System

Abstract

An object of the system according to the embodiment is to support sustainable life by performing optimal environment control based on a user's preference or behavior pattern.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a control unit, and a proposal unit. The collection unit collects data from various sensors. The analysis unit analyzes the data collected by the collection unit and learns user preferences and behavior patterns. The control unit controls the air conditioner and the lighting based on the analysis result obtained by the analysis unit. The proposal unit performs a proposal for supporting a sustainable life on the basis of a control result performed by the control unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal environmental control based on user preferences and behavioral patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to support a sustainable lifestyle by optimally controlling the environment based on the user's preferences and behavioral patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a proposal unit. The collection unit collects data from various sensors. The analysis unit analyzes the data collected by the collection unit and learns the user's preferences and behavioral patterns. The control unit controls the air conditioner and lighting based on the analysis results obtained by the analysis unit. The proposal unit makes proposals to support sustainable living based on the control results obtained by the control unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimally control the environment based on the user's preferences and behavioral patterns, and can support a sustainable lifestyle. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A life assistance system according to an embodiment of the present invention uses AI to analyze data collected by various sensors and make optimal suggestions tailored to the user's preferences and environment. Based on information accumulated as a life log, the life assistance system learns the user's air conditioner usage frequency, preferred temperature, lighting preferences, and behavioral patterns, and automatically controls the system to emphasize energy savings and comfort. The life assistance system also makes suggestions to support sustainable living in line with new lifestyles. For example, the life assistance system is equipped with temperature sensors, illuminance sensors, and motion sensors, and AI analyzes data collected by these sensors. Based on the information accumulated as a life log, the life assistance system then learns the user's air conditioner usage frequency, preferred temperature, lighting preferences, and behavioral patterns. For example, the system learns the user's preferred time of day to use the air conditioner, their preferred temperature, and their preferred brightness. The life assistance system then analyzes this data using AI to automatically control the system to emphasize energy savings and comfort. For example, the system automatically adjusts the air conditioner's temperature setting and optimizes the brightness of the lights. This allows the user to live a comfortable life. The life assistance system also makes suggestions to support sustainable living in line with new lifestyles. For example, this includes advice on reducing energy consumption and recommendations for recycling. This allows the life assistance system to not only enable users to live a comfortable life, but also to save energy and achieve a sustainable lifestyle. This allows the life assistance system to automatically control air conditioning and lighting based on the user's preferences and behavioral patterns, supporting a sustainable lifestyle. For example, automatically adjusting the air conditioning temperature setting can reduce energy consumption. Also, optimizing the brightness of lighting can reduce power waste. Furthermore, by making suggestions to support a sustainable lifestyle, the user can live an environmentally conscious lifestyle.

[0029] The life assistance system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a proposal unit. The collection unit collects data from various sensors. The various sensors include, but are not limited to, a temperature sensor, an illuminance sensor, and a motion sensor, for example. The collection unit collects indoor temperature data using, for example, a temperature sensor. The collection unit can also collect indoor brightness data using an illuminance sensor. The collection unit can also collect user motion data using a motion sensor. For example, the collection unit monitors indoor temperature in real time using a temperature sensor and collects data. The collection unit can also measure indoor brightness using an illuminance sensor and collect data. The collection unit can also detect user movement using a motion sensor and collect data. The analysis unit analyzes the data collected by the collection unit to learn the user's preferences and behavioral patterns. For example, the analysis unit analyzes the collected temperature data to identify the user's preferred temperature. The analysis unit can also analyze the collected illuminance data to identify the user's preferred brightness. The analysis unit can also analyze the collected operation data to identify the user's behavioral patterns. For example, the analysis unit can identify what time of day the user uses the air conditioner based on the collected temperature data. The analysis unit can also identify the user's preferred brightness level based on the collected illuminance data. The analysis unit can also identify the user's behavioral patterns based on the collected operation data. The control unit controls the air conditioner and lighting based on the analysis results obtained by the analysis unit. For example, the control unit can automatically adjust the set temperature of the air conditioner based on the analysis results. The control unit can also optimize the brightness of the lighting based on the analysis results. The control unit can also change the operation mode of the air conditioner based on the analysis results. For example, the control unit can automatically adjust the set temperature of the air conditioner based on the analysis results to maintain a comfortable indoor environment. The control unit can also optimize the brightness of the lighting based on the analysis results to provide a comfortable lighting environment. The control unit can also change the operation mode of the air conditioner based on the analysis results to improve energy efficiency.The suggestion unit makes suggestions to support a sustainable lifestyle based on the control results performed by the control unit. The suggestion unit, for example, provides advice on reducing energy consumption. The suggestion unit can also recommend recycling. The suggestion unit can also provide information to support a sustainable lifestyle. For example, the suggestion unit suggests specific methods for reducing energy consumption. The suggestion unit can also recommend recycling to support an environmentally conscious lifestyle. The suggestion unit can also provide information to support a sustainable lifestyle and encourage the user to take environmentally conscious actions. As a result, the life assistance system according to the embodiment can automatically control air conditioners and lighting based on the user's preferences and behavioral patterns to support a sustainable lifestyle. For example, by automatically adjusting the set temperature of an air conditioner, a comfortable indoor environment can be maintained. Furthermore, by optimizing the brightness of lighting, a comfortable lighting environment can be provided. Furthermore, by making suggestions to support a sustainable lifestyle, the user can live an environmentally conscious lifestyle.

[0030] The collection unit can collect data from a temperature sensor, an illuminance sensor, or a motion sensor. The collection unit collects indoor temperature data using a temperature sensor. For example, the collection unit monitors the indoor temperature in real time using a temperature sensor and collects data. The collection unit can also collect indoor brightness data using an illuminance sensor. For example, the collection unit measures the indoor brightness using an illuminance sensor and collects data. The collection unit can also collect user behavior data using a motion sensor. For example, the collection unit detects the user's movements using a motion sensor and collects data. In this way, by collecting data from various sensors, detailed information about the user's environment can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a temperature sensor to a generation AI and have the generation AI analyze the temperature data.

[0031] The analysis unit can analyze the collected data and learn the user's preferences and behavioral patterns. The analysis unit can analyze the collected temperature data and identify the user's preferred temperature. For example, the analysis unit can identify the time of day the user uses the air conditioner based on the collected temperature data. The analysis unit can also analyze the collected illuminance data and identify the user's preferred brightness. For example, the analysis unit can identify the user's preferred brightness based on the collected illuminance data. The analysis unit can also analyze the collected operation data and identify the user's behavioral patterns. For example, the analysis unit can identify the user's behavioral patterns based on the collected operation data. This enables more appropriate control by learning the user's preferences and behavioral patterns. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to learn the user's preferences and behavioral patterns.

[0032] The control unit can automatically adjust the set temperature of the air conditioner based on the analysis results. The control unit automatically adjusts the set temperature of the air conditioner based on the analysis results. For example, the control unit automatically adjusts the set temperature of the air conditioner based on the analysis results to maintain a comfortable indoor environment. The control unit can also change the operation mode of the air conditioner based on the analysis results. For example, the control unit can change the operation mode of the air conditioner based on the analysis results to improve energy efficiency. As a result, a comfortable indoor environment can be maintained by automatically adjusting the set temperature of the air conditioner. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the analysis results to a generation AI and cause the generation AI to adjust the set temperature of the air conditioner.

[0033] The control unit can adjust the brightness of the lighting based on the analysis results. The control unit adjusts the brightness of the lighting based on the analysis results. For example, the control unit can optimize the brightness of the lighting based on the analysis results, providing a comfortable lighting environment. The control unit can also adjust the color temperature of the lighting based on the analysis results. For example, the control unit can adjust the color temperature of the lighting based on the analysis results, providing a lighting environment that suits the user's preferences. In this way, a comfortable lighting environment can be provided by optimizing the brightness of the lighting. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the analysis results to the generation AI and cause the generation AI to adjust the brightness of the lighting.

[0034] The suggestion unit can provide advice to support a sustainable lifestyle. The suggestion unit provides advice to reduce energy consumption. For example, the suggestion unit recommends the use of energy-efficient appliances. The suggestion unit can also recommend recycling. For example, the suggestion unit suggests a method for separating recyclable materials. The suggestion unit can also provide information to support a sustainable lifestyle. For example, the suggestion unit suggests a specific method for reducing energy consumption. This allows the user to live an environmentally conscious life by providing advice to support a sustainable lifestyle. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input energy consumption data to a generation AI and cause the generation AI to execute advice to reduce energy consumption.

[0035] The suggestion unit can provide advice to reduce energy consumption. The suggestion unit provides advice to reduce energy consumption. For example, the suggestion unit recommends the use of energy-efficient equipment. The suggestion unit can also suggest specific methods to reduce energy consumption. For example, the suggestion unit can suggest a method to appropriately adjust the set temperature of an air conditioner. The suggestion unit can also suggest a method to reduce the amount of time that lighting is used. In this way, by providing advice to reduce energy consumption, the user can save energy. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input energy consumption data to a generation AI and cause the generation AI to execute advice to reduce energy consumption.

[0036] The suggestion unit can make recycling recommendations. The suggestion unit makes recycling recommendations. For example, the suggestion unit can suggest a method for separating recyclable materials. The suggestion unit can also suggest how to use recycling facilities. For example, the suggestion unit can specifically show how to separate recyclable materials, making it easier for users to practice recycling. The suggestion unit can also guide users on how to use recycling facilities, reducing the effort required for users to recycle. In this way, by making recycling recommendations, users can take environmentally conscious actions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input recycling data into a generation AI and cause the generation AI to execute recycling recommendations.

[0037] The collection unit can optimize the data collection timing of each sensor based on the user's behavioral patterns. The collection unit optimizes the data collection timing of each sensor based on the user's behavioral patterns. For example, the collection unit can start data collection from a temperature sensor in accordance with the time the user wakes up in the morning. The collection unit can also increase data collection from an illuminance sensor in accordance with the time the user returns home. The collection unit can also decrease data collection from a motion sensor in accordance with the time the user goes to bed. This enables efficient data collection by optimizing the data collection timing based on the user's behavioral patterns. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral pattern data into a generation AI and cause the generation AI to optimize the data collection timing.

[0038] The collection unit can dynamically adjust the data collection range of the sensor in response to changes in the user's living space. The collection unit dynamically adjusts the data collection range of the sensor in response to changes in the user's living space. For example, when the user is in the living room, the collection unit expands the data collection range of the sensor in the living room. Furthermore, when the user moves to the bedroom, the collection unit can also expand the data collection range of the sensor in the bedroom. Furthermore, when the user goes out, the collection unit can also reduce the overall data collection range. This enables more appropriate data collection by adjusting the data collection range in response to changes in the user's living space. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input the user's living space data to the generation AI and cause the generation AI to adjust the data collection range.

[0039] The collection unit can detect abnormal values ​​when collecting sensor data and issue an alert if an abnormality occurs. The collection unit can detect abnormal values ​​when collecting sensor data and issue an alert if an abnormality occurs. For example, the collection unit issues an alert if a temperature sensor detects an abnormally high temperature. The collection unit can also issue an alert if an illuminance sensor detects abnormal brightness. The collection unit can also issue an alert if a motion sensor detects abnormal movement. This enables a rapid response by detecting abnormal values ​​and issuing an alert if an abnormality occurs. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input sensor data to a generation AI and cause the generation AI to detect abnormal values ​​and issue an alert.

[0040] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting sensor data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting data from sensors in the home. Also, when the user is in the office, the collection unit can prioritize collecting data from sensors in the office. Also, when the user is out, the collection unit can prioritize collecting data from sensors outside the office. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0041] The collection unit can analyze the user's social media activity and collect related data when collecting sensor data. The collection unit analyzes the user's social media activity and collects related data when collecting sensor data. For example, if the user posts "It's hot" on social media, the collection unit can prioritize collecting temperature data. Also, if the user posts "It's dark" on social media, the collection unit can prioritize collecting illuminance data. Also, if the user posts "There's not much movement" on social media, the collection unit can prioritize collecting motion data. This enables more appropriate data collection by analyzing the user's social media activity and collecting related data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting sensor data. The collection unit customizes the collection method by reflecting the user's past feedback when collecting sensor data. For example, if the user has previously provided feedback that "temperature data is important," the collection unit can increase the frequency of temperature data collection. Furthermore, if the user has previously provided feedback that "illuminance data is important," the collection unit can increase the frequency of illuminance data collection. Furthermore, if the user has previously provided feedback that "operation data is important," the collection unit can increase the frequency of operation data collection. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a temperature analysis algorithm to temperature data. The analysis unit can also apply an illuminance analysis algorithm to illuminance data. The analysis unit can also apply a motion analysis algorithm to motion data. This enables more accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the current temperature data analysis by referring to the user's past temperature data analysis results. The analysis unit can also improve the accuracy of the current illuminance data analysis by referring to the user's past illuminance data analysis results. The analysis unit can also improve the accuracy of the current motion data analysis by referring to the user's past motion data analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze data by focusing on current data while referring to past data. The analysis unit can also prioritize analysis of data from a specific time period. This enables efficient analysis by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simpler language. The analysis unit can also dynamically adjust the use of technical terms according to the user's level of expertise. This allows for more appropriate information to be provided by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0049] During control, the control unit can analyze the user's past behavioral patterns and select the optimal control method. During control, the control unit analyzes the user's past behavioral patterns and selects the optimal control method. For example, the control unit adjusts the air conditioner's set temperature based on the user's previously preferred temperature settings. The control unit can also adjust the lighting brightness based on the user's previously preferred lighting brightness. The control unit can also analyze the user's past behavioral patterns and select the optimal control method. In this way, the optimal control method can be selected by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's past behavioral pattern data into a generation AI and cause the generation AI to select the optimal control method.

[0050] The control unit can customize the control means based on the user's current living situation during control. The control unit customizes the control means based on the user's current living situation during control. For example, when the user is at home, the control unit performs control that prioritizes comfort. Furthermore, when the user is out, the control unit can perform control that prioritizes energy conservation. Furthermore, when the user is sleeping, the control unit can perform control that prioritizes quietness. This enables more appropriate control by customizing the control means based on the user's current living situation. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the control means.

[0051] The control unit can improve the control method by reflecting user feedback during control. The control unit can improve the control method by reflecting user feedback during control. For example, if the user provides feedback that "the temperature is too high," the control unit can lower the air conditioner's set temperature. Also, if the user provides feedback that "the lighting is too dim," the control unit can increase the brightness of the lighting. The control unit can also dynamically improve the control method by reflecting user feedback. In this way, the control method can be dynamically improved by reflecting user feedback. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input user feedback data into a generation AI and cause the generation AI to improve the control method.

[0052] The control unit can select the optimal control method by taking into account the user's geographical location information when performing control. The control unit selects the optimal control method by taking into account the user's geographical location information when performing control. For example, when the user is at home, the control unit performs control that is optimal for the home environment. Furthermore, when the user is in the office, the control unit can also perform control that is optimal for the office environment. Furthermore, when the user is out, the control unit can also perform control that is optimal for the environment where the user is away from home. In this way, the optimal control method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal control method.

[0053] During control, the control unit can analyze the user's social media activity and suggest control measures. During control, the control unit analyzes the user's social media activity and suggest control measures. For example, if the user posts on social media that it's "hot," the control unit can suggest lowering the air conditioner's set temperature. Furthermore, if the user posts on social media that it's "dark," the control unit can suggest increasing the brightness of the lights. Furthermore, if the user posts on social media that there is "little movement," the control unit can suggest increasing the sensitivity of the motion sensor. In this way, by analyzing the user's social media activity, more appropriate control measures can be suggested. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's social media data into a generation AI and have the generation AI execute the suggested control measures.

[0054] The control unit can customize the control method by reflecting the user's past feedback during control. The control unit customizes the control method by reflecting the user's past feedback during control. For example, if the user previously provided feedback that the temperature is too high, the control unit can set the air conditioner's set temperature lower. Also, if the user previously provided feedback that the lighting is too dim, the control unit can set the lighting brightness higher. The control unit can also dynamically customize the control method by reflecting the user's past feedback. In this way, the control method can be dynamically customized by reflecting the user's past feedback. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input user feedback data to a generation AI and cause the generation AI to customize the control method.

[0055] The suggestion unit can analyze the user's past behavioral patterns and select the optimal suggestion method when making a suggestion. The suggestion unit analyzes the user's past behavioral patterns and selects the optimal suggestion method when making a suggestion. For example, the suggestion unit makes the optimal suggestion based on the suggestion content that the user has previously preferred. The suggestion unit can also analyze the user's past behavioral patterns and select the most effective suggestion method. The suggestion unit can also improve the suggestion method by referring to the user's past feedback. In this way, the optimal suggestion method can be selected by analyzing the user's past behavioral patterns. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past behavioral pattern data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0056] The suggestion unit can customize the proposed means based on the user's current living situation when making a suggestion. The suggestion unit customizes the proposed means based on the user's current living situation when making a suggestion. For example, when the user is at home, the suggestion unit makes a suggestion that emphasizes comfort. Furthermore, when the user is out, the suggestion unit can make a suggestion that emphasizes energy saving. Furthermore, when the user is sleeping, the suggestion unit can make a suggestion that emphasizes quietness. This enables more appropriate suggestions to be made by customizing the proposed means based on the user's current living situation. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the proposed means.

[0057] The suggestion unit can improve the suggestion method by reflecting user feedback when making a suggestion. The suggestion unit can improve the suggestion method by reflecting user feedback when making a suggestion. For example, if the user provides feedback that "the temperature is too high," the suggestion unit can suggest lowering the air conditioner's set temperature. Also, if the user provides feedback that "the lighting is too dim," the suggestion unit can suggest increasing the brightness of the lighting. Also, the suggestion unit can reflect user feedback and dynamically improve the suggestion method. In this way, the suggestion method can be dynamically improved by reflecting user feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data to the generation AI and cause the generation AI to improve the suggestion method.

[0058] The suggestion unit can select the optimal suggestion method by taking into account the user's geographical location information when making a suggestion. The suggestion unit selects the optimal suggestion method by taking into account the user's geographical location information when making a suggestion. For example, when the user is at home, the suggestion unit makes a suggestion that is optimal for the home environment. Furthermore, when the user is in the office, the suggestion unit can make a suggestion that is optimal for the office environment. Furthermore, when the user is out, the suggestion unit can make a suggestion that is optimal for the environment where the user is away from home. In this way, the optimal suggestion method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal suggestion method.

[0059] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest a suggestion means. When making a suggestion, the suggestion unit analyzes the user's social media activity and suggest a suggestion means. For example, if the user posts that it's "hot" on social media, the suggestion unit can suggest lowering the air conditioner's temperature setting. Also, if the user posts that it's "dark" on social media, the suggestion unit can suggest increasing the brightness of the lights. Also, if the user posts that there is "little movement" on social media, the suggestion unit can suggest increasing the sensitivity of the motion sensor. In this way, by analyzing the user's social media activity, more appropriate suggestion means can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into the generation AI and cause the generation AI to execute suggestions for suggestion means.

[0060] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. The suggestion unit customizes the suggestion method by reflecting the user's past feedback when making a suggestion. For example, if the user previously provided feedback that "the temperature is too high," the suggestion unit can suggest lowering the air conditioner's set temperature. Also, if the user previously provided feedback that "the lighting is too dim," the suggestion unit can suggest raising the lighting brightness. The suggestion unit can also dynamically customize the suggestion method by reflecting the user's past feedback. In this way, the suggestion method can be dynamically customized by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data to a generation AI and cause the generation AI to customize the suggestion method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The life assistance system may further include a health management unit. The health management unit collects health data of the user and provides it to the analysis unit. For example, the health management unit can collect data such as the user's heart rate, blood pressure, and sleep patterns. This allows the analysis unit to grasp the user's health condition and reflect it in the control of the entire life assistance system. For example, if the user's heart rate is high, the air conditioner's set temperature can be lowered to provide a comfortable environment. The brightness of the lighting can also be adjusted based on the user's sleep pattern to provide a better sleeping environment. Furthermore, the health management unit can provide advice on exercise and diet based on the user's health condition. This allows the life assistance system to comprehensively support the user's health.

[0063] The life assistance system may further include an energy management unit. The energy management unit provides a function for optimizing energy consumption within the home. For example, the energy management unit may monitor the energy consumption of each home appliance and make suggestions for reducing unnecessary energy consumption. The energy management unit may also manage a solar power generation system and a storage battery. Furthermore, the energy management unit may optimize energy consumption based on the user's behavioral patterns. For example, the energy management unit may automatically turn off the air conditioner and lights while the user is out. This allows the life assistance system to efficiently manage energy consumption within the home.

[0064] The life assistance system may further include a remote monitoring unit. The remote monitoring unit provides a function that allows the user to check the status of the home even when the user is away from home. For example, the remote monitoring unit can check the temperature and lighting status inside the home through a smartphone app. The remote monitoring unit can also check the status of the home in real time through a camera. Furthermore, the remote monitoring unit can operate home appliances based on the user's instructions. For example, the user can turn on the air conditioner while away from home. This allows the life assistance system to manage the status of the home even when the user is away from home.

[0065] The life assistance system may further include an environmental monitoring unit. The environmental monitoring unit collects environmental data inside and outside the home and provides it to the analysis unit. For example, the environmental monitoring unit may monitor indoor air quality using an air quality sensor. The environmental monitoring unit may also collect outside weather data and provide it to the analysis unit. Furthermore, the environmental monitoring unit may optimize the environmental data based on the user's behavioral patterns. For example, if the user has allergies, the operation of an air purifier may be optimized based on the air quality data. This allows the life assistance system to comprehensively manage the environments inside and outside the home.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data from various sensors. The various sensors include a temperature sensor, an illuminance sensor, a motion sensor, etc. For example, the collection unit collects indoor temperature data using a temperature sensor, collects indoor brightness data using an illuminance sensor, and collects user motion data using a motion sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the user's preferences and behavioral patterns. For example, the analysis unit analyzes the collected temperature data to identify the user's preferred temperature, the analysis unit analyzes the illuminance data to identify the user's preferred brightness, and the analysis unit analyzes the operation data to identify the user's behavioral patterns. Step 3: The control unit controls the air conditioner and lighting based on the analysis results obtained by the analysis unit. For example, the control unit automatically adjusts the air conditioner's set temperature, optimizes the lighting brightness, and changes the air conditioner's operating mode based on the analysis results. Step 4: The proposal unit makes proposals to support sustainable living based on the control results performed by the control unit. For example, it provides advice on reducing energy consumption, recommends recycling, and provides information to support sustainable living.

[0068] (Example 2) A life assistance system according to an embodiment of the present invention uses AI to analyze data collected by various sensors and make optimal suggestions tailored to the user's preferences and environment. Based on information accumulated as a life log, the life assistance system learns the user's air conditioner usage frequency, preferred temperature, lighting preferences, and behavioral patterns, and automatically controls the system to emphasize energy savings and comfort. The life assistance system also makes suggestions to support sustainable living in line with new lifestyles. For example, the life assistance system is equipped with temperature sensors, illuminance sensors, and motion sensors, and AI analyzes data collected by these sensors. Based on the information accumulated as a life log, the life assistance system then learns the user's air conditioner usage frequency, preferred temperature, lighting preferences, and behavioral patterns. For example, the system learns the user's preferred time of day to use the air conditioner, their preferred temperature, and their preferred brightness. The life assistance system then analyzes this data using AI to automatically control the system to emphasize energy savings and comfort. For example, the system automatically adjusts the air conditioner's temperature setting and optimizes the brightness of the lights. This allows the user to live a comfortable life. The life assistance system also makes suggestions to support sustainable living in line with new lifestyles. For example, this includes advice on reducing energy consumption and recommendations for recycling. This allows the life assistance system to not only enable users to live a comfortable life, but also to save energy and achieve a sustainable lifestyle. This allows the life assistance system to automatically control air conditioning and lighting based on the user's preferences and behavioral patterns, supporting a sustainable lifestyle. For example, automatically adjusting the air conditioning temperature setting can reduce energy consumption. Also, optimizing the brightness of lighting can reduce power waste. Furthermore, by making suggestions to support a sustainable lifestyle, the user can live an environmentally conscious lifestyle.

[0069] The life assistance system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a proposal unit. The collection unit collects data from various sensors. The various sensors include, but are not limited to, a temperature sensor, an illuminance sensor, and a motion sensor, for example. The collection unit collects indoor temperature data using, for example, a temperature sensor. The collection unit can also collect indoor brightness data using an illuminance sensor. The collection unit can also collect user motion data using a motion sensor. For example, the collection unit monitors indoor temperature in real time using a temperature sensor and collects data. The collection unit can also measure indoor brightness using an illuminance sensor and collect data. The collection unit can also detect user movement using a motion sensor and collect data. The analysis unit analyzes the data collected by the collection unit to learn the user's preferences and behavioral patterns. For example, the analysis unit analyzes the collected temperature data to identify the user's preferred temperature. The analysis unit can also analyze the collected illuminance data to identify the user's preferred brightness. The analysis unit can also analyze the collected operation data to identify the user's behavioral patterns. For example, the analysis unit can identify what time of day the user uses the air conditioner based on the collected temperature data. The analysis unit can also identify the user's preferred brightness level based on the collected illuminance data. The analysis unit can also identify the user's behavioral patterns based on the collected operation data. The control unit controls the air conditioner and lighting based on the analysis results obtained by the analysis unit. For example, the control unit can automatically adjust the set temperature of the air conditioner based on the analysis results. The control unit can also optimize the brightness of the lighting based on the analysis results. The control unit can also change the operation mode of the air conditioner based on the analysis results. For example, the control unit can automatically adjust the set temperature of the air conditioner based on the analysis results to maintain a comfortable indoor environment. The control unit can also optimize the brightness of the lighting based on the analysis results to provide a comfortable lighting environment. The control unit can also change the operation mode of the air conditioner based on the analysis results to improve energy efficiency.The suggestion unit makes suggestions to support a sustainable lifestyle based on the control results performed by the control unit. The suggestion unit, for example, provides advice on reducing energy consumption. The suggestion unit can also recommend recycling. The suggestion unit can also provide information to support a sustainable lifestyle. For example, the suggestion unit suggests specific methods for reducing energy consumption. The suggestion unit can also recommend recycling to support an environmentally conscious lifestyle. The suggestion unit can also provide information to support a sustainable lifestyle and encourage the user to take environmentally conscious actions. As a result, the life assistance system according to the embodiment can automatically control air conditioners and lighting based on the user's preferences and behavioral patterns to support a sustainable lifestyle. For example, by automatically adjusting the set temperature of an air conditioner, a comfortable indoor environment can be maintained. Furthermore, by optimizing the brightness of lighting, a comfortable lighting environment can be provided. Furthermore, by making suggestions to support a sustainable lifestyle, the user can live an environmentally conscious lifestyle.

[0070] The collection unit can collect data from a temperature sensor, an illuminance sensor, or a motion sensor. The collection unit collects indoor temperature data using a temperature sensor. For example, the collection unit monitors the indoor temperature in real time using a temperature sensor and collects data. The collection unit can also collect indoor brightness data using an illuminance sensor. For example, the collection unit measures the indoor brightness using an illuminance sensor and collects data. The collection unit can also collect user behavior data using a motion sensor. For example, the collection unit detects the user's movements using a motion sensor and collects data. In this way, by collecting data from various sensors, detailed information about the user's environment can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from a temperature sensor to a generation AI and have the generation AI analyze the temperature data.

[0071] The analysis unit can analyze the collected data and learn the user's preferences and behavioral patterns. The analysis unit can analyze the collected temperature data and identify the user's preferred temperature. For example, the analysis unit can identify the time of day the user uses the air conditioner based on the collected temperature data. The analysis unit can also analyze the collected illuminance data and identify the user's preferred brightness. For example, the analysis unit can identify the user's preferred brightness based on the collected illuminance data. The analysis unit can also analyze the collected operation data and identify the user's behavioral patterns. For example, the analysis unit can identify the user's behavioral patterns based on the collected operation data. This enables more appropriate control by learning the user's preferences and behavioral patterns. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to learn the user's preferences and behavioral patterns.

[0072] The control unit can automatically adjust the set temperature of the air conditioner based on the analysis results. The control unit automatically adjusts the set temperature of the air conditioner based on the analysis results. For example, the control unit automatically adjusts the set temperature of the air conditioner based on the analysis results to maintain a comfortable indoor environment. The control unit can also change the operation mode of the air conditioner based on the analysis results. For example, the control unit can change the operation mode of the air conditioner based on the analysis results to improve energy efficiency. As a result, a comfortable indoor environment can be maintained by automatically adjusting the set temperature of the air conditioner. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the analysis results to a generation AI and cause the generation AI to adjust the set temperature of the air conditioner.

[0073] The control unit can adjust the brightness of the lighting based on the analysis results. The control unit adjusts the brightness of the lighting based on the analysis results. For example, the control unit can optimize the brightness of the lighting based on the analysis results, providing a comfortable lighting environment. The control unit can also adjust the color temperature of the lighting based on the analysis results. For example, the control unit can adjust the color temperature of the lighting based on the analysis results, providing a lighting environment that suits the user's preferences. In this way, a comfortable lighting environment can be provided by optimizing the brightness of the lighting. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the analysis results to the generation AI and cause the generation AI to adjust the brightness of the lighting.

[0074] The suggestion unit can provide advice to support a sustainable lifestyle. The suggestion unit provides advice to reduce energy consumption. For example, the suggestion unit recommends the use of energy-efficient appliances. The suggestion unit can also recommend recycling. For example, the suggestion unit suggests a method for separating recyclable materials. The suggestion unit can also provide information to support a sustainable lifestyle. For example, the suggestion unit suggests a specific method for reducing energy consumption. This allows the user to live an environmentally conscious life by providing advice to support a sustainable lifestyle. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input energy consumption data to a generation AI and cause the generation AI to execute advice to reduce energy consumption.

[0075] The suggestion unit can provide advice to reduce energy consumption. The suggestion unit provides advice to reduce energy consumption. For example, the suggestion unit recommends the use of energy-efficient equipment. The suggestion unit can also suggest specific methods to reduce energy consumption. For example, the suggestion unit can suggest a method to appropriately adjust the set temperature of an air conditioner. The suggestion unit can also suggest a method to reduce the amount of time that lighting is used. In this way, by providing advice to reduce energy consumption, the user can save energy. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input energy consumption data to a generation AI and cause the generation AI to execute advice to reduce energy consumption.

[0076] The suggestion unit can make recycling recommendations. The suggestion unit makes recycling recommendations. For example, the suggestion unit can suggest a method for separating recyclable materials. The suggestion unit can also suggest how to use recycling facilities. For example, the suggestion unit can specifically show how to separate recyclable materials, making it easier for users to practice recycling. The suggestion unit can also guide users on how to use recycling facilities, reducing the effort required for users to recycle. In this way, by making recycling recommendations, users can take environmentally conscious actions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input recycling data into a generation AI and cause the generation AI to execute recycling recommendations.

[0077] The collection unit can estimate the user's emotions and adjust the data collection frequency of the sensors based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the data collection frequency of the sensors based on the estimated user emotions. For example, when the user is stressed, the collection unit can increase the data collection frequency to quickly respond to changes in the environment. The collection unit can also reduce the data collection frequency to reduce the load on the system when the user is relaxed. When the user is in a hurry, the collection unit can prioritize collecting only important data to enable a quick response. This enables more appropriate data collection by adjusting the data collection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the data collection frequency.

[0078] The collection unit can optimize the data collection timing of each sensor based on the user's behavioral patterns. The collection unit optimizes the data collection timing of each sensor based on the user's behavioral patterns. For example, the collection unit can start data collection from a temperature sensor in accordance with the time the user wakes up in the morning. The collection unit can also increase data collection from an illuminance sensor in accordance with the time the user returns home. The collection unit can also decrease data collection from a motion sensor in accordance with the time the user goes to bed. This enables efficient data collection by optimizing the data collection timing based on the user's behavioral patterns. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral pattern data into a generation AI and cause the generation AI to optimize the data collection timing.

[0079] The collection unit can dynamically adjust the data collection range of the sensor in response to changes in the user's living space. The collection unit dynamically adjusts the data collection range of the sensor in response to changes in the user's living space. For example, when the user is in the living room, the collection unit expands the data collection range of the sensor in the living room. Furthermore, when the user moves to the bedroom, the collection unit can also expand the data collection range of the sensor in the bedroom. Furthermore, when the user goes out, the collection unit can also reduce the overall data collection range. This enables more appropriate data collection by adjusting the data collection range in response to changes in the user's living space. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input the user's living space data to the generation AI and cause the generation AI to adjust the data collection range.

[0080] The collection unit can detect abnormal values ​​when collecting sensor data and issue an alert if an abnormality occurs. The collection unit can detect abnormal values ​​when collecting sensor data and issue an alert if an abnormality occurs. For example, the collection unit issues an alert if a temperature sensor detects an abnormally high temperature. The collection unit can also issue an alert if an illuminance sensor detects abnormal brightness. The collection unit can also issue an alert if a motion sensor detects abnormal movement. This enables a rapid response by detecting abnormal values ​​and issuing an alert if an abnormality occurs. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input sensor data to a generation AI and cause the generation AI to detect abnormal values ​​and issue an alert.

[0081] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user emotions. The collection unit estimates the user's emotions and selects the type of data to collect based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting temperature data. Also, if the user is relaxed, the collection unit can prioritize collecting illuminance data. Also, if the user is in a hurry, the collection unit can prioritize collecting motion data. This enables more appropriate data collection by selecting the type of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI select the type of data to collect.

[0082] When collecting sensor data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting sensor data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting data from sensors in the home. Also, when the user is in the office, the collection unit can prioritize collecting data from sensors in the office. Also, when the user is out, the collection unit can prioritize collecting data from sensors outside the office. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0083] The collection unit can analyze the user's social media activity and collect related data when collecting sensor data. The collection unit analyzes the user's social media activity and collects related data when collecting sensor data. For example, if the user posts "It's hot" on social media, the collection unit can prioritize collecting temperature data. Also, if the user posts "It's dark" on social media, the collection unit can prioritize collecting illuminance data. Also, if the user posts "There's not much movement" on social media, the collection unit can prioritize collecting motion data. This enables more appropriate data collection by analyzing the user's social media activity and collecting related data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0084] The collection unit can customize the collection method by reflecting the user's past feedback when collecting sensor data. The collection unit customizes the collection method by reflecting the user's past feedback when collecting sensor data. For example, if the user has previously provided feedback that "temperature data is important," the collection unit can increase the frequency of temperature data collection. Furthermore, if the user has previously provided feedback that "illuminance data is important," the collection unit can increase the frequency of illuminance data collection. Furthermore, if the user has previously provided feedback that "operation data is important," the collection unit can increase the frequency of operation data collection. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can use an analysis algorithm that emphasizes stress reduction. Also, if the user is relaxed, the analysis unit can use an analysis algorithm that emphasizes comfort. Also, if the user is in a hurry, the analysis unit can use an algorithm that performs quick analysis. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a temperature analysis algorithm to temperature data. The analysis unit can also apply an illuminance analysis algorithm to illuminance data. The analysis unit can also apply a motion analysis algorithm to motion data. This enables more accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0088] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the current temperature data analysis by referring to the user's past temperature data analysis results. The analysis unit can also improve the accuracy of the current illuminance data analysis by referring to the user's past illuminance data analysis results. The analysis unit can also improve the accuracy of the current motion data analysis by referring to the user's past motion data analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0090] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze data by focusing on current data while referring to past data. The analysis unit can also prioritize analysis of data from a specific time period. This enables efficient analysis by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0092] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simpler language. The analysis unit can also dynamically adjust the use of technical terms according to the user's level of expertise. This allows for more appropriate information to be provided by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0093] The control unit can estimate the user's emotions and adjust the control method based on the estimated user's emotions. The control unit can estimate the user's emotions and adjust the control method based on the estimated user's emotions. For example, if the user is feeling stressed, the control unit can select a control method that emphasizes comfort. If the user is relaxed, the control unit can select a control method that emphasizes energy conservation. If the user is in a hurry, the control unit can select a speedy control method. This enables more appropriate control by adjusting the control method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the control unit can be performed using AI, for example, or without AI. For example, the control unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the control method.

[0094] During control, the control unit can analyze the user's past behavioral patterns and select the optimal control method. During control, the control unit analyzes the user's past behavioral patterns and selects the optimal control method. For example, the control unit adjusts the air conditioner's set temperature based on the user's previously preferred temperature settings. The control unit can also adjust the lighting brightness based on the user's previously preferred lighting brightness. The control unit can also analyze the user's past behavioral patterns and select the optimal control method. In this way, the optimal control method can be selected by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's past behavioral pattern data into a generation AI and cause the generation AI to select the optimal control method.

[0095] The control unit can customize the control means based on the user's current living situation during control. The control unit customizes the control means based on the user's current living situation during control. For example, when the user is at home, the control unit performs control that prioritizes comfort. Furthermore, when the user is out, the control unit can perform control that prioritizes energy conservation. Furthermore, when the user is sleeping, the control unit can perform control that prioritizes quietness. This enables more appropriate control by customizing the control means based on the user's current living situation. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the control means.

[0096] The control unit can improve the control method by reflecting user feedback during control. The control unit can improve the control method by reflecting user feedback during control. For example, if the user provides feedback that "the temperature is too high," the control unit can lower the air conditioner's set temperature. Also, if the user provides feedback that "the lighting is too dim," the control unit can increase the brightness of the lighting. The control unit can also dynamically improve the control method by reflecting user feedback. In this way, the control method can be dynamically improved by reflecting user feedback. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input user feedback data into a generation AI and cause the generation AI to improve the control method.

[0097] The control unit can estimate the user's emotions and determine control priorities based on the estimated user emotions. The control unit can estimate the user's emotions and determine control priorities based on the estimated user emotions. For example, if the user is feeling stressed, the control unit can prioritize control that emphasizes comfort. Also, if the user is relaxed, the control unit can prioritize control that emphasizes energy conservation. Also, if the user is in a hurry, the control unit can prioritize quick control. This enables more appropriate control by determining control priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the control unit can be performed using, for example, an AI, or without an AI. For example, the control unit can input user emotion data into the generation AI and have the generation AI determine the control priorities.

[0098] The control unit can select the optimal control method by taking into account the user's geographical location information when performing control. The control unit selects the optimal control method by taking into account the user's geographical location information when performing control. For example, when the user is at home, the control unit performs control that is optimal for the home environment. Furthermore, when the user is in the office, the control unit can also perform control that is optimal for the office environment. Furthermore, when the user is out, the control unit can also perform control that is optimal for the environment where the user is away from home. In this way, the optimal control method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal control method.

[0099] During control, the control unit can analyze the user's social media activity and suggest control measures. During control, the control unit analyzes the user's social media activity and suggest control measures. For example, if the user posts on social media that it's "hot," the control unit can suggest lowering the air conditioner's set temperature. Furthermore, if the user posts on social media that it's "dark," the control unit can suggest increasing the brightness of the lights. Furthermore, if the user posts on social media that there is "little movement," the control unit can suggest increasing the sensitivity of the motion sensor. In this way, by analyzing the user's social media activity, more appropriate control measures can be suggested. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's social media data into a generation AI and have the generation AI execute the suggested control measures.

[0100] The control unit can customize the control method by reflecting the user's past feedback during control. The control unit customizes the control method by reflecting the user's past feedback during control. For example, if the user previously provided feedback that the temperature is too high, the control unit can set the air conditioner's set temperature lower. Also, if the user previously provided feedback that the lighting is too dim, the control unit can set the lighting brightness higher. The control unit can also dynamically customize the control method by reflecting the user's past feedback. In this way, the control method can be dynamically customized by reflecting the user's past feedback. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input user feedback data to a generation AI and cause the generation AI to customize the control method.

[0101] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This enables more appropriate suggestions to be made by adjusting the way the suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0102] The suggestion unit can analyze the user's past behavioral patterns and select the optimal suggestion method when making a suggestion. The suggestion unit analyzes the user's past behavioral patterns and selects the optimal suggestion method when making a suggestion. For example, the suggestion unit makes the optimal suggestion based on the suggestion content that the user has previously preferred. The suggestion unit can also analyze the user's past behavioral patterns and select the most effective suggestion method. The suggestion unit can also improve the suggestion method by referring to the user's past feedback. In this way, the optimal suggestion method can be selected by analyzing the user's past behavioral patterns. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past behavioral pattern data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0103] The suggestion unit can customize the proposed means based on the user's current living situation when making a suggestion. The suggestion unit customizes the proposed means based on the user's current living situation when making a suggestion. For example, when the user is at home, the suggestion unit makes a suggestion that emphasizes comfort. Furthermore, when the user is out, the suggestion unit can make a suggestion that emphasizes energy saving. Furthermore, when the user is sleeping, the suggestion unit can make a suggestion that emphasizes quietness. This enables more appropriate suggestions to be made by customizing the proposed means based on the user's current living situation. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the proposed means.

[0104] The suggestion unit can improve the suggestion method by reflecting user feedback when making a suggestion. The suggestion unit can improve the suggestion method by reflecting user feedback when making a suggestion. For example, if the user provides feedback that "the temperature is too high," the suggestion unit can suggest lowering the air conditioner's set temperature. Also, if the user provides feedback that "the lighting is too dim," the suggestion unit can suggest increasing the brightness of the lighting. Also, the suggestion unit can reflect user feedback and dynamically improve the suggestion method. In this way, the suggestion method can be dynamically improved by reflecting user feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data to the generation AI and cause the generation AI to improve the suggestion method.

[0105] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions that emphasize comfort. If the user is relaxed, the suggestion unit can prioritize suggestions that emphasize energy conservation. If the user is in a hurry, the suggestion unit can prioritize quick suggestions. This enables more appropriate suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of suggestions.

[0106] The suggestion unit can select the optimal suggestion method by taking into account the user's geographical location information when making a suggestion. The suggestion unit selects the optimal suggestion method by taking into account the user's geographical location information when making a suggestion. For example, when the user is at home, the suggestion unit makes a suggestion that is optimal for the home environment. Furthermore, when the user is in the office, the suggestion unit can make a suggestion that is optimal for the office environment. Furthermore, when the user is out, the suggestion unit can make a suggestion that is optimal for the environment where the user is away from home. In this way, the optimal suggestion method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal suggestion method.

[0107] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest a suggestion means. When making a suggestion, the suggestion unit analyzes the user's social media activity and suggest a suggestion means. For example, if the user posts that it's "hot" on social media, the suggestion unit can suggest lowering the air conditioner's temperature setting. Also, if the user posts that it's "dark" on social media, the suggestion unit can suggest increasing the brightness of the lights. Also, if the user posts that there is "little movement" on social media, the suggestion unit can suggest increasing the sensitivity of the motion sensor. In this way, by analyzing the user's social media activity, more appropriate suggestion means can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into the generation AI and cause the generation AI to execute suggestions for suggestion means.

[0108] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. The suggestion unit customizes the suggestion method by reflecting the user's past feedback when making a suggestion. For example, if the user previously provided feedback that "the temperature is too high," the suggestion unit can suggest lowering the air conditioner's set temperature. Also, if the user previously provided feedback that "the lighting is too dim," the suggestion unit can suggest raising the lighting brightness. The suggestion unit can also dynamically customize the suggestion method by reflecting the user's past feedback. In this way, the suggestion method can be dynamically customized by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data to a generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, control unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using a temperature sensor, an illuminance sensor, a motion sensor, etc., of the smart device 14 and transmits the data to the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the user's preferences and behavioral patterns. The control unit, realized, for example, by the specific processing unit 290 of the data processing device 12, controls the air conditioner and lighting based on the analysis results. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, makes suggestions to support sustainable living. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, control unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, control unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using a temperature sensor, illuminance sensor, motion sensor, etc., of the smart glasses 214 and transmits the data to the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to learn the user's preferences and behavioral patterns. The control unit, realized, for example, by the specific processing unit 290 of the data processing device 12, controls the air conditioner and lighting based on the analysis results. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, makes suggestions to support sustainable living. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, control unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, control unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using a temperature sensor, an illuminance sensor, a motion sensor, etc., of the headset-type terminal 314 and transmits the data to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to learn the user's preferences and behavioral patterns. The control unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and controls the air conditioner and lighting based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes suggestions to support sustainable living. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, control unit, and suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, control unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using a temperature sensor, an illuminance sensor, a motion sensor, etc., of the robot 414 and transmits the data to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to learn the user's preferences and behavioral patterns. The control unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and controls the air conditioner and lighting based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes suggestions to support sustainable living. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, control unit, and suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The life assistance system may further include a health management unit. The health management unit collects health data of the user and provides it to the analysis unit. For example, the health management unit can collect data such as the user's heart rate, blood pressure, and sleep patterns. This allows the analysis unit to grasp the user's health condition and reflect it in the control of the entire life assistance system. For example, if the user's heart rate is high, the air conditioner's set temperature can be lowered to provide a comfortable environment. The brightness of the lighting can also be adjusted based on the user's sleep pattern to provide a better sleeping environment. Furthermore, the health management unit can provide advice on exercise and diet based on the user's health condition. This allows the life assistance system to comprehensively support the user's health.

[0111] The life assistance system may further include a communication unit. The communication unit provides functions for facilitating dialogue between the user and the system. For example, the communication unit may recognize the user's voice commands using voice recognition technology and transmit instructions to each unit of the system. The communication unit may also provide appropriate answers to the user's questions. For example, if the user asks, "What's the weather like today?", the communication unit may obtain weather information from the Internet and convey it to the user. Furthermore, the communication unit may estimate the user's emotions and engage in dialogue according to the emotions. This allows the life assistance system to provide more personalized services through communication with the user.

[0112] The life assistance system may further include an entertainment unit. The entertainment unit provides entertainment content according to the user's preferences. For example, the entertainment unit may learn the user's music preferences and play appropriate music. The entertainment unit may also learn the user's movie preferences and recommend movies. The entertainment unit may also estimate the user's emotions and provide entertainment content according to the emotions. For example, if the user is feeling stressed, relaxing music may be played. In this way, the life assistance system can provide an entertainment experience that enriches the user's life.

[0113] The life assistance system may further include a security unit. The security unit provides functions for ensuring the safety of the user. For example, the security unit may use sensors on doors and windows to detect suspicious activity. The security unit may also use cameras to monitor the area around the user's home. The security unit may also estimate the user's emotions and implement security measures according to the emotions. For example, if the user feels anxious, the security unit may intensify the alarm. This allows the life assistance system to comprehensively support the user's safety.

[0114] The life assistance system may further include a learning support unit. The learning support unit provides functions for supporting the user's learning activities. For example, the learning support unit may manage the user's learning progress and propose an appropriate learning plan. The learning support unit may also provide learning materials that suit the user's learning style. Furthermore, the learning support unit may estimate the user's emotions and provide learning support according to the emotions. For example, if the user is lacking in concentration, the learning support unit may suggest taking a break. This allows the life assistance system to comprehensively support the user's learning activities.

[0115] The life assistance system may further include an energy management unit. The energy management unit provides a function for optimizing energy consumption within the home. For example, the energy management unit may monitor the energy consumption of each home appliance and make suggestions for reducing unnecessary energy consumption. The energy management unit may also manage a solar power generation system and a storage battery. Furthermore, the energy management unit may optimize energy consumption based on the user's behavioral patterns. For example, the energy management unit may automatically turn off the air conditioner and lights while the user is out. This allows the life assistance system to efficiently manage energy consumption within the home.

[0116] The life assistance system may further include a remote monitoring unit. The remote monitoring unit provides a function that allows the user to check the status of the home even when the user is away from home. For example, the remote monitoring unit can check the temperature and lighting status inside the home through a smartphone app. The remote monitoring unit can also check the status of the home in real time through a camera. Furthermore, the remote monitoring unit can operate home appliances based on the user's instructions. For example, the user can turn on the air conditioner while away from home. This allows the life assistance system to manage the status of the home even when the user is away from home.

[0117] The life assistance system may further include an environmental monitoring unit. The environmental monitoring unit collects environmental data inside and outside the home and provides it to the analysis unit. For example, the environmental monitoring unit may monitor indoor air quality using an air quality sensor. The environmental monitoring unit may also collect outside weather data and provide it to the analysis unit. Furthermore, the environmental monitoring unit may optimize the environmental data based on the user's behavioral patterns. For example, if the user has allergies, the operation of an air purifier may be optimized based on the air quality data. This allows the life assistance system to comprehensively manage the environments inside and outside the home.

[0118] The life assistance system may further include a fitness unit. The fitness unit provides functions for supporting the user's exercise habits. For example, the fitness unit may collect the user's exercise data and propose an appropriate exercise plan. The fitness unit may also manage the user's exercise history and monitor progress. Furthermore, the fitness unit may estimate the user's emotions and propose an exercise plan based on the emotions. For example, if the user is feeling stressed, the fitness unit may propose a relaxing exercise. This allows the life assistance system to comprehensively support the user's exercise habits.

[0119] The life assistance system may further include a diet management unit. The diet management unit provides functions for supporting the user's eating habits. For example, the diet management unit may collect the user's dietary data and propose an appropriate meal plan. The diet management unit may also manage the user's nutritional balance and support a healthy diet. Furthermore, the diet management unit may estimate the user's emotions and provide a meal plan based on the emotions. For example, if the user is feeling stressed, the diet management unit may propose a meal that will help the user relax. This allows the life assistance system to comprehensively support the user's eating habits.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects data from various sensors. The various sensors include a temperature sensor, an illuminance sensor, a motion sensor, etc. For example, the collection unit collects indoor temperature data using a temperature sensor, collects indoor brightness data using an illuminance sensor, and collects user motion data using a motion sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the user's preferences and behavioral patterns. For example, the analysis unit analyzes the collected temperature data to identify the user's preferred temperature, the analysis unit analyzes the illuminance data to identify the user's preferred brightness, and the analysis unit analyzes the operation data to identify the user's behavioral patterns. Step 3: The control unit controls the air conditioner and lighting based on the analysis results obtained by the analysis unit. For example, the control unit automatically adjusts the air conditioner's set temperature, optimizes the lighting brightness, and changes the air conditioner's operating mode based on the analysis results. Step 4: The proposal unit makes proposals to support sustainable living based on the control results performed by the control unit. For example, it provides advice on reducing energy consumption, recommends recycling, and provides information to support sustainable living.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] 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.

[0185] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data from various sensors; an analysis unit that analyzes the data collected by the collection unit and learns user preferences and behavior patterns; a control unit that controls an air conditioner and lighting based on the analysis result obtained by the analysis unit; a suggestion unit that makes suggestions to support a sustainable lifestyle based on the control results performed by the control unit. A system characterized by:

2. The collecting unit Collect data from temperature, light, and motion sensors 2. The system of claim 1.

3. The analysis unit Analyze collected data to learn user preferences and behavioral patterns 2. The system of claim 1.

4. The control unit Automatically adjust the air conditioner temperature setting based on the analysis results 2. The system of claim 1.

5. The control unit Adjusting lighting brightness based on analysis results 2. The system of claim 1.

6. The proposal unit Providing advice to support sustainable living 2. The system of claim 1.

7. The proposal unit Providing advice on how to reduce energy consumption 2. The system of claim 1.

8. The proposal unit Encourage recycling 2. The system of claim 1.

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