system

Abstract

The system according to this embodiment aims to provide optimal home appliance settings that correspond to the user's emotions and physical condition. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, an optimization unit, and a provision unit. The acquisition unit acquires data. The analysis unit analyzes the data acquired by the acquisition unit. The optimization unit optimizes the home appliance settings based on the data analyzed by the analysis unit. The provision unit provides the home appliance settings optimized by the optimization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the optimization of home appliance settings according to the user's emotions and physical conditions has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal home appliance setting according to the user's emotions and physical conditions.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, an optimization unit, and a provision unit. The acquisition unit acquires data. The analysis unit analyzes the data acquired by the acquisition unit. The optimization unit optimizes the home appliance setting based on the data analyzed by the analysis unit. The provision unit provides the home appliance setting optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal home appliance settings according to the user's emotions and physical condition. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Feelium system according to an embodiment of the present invention is a system that integrates generating AI and smart home appliances to automatically provide an optimal environment tailored to the user's daily emotions and physical condition. The Feelium system acquires heart rate and sleep data from a smartwatch or smartphone, and the AI ​​estimates stress and fatigue levels. Furthermore, it analyzes the user's speech and facial expression data to understand their emotional state for the day. Based on this information, it optimizes appliance settings such as lighting, music, and room temperature to create a relaxing space. For example, if the user is feeling stressed, the Feelium system changes the lighting to a warm color, plays relaxing music, and adjusts the room temperature to a comfortable level. This allows the user to have a relaxing space. The Feelium system can also be set to automatically turn on the lights and play relaxing music when the user returns home. This reduces daily stress and fatigue, allowing the user to live a comfortable life. The Feelium system is a next-generation smart home agent that makes each user's life more comfortable and healthy. As a result, the Feelium system can automatically provide an optimal environment tailored to the user's emotions and physical condition.

[0029] The Feelium system according to this embodiment comprises an acquisition unit, an analysis unit, an optimization unit, and a provision unit. The acquisition unit acquires data. For example, the acquisition unit acquires heart rate and sleep data from a smartwatch or smartphone. For example, the acquisition unit acquires heart rate data in real time to monitor the user's stress level. The acquisition unit can also acquire sleep data intensively at night to evaluate the user's fatigue level. Furthermore, the acquisition unit can periodically acquire the user's activity data to evaluate their activity level during the day. The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes the user's speech and facial expression data to understand their emotional state for the day. For example, the analysis unit uses speech recognition technology to analyze the user's speech content and estimate their emotional state. Furthermore, the analysis unit can use image recognition technology to analyze the user's facial expression data to understand their emotional state. Furthermore, the analysis unit can analyze the user's heart rate data and sleep data to evaluate their stress level and fatigue level. The optimization unit optimizes the home appliance settings based on the data analyzed by the analysis unit. The optimization unit optimizes appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit may change the lighting to a warmer color, play relaxing music, and adjust the room temperature to a comfortable level. The optimization unit can also adjust appliance settings to maintain a comfortable environment if the user is relaxed. Furthermore, if the user is tired, the optimization unit can optimize appliance settings to provide an environment that promotes rest. The provisioning unit provides the appliance settings optimized by the optimization unit. For example, the provisioning unit provides the user with optimized lighting and music settings. For example, the provisioning unit may set the lights to automatically turn on and relaxing music to play when the user returns home. The provisioning unit can also adjust the optimized appliance settings in real time to provide the user with a relaxing environment. As a result, the Feelium system according to this embodiment can automatically provide an optimal environment tailored to the user's daily emotions and physical condition.

[0030] The data acquisition unit acquires data. For example, it acquires heart rate and sleep data from smartwatches and smartphones. Specifically, smartwatches are worn on the user's wrist and measure heart rate and activity levels in real time using heart rate sensors and accelerometers. This allows for accurate understanding of the user's stress level and activity level. Smartphones use accelerometers and microphones to detect the user's movements, such as turning over in sleep or snoring, in order to acquire sleep data. This allows for detailed analysis of the user's sleep quality and sleep cycle. Furthermore, the data acquisition unit can periodically acquire the user's activity data and evaluate their activity level during the day. For example, it can use the GPS function of smartwatches and smartphones to record the user's distance traveled and steps taken, and evaluate their daily activity level. This allows the data acquisition unit to comprehensively understand the user's health status and lifestyle and collect data to provide to the analysis and optimization units. The data acquisition unit sends this data to a cloud server so that the analysis and optimization units can access it. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data acquisition unit to collect data efficiently and effectively, improving the overall performance of the Feelium system.

[0031] The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes the user's speech and facial expression data to understand their emotional state for the day. Specifically, it uses speech recognition technology to analyze the content of the user's speech and estimate their emotional state. Speech recognition technology analyzes the user's voice tone and speaking patterns to identify emotions such as joy, anger, and sadness. It can also use image recognition technology to analyze the user's facial expression data and understand their emotional state. Image recognition technology analyzes the user's facial expressions, eye movements, and mouth shape to identify emotions. Furthermore, the analysis unit can also analyze the user's heart rate data and sleep data to evaluate stress levels and fatigue. For example, it analyzes heart rate data to determine whether the user is feeling stressed, and analyzes sleep data to evaluate the quality of the user's sleep and their fatigue level. This allows the analysis unit to comprehensively understand the user's emotional and health state and generate information to provide to the optimization unit. In addition, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. For example, based on past heart rate and sleep data, it can predict changes in the user's health status and emotional tendencies, and plan future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the reliability and safety of the entire Feelium system.

[0032] The optimization unit optimizes appliance settings based on data analyzed by the analysis unit. Specifically, it optimizes appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit changes the lighting to a warmer color, plays relaxing music, and adjusts the room temperature to a comfortable level. By adjusting the color temperature and brightness of the lighting, it soothes the user's mood and provides a relaxing environment. Furthermore, for music selection, it chooses songs with high relaxation effects based on the user's preferences and past data. In addition, it controls the air conditioner or heater to adjust the room temperature to a comfortable level for the user. When the user is relaxed, the optimization unit can also adjust appliance settings to maintain a comfortable environment. For example, it keeps the lighting brightness at a moderate level, adjusts the music volume, and maintains a constant room temperature to help the user remain relaxed. Furthermore, if the user is tired, the optimization unit can optimize appliance settings to provide an environment that encourages rest. For example, it dims the lights, plays quiet music, and sets the room temperature slightly lower to provide an environment where the user can rest comfortably. In this way, the optimization unit can provide an optimal environment tailored to the user's emotional and physical state, improving the user's quality of life. Furthermore, the optimization unit can collect user feedback and continuously improve the accuracy and effectiveness of home appliance settings. For example, it can receive feedback on how users felt about the provided environment and adjust the optimization algorithm based on that information. This allows the optimization unit to always provide the optimal environment for the user.

[0033] The service provider provides home appliance settings optimized by the optimization unit. Specifically, it provides users with optimized lighting and music settings. For example, it can set the lights to automatically turn on and relaxing music to play when the user returns home. The service provider works in conjunction with smart home devices to detect the user's return home and simultaneously provide the optimized environment. The service provider can also adjust the optimized home appliance settings in real time to provide the user with a relaxing environment. For example, it can adjust the brightness of the lights and the volume of the music as needed while the user is in the living room to maintain a comfortable environment. Furthermore, the service provider can provide home appliance settings at the optimal time based on the user's schedule and daily routine. For example, it can gradually dim the lights and play relaxing music to match the user's bedtime, providing a comfortable sleeping environment. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the environment it provides. For example, it can provide feedback on how the user felt about the provided environment and adjust the service algorithm based on that information. This allows the service provider to always provide the optimal environment for the user. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with the optimal environment, improving the overall reliability of the Feelium system and user satisfaction.

[0034] The data acquisition unit can acquire heart rate and sleep data from smartwatches and smartphones. For example, the data acquisition unit can acquire heart rate data from a smartwatch in real time to monitor the user's stress level. The data acquisition unit can also acquire sleep data from a smartphone mainly at night to evaluate the user's fatigue level. The data acquisition unit can also periodically acquire activity data from smartwatches and smartphones to evaluate the activity level during the day. This allows the data acquisition unit to understand the user's physical condition in real time. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input heart rate data acquired from a smartwatch into a generating AI and have the generating AI perform stress level estimation.

[0035] The analysis unit can analyze the user's speech and facial expression data to understand their emotional state for the day. For example, the analysis unit can use speech recognition technology to analyze the user's speech and estimate their emotional state. For example, if the user is smiling while speaking, the analysis unit will determine that they are in a positive emotional state. The analysis unit can also determine that if the user has a blank or sad expression, they are in a negative emotional state. The analysis unit can also use image recognition technology to analyze the user's facial expression data and understand their emotional state. For example, the analysis unit can calculate an emotional score based on changes in the user's facial expression. This allows the analysis unit to understand the user's emotional state. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's speech data into a generating AI and have the generating AI perform the estimation of the emotional state.

[0036] The optimization unit can optimize appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit may change the lighting to a warmer color, play relaxing music, and adjust the room temperature to a comfortable level. For example, if the user is relaxed, the optimization unit may also adjust appliance settings to maintain a comfortable environment. For example, if the user is tired, the optimization unit may optimize appliance settings to provide an environment that encourages rest. In this way, the optimization unit can provide the user with a relaxing space. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's emotional state into a generating AI and have the generating AI execute the optimal appliance settings.

[0037] The service provider can provide users with optimized home appliance settings. For example, the service provider can provide users with optimized lighting and music settings. For example, the service provider can set the lights to automatically turn on and relaxing music to play when the user returns home. The service provider can also adjust the optimized home appliance settings in real time to provide the user with a relaxing environment. In this way, the service provider can provide the user with the best possible environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the optimized home appliance settings into a generating AI and have the generating AI execute the home appliance settings to be provided to the user.

[0038] The analysis unit can analyze user speech and facial expression data using speech recognition and image recognition technologies. For example, the analysis unit can analyze the content of user speech using speech recognition technology and estimate the emotional state. For example, if the user is smiling while speaking, the analysis unit will determine that the emotional state is positive. The analysis unit can also determine that if the user has a blank or sad expression, the emotional state is negative. The analysis unit can also analyze user facial expression data using image recognition technology to understand the emotional state. For example, the analysis unit can calculate an emotional score based on changes in the user's facial expression. This allows the analysis unit to analyze user speech and facial expression data with high accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user speech data into a generating AI and have the generating AI perform the estimation of the emotional state.

[0039] The data acquisition unit can analyze the user's past data acquisition history and select the optimal acquisition method. For example, the data acquisition unit can identify time periods in the past when the user was in a high-stress state and focus on acquiring data during those times. For example, the data acquisition unit can analyze days in the past when the user had good sleep and refer to the data acquisition method used on those days. For example, the data acquisition unit can review the data acquisition method on days in the past when the user felt fatigued and reflect any improvements. As a result, the data acquisition unit can acquire data in the most optimal way based on the user's past data acquisition history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past data acquisition history into a generating AI and have the generating AI select the optimal acquisition method.

[0040] The data acquisition unit can filter data based on the user's current activity status and environment. For example, if the user is exercising, the data acquisition unit can prioritize acquiring heart rate data and evaluate exercise intensity. If the user is working, the data acquisition unit can monitor stress levels and suggest appropriate break times. If the user is relaxing, the data acquisition unit can acquire ambient sound data and analyze the relaxation effect. This allows the data acquisition unit to acquire data tailored to the user's activity status and environment. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input user activity status and environmental data into a generating AI and have the generating AI perform data filtering.

[0041] The data acquisition unit can prioritize acquiring highly relevant data by considering the user's geographical location information when acquiring data. For example, if the user is at home, the data acquisition unit can prioritize acquiring indoor environment data to provide a comfortable environment. If the user is out, for example, the data acquisition unit can prioritize acquiring external environment data to provide appropriate advice. If the user is at work, for example, the data acquisition unit can prioritize acquiring stress levels and suggest appropriate break times. In this way, the data acquisition unit can acquire highly relevant data based on the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant data.

[0042] The data acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring data. For example, if the user is stressed on social media, the data acquisition unit can prioritize acquiring heart rate data and assess the stress level. For example, if the user is relaxed on social media, the data acquisition unit can prioritize acquiring sleep data and analyze the quality of sleep. For example, if the user is tired on social media, the data acquisition unit can prioritize acquiring activity data and assess the degree of fatigue. In this way, the data acquisition unit can acquire relevant data based on the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if heart rate data is important, the analysis unit can perform a detailed analysis to evaluate the stress level. For example, if sleep data is important, the analysis unit can also perform a detailed analysis to evaluate the quality of sleep. For example, if activity data is important, the analysis unit can also perform a detailed analysis to evaluate the degree of fatigue. This allows the analysis unit to perform detailed analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to assess stress levels to heart rate data. For example, the analysis unit can apply an algorithm to assess sleep quality to sleep data. For example, the analysis unit can apply an algorithm to assess fatigue levels to activity data. This allows the analysis unit to perform appropriate analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data acquisition timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent heart rate data to evaluate stress levels. The analysis unit may also prioritize the analysis of the most recent sleep data to evaluate sleep quality. The analysis unit may also prioritize the analysis of the most recent activity data to evaluate fatigue levels. This allows the analysis unit to perform analyses preferentially based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the data acquisition timing to a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can adjust the order of analysis by considering the relationship between heart rate data and sleep data. The analysis unit can also adjust the order of analysis by considering the relationship between activity data and heart rate data. The analysis unit can also adjust the order of analysis by considering the relationship between sleep data and activity data. This allows the analysis unit to perform analysis in an appropriate order based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The optimization unit can improve the accuracy of optimization by considering the interrelationships between data during the optimization process. For example, the optimization unit can consider the interrelationships between heart rate data and sleep data to provide an optimal environment. The optimization unit can also consider the interrelationships between activity data and heart rate data to provide an optimal environment. The optimization unit can also consider the interrelationships between sleep data and activity data to provide an optimal environment. In this way, the optimization unit can provide an optimal environment based on the interrelationships of data. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the interrelationships of data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0048] The optimization unit can perform optimization while taking user attribute information into consideration. For example, the optimization unit can provide an optimal environment according to the user's age. For example, the optimization unit can also provide an optimal environment according to the user's gender. For example, the optimization unit can also provide an optimal environment according to the user's health condition. In this way, the optimization unit can provide an optimal environment based on the user's attribute information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input user attribute information into a generating AI and have the generating AI perform the optimization.

[0049] The optimization unit can perform optimization while considering the geographical distribution of the data. For example, if the user is at home, the optimization unit can optimize the indoor environment. For example, if the user is out, the optimization unit can also optimize the external environment. For example, if the user is at work, the optimization unit can also optimize the work environment. In this way, the optimization unit can provide an optimal environment based on the geographical distribution of the data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical distribution data into a generating AI and have the generating AI perform the optimization.

[0050] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the data during the optimization process. For example, the optimization unit can refer to the latest research on heart rate data to provide an optimal environment. The optimization unit can also refer to the latest research on sleep data to provide an optimal environment. The optimization unit can also refer to the latest research on activity data to provide an optimal environment. This allows the optimization unit to provide an optimal environment based on relevant literature on the data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input relevant literature data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0051] The service provider can select the optimal service method by referring to the user's past appliance setting history when providing the service. For example, the service provider can refer to the lighting settings the user has previously preferred and provide similar settings. For example, the service provider can refer to the music settings the user has previously preferred and provide similar settings. For example, the service provider can refer to the room temperature settings the user has previously preferred and provide similar settings. This allows the service provider to provide appliance settings in the most optimal way based on the user's past appliance setting history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the past appliance setting history into a generating AI and have the generating AI select the optimal service method.

[0052] The service provider can adjust appliance settings based on the user's current activity and environment at the time of service provision. For example, if the user is relaxing, the service provider can change the lighting to a warm color and play relaxing music. For example, if the user is working, the service provider can provide lighting and music that promotes concentration. For example, if the user is exercising, the service provider can provide lighting and music that provides energy. In this way, the service provider can provide appliance settings that are appropriate for the user's activity and environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user activity and environment data into a generating AI and have the generating AI perform the adjustment of appliance settings.

[0053] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to provide home appliance settings in the most optimal way based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal service delivery method.

[0054] The service provider can provide multilingual appliance settings at the time of service delivery, according to the user's language settings. For example, the service provider can automatically set the language of the appliance settings based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. For example, the service provider can provide appliance settings in a specific language if the user selects a particular language. In this way, the service provider can provide multilingual appliance settings based on the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language settings into a generating AI and have the generating AI perform the task of providing multilingual appliance settings.

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

[0056] The data acquisition unit can analyze the user's past data acquisition history and select the optimal acquisition method. For example, it can identify time periods when the user was in a high-stress state in the past and focus data acquisition on those times. It can also analyze days when the user had good sleep in the past and refer to the data acquisition method for those days. Furthermore, it can review the data acquisition method on days when the user felt fatigued in the past and reflect improvements. As a result, the data acquisition unit can acquire data in the most optimal way based on the user's past data acquisition history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past data acquisition history into a generating AI and have the generating AI select the optimal acquisition method.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if heart rate data is important, a detailed analysis can be performed to evaluate the stress level. Similarly, if sleep data is important, a detailed analysis can be performed to evaluate the quality of sleep. Furthermore, if activity data is important, a detailed analysis can be performed to evaluate the degree of fatigue. This allows the analysis unit to perform detailed analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The optimization unit can improve the accuracy of optimization by considering the interrelationships between data during the optimization process. For example, it can consider the interrelationships between heart rate data and sleep data to provide an optimal environment. It can also consider the interrelationships between activity data and heart rate data to provide an optimal environment. Furthermore, it can consider the interrelationships between sleep data and activity data to provide an optimal environment. In this way, the optimization unit can provide an optimal environment based on the interrelationships of data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the interrelationships of data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0059] The service provider can select the optimal service method by referring to the user's past appliance setting history when providing the service. For example, it can refer to the lighting settings the user has previously preferred and provide similar settings. It can also refer to the music settings the user has previously preferred and provide similar settings. Furthermore, it can refer to the room temperature settings the user has previously preferred and provide similar settings. In this way, the service provider can provide appliance settings in the most optimal way based on the user's past appliance setting history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the past appliance setting history into a generating AI and have the generating AI select the optimal service method.

[0060] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the service provider to provide home appliance settings in the most optimal way based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal service delivery method.

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

[0062] Step 1: The acquisition unit acquires data. The acquisition unit acquires heart rate and sleep data from, for example, a smartwatch or smartphone. The acquisition unit can, for example, acquire heart rate data in real time to monitor the user's stress level. The acquisition unit can also concentrate on acquiring sleep data at night to evaluate the user's fatigue level. Furthermore, the acquisition unit can periodically acquire the user's activity data to evaluate their activity level during the day. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes the user's speech and facial expression data to understand their emotional state for the day. For example, the analysis unit uses speech recognition technology to analyze the content of the user's speech and estimate their emotional state. The analysis unit can also use image recognition technology to analyze the user's facial expression data and understand their emotional state. Furthermore, the analysis unit can analyze the user's heart rate data and sleep data to evaluate stress levels and fatigue levels. Step 3: The optimization unit optimizes appliance settings based on the data analyzed by the analysis unit. The optimization unit optimizes appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit may change the lighting to a warmer color, play relaxing music, and adjust the room temperature to a comfortable level. The optimization unit can also adjust appliance settings to maintain a comfortable environment if the user is relaxed. Furthermore, if the user is tired, the optimization unit can optimize appliance settings to provide an environment that promotes rest. Step 4: The service unit provides the optimized appliance settings from the optimization unit. For example, the service unit provides the user with optimized lighting and music settings. For example, the service unit can set the lights to automatically turn on and relaxing music to play when the user returns home. The service unit can also adjust the optimized appliance settings in real time to provide the user with a relaxing environment.

[0063] (Example of form 2) The Feelium system according to an embodiment of the present invention is a system that integrates generating AI and smart home appliances to automatically provide an optimal environment tailored to the user's daily emotions and physical condition. The Feelium system acquires heart rate and sleep data from a smartwatch or smartphone, and the AI ​​estimates stress and fatigue levels. Furthermore, it analyzes the user's speech and facial expression data to understand their emotional state for the day. Based on this information, it optimizes appliance settings such as lighting, music, and room temperature to create a relaxing space. For example, if the user is feeling stressed, the Feelium system changes the lighting to a warm color, plays relaxing music, and adjusts the room temperature to a comfortable level. This allows the user to have a relaxing space. The Feelium system can also be set to automatically turn on the lights and play relaxing music when the user returns home. This reduces daily stress and fatigue, allowing the user to live a comfortable life. The Feelium system is a next-generation smart home agent that makes each user's life more comfortable and healthy. As a result, the Feelium system can automatically provide an optimal environment tailored to the user's emotions and physical condition.

[0064] The Feelium system according to this embodiment comprises an acquisition unit, an analysis unit, an optimization unit, and a provision unit. The acquisition unit acquires data. For example, the acquisition unit acquires heart rate and sleep data from a smartwatch or smartphone. For example, the acquisition unit acquires heart rate data in real time to monitor the user's stress level. The acquisition unit can also acquire sleep data intensively at night to evaluate the user's fatigue level. Furthermore, the acquisition unit can periodically acquire the user's activity data to evaluate their activity level during the day. The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes the user's speech and facial expression data to understand their emotional state for the day. For example, the analysis unit uses speech recognition technology to analyze the user's speech content and estimate their emotional state. Furthermore, the analysis unit can use image recognition technology to analyze the user's facial expression data to understand their emotional state. Furthermore, the analysis unit can analyze the user's heart rate data and sleep data to evaluate their stress level and fatigue level. The optimization unit optimizes the home appliance settings based on the data analyzed by the analysis unit. The optimization unit optimizes appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit may change the lighting to a warmer color, play relaxing music, and adjust the room temperature to a comfortable level. The optimization unit can also adjust appliance settings to maintain a comfortable environment if the user is relaxed. Furthermore, if the user is tired, the optimization unit can optimize appliance settings to provide an environment that promotes rest. The provisioning unit provides the appliance settings optimized by the optimization unit. For example, the provisioning unit provides the user with optimized lighting and music settings. For example, the provisioning unit may set the lights to automatically turn on and relaxing music to play when the user returns home. The provisioning unit can also adjust the optimized appliance settings in real time to provide the user with a relaxing environment. As a result, the Feelium system according to this embodiment can automatically provide an optimal environment tailored to the user's daily emotions and physical condition.

[0065] The data acquisition unit acquires data. For example, it acquires heart rate and sleep data from smartwatches and smartphones. Specifically, smartwatches are worn on the user's wrist and measure heart rate and activity levels in real time using heart rate sensors and accelerometers. This allows for accurate understanding of the user's stress level and activity level. Smartphones use accelerometers and microphones to detect the user's movements, such as turning over in sleep or snoring, in order to acquire sleep data. This allows for detailed analysis of the user's sleep quality and sleep cycle. Furthermore, the data acquisition unit can periodically acquire the user's activity data and evaluate their activity level during the day. For example, it can use the GPS function of smartwatches and smartphones to record the user's distance traveled and steps taken, and evaluate their daily activity level. This allows the data acquisition unit to comprehensively understand the user's health status and lifestyle and collect data to provide to the analysis and optimization units. The data acquisition unit sends this data to a cloud server so that the analysis and optimization units can access it. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data acquisition unit to collect data efficiently and effectively, improving the overall performance of the Feelium system.

[0066] The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes the user's speech and facial expression data to understand their emotional state for the day. Specifically, it uses speech recognition technology to analyze the content of the user's speech and estimate their emotional state. Speech recognition technology analyzes the user's voice tone and speaking patterns to identify emotions such as joy, anger, and sadness. It can also use image recognition technology to analyze the user's facial expression data and understand their emotional state. Image recognition technology analyzes the user's facial expressions, eye movements, and mouth shape to identify emotions. Furthermore, the analysis unit can also analyze the user's heart rate data and sleep data to evaluate stress levels and fatigue. For example, it analyzes heart rate data to determine whether the user is feeling stressed, and analyzes sleep data to evaluate the quality of the user's sleep and their fatigue level. This allows the analysis unit to comprehensively understand the user's emotional and health state and generate information to provide to the optimization unit. In addition, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. For example, based on past heart rate and sleep data, it can predict changes in the user's health status and emotional tendencies, and plan future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the reliability and safety of the entire Feelium system.

[0067] The optimization unit optimizes appliance settings based on data analyzed by the analysis unit. Specifically, it optimizes appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit changes the lighting to a warmer color, plays relaxing music, and adjusts the room temperature to a comfortable level. By adjusting the color temperature and brightness of the lighting, it soothes the user's mood and provides a relaxing environment. Furthermore, for music selection, it chooses songs with high relaxation effects based on the user's preferences and past data. In addition, it controls the air conditioner or heater to adjust the room temperature to a comfortable level for the user. When the user is relaxed, the optimization unit can also adjust appliance settings to maintain a comfortable environment. For example, it keeps the lighting brightness at a moderate level, adjusts the music volume, and maintains a constant room temperature to help the user remain relaxed. Furthermore, if the user is tired, the optimization unit can optimize appliance settings to provide an environment that encourages rest. For example, it dims the lights, plays quiet music, and sets the room temperature slightly lower to provide an environment where the user can rest comfortably. In this way, the optimization unit can provide an optimal environment tailored to the user's emotional and physical state, improving the user's quality of life. Furthermore, the optimization unit can collect user feedback and continuously improve the accuracy and effectiveness of home appliance settings. For example, it can receive feedback on how users felt about the provided environment and adjust the optimization algorithm based on that information. This allows the optimization unit to always provide the optimal environment for the user.

[0068] The service provider provides home appliance settings optimized by the optimization unit. Specifically, it provides users with optimized lighting and music settings. For example, it can set the lights to automatically turn on and relaxing music to play when the user returns home. The service provider works in conjunction with smart home devices to detect the user's return home and simultaneously provide the optimized environment. The service provider can also adjust the optimized home appliance settings in real time to provide the user with a relaxing environment. For example, it can adjust the brightness of the lights and the volume of the music as needed while the user is in the living room to maintain a comfortable environment. Furthermore, the service provider can provide home appliance settings at the optimal time based on the user's schedule and daily routine. For example, it can gradually dim the lights and play relaxing music to match the user's bedtime, providing a comfortable sleeping environment. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the environment it provides. For example, it can provide feedback on how the user felt about the provided environment and adjust the service algorithm based on that information. This allows the service provider to always provide the optimal environment for the user. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with the optimal environment, improving the overall reliability of the Feelium system and user satisfaction.

[0069] The data acquisition unit can acquire heart rate and sleep data from smartwatches and smartphones. For example, the data acquisition unit can acquire heart rate data from a smartwatch in real time to monitor the user's stress level. The data acquisition unit can also acquire sleep data from a smartphone mainly at night to evaluate the user's fatigue level. The data acquisition unit can also periodically acquire activity data from smartwatches and smartphones to evaluate the activity level during the day. This allows the data acquisition unit to understand the user's physical condition in real time. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input heart rate data acquired from a smartwatch into a generating AI and have the generating AI perform stress level estimation.

[0070] The analysis unit can analyze the user's speech and facial expression data to understand their emotional state for the day. For example, the analysis unit can use speech recognition technology to analyze the user's speech and estimate their emotional state. For example, if the user is smiling while speaking, the analysis unit will determine that they are in a positive emotional state. The analysis unit can also determine that if the user has a blank or sad expression, they are in a negative emotional state. The analysis unit can also use image recognition technology to analyze the user's facial expression data and understand their emotional state. For example, the analysis unit can calculate an emotional score based on changes in the user's facial expression. This allows the analysis unit to understand the user's emotional state. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's speech data into a generating AI and have the generating AI perform the estimation of the emotional state.

[0071] The optimization unit can optimize appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit may change the lighting to a warmer color, play relaxing music, and adjust the room temperature to a comfortable level. For example, if the user is relaxed, the optimization unit may also adjust appliance settings to maintain a comfortable environment. For example, if the user is tired, the optimization unit may optimize appliance settings to provide an environment that encourages rest. In this way, the optimization unit can provide the user with a relaxing space. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's emotional state into a generating AI and have the generating AI execute the optimal appliance settings.

[0072] The service provider can provide users with optimized home appliance settings. For example, the service provider can provide users with optimized lighting and music settings. For example, the service provider can set the lights to automatically turn on and relaxing music to play when the user returns home. The service provider can also adjust the optimized home appliance settings in real time to provide the user with a relaxing environment. In this way, the service provider can provide the user with the best possible environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the optimized home appliance settings into a generating AI and have the generating AI execute the home appliance settings to be provided to the user.

[0073] The analysis unit can analyze user speech and facial expression data using speech recognition and image recognition technologies. For example, the analysis unit can analyze the content of user speech using speech recognition technology and estimate the emotional state. For example, if the user is smiling while speaking, the analysis unit will determine that the emotional state is positive. The analysis unit can also determine that if the user has a blank or sad expression, the emotional state is negative. The analysis unit can also analyze user facial expression data using image recognition technology to understand the emotional state. For example, the analysis unit can calculate an emotional score based on changes in the user's facial expression. This allows the analysis unit to analyze user speech and facial expression data with high accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user speech data into a generating AI and have the generating AI perform the estimation of the emotional state.

[0074] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can frequently acquire heart rate data and monitor the stress level in real time. For example, if the user is relaxed, the data acquisition unit can concentrate sleep data acquisition at night and analyze the quality of sleep in detail. For example, if the user is tired, the data acquisition unit can periodically acquire daytime activity data and evaluate the degree of fatigue. This allows the data acquisition unit to acquire data at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of data acquisition.

[0075] The data acquisition unit can analyze the user's past data acquisition history and select the optimal acquisition method. For example, the data acquisition unit can identify time periods in the past when the user was in a high-stress state and focus on acquiring data during those times. For example, the data acquisition unit can analyze days in the past when the user had good sleep and refer to the data acquisition method used on those days. For example, the data acquisition unit can review the data acquisition method on days in the past when the user felt fatigued and reflect any improvements. As a result, the data acquisition unit can acquire data in the most optimal way based on the user's past data acquisition history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past data acquisition history into a generating AI and have the generating AI select the optimal acquisition method.

[0076] The data acquisition unit can filter data based on the user's current activity status and environment. For example, if the user is exercising, the data acquisition unit can prioritize acquiring heart rate data and evaluate exercise intensity. If the user is working, the data acquisition unit can monitor stress levels and suggest appropriate break times. If the user is relaxing, the data acquisition unit can acquire ambient sound data and analyze the relaxation effect. This allows the data acquisition unit to acquire data tailored to the user's activity status and environment. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input user activity status and environmental data into a generating AI and have the generating AI perform data filtering.

[0077] The data acquisition unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated user emotions. For example, if the user is stressed, the data acquisition unit may prioritize acquiring heart rate data and assess the stress level. If the user is relaxed, the data acquisition unit may also prioritize acquiring sleep data and analyze the quality of sleep. If the user is tired, the data acquisition unit may also prioritize acquiring activity data and assess the degree of fatigue. This allows the data acquisition unit to acquire data with a priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0078] The data acquisition unit can prioritize acquiring highly relevant data by considering the user's geographical location information when acquiring data. For example, if the user is at home, the data acquisition unit can prioritize acquiring indoor environment data to provide a comfortable environment. If the user is out, for example, the data acquisition unit can prioritize acquiring external environment data to provide appropriate advice. If the user is at work, for example, the data acquisition unit can prioritize acquiring stress levels and suggest appropriate break times. In this way, the data acquisition unit can acquire highly relevant data based on the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant data.

[0079] The data acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring data. For example, if the user is stressed on social media, the data acquisition unit can prioritize acquiring heart rate data and assess the stress level. For example, if the user is relaxed on social media, the data acquisition unit can prioritize acquiring sleep data and analyze the quality of sleep. For example, if the user is tired on social media, the data acquisition unit can prioritize acquiring activity data and assess the degree of fatigue. In this way, the data acquisition unit can acquire relevant data based on the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results to deepen understanding. For example, if the user is tired, the analysis unit can also provide concise analysis results that get straight to the point. In this way, the analysis unit can provide analysis results in an appropriate presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if heart rate data is important, the analysis unit can perform a detailed analysis to evaluate the stress level. For example, if sleep data is important, the analysis unit can also perform a detailed analysis to evaluate the quality of sleep. For example, if activity data is important, the analysis unit can also perform a detailed analysis to evaluate the degree of fatigue. This allows the analysis unit to perform detailed analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to assess stress levels to heart rate data. For example, the analysis unit can apply an algorithm to assess sleep quality to sleep data. For example, the analysis unit can apply an algorithm to assess fatigue levels to activity data. This allows the analysis unit to perform appropriate analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result to deepen understanding. For example, if the user is tired, the analysis unit can also provide a concise and easy-to-read analysis result. This allows the analysis unit to provide analysis results of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the data acquisition timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent heart rate data to evaluate stress levels. The analysis unit may also prioritize the analysis of the most recent sleep data to evaluate sleep quality. The analysis unit may also prioritize the analysis of the most recent activity data to evaluate fatigue levels. This allows the analysis unit to perform analyses preferentially based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the data acquisition timing to a generating AI and have the generating AI determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can adjust the order of analysis by considering the relationship between heart rate data and sleep data. The analysis unit can also adjust the order of analysis by considering the relationship between activity data and heart rate data. The analysis unit can also adjust the order of analysis by considering the relationship between sleep data and activity data. This allows the analysis unit to perform analysis in an appropriate order based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. For example, if the user is stressed, the optimization unit may prioritize optimizing for a relaxing environment. If the user is relaxed, the optimization unit may also optimize to maintain a comfortable environment. If the user is tired, the optimization unit may also prioritize optimizing for an environment that encourages rest. This allows the optimization unit to perform optimization using appropriate criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may 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 processes in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the optimization criteria.

[0087] The optimization unit can improve the accuracy of optimization by considering the interrelationships between data during the optimization process. For example, the optimization unit can consider the interrelationships between heart rate data and sleep data to provide an optimal environment. The optimization unit can also consider the interrelationships between activity data and heart rate data to provide an optimal environment. The optimization unit can also consider the interrelationships between sleep data and activity data to provide an optimal environment. In this way, the optimization unit can provide an optimal environment based on the interrelationships of data. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the interrelationships of data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0088] The optimization unit can perform optimization while taking user attribute information into consideration. For example, the optimization unit can provide an optimal environment according to the user's age. For example, the optimization unit can also provide an optimal environment according to the user's gender. For example, the optimization unit can also provide an optimal environment according to the user's health condition. In this way, the optimization unit can provide an optimal environment based on the user's attribute information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input user attribute information into a generating AI and have the generating AI perform the optimization.

[0089] The optimization unit can estimate the user's emotions and adjust the order in which the optimization results are displayed based on the estimated emotions. For example, if the user is stressed, the optimization unit may prioritize displaying relaxing environments. If the user is relaxed, the optimization unit may also prioritize displaying environments that maintain a comfortable environment. If the user is tired, the optimization unit may also prioritize displaying environments that encourage rest. This allows the optimization unit to display the optimization results in an appropriate order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the optimization results.

[0090] The optimization unit can perform optimization while considering the geographical distribution of the data. For example, if the user is at home, the optimization unit can optimize the indoor environment. For example, if the user is out, the optimization unit can also optimize the external environment. For example, if the user is at work, the optimization unit can also optimize the work environment. In this way, the optimization unit can provide an optimal environment based on the geographical distribution of the data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical distribution data into a generating AI and have the generating AI perform the optimization.

[0091] The optimization unit can improve the accuracy of optimization by referring to relevant literature on the data during the optimization process. For example, the optimization unit can refer to the latest research on heart rate data to provide an optimal environment. The optimization unit can also refer to the latest research on sleep data to provide an optimal environment. The optimization unit can also refer to the latest research on activity data to provide an optimal environment. This allows the optimization unit to provide an optimal environment based on relevant literature on the data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input relevant literature data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0092] The service provider can estimate the user's emotions and determine the priority of the appliance settings to be provided based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize providing relaxing lighting settings. For example, if the user is relaxed, the service provider may prioritize providing comfortable music settings. For example, if the user is tired, the service provider may prioritize providing comfortable room temperature settings. In this way, the service provider can provide appliance settings with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of appliance settings.

[0093] The service provider can select the optimal service method by referring to the user's past appliance setting history when providing the service. For example, the service provider can refer to the lighting settings the user has previously preferred and provide similar settings. For example, the service provider can refer to the music settings the user has previously preferred and provide similar settings. For example, the service provider can refer to the room temperature settings the user has previously preferred and provide similar settings. This allows the service provider to provide appliance settings in the most optimal way based on the user's past appliance setting history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the past appliance setting history into a generating AI and have the generating AI select the optimal service method.

[0094] The service provider can adjust appliance settings based on the user's current activity and environment at the time of service provision. For example, if the user is relaxing, the service provider can change the lighting to a warm color and play relaxing music. For example, if the user is working, the service provider can provide lighting and music that promotes concentration. For example, if the user is exercising, the service provider can provide lighting and music that provides energy. In this way, the service provider can provide appliance settings that are appropriate for the user's activity and environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user activity and environment data into a generating AI and have the generating AI perform the adjustment of appliance settings.

[0095] The service provider can estimate the user's emotions and adjust the display method of the appliance settings based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is tired, the service provider can also provide a concise display method that gets straight to the point. This allows the service provider to provide appliance settings in an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0096] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to provide home appliance settings in the most optimal way based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal service delivery method.

[0097] The service provider can provide multilingual appliance settings at the time of service delivery, according to the user's language settings. For example, the service provider can automatically set the language of the appliance settings based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. For example, the service provider can provide appliance settings in a specific language if the user selects a particular language. In this way, the service provider can provide multilingual appliance settings based on the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language settings into a generating AI and have the generating AI perform the task of providing multilingual appliance settings.

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

[0099] The data acquisition unit can estimate the user's emotions and adjust the data acquisition frequency based on the estimated emotions. For example, if the user is stressed, heart rate data can be acquired frequently to monitor stress levels in real time. If the user is relaxed, sleep data can be concentrated at night to analyze sleep quality in detail. Furthermore, if the user is tired, daytime activity data can be acquired periodically to assess fatigue levels. This allows the data acquisition unit to acquire data at an appropriate frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input user emotion data into the generative AI and have the generative AI adjust the data acquisition frequency.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis of heart rate data can be prioritized to assess the stress level. If the user is relaxed, the analysis of sleep data can be prioritized to assess the quality of sleep. Furthermore, if the user is tired, the analysis of activity data can be prioritized to assess the degree of fatigue. This allows the analysis unit to analyze data with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0101] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. For example, if the user is stressed, the optimization unit can prioritize optimizing for a relaxing environment. If the user is relaxed, the optimization unit can also optimize to maintain a comfortable environment. Furthermore, if the user is tired, the optimization unit can prioritize optimizing for an environment that encourages rest. This allows the optimization unit to perform optimization using appropriate criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the optimization criteria.

[0102] The service provider can estimate the user's emotions and determine the priority of the appliance settings to be provided based on the estimated emotions. For example, if the user is stressed, it can prioritize providing relaxing lighting settings. If the user is relaxed, it can prioritize providing comfortable music settings. Furthermore, if the user is tired, it can prioritize providing comfortable room temperature settings. In this way, the service provider can provide appliance settings with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of appliance settings.

[0103] The service provider can estimate the user's emotions and adjust the display method of the appliance settings based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is tired, a concise display method that gets straight to the point can be provided. In this way, the service provider can provide appliance settings with an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0104] The data acquisition unit can analyze the user's past data acquisition history and select the optimal acquisition method. For example, it can identify time periods when the user was in a high-stress state in the past and focus data acquisition on those times. It can also analyze days when the user had good sleep in the past and refer to the data acquisition method for those days. Furthermore, it can review the data acquisition method on days when the user felt fatigued in the past and reflect improvements. As a result, the data acquisition unit can acquire data in the most optimal way based on the user's past data acquisition history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past data acquisition history into a generating AI and have the generating AI select the optimal acquisition method.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if heart rate data is important, a detailed analysis can be performed to evaluate the stress level. Similarly, if sleep data is important, a detailed analysis can be performed to evaluate the quality of sleep. Furthermore, if activity data is important, a detailed analysis can be performed to evaluate the degree of fatigue. This allows the analysis unit to perform detailed analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0106] The optimization unit can improve the accuracy of optimization by considering the interrelationships between data during the optimization process. For example, it can consider the interrelationships between heart rate data and sleep data to provide an optimal environment. It can also consider the interrelationships between activity data and heart rate data to provide an optimal environment. Furthermore, it can consider the interrelationships between sleep data and activity data to provide an optimal environment. In this way, the optimization unit can provide an optimal environment based on the interrelationships of data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the interrelationships of data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0107] The service provider can select the optimal service method by referring to the user's past appliance setting history when providing the service. For example, it can refer to the lighting settings the user has previously preferred and provide similar settings. It can also refer to the music settings the user has previously preferred and provide similar settings. Furthermore, it can refer to the room temperature settings the user has previously preferred and provide similar settings. In this way, the service provider can provide appliance settings in the most optimal way based on the user's past appliance setting history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the past appliance setting history into a generating AI and have the generating AI select the optimal service method.

[0108] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the service provider to provide home appliance settings in the most optimal way based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal service delivery method.

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

[0110] Step 1: The acquisition unit acquires data. The acquisition unit acquires heart rate and sleep data from, for example, a smartwatch or smartphone. The acquisition unit can, for example, acquire heart rate data in real time to monitor the user's stress level. The acquisition unit can also concentrate on acquiring sleep data at night to evaluate the user's fatigue level. Furthermore, the acquisition unit can periodically acquire the user's activity data to evaluate their activity level during the day. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes the user's speech and facial expression data to understand their emotional state for the day. For example, the analysis unit uses speech recognition technology to analyze the content of the user's speech and estimate their emotional state. The analysis unit can also use image recognition technology to analyze the user's facial expression data and understand their emotional state. Furthermore, the analysis unit can analyze the user's heart rate data and sleep data to evaluate stress levels and fatigue levels. Step 3: The optimization unit optimizes appliance settings based on the data analyzed by the analysis unit. The optimization unit optimizes appliance settings such as lighting, music, and room temperature. For example, if the user is feeling stressed, the optimization unit may change the lighting to a warmer color, play relaxing music, and adjust the room temperature to a comfortable level. The optimization unit can also adjust appliance settings to maintain a comfortable environment if the user is relaxed. Furthermore, if the user is tired, the optimization unit can optimize appliance settings to provide an environment that promotes rest. Step 4: The service unit provides the optimized appliance settings from the optimization unit. For example, the service unit provides the user with optimized lighting and music settings. For example, the service unit can set the lights to automatically turn on and relaxing music to play when the user returns home. The service unit can also adjust the optimized appliance settings in real time to provide the user with a relaxing environment.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the acquisition unit, analysis unit, optimization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the computer 36 of the smart device 14 and acquires heart rate and sleep data from a smartwatch or smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the user's emotional state. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the home appliance settings based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the optimized home appliance settings to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the acquisition unit, analysis unit, optimization unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the computer 36 of the smart glasses 214 and acquires heart rate and sleep data from a smartwatch or smartphone. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the user's emotional state. The optimization unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes home appliance settings based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the optimized home appliance settings to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the acquisition unit, analysis unit, optimization unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the computer 36 of the headset terminal 314 and acquires heart rate and sleep data from a smartwatch or smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the user's emotional state. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the home appliance settings based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the optimized home appliance settings to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the acquisition unit, analysis unit, optimization unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the computer 36 of the robot 414 and acquires heart rate and sleep data from a smartwatch or smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data to understand the user's emotional state. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the home appliance settings based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the optimized home appliance settings to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data acquisition unit that acquires data, An analysis unit analyzes the data acquired by the acquisition unit, An optimization unit optimizes home appliance settings based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides home appliance settings optimized by the optimization unit. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain heart rate and sleep data from smartwatches and smartphones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes the user's speech and facial expression data to understand their emotional state on that day. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, Optimize appliance settings such as lighting, music, and room temperature. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides users with optimized home appliance settings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Using speech recognition and image recognition technologies, we analyze user speech and facial expression data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past data acquisition history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring data, the system prioritizes retrieving highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring data, we analyze the user's social media activity and retrieve relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, It estimates user sentiment and adjusts optimization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, consider the interrelationships between data to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, It estimates the user's emotions and adjusts the order in which the optimization results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature on the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the home appliance settings to be offered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system will refer to the user's past appliance setting history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the appliance settings will be adjusted based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how home appliance settings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, multilingual home appliance settings will be provided according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data acquisition unit that acquires data, An analysis unit analyzes the data acquired by the acquisition unit, An optimization unit optimizes the home appliance settings based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides home appliance settings optimized by the optimization unit. A system characterized by the following features.

2. The acquisition unit is, Obtain heart rate and sleep data from smartwatches and smartphones. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the user's speech and facial expression data to understand their emotional state on that day. The system according to feature 1.

4. The optimization unit, Optimize appliance settings such as lighting, music, and room temperature. The system according to feature 1.

5. The aforementioned supply unit is, Provides users with optimized home appliance settings. The system according to feature 1.

6. The aforementioned analysis unit, Using speech recognition and image recognition technologies, we analyze user speech and facial expression data. The system according to feature 1.

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

8. The acquisition unit is, Analyze the user's past data acquisition history and select the optimal acquisition method. The system according to feature 1.

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