system

Abstract

The system according to this embodiment aims to optimize the home environment based on the behavioral patterns and preferences of the residents and to propose products and services accordingly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects lifestyle logs using various sensors. The analysis unit analyzes the lifestyle logs collected by the collection unit to understand the behavioral patterns and preferences of the residents. Based on the analysis results obtained by the analysis unit, the proposal unit optimizes the home environment and proposes products and services that meet the user's needs.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the home environment has not been sufficiently optimized or products and services have not been sufficiently proposed based on the behavior patterns and preferences of the residents.

[0005] The system according to the embodiment aims to optimize the home environment and propose products and services based on the behavior patterns and preferences of the residents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects lifestyle logs using various sensors. The analysis unit analyzes the lifestyle logs collected by the collection unit to understand the behavioral patterns and preferences of the residents. Based on the analysis results obtained by the analysis unit, the proposal unit optimizes the home environment and proposes products and services tailored to the user's needs. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the home environment based on the behavioral patterns and preferences of the residents and propose products and services accordingly. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The Smart Life Concierge according to an embodiment of the present invention is a system that uses various data collected within a smart home to understand the lifestyle of the inhabitants and proposes the most suitable products and services. By analyzing lifestyle logs collected by various sensors, the Smart Life Concierge can grasp in detail the daily behavior patterns and preferences of the inhabitants. Next, based on the analysis results, it optimizes the home environment to suit the individual needs of the user and recommends products and services that the user requires. This makes it possible to further personalize the smart home experience and improve convenience and comfort. For example, the Smart Life Concierge collects data such as the usage status of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of the inhabitants. For example, by collecting data on air conditioner usage, changes in indoor temperature, and the movement patterns of the inhabitants and analyzing it with AI, it is possible to understand the behavior patterns and preferences of the inhabitants. Next, based on the analysis results, it optimizes the home environment to suit the individual needs of the user. For example, if an inhabitant frequently uses the air conditioner during a specific time period, it can set the air conditioner to automatically turn on during that time. Also, if an inhabitant prefers a specific brightness level of lighting, it can automatically adjust the brightness to that level. This makes it possible to provide a comfortable environment tailored to the inhabitants' preferences. Furthermore, it recommends products and services that the user requires. For example, if residents desire an eco-conscious lifestyle, energy-efficient appliances and services that help save money can be suggested. Also, if residents are interested in new products and services, products and services tailored to their needs can be recommended. This can further enrich the lives of residents. This system makes it possible to further personalize the smart home experience and improve convenience and comfort. For instance, if residents frequently use the air conditioner at certain times, setting it to automatically turn on during those times can provide a comfortable environment tailored to their preferences. Furthermore, if residents desire an eco-conscious lifestyle, suggesting energy-efficient appliances and services that help save money can further enrich their lives.This allows the Smart Life Concierge to make the lives of residents more comfortable and convenient.

[0029] The smart life concierge according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects lifestyle logs using various sensors. The data collection unit collects data such as the usage status of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of residents. For example, the data collection unit can detect the usage status of air conditioners with sensors and collect data. The data collection unit can also detect the usage status of lighting with sensors and collect data. Furthermore, the data collection unit can detect the usage status of home appliances with sensors and collect data. For example, the data collection unit records the usage time and power consumption of air conditioners and collects data. It records the usage time and brightness settings of lighting and collects data. It records the usage time and power consumption of home appliances and collects data. The analysis unit analyzes the lifestyle logs collected by the data collection unit to understand the behavior patterns and preferences of the residents. For example, the analysis unit can analyze the collected data to understand the behavior patterns of the residents. Furthermore, the analysis unit can analyze the collected data to understand the preferences of the residents. Furthermore, the analysis department can analyze the collected data to comprehensively understand the behavioral patterns and preferences of the residents. For example, the analysis department can analyze the usage of air conditioners and changes in indoor temperature to understand the residents' behavioral patterns. It can also analyze the usage of lighting and brightness settings to understand the residents' preferences. It can analyze the usage and power consumption of home appliances to comprehensively understand the residents' behavioral patterns and preferences. Based on the analysis results obtained by the analysis department, the proposal department optimizes the home environment and proposes products and services that meet the user's needs. For example, the proposal department can optimize the home environment to meet the user's needs based on the analysis results. It can also propose products and services that the user needs based on the analysis results. Furthermore, the proposal department can comprehensively optimize the home environment and propose products and services that meet the user's needs based on the analysis results. For example, if a resident frequently uses the air conditioner during a specific time period, the proposal department can set it to automatically turn on during that time period. If a resident prefers a specific brightness level of lighting, the proposal department can automatically adjust the brightness to that level.If residents desire an eco-conscious lifestyle, the system will propose energy-efficient home appliances and services that help save money. This allows the Smart Life Concierge, according to this embodiment, to understand the user's lifestyle and propose the most suitable products and services.

[0030] The data collection unit collects lifestyle logs using various sensors. For example, it collects data on the usage of air conditioners, lighting, and home appliances, as well as indoor temperature and humidity, and the movement patterns of residents. Specifically, it detects air conditioner usage with sensors and records usage time and power consumption. This allows for understanding air conditioner operating time and energy consumption trends. Similarly, it detects lighting usage time and brightness settings with sensors and collects data. This allows for understanding lighting usage patterns and preferred brightness levels. Furthermore, it also collects data on the usage of home appliances by recording usage time and power consumption with sensors. For example, it records the usage of appliances such as refrigerators, washing machines, and televisions in detail, allowing for understanding energy consumption trends and usage frequency. It also collects real-time data on indoor temperature and humidity with sensors, providing information to maintain a comfortable environment. Regarding residents' movement patterns, for example, sensors installed on doors and windows detect people entering and leaving, and collect data. This allows for understanding the residents' daily rhythms and behavioral patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes lifestyle logs collected by the data collection department to understand the behavioral patterns and preferences of residents. Specifically, it analyzes air conditioner usage and indoor temperature changes to understand residents' behavioral patterns. For example, by analyzing data on air conditioner usage time and power consumption, it can identify when residents tend to use air conditioners and suggest settings to maintain a comfortable temperature during those times. By analyzing lighting usage and brightness settings, it can understand residents' preferred brightness and usage patterns. For example, if a resident prefers a specific brightness at a particular time, it can suggest settings to automatically adjust the lighting during that time. By analyzing the usage and power consumption of home appliances, it can comprehensively understand residents' behavioral patterns and preferences. For example, by analyzing the frequency of use and energy consumption trends of appliances such as refrigerators, washing machines, and televisions, it can suggest energy-efficient usage methods and ways to save energy. Furthermore, based on the collected data, the analysis department can comprehensively understand residents' behavioral patterns and preferences and predict long-term trends and changes. For example, by analyzing seasonal air conditioner usage patterns and lighting usage trends, it can predict future energy consumption and suggest optimal settings. This allows the analysis department to provide a foundation for making optimal suggestions tailored to the lifestyles of the residents.

[0032] The Proposal Department, based on the analysis results obtained by the Analysis Department, optimizes the home environment and proposes products and services tailored to the user's needs. Specifically, if residents frequently use the air conditioner during certain times, the department sets it to automatically turn on during those times. This allows residents to maintain a comfortable environment while reducing energy waste. Similarly, if residents prefer a specific level of lighting, the department sets it to automatically adjust to that level, ensuring they always live in comfortable lighting. Furthermore, if residents desire an eco-conscious lifestyle, the department proposes energy-efficient appliances and services that help conserve energy. For example, it suggests energy-efficient refrigerators and washing machines, and televisions with energy-saving modes to reduce energy consumption. The Proposal Department can also propose products and services tailored to the user's lifestyle and preferences. For example, it proposes smart devices and services that help with health management for health-conscious users, supporting a comfortable life. In addition, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of its proposals. For example, it monitors how effective the proposed settings and products actually are and makes adjustments or improvements as needed. This allows the proposal department to provide users with the optimal home environment, products, and services, supporting a comfortable and efficient lifestyle.

[0033] The data collection unit can collect data such as the usage status of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of residents. For example, the data collection unit can detect the usage status of air conditioners using sensors and collect data. The data collection unit can also detect the usage status of lighting using sensors and collect data. The data collection unit can also detect the usage status of home appliances using sensors and collect data. For example, the data collection unit can record the usage time and power consumption of air conditioners and collect data. The data collection unit can record the usage time and brightness settings of lighting and collect data. The data collection unit can record the usage time and power consumption of home appliances and collect data. By collecting diverse data in this way, it is possible to understand the behavior patterns and preferences of residents in detail. 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 collect data using an AI model that detects the usage status of air conditioners using sensors and collects data.

[0034] The analysis unit can analyze the collected data to understand the behavioral patterns and preferences of the residents. For example, the analysis unit can analyze the collected data to understand the behavioral patterns of the residents. The analysis unit can also analyze the collected data to understand the preferences of the residents. The analysis unit can also analyze the collected data to comprehensively understand the behavioral patterns and preferences of the residents. For example, the analysis unit can analyze the usage of air conditioners and changes in indoor temperature to understand the behavioral patterns of the residents. The analysis unit can analyze the usage of lighting and brightness settings to understand the preferences of the residents. The analysis unit can analyze the usage and power consumption of home appliances to comprehensively understand the behavioral patterns and preferences of the residents. This allows for a detailed understanding of the behavioral patterns and preferences of the residents through data analysis. 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 the collected data into an AI model and have the AI ​​perform the task of understanding the behavioral patterns and preferences of the residents.

[0035] The proposal unit can optimize the home environment to meet user needs based on the analysis results. For example, the proposal unit can optimize the home environment to meet user needs based on the analysis results. The proposal unit can also propose products and services that the user needs based on the analysis results. The proposal unit can also comprehensively optimize the home environment and propose products and services to meet user needs based on the analysis results. For example, if the resident frequently uses the air conditioner during certain times of the day, the proposal unit can set it to automatically turn on during those times. If the resident prefers a certain level of lighting brightness, the proposal unit can automatically adjust it to that level. If the resident desires an eco-conscious lifestyle, the proposal unit can propose energy-efficient home appliances and services that help save money. This makes it possible to optimize the home environment to meet user needs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the analysis results into an AI model and have the AI ​​perform the optimization of the home environment and propose products and services.

[0036] The proposal department can propose products and services that users need based on the analysis results. For example, the proposal department can propose products and services that users need based on the analysis results. The proposal department can also optimize the home environment to suit the user's needs based on the analysis results. The proposal department can also comprehensively optimize the home environment and propose products and services that suit the user's needs based on the analysis results. For example, if the residents are looking for an eco-conscious lifestyle, the proposal department will propose energy-efficient home appliances and services that help save money. If the residents are interested in new things and services, the proposal department will propose products and services that meet those needs. This allows the proposal department to appropriately propose products and services that users need. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results into an AI model and have the AI ​​make product and service proposals.

[0037] The proposed unit includes a control unit that controls air conditioners and lighting. The control unit can, for example, set the temperature of the air conditioner. The control unit can also adjust the brightness of the lighting. The control unit can also control home appliances. For example, the control unit adjusts the temperature of the air conditioner to a comfortable setting. The control unit adjusts the brightness of the lighting to the occupants' preferences. The control unit controls home appliances according to their usage. This makes it possible to optimize the home environment through the control of air conditioners and lighting. Some or all of the above-mentioned processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the temperature setting of the air conditioner into an AI model and have the AI ​​execute the optimal temperature setting.

[0038] The proposal unit includes a recommendation unit that proposes new products and services. The recommendation unit can, for example, propose new products. The recommendation unit can also recommend services. The recommendation unit can also propose new products and services tailored to the user's needs. For example, if a resident desires an eco-conscious lifestyle, the recommendation unit will propose energy-efficient home appliances and services that help save money. If a resident is interested in new products and services, the recommendation unit will propose products and services that meet those needs. This makes it possible to propose new products and services. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's needs into an AI model and have the AI ​​execute proposals for new products and services.

[0039] The data collection unit can analyze the residents' past behavioral patterns and select the optimal data collection method. For example, the data collection unit prioritizes collecting data on household appliances that residents have frequently used in the past. Based on the residents' past movement patterns, the data collection unit concentrates data collection during specific time periods. Based on the residents' past behavioral patterns, the data collection unit intensifies data collection during specific events. This enables efficient data collection by selecting the optimal data collection method based on past behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the residents' past behavioral patterns into an AI model and have the AI ​​select the optimal data collection method.

[0040] The data collection unit can filter data based on the resident's current activities and areas of interest during data collection. For example, if the resident is cooking, the unit will prioritize collecting data on kitchen appliances. If the resident is relaxing in the living room, the unit will collect data on air conditioning and lighting. If the resident is out, the unit will collect security-related data. By filtering data based on current activities and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the resident's current activities and areas of interest into an AI model and have the AI ​​perform the data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the residents during data collection. For example, if a resident is at home, the data collection unit prioritizes the collection of indoor temperature and humidity data. If a resident is away from home, the data collection unit prioritizes the collection of external environmental data. If a resident is in a specific location, the data collection unit prioritizes the collection of data related to that location. This enables more appropriate data collection by prioritizing the collection of highly relevant data based on geographical location 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 geographical location information of residents into an AI model and prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze residents' social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to residents' interests based on information they share on social media. The data collection unit can collect relevant data based on information about accounts that residents follow on social media. The data collection unit can collect data related to events that residents participate in on social media. This allows for the collection of more relevant data by collecting relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input residents' social media activity into an AI model and collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during data analysis. For example, the analysis unit performs a detailed analysis on highly important data to provide deep insights. For less important data, the analysis unit performs a concise analysis to provide quick feedback. For moderately important data, the analysis unit performs an analysis with a moderate level of detail to provide balanced feedback. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into an AI model and have the AI ​​adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit applies a health analysis algorithm to health-related data, an entertainment analysis algorithm to entertainment-related data, and an environmental analysis algorithm to environmental-related data. By applying different analysis algorithms depending on the data category, more appropriate data analysis becomes possible. 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 the data category into an AI model and apply different analysis algorithms to the AI.

[0045] The analysis unit can prioritize analyses based on the data collection timing during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can analyze long-term trends based on historical data. The analysis unit can prioritize the analysis of data from a specific period and provide insights for that period. This enables efficient data analysis by prioritizing analyses based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the data collection timing into an AI model and have the AI ​​determine the analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide deeper insights. It can also analyze moderately relevant data to provide balanced feedback. Finally, it can briefly analyze low-relevance data to provide rapid feedback. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of the data into an AI model and have the AI ​​adjust the order of analysis.

[0047] The proposal function can adjust the level of detail of its proposals based on the importance of the user's needs. For example, for high-priority needs, the proposal function provides detailed proposals and deep insights. For low-priority needs, it provides concise proposals and quick feedback. For medium-priority needs, it provides proposals with a moderate level of detail and balanced feedback. By adjusting the level of detail of proposals based on the importance of the user's needs, more appropriate proposals can be made. Some or all of the above processing in the proposal function may be performed using AI, for example, or not. For example, the proposal function can input the importance of the user's needs into an AI model and have the AI ​​adjust the level of detail of the proposals.

[0048] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, the suggestion unit applies a health suggestion algorithm to health-related needs, an entertainment suggestion algorithm to entertainment-related needs, and an environmental suggestion algorithm to environmental-related needs. By applying different suggestion algorithms depending on the user's category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's category into an AI model and apply different suggestion algorithms to the AI.

[0049] The proposal department can prioritize proposals based on when the user's needs were submitted. For example, the proposal department might prioritize recent needs and provide real-time feedback. The proposal department might make long-term proposals based on past needs. The proposal department might prioritize needs for a specific period and provide insights for that period. This allows for more appropriate proposals by prioritizing proposals based on when the user's needs were submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input the timing of the user's needs submission into an AI model and have the AI ​​determine the priority of proposals.

[0050] The suggestion function can adjust the order of suggestions based on the relevance of the user's needs. For example, it may prioritize highly relevant needs to provide deeper insights. It may also suggest moderately relevant needs to provide balanced feedback. Finally, it may briefly suggest less relevant needs to provide quick feedback. By adjusting the order of suggestions based on the relevance of the user's needs, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input the relevance of the user's needs into an AI model and have the AI ​​adjust the order of suggestions.

[0051] The control unit can analyze the resident's past usage history and select the optimal control method during control. For example, the control unit can set the optimal temperature based on the resident's preferred air conditioner settings in the past. The control unit can set the optimal brightness based on the resident's past lighting usage history. The control unit can select the optimal control method based on the resident's past use history of home appliances. By selecting the optimal control method based on the resident's past usage history, a more comfortable environment can be provided. 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 resident's past usage history into an AI model and have the AI ​​select the optimal control method.

[0052] The control unit can customize the control means based on the resident's current living situation during control. For example, if the resident is relaxing in the living room, the control unit adjusts the lighting to a calming brightness. If the resident is resting in the bedroom, the control unit adjusts the air conditioner temperature to a comfortable setting. If the resident is cooking in the kitchen, the control unit sets the exhaust fan speed to a higher setting. In this way, a more comfortable environment can be provided by customizing the control means based on the resident'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 the resident's current living situation into an AI model and have the AI ​​customize the control means.

[0053] The control unit can select the optimal control method when controlling a resident, taking into account their geographical location. For example, if a resident is at home, the control unit will optimally adjust the indoor temperature and humidity. If a resident is away from home, the control unit will prioritize security-related controls. If a resident is in a specific location, the control unit will prioritize controls related to that location. By selecting the optimal control method based on the resident's geographical location, a more comfortable environment can be provided. 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 resident's geographical location into an AI model and have the AI ​​select the optimal control method.

[0054] The control unit can analyze the residents' social media activities during control and propose control measures. For example, the control unit can propose controls related to the residents' interests based on information shared on social media. The control unit can propose controls related to the accounts that residents follow on social media. The control unit can propose controls related to events that residents participate in on social media. In this way, a more comfortable environment can be provided by proposing control measures based on the residents' social media activities. 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 residents' social media activities into an AI model and propose control measures.

[0055] The recommendation unit can provide optimal suggestions by referring to the resident's past purchase history when making suggestions. For example, the recommendation unit suggests relevant products based on the products the resident has purchased in the past. The recommendation unit prioritizes suggesting products of a specific brand based on the resident's past purchase history. The recommendation unit analyzes the resident's past purchase history and suggests the most suitable products. This allows for more appropriate suggestions by providing optimal suggestions based on the resident's past purchase history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the resident's past purchase history into an AI model and have the AI ​​provide optimal suggestions.

[0056] The recommendation unit can customize its suggestions based on the resident's current living situation. For example, if the resident is relaxing in the living room, it will suggest entertainment-related products. If the resident is resting in the bedroom, it will suggest products that support comfortable sleep. If the resident is cooking in the kitchen, it will suggest cooking-related products. By customizing the suggestions based on the resident's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the resident's current living situation into an AI model and have the AI ​​customize the suggestions.

[0057] The recommendation unit can provide optimal suggestions by considering the resident's geographical location information when making suggestions. For example, if the resident is at home, the recommendation unit will suggest products to be used indoors. If the resident is out, the recommendation unit will suggest products to be used while out. If the resident is in a specific location, the recommendation unit will suggest products related to that location. This allows for more appropriate suggestions by providing optimal suggestions based on the resident's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the resident's geographical location information into an AI model and have the AI ​​provide optimal suggestions.

[0058] The recommendation unit can analyze the resident's social media activity and propose methods for making suggestions. For example, the recommendation unit can suggest products related to the resident's interests based on information shared on social media. The recommendation unit can suggest related products based on information about accounts followed by the resident on social media. The recommendation unit can suggest products related to events the resident is participating in on social media. By proposing methods for making suggestions based on the resident's social media activity, more appropriate suggestions can be made. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the resident's social media activity into an AI model and propose methods for making suggestions.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can monitor the health status of residents and collect health data. For example, the data collection unit can detect vital signs such as heart rate, blood pressure, and body temperature using sensors and collect data. The data collection unit can also monitor sleep quality and exercise levels and collect data. This allows for a detailed understanding of the residents' health status and can be used for health management. 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 health data into an AI model and have the AI ​​perform health status monitoring.

[0061] The suggestion function can propose leisure activities based on the hobbies and interests of the residents. For example, if a resident enjoys outdoor activities, it can suggest nearby hiking trails or campgrounds. If a resident enjoys cultural activities, it can suggest events at nearby art museums or theaters. If a resident enjoys sports, it can suggest nearby sports facilities or events. In this way, by suggesting leisure activities based on the hobbies and interests of the residents, a more fulfilling life can be provided. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input the resident's hobbies and interests into an AI model and have the AI ​​execute the leisure activity suggestions.

[0062] The suggestion unit can analyze the past behavioral patterns of residents and propose optimal leisure activities. For example, it can suggest relevant leisure activities based on places the resident has frequently visited in the past. It can also suggest leisure activities suitable for a specific time of day based on the resident's past behavioral patterns. By analyzing the resident's past behavioral patterns, it can propose leisure activities suitable for a specific event. In this way, by suggesting optimal leisure activities based on past behavioral patterns, it can provide a more fulfilling life. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the resident's past behavioral patterns into an AI model and have the AI ​​propose optimal leisure activities.

[0063] The suggestion unit can propose optimal leisure activities considering the geographical location information of the residents. For example, if a resident is at home, it suggests nearby leisure activities. If a resident is out, it suggests leisure activities that can be enjoyed at their destination. If a resident is in a specific location, it suggests leisure activities related to that location. By suggesting optimal leisure activities based on the resident's geographical location information, it is possible to provide a more fulfilling life. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the resident's geographical location information into an AI model and have the AI ​​suggest optimal leisure activities.

[0064] The suggestion unit can analyze residents' social media activity and propose leisure activities. For example, it can suggest leisure activities related to residents' interests based on information they have shared on social media. It can also suggest relevant leisure activities based on information about accounts residents follow on social media. It can suggest leisure activities related to events residents participate in on social media. By suggesting leisure activities based on residents' social media activity, it can provide a more fulfilling life for them. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input residents' social media activity into an AI model and have the AI ​​make leisure activity suggestions.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects lifestyle logs using various sensors. For example, it collects data on the usage of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of residents. The data collection unit can record and collect data on air conditioner usage and power consumption, lighting usage time and brightness settings, and home appliance usage time and power consumption. Step 2: The analysis unit analyzes the lifestyle logs collected by the data collection unit to understand the residents' behavior patterns and preferences. For example, it analyzes air conditioner usage and indoor temperature changes to understand residents' behavior patterns. It analyzes lighting usage and brightness settings to understand residents' preferences. It analyzes the usage and power consumption of home appliances to comprehensively understand residents' behavior patterns and preferences. Step 3: Based on the analysis results obtained by the analysis department, the proposal department optimizes the home environment and proposes products and services tailored to the user's needs. For example, if residents frequently use the air conditioner at certain times, the department will set it to automatically turn on during those times. If residents prefer a specific level of lighting brightness, the department will automatically adjust it to that level. If residents desire an eco-friendly lifestyle, the department will propose energy-efficient home appliances and services that help save money.

[0067] (Example of form 2) The Smart Life Concierge according to an embodiment of the present invention is a system that uses various data collected within a smart home to understand the lifestyle of the inhabitants and proposes the most suitable products and services. By analyzing lifestyle logs collected by various sensors, the Smart Life Concierge can grasp in detail the daily behavior patterns and preferences of the inhabitants. Next, based on the analysis results, it optimizes the home environment to suit the individual needs of the user and recommends products and services that the user requires. This makes it possible to further personalize the smart home experience and improve convenience and comfort. For example, the Smart Life Concierge collects data such as the usage status of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of the inhabitants. For example, by collecting data on air conditioner usage, changes in indoor temperature, and the movement patterns of the inhabitants and analyzing it with AI, it is possible to understand the behavior patterns and preferences of the inhabitants. Next, based on the analysis results, it optimizes the home environment to suit the individual needs of the user. For example, if an inhabitant frequently uses the air conditioner during a specific time period, it can set the air conditioner to automatically turn on during that time. Also, if an inhabitant prefers a specific brightness level of lighting, it can automatically adjust the brightness to that level. This makes it possible to provide a comfortable environment tailored to the inhabitants' preferences. Furthermore, it recommends products and services that the user requires. For example, if residents desire an eco-conscious lifestyle, energy-efficient appliances and services that help save money can be suggested. Also, if residents are interested in new products and services, products and services tailored to their needs can be recommended. This can further enrich the lives of residents. This system makes it possible to further personalize the smart home experience and improve convenience and comfort. For instance, if residents frequently use the air conditioner at certain times, setting it to automatically turn on during those times can provide a comfortable environment tailored to their preferences. Furthermore, if residents desire an eco-conscious lifestyle, suggesting energy-efficient appliances and services that help save money can further enrich their lives.This allows the Smart Life Concierge to make the lives of residents more comfortable and convenient.

[0068] The smart life concierge according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects lifestyle logs using various sensors. The data collection unit collects data such as the usage status of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of residents. For example, the data collection unit can detect the usage status of air conditioners with sensors and collect data. The data collection unit can also detect the usage status of lighting with sensors and collect data. Furthermore, the data collection unit can detect the usage status of home appliances with sensors and collect data. For example, the data collection unit records the usage time and power consumption of air conditioners and collects data. It records the usage time and brightness settings of lighting and collects data. It records the usage time and power consumption of home appliances and collects data. The analysis unit analyzes the lifestyle logs collected by the data collection unit to understand the behavior patterns and preferences of the residents. For example, the analysis unit can analyze the collected data to understand the behavior patterns of the residents. Furthermore, the analysis unit can analyze the collected data to understand the preferences of the residents. Furthermore, the analysis department can analyze the collected data to comprehensively understand the behavioral patterns and preferences of the residents. For example, the analysis department can analyze the usage of air conditioners and changes in indoor temperature to understand the residents' behavioral patterns. It can also analyze the usage of lighting and brightness settings to understand the residents' preferences. It can analyze the usage and power consumption of home appliances to comprehensively understand the residents' behavioral patterns and preferences. Based on the analysis results obtained by the analysis department, the proposal department optimizes the home environment and proposes products and services that meet the user's needs. For example, the proposal department can optimize the home environment to meet the user's needs based on the analysis results. It can also propose products and services that the user needs based on the analysis results. Furthermore, the proposal department can comprehensively optimize the home environment and propose products and services that meet the user's needs based on the analysis results. For example, if a resident frequently uses the air conditioner during a specific time period, the proposal department can set it to automatically turn on during that time period. If a resident prefers a specific brightness level of lighting, the proposal department can automatically adjust the brightness to that level.If residents desire an eco-conscious lifestyle, the system will propose energy-efficient home appliances and services that help save money. This allows the Smart Life Concierge, according to this embodiment, to understand the user's lifestyle and propose the most suitable products and services.

[0069] The data collection unit collects lifestyle logs using various sensors. For example, it collects data on the usage of air conditioners, lighting, and home appliances, as well as indoor temperature and humidity, and the movement patterns of residents. Specifically, it detects air conditioner usage with sensors and records usage time and power consumption. This allows for understanding air conditioner operating time and energy consumption trends. Similarly, it detects lighting usage time and brightness settings with sensors and collects data. This allows for understanding lighting usage patterns and preferred brightness levels. Furthermore, it also collects data on the usage of home appliances by recording usage time and power consumption with sensors. For example, it records the usage of appliances such as refrigerators, washing machines, and televisions in detail, allowing for understanding energy consumption trends and usage frequency. It also collects real-time data on indoor temperature and humidity with sensors, providing information to maintain a comfortable environment. Regarding residents' movement patterns, for example, sensors installed on doors and windows detect people entering and leaving, and collect data. This allows for understanding the residents' daily rhythms and behavioral patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis department analyzes lifestyle logs collected by the data collection department to understand the behavioral patterns and preferences of residents. Specifically, it analyzes air conditioner usage and indoor temperature changes to understand residents' behavioral patterns. For example, by analyzing data on air conditioner usage time and power consumption, it can identify when residents tend to use air conditioners and suggest settings to maintain a comfortable temperature during those times. By analyzing lighting usage and brightness settings, it can understand residents' preferred brightness and usage patterns. For example, if a resident prefers a specific brightness at a particular time, it can suggest settings to automatically adjust the lighting during that time. By analyzing the usage and power consumption of home appliances, it can comprehensively understand residents' behavioral patterns and preferences. For example, by analyzing the frequency of use and energy consumption trends of appliances such as refrigerators, washing machines, and televisions, it can suggest energy-efficient usage methods and ways to save energy. Furthermore, based on the collected data, the analysis department can comprehensively understand residents' behavioral patterns and preferences and predict long-term trends and changes. For example, by analyzing seasonal air conditioner usage patterns and lighting usage trends, it can predict future energy consumption and suggest optimal settings. This allows the analysis department to provide a foundation for making optimal suggestions tailored to the lifestyles of the residents.

[0071] The Proposal Department, based on the analysis results obtained by the Analysis Department, optimizes the home environment and proposes products and services tailored to the user's needs. Specifically, if residents frequently use the air conditioner during certain times, the department sets it to automatically turn on during those times. This allows residents to maintain a comfortable environment while reducing energy waste. Similarly, if residents prefer a specific level of lighting, the department sets it to automatically adjust to that level, ensuring they always live in comfortable lighting. Furthermore, if residents desire an eco-conscious lifestyle, the department proposes energy-efficient appliances and services that help conserve energy. For example, it suggests energy-efficient refrigerators and washing machines, and televisions with energy-saving modes to reduce energy consumption. The Proposal Department can also propose products and services tailored to the user's lifestyle and preferences. For example, it proposes smart devices and services that help with health management for health-conscious users, supporting a comfortable life. In addition, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of its proposals. For example, it monitors how effective the proposed settings and products actually are and makes adjustments or improvements as needed. This allows the proposal department to provide users with the optimal home environment, products, and services, supporting a comfortable and efficient lifestyle.

[0072] The data collection unit can collect data such as the usage status of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of residents. For example, the data collection unit can detect the usage status of air conditioners using sensors and collect data. The data collection unit can also detect the usage status of lighting using sensors and collect data. The data collection unit can also detect the usage status of home appliances using sensors and collect data. For example, the data collection unit can record the usage time and power consumption of air conditioners and collect data. The data collection unit can record the usage time and brightness settings of lighting and collect data. The data collection unit can record the usage time and power consumption of home appliances and collect data. By collecting diverse data in this way, it is possible to understand the behavior patterns and preferences of residents in detail. 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 collect data using an AI model that detects the usage status of air conditioners using sensors and collects data.

[0073] The analysis unit can analyze the collected data to understand the behavioral patterns and preferences of the residents. For example, the analysis unit can analyze the collected data to understand the behavioral patterns of the residents. The analysis unit can also analyze the collected data to understand the preferences of the residents. The analysis unit can also analyze the collected data to comprehensively understand the behavioral patterns and preferences of the residents. For example, the analysis unit can analyze the usage of air conditioners and changes in indoor temperature to understand the behavioral patterns of the residents. The analysis unit can analyze the usage of lighting and brightness settings to understand the preferences of the residents. The analysis unit can analyze the usage and power consumption of home appliances to comprehensively understand the behavioral patterns and preferences of the residents. This allows for a detailed understanding of the behavioral patterns and preferences of the residents through data analysis. 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 the collected data into an AI model and have the AI ​​perform the task of understanding the behavioral patterns and preferences of the residents.

[0074] The proposal unit can optimize the home environment to meet user needs based on the analysis results. For example, the proposal unit can optimize the home environment to meet user needs based on the analysis results. The proposal unit can also propose products and services that the user needs based on the analysis results. The proposal unit can also comprehensively optimize the home environment and propose products and services to meet user needs based on the analysis results. For example, if the resident frequently uses the air conditioner during certain times of the day, the proposal unit can set it to automatically turn on during those times. If the resident prefers a certain level of lighting brightness, the proposal unit can automatically adjust it to that level. If the resident desires an eco-conscious lifestyle, the proposal unit can propose energy-efficient home appliances and services that help save money. This makes it possible to optimize the home environment to meet user needs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the analysis results into an AI model and have the AI ​​perform the optimization of the home environment and propose products and services.

[0075] The proposal department can propose products and services that users need based on the analysis results. For example, the proposal department can propose products and services that users need based on the analysis results. The proposal department can also optimize the home environment to suit the user's needs based on the analysis results. The proposal department can also comprehensively optimize the home environment and propose products and services that suit the user's needs based on the analysis results. For example, if the residents are looking for an eco-conscious lifestyle, the proposal department will propose energy-efficient home appliances and services that help save money. If the residents are interested in new things and services, the proposal department will propose products and services that meet those needs. This allows the proposal department to appropriately propose products and services that users need. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results into an AI model and have the AI ​​make product and service proposals.

[0076] The proposed unit includes a control unit that controls air conditioners and lighting. The control unit can, for example, set the temperature of the air conditioner. The control unit can also adjust the brightness of the lighting. The control unit can also control home appliances. For example, the control unit adjusts the temperature of the air conditioner to a comfortable setting. The control unit adjusts the brightness of the lighting to the occupants' preferences. The control unit controls home appliances according to their usage. This makes it possible to optimize the home environment through the control of air conditioners and lighting. Some or all of the above-mentioned processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the temperature setting of the air conditioner into an AI model and have the AI ​​execute the optimal temperature setting.

[0077] The proposal unit includes a recommendation unit that proposes new products and services. The recommendation unit can, for example, propose new products. The recommendation unit can also recommend services. The recommendation unit can also propose new products and services tailored to the user's needs. For example, if a resident desires an eco-conscious lifestyle, the recommendation unit will propose energy-efficient home appliances and services that help save money. If a resident is interested in new products and services, the recommendation unit will propose products and services that meet those needs. This makes it possible to propose new products and services. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's needs into an AI model and have the AI ​​execute proposals for new products and services.

[0078] The data collection unit can estimate the emotions of the residents and adjust the timing of data collection based on the estimated emotions. For example, if the residents are relaxed, the data collection unit will collect data frequently to obtain a detailed lifestyle log. If the residents are stressed, the data collection unit will reduce the frequency of data collection to respect their privacy. If the residents are active, the data collection unit will collect data in real time and provide immediate feedback. This allows for more appropriate data collection by adjusting the timing of data collection according to the residents' 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 adjust the timing of data collection using an AI model that estimates the emotions of the residents.

[0079] The data collection unit can analyze the residents' past behavioral patterns and select the optimal data collection method. For example, the data collection unit prioritizes collecting data on household appliances that residents have frequently used in the past. Based on the residents' past movement patterns, the data collection unit concentrates data collection during specific time periods. Based on the residents' past behavioral patterns, the data collection unit intensifies data collection during specific events. This enables efficient data collection by selecting the optimal data collection method based on past behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the residents' past behavioral patterns into an AI model and have the AI ​​select the optimal data collection method.

[0080] The data collection unit can filter data based on the resident's current activities and areas of interest during data collection. For example, if the resident is cooking, the unit will prioritize collecting data on kitchen appliances. If the resident is relaxing in the living room, the unit will collect data on air conditioning and lighting. If the resident is out, the unit will collect security-related data. By filtering data based on current activities and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the resident's current activities and areas of interest into an AI model and have the AI ​​perform the data filtering.

[0081] The data collection unit can estimate the emotions of the residents and determine the priority of data to collect based on the estimated emotions. For example, if the residents are relaxed, the data collection unit will prioritize collecting entertainment-related data. If the residents are stressed, the data collection unit will prioritize collecting health-related data. If the residents are active, the data collection unit will prioritize collecting exercise-related data. This allows for the priority collection of more important data by prioritizing data according to the residents' 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 determine the priority of data to collect using an AI model that estimates the emotions of the residents.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the residents during data collection. For example, if a resident is at home, the data collection unit prioritizes the collection of indoor temperature and humidity data. If a resident is away from home, the data collection unit prioritizes the collection of external environmental data. If a resident is in a specific location, the data collection unit prioritizes the collection of data related to that location. This enables more appropriate data collection by prioritizing the collection of highly relevant data based on geographical location 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 geographical location information of residents into an AI model and prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze residents' social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to residents' interests based on information they share on social media. The data collection unit can collect relevant data based on information about accounts that residents follow on social media. The data collection unit can collect data related to events that residents participate in on social media. This allows for the collection of more relevant data by collecting relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input residents' social media activity into an AI model and collect relevant data.

[0084] The analysis unit can estimate the emotions of the residents and adjust the data analysis method based on the estimated emotions. For example, if the residents are relaxed, the analysis unit performs a detailed data analysis to provide deep insights. If the residents are stressed, the analysis unit performs a concise data analysis to provide quick feedback. If the residents are active, the analysis unit performs data analysis in real time to provide immediate feedback. This allows for more appropriate data analysis by adjusting the data analysis method according to the residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can adjust the data analysis method using an AI model that estimates the emotions of the residents.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during data analysis. For example, the analysis unit performs a detailed analysis on highly important data to provide deep insights. For less important data, the analysis unit performs a concise analysis to provide quick feedback. For moderately important data, the analysis unit performs an analysis with a moderate level of detail to provide balanced feedback. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into an AI model and have the AI ​​adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit applies a health analysis algorithm to health-related data, an entertainment analysis algorithm to entertainment-related data, and an environmental analysis algorithm to environmental-related data. By applying different analysis algorithms depending on the data category, more appropriate data analysis becomes possible. 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 the data category into an AI model and apply different analysis algorithms to the AI.

[0087] The analysis unit can estimate the emotions of the inhabitants and adjust the display method of the analysis results based on the estimated emotions. For example, if the inhabitants are relaxed, the analysis unit will display detailed analysis results. If the inhabitants are stressed, the analysis unit will display concise analysis results. If the inhabitants are active, the analysis unit will display analysis results in real time. This allows for more appropriate feedback by adjusting the display method of the analysis results according to the emotions of the inhabitants. 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 adjust the display method of the analysis results using an AI model that estimates the emotions of the inhabitants.

[0088] The analysis unit can prioritize analyses based on the data collection timing during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can analyze long-term trends based on historical data. The analysis unit can prioritize the analysis of data from a specific period and provide insights for that period. This enables efficient data analysis by prioritizing analyses based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the data collection timing into an AI model and have the AI ​​determine the analysis priorities.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide deeper insights. It can also analyze moderately relevant data to provide balanced feedback. Finally, it can briefly analyze low-relevance data to provide rapid feedback. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of the data into an AI model and have the AI ​​adjust the order of analysis.

[0090] The suggestion unit can estimate the emotions of the residents and adjust the way it presents its suggestions based on those estimated emotions. For example, if the residents are relaxed, the suggestion unit can provide detailed suggestions and deeper insights. If the residents are stressed, the suggestion unit can provide concise suggestions and rapid feedback. If the residents are active, the suggestion unit can provide suggestions in real time and immediate feedback. This allows for more appropriate suggestions by adjusting the way they are presented according to the residents' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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, for example, or not using AI. For example, the suggestion unit can adjust the way it presents its suggestions using an AI model that estimates the emotions of residents.

[0091] The proposal function can adjust the level of detail of its proposals based on the importance of the user's needs. For example, for high-priority needs, the proposal function provides detailed proposals and deep insights. For low-priority needs, it provides concise proposals and quick feedback. For medium-priority needs, it provides proposals with a moderate level of detail and balanced feedback. By adjusting the level of detail of proposals based on the importance of the user's needs, more appropriate proposals can be made. Some or all of the above processing in the proposal function may be performed using AI, for example, or not. For example, the proposal function can input the importance of the user's needs into an AI model and have the AI ​​adjust the level of detail of the proposals.

[0092] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, the suggestion unit applies a health suggestion algorithm to health-related needs, an entertainment suggestion algorithm to entertainment-related needs, and an environmental suggestion algorithm to environmental-related needs. By applying different suggestion algorithms depending on the user's category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's category into an AI model and apply different suggestion algorithms to the AI.

[0093] The suggestion unit can estimate the resident's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the resident is relaxed, the suggestion unit will provide detailed suggestions and deep insights. If the resident is stressed, the suggestion unit will provide concise suggestions and quick feedback. If the resident is active, the suggestion unit will provide suggestions in real time and immediate feedback. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the resident'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 processing described above in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can adjust the length of the suggestion using an AI model that estimates the resident's emotions.

[0094] The proposal department can prioritize proposals based on when the user's needs were submitted. For example, the proposal department might prioritize recent needs and provide real-time feedback. The proposal department might make long-term proposals based on past needs. The proposal department might prioritize needs for a specific period and provide insights for that period. This allows for more appropriate proposals by prioritizing proposals based on when the user's needs were submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input the timing of the user's needs submission into an AI model and have the AI ​​determine the priority of proposals.

[0095] The suggestion function can adjust the order of suggestions based on the relevance of the user's needs. For example, it may prioritize highly relevant needs to provide deeper insights. It may also suggest moderately relevant needs to provide balanced feedback. Finally, it may briefly suggest less relevant needs to provide quick feedback. By adjusting the order of suggestions based on the relevance of the user's needs, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input the relevance of the user's needs into an AI model and have the AI ​​adjust the order of suggestions.

[0096] The control unit can estimate the emotions of the occupants and adjust the control methods for the air conditioner and lighting based on the estimated emotions. For example, if the occupants are relaxed, the control unit adjusts the air conditioner temperature to a comfortable setting. If the occupants are stressed, the control unit adjusts the brightness of the lighting to a calming setting. If the occupants are active, the control unit sets the airflow of the air conditioner to a stronger setting. In this way, a more comfortable environment can be provided by adjusting the control methods for the air conditioner and lighting according to the emotions of the occupants. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can adjust the control methods for the air conditioner and lighting using an AI model that estimates the emotions of the occupants.

[0097] The control unit can analyze the resident's past usage history and select the optimal control method during control. For example, the control unit can set the optimal temperature based on the resident's preferred air conditioner settings in the past. The control unit can set the optimal brightness based on the resident's past lighting usage history. The control unit can select the optimal control method based on the resident's past use history of home appliances. By selecting the optimal control method based on the resident's past usage history, a more comfortable environment can be provided. 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 resident's past usage history into an AI model and have the AI ​​select the optimal control method.

[0098] The control unit can customize the control means based on the resident's current living situation during control. For example, if the resident is relaxing in the living room, the control unit adjusts the lighting to a calming brightness. If the resident is resting in the bedroom, the control unit adjusts the air conditioner temperature to a comfortable setting. If the resident is cooking in the kitchen, the control unit sets the exhaust fan speed to a higher setting. In this way, a more comfortable environment can be provided by customizing the control means based on the resident'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 the resident's current living situation into an AI model and have the AI ​​customize the control means.

[0099] The control unit can estimate the emotions of the inhabitants and determine control priorities based on the estimated emotions. For example, if the inhabitants are relaxed, the control unit prioritizes adjusting the air conditioner temperature. If the inhabitants are stressed, the control unit prioritizes adjusting the brightness of the lighting. If the inhabitants are active, the control unit prioritizes controlling household appliances. This allows for a more comfortable environment by determining control priorities according to the emotions of the inhabitants. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can determine control priorities using an AI model that estimates the emotions of the inhabitants.

[0100] The control unit can select the optimal control method when controlling a resident, taking into account their geographical location. For example, if a resident is at home, the control unit will optimally adjust the indoor temperature and humidity. If a resident is away from home, the control unit will prioritize security-related controls. If a resident is in a specific location, the control unit will prioritize controls related to that location. By selecting the optimal control method based on the resident's geographical location, a more comfortable environment can be provided. 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 resident's geographical location into an AI model and have the AI ​​select the optimal control method.

[0101] The control unit can analyze the residents' social media activities during control and propose control measures. For example, the control unit can propose controls related to the residents' interests based on information shared on social media. The control unit can propose controls related to the accounts that residents follow on social media. The control unit can propose controls related to events that residents participate in on social media. In this way, a more comfortable environment can be provided by proposing control measures based on the residents' social media activities. 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 residents' social media activities into an AI model and propose control measures.

[0102] The recommendation system can estimate the emotions of the residents and adjust how it suggests new products and services based on those estimated emotions. For example, if the residents are relaxed, the recommendation system will provide detailed suggestions and deep insights. If the residents are stressed, the recommendation system will provide concise suggestions and quick feedback. If the residents are active, the recommendation system will provide real-time suggestions and immediate feedback. This allows for more appropriate suggestions by adjusting how new products and services are suggested according to the residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, 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 recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can adjust how it suggests new products and services using an AI model that estimates the emotions of residents.

[0103] The recommendation unit can provide optimal suggestions by referring to the resident's past purchase history when making suggestions. For example, the recommendation unit suggests relevant products based on the products the resident has purchased in the past. The recommendation unit prioritizes suggesting products of a specific brand based on the resident's past purchase history. The recommendation unit analyzes the resident's past purchase history and suggests the most suitable products. This allows for more appropriate suggestions by providing optimal suggestions based on the resident's past purchase history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the resident's past purchase history into an AI model and have the AI ​​provide optimal suggestions.

[0104] The recommendation unit can customize its suggestions based on the resident's current living situation. For example, if the resident is relaxing in the living room, it will suggest entertainment-related products. If the resident is resting in the bedroom, it will suggest products that support comfortable sleep. If the resident is cooking in the kitchen, it will suggest cooking-related products. By customizing the suggestions based on the resident's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the resident's current living situation into an AI model and have the AI ​​customize the suggestions.

[0105] The recommendation unit can estimate the resident's emotions and prioritize suggestions based on those emotions. For example, if the resident is relaxed, the recommendation unit will prioritize entertainment-related suggestions. If the resident is stressed, the recommendation unit will prioritize health-related suggestions. If the resident is active, the recommendation unit will prioritize exercise-related suggestions. This allows for more appropriate suggestions by prioritizing suggestions according to the resident'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 processing described above in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can use an AI model that estimates the resident's emotions to determine the priority of suggestions.

[0106] The recommendation unit can provide optimal suggestions by considering the resident's geographical location information when making suggestions. For example, if the resident is at home, the recommendation unit will suggest products to be used indoors. If the resident is out, the recommendation unit will suggest products to be used while out. If the resident is in a specific location, the recommendation unit will suggest products related to that location. This allows for more appropriate suggestions by providing optimal suggestions based on the resident's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the resident's geographical location information into an AI model and have the AI ​​provide optimal suggestions.

[0107] The recommendation unit can analyze the resident's social media activity and propose methods for making suggestions. For example, the recommendation unit can suggest products related to the resident's interests based on information shared on social media. The recommendation unit can suggest related products based on information about accounts followed by the resident on social media. The recommendation unit can suggest products related to events the resident is participating in on social media. By proposing methods for making suggestions based on the resident's social media activity, more appropriate suggestions can be made. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the resident's social media activity into an AI model and propose methods for making suggestions.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data collection unit can monitor the health status of residents and collect health data. For example, the data collection unit can detect vital signs such as heart rate, blood pressure, and body temperature using sensors and collect data. The data collection unit can also monitor sleep quality and exercise levels and collect data. This allows for a detailed understanding of the residents' health status and can be used for health management. 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 health data into an AI model and have the AI ​​perform health status monitoring.

[0110] The analysis unit can estimate the emotions of residents and adjust the method of analyzing health data based on the estimated emotions. For example, if a resident is relaxed, it can perform a detailed health data analysis to provide deep insights. If a resident is stressed, it can perform a concise health data analysis to provide quick feedback. If a resident is active, it can perform a real-time health data analysis to provide immediate feedback. This allows for more appropriate health management by adjusting the method of analyzing health data according to the emotions of residents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can adjust the method of analyzing health data using an AI model that estimates the emotions of residents.

[0111] The suggestion function can propose leisure activities based on the hobbies and interests of the residents. For example, if a resident enjoys outdoor activities, it can suggest nearby hiking trails or campgrounds. If a resident enjoys cultural activities, it can suggest events at nearby art museums or theaters. If a resident enjoys sports, it can suggest nearby sports facilities or events. In this way, by suggesting leisure activities based on the hobbies and interests of the residents, a more fulfilling life can be provided. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input the resident's hobbies and interests into an AI model and have the AI ​​execute the leisure activity suggestions.

[0112] The data collection unit can estimate the emotions of the residents and adjust the accuracy of data collection based on the estimated emotions. For example, if the residents are relaxed, detailed data collection is performed to obtain a highly accurate lifestyle log. If the residents are stressed, the accuracy of data collection is reduced to respect their privacy. If the residents are active, data is collected in real time to provide immediate feedback. This allows for more appropriate data collection by adjusting the accuracy of data collection according to the residents' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 adjust the accuracy of data collection using an AI model that estimates the emotions of the residents.

[0113] The suggestion unit can analyze the past behavioral patterns of residents and propose optimal leisure activities. For example, it can suggest relevant leisure activities based on places the resident has frequently visited in the past. It can also suggest leisure activities suitable for a specific time of day based on the resident's past behavioral patterns. By analyzing the resident's past behavioral patterns, it can propose leisure activities suitable for a specific event. In this way, by suggesting optimal leisure activities based on past behavioral patterns, it can provide a more fulfilling life. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the resident's past behavioral patterns into an AI model and have the AI ​​propose optimal leisure activities.

[0114] The data collection unit can estimate the emotions of the residents and select the types of data to collect based on the estimated emotions. For example, if the residents are relaxed, entertainment-related data can be prioritized for collection. If the residents are stressed, health-related data can be prioritized for collection. If the residents are active, exercise-related data can be prioritized for collection. This allows for the priority collection of more important data by selecting the types of data to collect according to the emotions of the residents. 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 select the types of data to collect using an AI model that estimates the emotions of the residents.

[0115] The suggestion unit can propose optimal leisure activities considering the geographical location information of the residents. For example, if a resident is at home, it suggests nearby leisure activities. If a resident is out, it suggests leisure activities that can be enjoyed at their destination. If a resident is in a specific location, it suggests leisure activities related to that location. By suggesting optimal leisure activities based on the resident's geographical location information, it is possible to provide a more fulfilling life. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the resident's geographical location information into an AI model and have the AI ​​suggest optimal leisure activities.

[0116] The analysis unit can estimate the emotions of the residents and adjust the display method of the analysis results based on the estimated emotions. For example, if the residents are relaxed, detailed analysis results are displayed. If the residents are stressed, concise analysis results are displayed. If the residents are active, analysis results are displayed in real time. This allows for more appropriate feedback by adjusting the display method of the analysis results according to the emotions of the residents. 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 adjust the display method of the analysis results using an AI model that estimates the emotions of the residents.

[0117] The suggestion unit can analyze residents' social media activity and propose leisure activities. For example, it can suggest leisure activities related to residents' interests based on information they have shared on social media. It can also suggest relevant leisure activities based on information about accounts residents follow on social media. It can suggest leisure activities related to events residents participate in on social media. By suggesting leisure activities based on residents' social media activity, it can provide a more fulfilling life for them. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input residents' social media activity into an AI model and have the AI ​​make leisure activity suggestions.

[0118] The data collection unit can estimate the emotions of the residents and prioritize the data to be collected based on the estimated emotions. For example, if the residents are relaxed, entertainment-related data will be prioritized for collection. If the residents are stressed, health-related data will be prioritized for collection. If the residents are active, exercise-related data will be prioritized for collection. This allows for the collection of more important data by prioritizing the data to be collected according to the emotions of the residents. 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 use an AI model to estimate the emotions of the residents to prioritize the data to be collected.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects lifestyle logs using various sensors. For example, it collects data on the usage of air conditioners, lighting, and home appliances, indoor temperature and humidity, and the movement patterns of residents. The data collection unit can record and collect data on air conditioner usage and power consumption, lighting usage time and brightness settings, and home appliance usage time and power consumption. Step 2: The analysis unit analyzes the lifestyle logs collected by the data collection unit to understand the residents' behavior patterns and preferences. For example, it analyzes air conditioner usage and indoor temperature changes to understand residents' behavior patterns. It analyzes lighting usage and brightness settings to understand residents' preferences. It analyzes the usage and power consumption of home appliances to comprehensively understand residents' behavior patterns and preferences. Step 3: Based on the analysis results obtained by the analysis department, the proposal department optimizes the home environment and proposes products and services tailored to the user's needs. For example, if residents frequently use the air conditioner at certain times, the department will set it to automatically turn on during those times. If residents prefer a specific level of lighting brightness, the department will automatically adjust it to that level. If residents desire an eco-friendly lifestyle, the department will propose energy-efficient home appliances and services that help save money.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the smart device 14 and processes the data with the control unit 46A. 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 optimal suggestions to the user based on the analysis results. The control unit is implemented in the specific processing unit 46A of the smart device 14 and controls air conditioners and lighting. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for new products and services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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 (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 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and recommendation 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 data using the camera 42 and sensors of the smart glasses 214 and processes the data with the control unit 46A. 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 optimal suggestions to the user based on the analysis results. The control unit is implemented in the specific processing unit 46A of the smart glasses 214 and controls air conditioning and lighting. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for new products and services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In 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.

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

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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 part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and recommendation 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 data using the camera 42 and sensors of the headset terminal 314 and processes the data with the control unit 46A. 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 optimal suggestions to the user based on the analysis results. The control unit is implemented in the specific processing unit 46A of the headset terminal 314 and controls air conditioning and lighting. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for new products and services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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 (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 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.

[0172] 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 external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0173] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and recommendation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and sensors of the robot 414 and processes the data with the control unit 46A. 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 optimal suggestions to the user based on the analysis results. The control unit is implemented in the specific processing unit 46A of the robot 414 and controls air conditioning and lighting. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions for new products and services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A collection unit that collects lifestyle logs using various sensors, The analysis unit analyzes the lifestyle logs collected by the aforementioned collection unit to understand the behavioral patterns and preferences of the residents, Based on the analysis results obtained by the aforementioned analysis unit, the system includes a proposal unit that optimizes the home environment and proposes products and services tailored to the user's needs. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data on the usage of air conditioners, lighting, and other home appliances, as well as indoor temperature and humidity, and the movement patterns of residents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to understand the behavioral patterns and preferences of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we will optimize the home environment to meet the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we propose products and services that users need. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It includes a control unit that controls the air conditioner and lighting. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, It features a recommendation section that suggests new products and services. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the emotions of the residents and adjusts the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the past behavioral patterns of residents and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the current activities and areas of interest of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the emotions of the residents and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the social media activity of residents is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate the emotions of the residents and adjust the data analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During data analysis, adjust the level of detail of the analysis based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing data, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the emotions of the residents and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing data, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During data analysis, adjust the order of analysis based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We estimate the emotions of the residents and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, The system estimates the residents' emotions and adjusts the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the user's needs were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, It estimates the emotions of the residents and adjusts the control methods for air conditioning and lighting based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The control unit, During control, the system analyzes the residents' past usage history to select the optimal control method. The system described in Appendix 2, characterized by the features described herein. (Note 28) The control unit, During control, the control methods are customized based on the current living situation of the residents. The system described in Appendix 2, characterized by the features described herein. (Note 29) The control unit, It estimates the emotions of the residents and determines the priority of control based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The control unit, During control, the optimal control method is selected by considering the geographical location information of the residents. The system described in Appendix 2, characterized by the features described herein. (Note 31) The control unit, During the control process, we analyze the residents' social media activity and propose control methods. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned recommendation section is, We estimate the emotions of the residents and adjust how we propose new products and services based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned recommendation section is, When making a proposal, we refer to the prospective resident's past purchase history to provide the most suitable suggestion. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned recommendation section is, When making a proposal, customize the proposal based on the current living situation of the residents. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned recommendation section is, The system estimates the emotions of the residents and prioritizes proposals based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned recommendation section is, When making a proposal, we take into account the geographical location information of the residents to provide the most suitable proposal. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned recommendation section is, When making a proposal, we analyze the social media activity of residents and propose methods for doing so. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects lifestyle logs using various sensors, The analysis unit analyzes the lifestyle logs collected by the aforementioned collection unit to understand the behavioral patterns and preferences of the residents, Based on the analysis results obtained by the aforementioned analysis unit, the system includes a proposal unit that optimizes the home environment and proposes products and services tailored to the user's needs. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data on the usage of air conditioners, lighting, and other home appliances, as well as indoor temperature and humidity, and the movement patterns of residents. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to understand the behavioral patterns and preferences of the residents. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we will optimize the home environment to meet the user's needs. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we propose products and services that users need. The system according to feature 1.

6. The aforementioned proposal section is, It includes a control unit that controls the air conditioner and lighting. The system according to feature 1.

7. The aforementioned proposal section is, It features a recommendation section that suggests new products and services. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the emotions of the residents and adjusts the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the past behavioral patterns of residents and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on the current activities and areas of interest of the residents. The system according to feature 1.

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