the priority of information to collect based on 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. The data collection unit can also collect highly relevant information based on the user's travel history. For example, the data collection unit can also collect region-specific information based on the user's current location. This allows for the provision of region-specific information by collecting highly relevant information while considering 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 collect highly relevant information.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also collect information shared by the user's social media followers and friends. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant information. This allows the system to provide information tailored to the user's interests by analyzing the user's social media activity and collecting relevant information. 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 collect relevant information.

[0080] 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 stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis result can be provided that is easy for the user to understand. 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 without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a health data analysis algorithm to information related to health management. For example, the analysis unit can also apply an energy efficiency analysis algorithm to information related to energy management. For example, the analysis unit can also apply a crime prevention data analysis algorithm to information related to crime prevention. By applying different analysis algorithms depending on the category of information, it becomes possible to perform optimal analysis for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] 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 stressed, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can provide a summarized analysis for quick understanding. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. 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 without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also analyze older information as needed. The analysis unit may also adjust the priority of analysis according to the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. This allows for the prioritization of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit may provide detailed suggestions. If the user is in a hurry, the suggestion unit may provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0087] The proposal unit can adjust the level of detail of a proposal based on the importance of the information. For example, the proposal unit can provide detailed proposals for highly important information. For example, it can provide simplified proposals for less important information. The proposal unit can also determine the priority of proposals based on the importance of the information. This allows important information to be proposed in detail by adjusting the level of detail of proposals based on the importance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0088] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, the proposal unit can apply a health data analysis algorithm to proposals related to health management. For example, the proposal unit can also apply an energy efficiency analysis algorithm to proposals related to energy management. For example, the proposal unit can also apply a crime prevention data analysis algorithm to proposals related to crime prevention. By applying different proposal algorithms depending on the category of information, it becomes possible to make optimal proposals for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input information category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide summarized suggestions that can be quickly understood. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0090] The proposal unit can determine the priority of proposals based on the timing of information collection when making a proposal. For example, the proposal unit will prioritize the most recent information. The proposal unit can also make proposals for older information as needed. The proposal unit can also adjust the priority of proposals according to the timing of information collection. This allows for prioritizing the most recent information by determining the priority of proposals based on the timing of information collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information collection timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0091] The proposal unit can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal unit may prioritize proposing highly relevant information. For example, the proposal unit may postpone proposing less relevant information. The proposal unit can also adjust the order of proposals according to the relevance of the information. This allows the proposal unit to prioritize proposing highly relevant information by adjusting the order of proposals based on the relevance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0092] The control unit can estimate the user's emotions and adjust the control method based on the estimated emotions. For example, if the user is stressed, the control unit can provide a simple and easily understandable control method. For example, if the user is relaxed, the control unit can also provide a more detailed control method. For example, if the user is in a hurry, the control unit can provide a more concise control method. By adjusting the control method based on the user's emotions, the control unit can provide a control method that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the control method.

[0093] The control unit can analyze the user's past behavior history and select the optimal control method during control. For example, the control unit can prioritize providing control methods that the user has frequently used in the past. The control unit can also analyze the user's past behavior patterns and provide the optimal control method for a specific time period. For example, the control unit can select a control method based on the user's past behavior history that corresponds to a specific event or situation. This enables efficient control by analyzing the user's past behavior history and selecting the optimal control method. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user behavior history data into a generating AI and have the generating AI select the optimal control method.

[0094] The control unit can customize the control means based on the user's current living situation during control. For example, the control unit can provide a control method related to a project the user is currently working on. The control unit can also adjust the content of the control according to the user's current living situation (e.g., at work, on vacation). The control unit can also provide a relevant control method based on the user's areas of interest. This allows for optimal control for the user by customizing the control means based on 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.

[0095] The control unit can estimate the user's emotions and determine control priorities based on the estimated emotions. For example, if the user is stressed, the control unit may prioritize high-priority controls. For example, if the user is relaxed, the control unit may also prioritize detailed controls. For example, if the user is in a hurry, the control unit may also prioritize controls that can be executed quickly. This allows for prioritizing important controls by determining control priorities based on 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 control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI determine the control priorities.

[0096] The control unit can select the optimal control method during control, taking into account the user's geographical location information. For example, the control unit can provide a control method related to the user's current location. The control unit can also provide a highly relevant control method based on the user's travel history. For example, the control unit can provide a region-specific control method based on the user's current location information. This enables region-specific control by selecting the optimal control method while considering the user's geographical location information. Some or all of the above-described processes 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.

[0097] The control unit can analyze the user's social media activity during control and propose control methods. For example, the control unit can provide control methods related to topics the user has shown interest in on social media. The control unit can also provide control methods based on information shared by the user's social media followers and friends. For example, the control unit can analyze the content of the user's social media posts and provide relevant control methods. This enables control tailored to the user's interests by analyzing the user's social media activity and proposing control methods. 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 suggestions for control methods.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The Home Guardian Agent system can further estimate the user's emotions and adjust the health management assistant's suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing stretches or breathing exercises. If the user is relaxed, it can suggest a more active exercise plan. Furthermore, if the user is tired, it can suggest taking a break. This allows for more effective health support by providing health management suggestions tailored to the user's emotions.

[0100] The Home Guardian Agent system can further analyze the user's past behavior history and suggest the most suitable tidying method. For example, it can prioritize suggesting storage locations that the user has frequently used in the past. It can also analyze the user's past tidying patterns and suggest efficient tidying procedures. Furthermore, based on the user's past behavior history, it can suggest tidying at specific times of day. This makes it possible to suggest tidying methods that take the user's past behavior history into consideration, resulting in more efficient tidying.

[0101] The Home Guardian Agent system can further estimate the user's emotions and adjust the security agent's warning method based on those emotions. For example, if the user is stressed, the warning sound can be made more subdued. If the user is relaxed, a detailed warning message can be provided. Furthermore, if the user is in a hurry, a concise warning message can be provided. This enables security warnings that are tailored to the user's emotions, leading to a more appropriate response.

[0102] The Home Guardian Agent system can further adjust the suggestions of the energy efficiency optimization agent based on the user's current lifestyle. For example, if the user is at work, it can adjust the lighting to reduce energy consumption. If the user is on vacation, it can adjust the air conditioning settings to maintain a comfortable environment. Furthermore, if the user is away from home, it can suggest turning off unnecessary appliances. This enables energy efficiency optimization tailored to the user's lifestyle, resulting in more effective energy management.

[0103] The Home Guardian Agent system can further estimate the user's emotions and adjust the guidance provided by the lost item support agent based on those emotions. For example, if the user is stressed, the guidance can be concise and easy to understand. If the user is relaxed, detailed guidance can be provided. Furthermore, if the user is in a hurry, guidance can be provided quickly. This enables lost item support that is tailored to the user's emotions, resulting in more effective support.

[0104] The Home Guardian Agent system can further adjust the health management assistant's suggestions based on the user's geographical location. For example, if the user is outdoors, it can suggest stretches and exercises that can be done outdoors. If the user is at home, it can suggest exercise plans that can be done indoors. Furthermore, if the user is at a gym, it can suggest exercise plans that utilize the gym's equipment. This enables health management suggestions tailored to the user's geographical location, resulting in more effective health support.

[0105] The Home Guardian Agent system can further estimate the user's emotions and adjust the suggestions of the tidying guidance agent based on those emotions. For example, if the user is feeling stressed, it can suggest a simple and quick way to tidy up. If the user is relaxed, it can suggest a detailed tidying procedure. Furthermore, if the user is in a hurry, it can suggest a way to tidy up quickly. This enables tidying guidance tailored to the user's emotions, resulting in more effective tidying support.

[0106] The Home Guardian Agent system can further analyze users' social media activity and provide relevant crime prevention information. For example, if a user shows interest in crime prevention on social media, it can provide the latest crime prevention information. It can also collect and provide crime prevention information shared by the user's followers and friends. Furthermore, it can analyze the content of users' posts and provide relevant crime prevention information. This makes it possible to provide crime prevention information based on the user's social media activity, leading to more effective crime prevention measures.

[0107] The Home Guardian Agent system can further estimate the user's emotions and adjust the suggestions of the energy efficiency optimization agent based on those emotions. For example, if the user is stressed, it can offer simple suggestions to reduce energy consumption. If the user is relaxed, it can offer more detailed energy efficiency improvement measures. Furthermore, if the user is in a hurry, it can offer energy efficiency improvement measures that can be implemented quickly. This enables energy efficiency optimization tailored to the user's emotions, resulting in more effective energy management.

[0108] The Home Guardian Agent system can further analyze the user's past activity history and suggest the most optimal way to help find lost items. For example, it can prioritize providing location information for items the user has frequently searched for in the past. It can also analyze the user's past search patterns and suggest efficient search procedures. Furthermore, based on the user's past activity history, it can suggest searching during specific time periods. This enables search support that takes the user's past activity history into account, resulting in more efficient item searching.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects information. The data collection unit can collect, for example, sensor data, user data, and environmental data. For example, the data collection unit can collect indoor temperature data using a temperature sensor. The data collection unit can also collect indoor video data using a camera. Furthermore, the data collection unit can collect audio data using a microphone. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can analyze the collected temperature data to understand the temperature changes in the room. The analysis unit can also analyze the collected video data to understand the conditions in the room. Furthermore, the analysis unit can analyze the collected audio data to understand the sound conditions in the room. Step 3: The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. The proposal unit can, for example, suggest recommended actions, areas for improvement, and optimization plans. For example, the proposal unit may suggest lowering the air conditioner's temperature setting when the room temperature is high. It can also suggest adjusting the brightness of the lighting according to the room conditions. Furthermore, it can suggest adjusting the volume according to the sound conditions in the room. Step 4: The control unit performs control based on the content proposed by the proposal unit. The control unit can, for example, operate devices, change system settings, and adjust processes. For example, the control unit can lower the set temperature of an air conditioner. The control unit can also adjust the brightness of the lighting. Furthermore, the control unit can also adjust the volume.

[0111] 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.

[0112] 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 text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and control 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 indoor video and audio data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The control unit is implemented in the control unit 46A of the smart device 14 and operates the device based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and control unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects indoor video and audio data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The control unit is implemented in the control unit 46A of the smart glasses 214 and operates the device based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and control unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects indoor video and audio data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The control unit is implemented in the control unit 46A of the headset terminal 314 and operates the device based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and control unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects video and audio data from the room using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The control unit is implemented in the control unit 46A of the robot 414 and operates the device based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, The system includes a control unit that performs control based on the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect location information of items, The aforementioned proposal section is, I will guide you to that place. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect camera footage, The aforementioned analysis unit, Detecting abnormal movements, The aforementioned proposal section is, Issue a warning The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information about the condition of the room, The aforementioned analysis unit, We analyzed efficient tidying methods, The aforementioned proposal section is, Suggesting storage locations The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect user movements and postures, The aforementioned analysis unit, Analyze appropriate stretching and rest times, The aforementioned proposal section is, Suggest an exercise plan The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect energy usage patterns, The aforementioned analysis unit, Analyze the optimal settings, The control unit, Adjust the settings for air conditioners, lighting, and other home appliances. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Estimate the user's emotions, Adjust the timing of information collection based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is By analyzing the user's past behavior history, Select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, Filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate the user's emotions, Prioritize the information to collect based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, Prioritize the collection of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, Analyze users' social media activity, Gather relevant information The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Estimate the user's emotions, Adjust the way the analysis is presented based on the estimated user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, Adjust the level of detail in the analysis based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, Apply different analysis algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Estimate the user's emotions, Adjust the length of the analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, Prioritize analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, Adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, Estimate the user's emotions, Adjust the way suggestions are presented based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, Adjust the level of detail in the proposal based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, Apply different proposed algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the user's emotions, Adjust the length of the suggestion based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, Prioritize proposals based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, Adjust the order of suggestions based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, Estimate the user's emotions, Adjust the control method based on the estimated user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, During control, Analyze the user's past behavior history to select the optimal control method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The control unit, During control, Customize control methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The control unit, Estimate the user's emotions, Prioritize controls based on estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The control unit, During control, The optimal control method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The control unit, During control, Analyzing users' social media activity and proposing control measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, The system includes a control unit that performs control based on the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect location information of items, The aforementioned proposal section is, I will guide you to that place. The system according to feature 1.

3. The aforementioned collection unit is Collect camera footage, The aforementioned analysis unit, It detects abnormal movements, The aforementioned proposal section is, Issue a warning The system according to feature 1.

4. The aforementioned collection unit is Collect information about the condition of the room, The aforementioned analysis unit, We analyzed efficient tidying methods, The aforementioned proposal section is, Suggesting storage locations The system according to feature 1.

5. The aforementioned collection unit is Collect user movements and postures, The aforementioned analysis unit, Analyze appropriate stretching and rest times, The aforementioned proposal section is, Suggest an exercise plan The system according to feature 1.

6. The aforementioned collection unit is Collect energy usage patterns, The aforementioned analysis unit, Analyze the optimal settings, The control unit, Adjust the settings for air conditioners, lighting, and other home appliances. The system according to feature 1.

7. The aforementioned collection unit is To estimate the user's emotions, Adjust the timing of information collection based on estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is By analyzing the user's past behavior history, Select the optimal information gathering method. The system according to feature 1.

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