system

Abstract

The system according to the embodiment aims to provide a safe and comfortable living environment for elderly people with dementia. [Solution] A system according to an embodiment includes a learning unit, a lighting unit, a temperature control unit, and a security unit. The learning unit learns user behavior patterns. The lighting unit automatically adjusts lighting based on the behavior patterns learned by the learning unit. The temperature control unit automatically adjusts temperature based on the behavior patterns learned by the learning unit. The security unit automatically adjusts security based on the behavior patterns learned by the learning unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not been able to fully optimize living environments for elderly people with dementia, which places a heavy burden on caregivers.

[0005] The system according to the embodiment aims to provide a safe and comfortable living environment for elderly people with dementia. [Means for solving the problem]

[0006] A system according to an embodiment includes a learning unit, a lighting unit, a temperature control unit, and a security unit. The learning unit learns a user's behavioral patterns. The lighting unit automatically adjusts lighting based on the behavioral patterns learned by the learning unit. The temperature control unit automatically adjusts temperature based on the behavioral patterns learned by the learning unit. The security unit automatically adjusts security based on the behavioral patterns learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a safe and comfortable living environment for elderly people with dementia. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-driven smart home system according to an embodiment of the present invention provides a safe and comfortable living environment for elderly people with dementia. This system employs smart devices equipped with lighting, temperature control, and security functions. AI learns each user's behavioral patterns and habits to provide an optimal living space. This aims to support the independent living of elderly people with dementia and reduce the burden on caregivers. For example, smart lighting automatically turns on and off based on the user's behavioral patterns. The temperature control system maintains the optimal room temperature based on the user's preferences and health status. The security function monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. Furthermore, generative AI is utilized to provide personalized services. For example, if a user prefers a certain type of music, the AI ​​can play music based on that information. Similarly, if a user prefers a certain type of food, the AI ​​can suggest meals based on that information. This allows the AI-driven smart home system to support the independent living of elderly people with dementia and reduce the burden on caregivers. For example, AI can learn a user's behavioral patterns, making it easier for caregivers to understand the user's condition. Automatic adjustments to smart devices also reduce the burden on caregivers. The goal is to contribute to solving the challenges of dementia care in an aging society and improve the quality of life of elderly people with dementia and their families.

[0029] An AI-driven smart home system according to an embodiment includes a learning unit, a lighting unit, a temperature control unit, and a security unit. The learning unit learns a user's behavioral patterns. For example, the learning unit learns a user's pattern of going to the bedroom at a specific time each day. The learning unit can also learn a user's pattern of preferring a specific temperature. The learning unit can also collect data to provide an optimal living environment based on the user's behavioral patterns. The lighting unit automatically adjusts lighting based on the behavioral patterns learned by the learning unit. For example, the lighting unit automatically turns the lights on and off according to the time the user goes to the bedroom. The lighting unit can also adjust the brightness and color of the lights according to the user's preferences. The lighting unit can also automatically set a lighting schedule based on the user's behavioral patterns. The temperature control unit automatically adjusts the temperature based on the behavioral patterns learned by the learning unit. For example, if the user prefers a specific temperature, the temperature control unit adjusts the temperature to maintain that temperature. The temperature control unit can also adjust the temperature according to the user's health condition. The temperature control unit can also automatically set a temperature schedule based on the user's behavioral patterns. The security unit automatically adjusts security based on the behavioral patterns learned by the learning unit. For example, the security unit monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. The security unit can also automatically set a security schedule based on the user's behavioral patterns. The security unit can also customize security settings according to the user's preferences. As a result, the AI-driven smart home system according to the embodiment can support the independent living of elderly people with dementia and reduce the burden on caregivers.

[0030] The learning unit can adjust the temperature control system based on the user's preferences and health condition. For example, the learning unit learns the user's preferred temperature and adjusts the temperature control system based on that information. For example, if the user prefers a specific temperature, the learning unit adjusts the temperature control system to maintain that temperature. The learning unit can also monitor the user's health condition and adjust the temperature if the user is not feeling well. For example, the learning unit can monitor the user's body temperature and heart rate and adjust the temperature control system based on that information. The learning unit can also automatically set a temperature schedule based on the user's behavioral patterns. This makes it possible to provide a comfortable living environment by adjusting the temperature based on the user's preferences and health condition.

[0031] The learning unit can play music based on the user's behavioral patterns. For example, the learning unit learns a user's preference for specific music at specific times and plays music based on that information. For example, if a user listens to specific music every morning, the learning unit plays music at that time. The learning unit can also automatically set a music playback schedule based on the user's behavioral patterns. For example, the learning unit plays relaxing music at times when the user wants to relax. The learning unit can also select music genres and artists according to the user's preferences. In this way, playing music based on the user's behavioral patterns can improve the user's comfort.

[0032] The learning unit can make meal suggestions based on the user's behavioral patterns. For example, the learning unit learns a pattern in which the user prefers certain meals at certain times and makes meal suggestions based on that information. For example, if the user prefers a certain meal every night, the learning unit can make meal suggestions based on that time. The learning unit can also automatically set a meal schedule based on the user's behavioral patterns. For example, if the user prefers healthy meals, the learning unit can suggest nutritionally balanced meals based on that information. The learning unit can also customize meal menus according to the user's preferences. In this way, meal suggestions based on the user's behavioral patterns can support the user's health.

[0033] The learning unit can analyze the user's past behavioral patterns and select the optimal learning algorithm. The learning unit selects the optimal learning algorithm based on, for example, behaviors the user frequently performed in the past. For example, the learning unit detects abnormal behavior from the user's past behavioral patterns and selects a learning algorithm that corresponds to that behavior. The learning unit can also analyze changes in the user's behavioral patterns and adaptively update the learning algorithm. For example, the learning unit monitors changes in the user's behavioral patterns in real time and updates the learning algorithm based on that information. In this way, by analyzing past behavioral patterns, the optimal learning algorithm can be selected and learning accuracy can be improved.

[0034] When learning a behavioral pattern, the learning unit can customize the learning content taking into account the user's health condition and lifestyle rhythm. The learning unit, for example, monitors the user's health condition and reduces the learning content if the user is not feeling well. For example, the learning unit monitors the user's body temperature and heart rate and adjusts the learning content based on that information. The learning unit can also adjust the timing of the learning to match the user's lifestyle rhythm. For example, the learning unit sets the timing of the learning to match the user's wake-up time and bedtime. The learning unit can also provide optimal learning content based on the user's health data. For example, the learning unit customizes the learning content according to the user's health condition. In this way, optimal learning content can be provided by taking the user's health condition and lifestyle rhythm into consideration.

[0035] The learning unit can improve the learning algorithm by reflecting user feedback when learning behavioral patterns. For example, the learning unit collects user feedback and reflects it in the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm based on user opinions and usage history. The learning unit can also analyze user feedback and adjust the parameters of the learning algorithm. For example, the learning unit optimizes the parameters of the learning algorithm based on user feedback. In this way, the accuracy of the learning algorithm can be improved by reflecting user feedback.

[0036] When learning behavioral patterns, the learning unit can adjust the learning content taking into account the user's geographical location information. For example, when the user is in a specific location, the learning unit learns behavioral patterns related to that location. For example, the learning unit provides optimal learning content based on the user's geographical location information. Furthermore, when the user is traveling, the learning unit can also learn behavioral patterns related to the user's destination. For example, the learning unit collects the user's GPS data and adjusts the learning content based on that information. In this way, optimal learning content can be provided by taking into account the user's geographical location information.

[0037] When learning behavioral patterns, the learning unit can analyze the user's social media activities and learn related behavioral patterns. The learning unit, for example, analyzes the content posted by the user on social media to learn related behavioral patterns. For example, the learning unit learns related behavioral patterns by referring to the activities of the user's friends on social media. The learning unit can also learn related behavioral patterns based on the user's check-in information on social media. For example, the learning unit collects the user's social media data and adjusts the learning content based on that information. In this way, by analyzing the user's social media activities, related behavioral patterns can be learned and accuracy can be improved.

[0038] When learning a behavioral pattern, the learning unit can customize the learning method by reflecting the user's past feedback. The learning unit customizes the learning method, for example, based on the user's past feedback. For example, the learning unit provides an optimal learning method based on the user's opinions and usage history. The learning unit can also analyze the user's past feedback and improve the learning algorithm. For example, the learning unit optimizes the parameters of the learning algorithm based on the user's feedback. In this way, the optimal learning method can be provided by reflecting the user's past feedback.

[0039] When adjusting the lighting, the lighting unit can select optimal settings by referring to the user's past lighting setting history. The lighting unit provides optimal settings, for example, based on lighting settings that the user has preferred in the past. For example, the lighting unit analyzes the user's past lighting setting history and suggests an optimal lighting pattern. The lighting unit can also adjust the brightness and color of the lighting based on the user's past lighting setting history. For example, the lighting unit collects the user's lighting setting change history and selects optimal settings based on that information. In this way, optimal lighting settings can be provided by referring to the past lighting setting history.

[0040] When adjusting the lighting, the lighting unit can customize the lighting settings according to the user's current activity. For example, if the user is reading, the lighting unit provides lighting suitable for reading. For example, the lighting unit detects the user's current activity using a sensor and customizes the lighting settings based on that information. Furthermore, if the user is relaxing, the lighting unit can also provide lighting suitable for relaxation. For example, the lighting unit acquires the user's activity from user input and customizes the lighting settings based on that information. Furthermore, if the user is working, the lighting unit can also provide lighting suitable for that work. For example, the lighting unit adjusts the brightness and color of the lighting based on the user's activity. In this way, the lighting settings can be customized according to the user's current activity, thereby providing an optimal lighting environment.

[0041] The lighting unit can improve lighting settings by reflecting user feedback when adjusting the lighting. For example, the lighting unit adjusts the brightness and color of the lighting based on user feedback. For example, the lighting unit collects user opinions and usage history and improves the lighting settings based on that information. The lighting unit can also analyze user feedback and provide optimal lighting settings. For example, the lighting unit optimizes lighting setting parameters based on user feedback. The lighting unit can also customize lighting settings by reflecting user feedback. For example, the lighting unit adjusts the lighting schedule based on user feedback. In this way, optimal lighting settings can be provided by reflecting user feedback.

[0042] When adjusting the lighting, the lighting unit can select optimal lighting settings by taking into account the user's geographical location information. For example, when the user is in a specific location, the lighting unit provides lighting settings appropriate for that location. For example, the lighting unit obtains the user's geographical location information from GPS data and selects optimal lighting settings based on that information. The lighting unit can also provide optimal lighting settings based on the user's geographical location information. For example, when the user is traveling, the lighting unit provides lighting settings appropriate for the destination. The lighting unit can also adjust the lighting schedule based on the user's geographical location information. In this way, optimal lighting settings can be provided by taking into account the user's geographical location information.

[0043] The lighting unit can analyze the user's social media activity and suggest relevant lighting settings when adjusting the lighting. For example, the lighting unit can analyze the content of the user's social media posts and suggest relevant lighting settings. For example, the lighting unit can suggest relevant lighting settings by referring to the activity of the user's friends on social media. The lighting unit can also suggest relevant lighting settings based on the user's social media check-in information. For example, the lighting unit can collect the user's social media data and suggest lighting settings based on that information. In this way, by analyzing the user's social media activity, relevant lighting settings can be suggested, providing a comfortable lighting environment.

[0044] The lighting unit can customize lighting settings by reflecting the user's past feedback when adjusting the lighting. For example, the lighting unit adjusts the brightness and color of the lighting based on the user's past feedback. For example, the lighting unit collects the user's opinions and usage history and customizes the lighting settings based on that information. The lighting unit can also analyze the user's past feedback to provide optimal lighting settings. For example, the lighting unit optimizes lighting setting parameters based on the user's feedback. The lighting unit can also customize lighting settings by reflecting the user's past feedback. For example, the lighting unit adjusts the lighting schedule based on the user's feedback. In this way, optimal lighting settings can be provided by reflecting the user's past feedback.

[0045] When managing the temperature, the temperature management unit can select the optimal setting by referring to the user's past temperature setting history. The temperature management unit provides the optimal setting, for example, based on the user's past preferred temperature settings. For example, the temperature management unit analyzes the user's past temperature setting history and suggests the optimal temperature pattern. The temperature management unit can also adjust the temperature based on the user's past temperature setting history. For example, the temperature management unit collects the user's temperature setting change history and selects the optimal setting based on that information. In this way, the optimal temperature setting can be provided by referring to the past temperature setting history.

[0046] During temperature management, the temperature management unit can customize the temperature setting according to the user's current health condition and activity. The temperature management unit, for example, monitors the user's health condition and adjusts the temperature if the user is not feeling well. For example, the temperature management unit monitors the user's body temperature and heart rate and adjusts the temperature based on that information. The temperature management unit can also provide an optimal temperature according to the user's activity. For example, the temperature management unit obtains the user's activity from sensor information and customizes the temperature setting based on that information. The temperature management unit can also provide an optimal temperature setting based on the user's health data. For example, the temperature management unit customizes the temperature setting according to the user's health condition. In this way, a comfortable temperature environment can be provided by customizing the temperature setting according to the user's current health condition and activity.

[0047] The temperature management unit can improve the temperature setting by reflecting user feedback during temperature management. The temperature management unit, for example, adjusts the temperature based on user feedback. For example, the temperature management unit collects user opinions and usage history and improves the temperature setting based on that information. The temperature management unit can also analyze user feedback and provide optimal temperature settings. For example, the temperature management unit optimizes temperature setting parameters based on user feedback. The temperature management unit can also customize the temperature setting by reflecting user feedback. For example, the temperature management unit adjusts the temperature schedule based on user feedback. In this way, optimal temperature settings can be provided by reflecting user feedback.

[0048] The temperature management unit can select the optimal temperature setting by taking into account the user's geographical location information when managing the temperature. For example, when the user is in a specific location, the temperature management unit provides a temperature setting appropriate for that location. For example, the temperature management unit obtains the user's geographical location information from GPS data and selects the optimal temperature setting based on that information. The temperature management unit can also provide the optimal temperature setting based on the user's geographical location information. For example, when the user is traveling, the temperature management unit provides a temperature setting appropriate for the destination. The temperature management unit can also adjust the temperature schedule based on the user's geographical location information. In this way, the optimal temperature setting can be provided by taking into account the user's geographical location information.

[0049] The temperature control unit can analyze the user's social media activity and suggest relevant temperature settings when controlling the temperature. The temperature control unit, for example, analyzes the content of the user's social media posts and suggests relevant temperature settings. For example, the temperature control unit can refer to the activity of the user's friends on social media to suggest relevant temperature settings. The temperature control unit can also suggest relevant temperature settings based on the user's social media check-in information. For example, the temperature control unit collects the user's social media data and suggests temperature settings based on that information. In this way, by analyzing the user's social media activity, it is possible to suggest relevant temperature settings and provide a comfortable temperature environment.

[0050] The temperature management unit can customize the temperature setting by reflecting the user's past feedback when managing the temperature. The temperature management unit, for example, adjusts the temperature based on the user's past feedback. For example, the temperature management unit collects the user's opinions and usage history and customizes the temperature setting based on that information. The temperature management unit can also analyze the user's past feedback and provide the optimal temperature setting. For example, the temperature management unit optimizes the temperature setting parameters based on the user's feedback. The temperature management unit can also customize the temperature setting by reflecting the user's past feedback. For example, the temperature management unit adjusts the temperature schedule based on the user's feedback. In this way, the optimal temperature setting can be provided by reflecting the user's past feedback.

[0051] When adjusting security, the security unit can select optimal settings by referring to the user's past security history. The security unit provides optimal settings, for example, based on security settings that the user has preferred in the past. For example, the security unit analyzes the user's past security history and proposes an optimal security pattern. The security unit can also adjust security settings based on the user's past security history. For example, the security unit collects a history of changes to the user's security settings and selects optimal settings based on that information. In this way, optimal security settings can be provided by referring to the past security history.

[0052] When adjusting security, the security unit can customize security settings according to the user's current living situation and activities. The security unit, for example, monitors the user's living situation and adjusts security settings according to the situation. For example, the security unit obtains the user's daily routines and the frequency of specific actions from sensor information and customizes security settings based on that information. The security unit can also provide optimal security settings according to the user's activities. For example, the security unit obtains the user's activities from user input and customizes security settings based on that information. The security unit can also provide optimal security settings based on the user's living data. For example, the security unit customizes security settings according to the user's living situation. In this way, by customizing security settings according to the user's current living situation and activities, an optimal security environment can be provided.

[0053] The security unit can improve security settings by reflecting user feedback when adjusting security. The security unit, for example, adjusts security settings based on user feedback. For example, the security unit collects user opinions and usage history and improves security settings based on that information. The security unit can also analyze user feedback and provide optimal security settings. For example, the security unit optimizes security setting parameters based on user feedback. The security unit can also customize security settings by reflecting user feedback. For example, the security unit adjusts a security schedule based on user feedback. In this way, optimal security settings can be provided by reflecting user feedback.

[0054] When adjusting security, the security unit can select optimal security settings by taking into account the user's geographical location information. For example, when the user is in a specific location, the security unit provides security settings appropriate for that location. For example, the security unit obtains the user's geographical location information from GPS data and selects optimal security settings based on that information. The security unit can also provide optimal security settings based on the user's geographical location information. For example, when the user is traveling, the security unit provides security settings appropriate for the user's destination. The security unit can also adjust the security schedule based on the user's geographical location information. In this way, optimal security settings can be provided by taking the user's geographical location information into account.

[0055] When adjusting security, the security unit can analyze the user's social media activity and suggest relevant security settings. For example, the security unit can analyze the content of the user's social media posts and suggest relevant security settings. For example, the security unit can refer to the activities of the user's friends on social media to suggest relevant security settings. The security unit can also suggest relevant security settings based on the user's social media check-in information. For example, the security unit can collect the user's social media data and suggest security settings based on that information. In this way, by analyzing the user's social media activity, relevant security settings can be suggested and an optimal security environment can be provided.

[0056] When adjusting security settings, the security unit can customize the security settings by reflecting the user's past feedback. The security unit adjusts the security settings based on, for example, the user's past feedback. For example, the security unit collects the user's opinions and usage history and customizes the security settings based on that information. The security unit can also analyze the user's past feedback to provide optimal security settings. For example, the security unit optimizes the security setting parameters based on the user's feedback. The security unit can also customize the security settings by reflecting the user's past feedback. For example, the security unit adjusts the security schedule based on the user's feedback. In this way, optimal security settings can be provided by reflecting the user's past feedback.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The learning unit can automatically diffuse a scent that the user prefers based on the user's behavioral patterns. For example, the learning unit can diffuse a lavender scent when the user wants to relax. The learning unit can also diffuse a mint scent when the user wants to concentrate. Furthermore, the learning unit can adjust the strength of the scent based on the user's behavioral patterns. This allows the user's comfort to be improved by diffusing a scent based on the user's behavioral patterns.

[0059] The learning unit can automatically play video content that the user likes based on the user's behavioral patterns. For example, the learning unit can play videos of natural scenery when the user wants to relax. The learning unit can also play quiet background videos when the user wants to concentrate. Furthermore, the learning unit can automatically set a video playback schedule based on the user's behavioral patterns. This can improve the user's comfort by playing video content based on the user's behavioral patterns.

[0060] The learning unit can automatically provide a drink that the user prefers based on the user's behavioral patterns. For example, the learning unit can provide herbal tea when the user wants to relax. The learning unit can also provide coffee when the user wants to concentrate. Furthermore, the learning unit can automatically set a drink serving schedule based on the user's behavioral patterns. This can improve the user's comfort by providing drinks based on the user's behavioral patterns.

[0061] The learning unit can automatically select a news genre that the user prefers based on the user's behavioral patterns. For example, the learning unit can provide entertainment news when the user wants to relax. The learning unit can also provide business news when the user wants to concentrate. Furthermore, the learning unit can automatically set a news delivery schedule based on the user's behavioral patterns. This can improve the user's comfort by selecting a news genre based on the user's behavioral patterns.

[0062] The learning unit can automatically suggest exercise programs that the user prefers based on the user's behavioral patterns. For example, the learning unit can suggest yoga when the user wants to relax. The learning unit can also suggest aerobics when the user wants to energize. Furthermore, the learning unit can automatically set a suggested schedule for exercise programs based on the user's behavioral patterns. This makes it possible to support the user's health by suggesting exercise programs based on the user's behavioral patterns.

[0063] The learning unit can automatically suggest a relaxation method that the user prefers based on the user's behavioral patterns. For example, the learning unit can suggest meditation when the user wants to relax. The learning unit can also suggest stretching when the user wants to cheer up. Furthermore, the learning unit can automatically set a relaxation method suggestion schedule based on the user's behavioral patterns. This can improve the user's comfort by suggesting relaxation methods based on the user's behavioral patterns.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The learning unit learns the user's behavioral patterns. For example, the learning unit learns the user's pattern of going to the bedroom at a specific time each day, or their preference for a specific temperature. The learning unit also collects data based on the user's behavioral patterns to provide an optimal living environment. Step 2: The lighting unit automatically adjusts the lighting based on the behavioral patterns learned by the learning unit. For example, the lighting unit automatically turns the lights on and off when the user goes to the bedroom, and adjusts the brightness and color of the lights according to the user's preferences. The lighting unit also automatically sets a lighting schedule based on the user's behavioral patterns. Step 3: The temperature control unit automatically adjusts the temperature based on the behavioral patterns learned by the learning unit. For example, if the user prefers a specific temperature, the temperature control unit adjusts the temperature to maintain that temperature, and adjusts the temperature according to the user's health condition. The temperature control unit also automatically sets a temperature schedule based on the user's behavioral patterns. Step 4: The security unit automatically adjusts security based on the behavioral patterns learned by the learning unit. For example, the security unit monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. The security unit also automatically sets a security schedule based on the user's behavioral patterns and customizes security settings according to the user's preferences.

[0066] (Example 2) An AI-driven smart home system according to an embodiment of the present invention provides a safe and comfortable living environment for elderly people with dementia. This system employs smart devices equipped with lighting, temperature control, and security functions. AI learns each user's behavioral patterns and habits to provide an optimal living space. This aims to support the independent living of elderly people with dementia and reduce the burden on caregivers. For example, smart lighting automatically turns on and off based on the user's behavioral patterns. The temperature control system maintains the optimal room temperature based on the user's preferences and health status. The security function monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. Furthermore, generative AI is utilized to provide personalized services. For example, if a user prefers a certain type of music, the AI ​​can play music based on that information. Similarly, if a user prefers a certain type of food, the AI ​​can suggest meals based on that information. This allows the AI-driven smart home system to support the independent living of elderly people with dementia and reduce the burden on caregivers. For example, AI can learn a user's behavioral patterns, making it easier for caregivers to understand the user's condition. Automatic adjustments to smart devices also reduce the burden on caregivers. The goal is to contribute to solving the challenges of dementia care in an aging society and improve the quality of life of elderly people with dementia and their families.

[0067] An AI-driven smart home system according to an embodiment includes a learning unit, a lighting unit, a temperature control unit, and a security unit. The learning unit learns a user's behavioral patterns. For example, the learning unit learns a user's pattern of going to the bedroom at a specific time each day. The learning unit can also learn a user's pattern of preferring a specific temperature. The learning unit can also collect data to provide an optimal living environment based on the user's behavioral patterns. The lighting unit automatically adjusts lighting based on the behavioral patterns learned by the learning unit. For example, the lighting unit automatically turns the lights on and off according to the time the user goes to the bedroom. The lighting unit can also adjust the brightness and color of the lights according to the user's preferences. The lighting unit can also automatically set a lighting schedule based on the user's behavioral patterns. The temperature control unit automatically adjusts the temperature based on the behavioral patterns learned by the learning unit. For example, if the user prefers a specific temperature, the temperature control unit adjusts the temperature to maintain that temperature. The temperature control unit can also adjust the temperature according to the user's health condition. The temperature control unit can also automatically set a temperature schedule based on the user's behavioral patterns. The security unit automatically adjusts security based on the behavioral patterns learned by the learning unit. For example, the security unit monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. The security unit can also automatically set a security schedule based on the user's behavioral patterns. The security unit can also customize security settings according to the user's preferences. As a result, the AI-driven smart home system according to the embodiment can support the independent living of elderly people with dementia and reduce the burden on caregivers.

[0068] The learning unit can adjust the temperature control system based on the user's preferences and health condition. For example, the learning unit learns the user's preferred temperature and adjusts the temperature control system based on that information. For example, if the user prefers a specific temperature, the learning unit adjusts the temperature control system to maintain that temperature. The learning unit can also monitor the user's health condition and adjust the temperature if the user is not feeling well. For example, the learning unit can monitor the user's body temperature and heart rate and adjust the temperature control system based on that information. The learning unit can also automatically set a temperature schedule based on the user's behavioral patterns. This makes it possible to provide a comfortable living environment by adjusting the temperature based on the user's preferences and health condition.

[0069] The learning unit can play music based on the user's behavioral patterns. For example, the learning unit learns a user's preference for specific music at specific times and plays music based on that information. For example, if a user listens to specific music every morning, the learning unit plays music at that time. The learning unit can also automatically set a music playback schedule based on the user's behavioral patterns. For example, the learning unit plays relaxing music at times when the user wants to relax. The learning unit can also select music genres and artists according to the user's preferences. In this way, playing music based on the user's behavioral patterns can improve the user's comfort.

[0070] The learning unit can make meal suggestions based on the user's behavioral patterns. For example, the learning unit learns a pattern in which the user prefers certain meals at certain times and makes meal suggestions based on that information. For example, if the user prefers a certain meal every night, the learning unit can make meal suggestions based on that time. The learning unit can also automatically set a meal schedule based on the user's behavioral patterns. For example, if the user prefers healthy meals, the learning unit can suggest nutritionally balanced meals based on that information. The learning unit can also customize meal menus according to the user's preferences. In this way, meal suggestions based on the user's behavioral patterns can support the user's health.

[0071] The learning unit can estimate the user's emotions and adjust the learning frequency of behavioral patterns based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit reduces the learning frequency to reduce the user's burden. For example, the learning unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the learning unit can increase the learning frequency to acquire more detailed behavioral patterns. For example, the learning unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the learning unit can temporarily reduce the learning frequency to avoid interfering with the user's actions. For example, the learning unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This reduces the user's burden by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The learning unit can analyze the user's past behavioral patterns and select the optimal learning algorithm. The learning unit selects the optimal learning algorithm based on, for example, behaviors the user frequently performed in the past. For example, the learning unit detects abnormal behavior from the user's past behavioral patterns and selects a learning algorithm that corresponds to that behavior. The learning unit can also analyze changes in the user's behavioral patterns and adaptively update the learning algorithm. For example, the learning unit monitors changes in the user's behavioral patterns in real time and updates the learning algorithm based on that information. In this way, by analyzing past behavioral patterns, the optimal learning algorithm can be selected and learning accuracy can be improved.

[0073] When learning a behavioral pattern, the learning unit can customize the learning content taking into account the user's health condition and lifestyle rhythm. The learning unit, for example, monitors the user's health condition and reduces the learning content if the user is not feeling well. For example, the learning unit monitors the user's body temperature and heart rate and adjusts the learning content based on that information. The learning unit can also adjust the timing of the learning to match the user's lifestyle rhythm. For example, the learning unit sets the timing of the learning to match the user's wake-up time and bedtime. The learning unit can also provide optimal learning content based on the user's health data. For example, the learning unit customizes the learning content according to the user's health condition. In this way, optimal learning content can be provided by taking the user's health condition and lifestyle rhythm into consideration.

[0074] The learning unit can improve the learning algorithm by reflecting user feedback when learning behavioral patterns. For example, the learning unit collects user feedback and reflects it in the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm based on user opinions and usage history. The learning unit can also analyze user feedback and adjust the parameters of the learning algorithm. For example, the learning unit optimizes the parameters of the learning algorithm based on user feedback. In this way, the accuracy of the learning algorithm can be improved by reflecting user feedback.

[0075] The learning unit can estimate the user's emotions and prioritize the behavioral patterns to be learned based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit prioritizes learning behavioral patterns that promote relaxation. For example, the learning unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the learning unit can prioritize learning daily behavioral patterns. For example, the learning unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the learning unit can prioritize learning important behavioral patterns. For example, the learning unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This enables more appropriate learning by prioritizing the behavioral patterns to be learned based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When learning behavioral patterns, the learning unit can adjust the learning content taking into account the user's geographical location information. For example, when the user is in a specific location, the learning unit learns behavioral patterns related to that location. For example, the learning unit provides optimal learning content based on the user's geographical location information. Furthermore, when the user is traveling, the learning unit can also learn behavioral patterns related to the user's destination. For example, the learning unit collects the user's GPS data and adjusts the learning content based on that information. In this way, optimal learning content can be provided by taking into account the user's geographical location information.

[0077] When learning behavioral patterns, the learning unit can analyze the user's social media activities and learn related behavioral patterns. The learning unit, for example, analyzes the content posted by the user on social media to learn related behavioral patterns. For example, the learning unit learns related behavioral patterns by referring to the activities of the user's friends on social media. The learning unit can also learn related behavioral patterns based on the user's check-in information on social media. For example, the learning unit collects the user's social media data and adjusts the learning content based on that information. In this way, by analyzing the user's social media activities, related behavioral patterns can be learned and accuracy can be improved.

[0078] When learning a behavioral pattern, the learning unit can customize the learning method by reflecting the user's past feedback. The learning unit customizes the learning method, for example, based on the user's past feedback. For example, the learning unit provides an optimal learning method based on the user's opinions and usage history. The learning unit can also analyze the user's past feedback and improve the learning algorithm. For example, the learning unit optimizes the parameters of the learning algorithm based on the user's feedback. In this way, the optimal learning method can be provided by reflecting the user's past feedback.

[0079] The lighting unit can estimate the user's emotions and adjust the brightness and color of the lighting based on the estimated user emotions. For example, the lighting unit provides warm-colored lighting when the user is relaxed. For example, the lighting unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The lighting unit can also provide bright white lighting when the user is concentrating. For example, the lighting unit records the user's voice and estimates the user's emotions using voice analysis technology. The lighting unit can also provide soft light when the user is feeling drowsy. For example, the lighting unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the lighting unit to adjust the brightness and color of the lighting based on the user's emotions, thereby providing a comfortable lighting environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When adjusting the lighting, the lighting unit can select optimal settings by referring to the user's past lighting setting history. The lighting unit provides optimal settings, for example, based on lighting settings that the user has preferred in the past. For example, the lighting unit analyzes the user's past lighting setting history and suggests an optimal lighting pattern. The lighting unit can also adjust the brightness and color of the lighting based on the user's past lighting setting history. For example, the lighting unit collects the user's lighting setting change history and selects optimal settings based on that information. In this way, optimal lighting settings can be provided by referring to the past lighting setting history.

[0081] When adjusting the lighting, the lighting unit can customize the lighting settings according to the user's current activity. For example, if the user is reading, the lighting unit provides lighting suitable for reading. For example, the lighting unit detects the user's current activity using a sensor and customizes the lighting settings based on that information. Furthermore, if the user is relaxing, the lighting unit can also provide lighting suitable for relaxation. For example, the lighting unit acquires the user's activity from user input and customizes the lighting settings based on that information. Furthermore, if the user is working, the lighting unit can also provide lighting suitable for that work. For example, the lighting unit adjusts the brightness and color of the lighting based on the user's activity. In this way, the lighting settings can be customized according to the user's current activity, thereby providing an optimal lighting environment.

[0082] The lighting unit can improve lighting settings by reflecting user feedback when adjusting the lighting. For example, the lighting unit adjusts the brightness and color of the lighting based on user feedback. For example, the lighting unit collects user opinions and usage history and improves the lighting settings based on that information. The lighting unit can also analyze user feedback and provide optimal lighting settings. For example, the lighting unit optimizes lighting setting parameters based on user feedback. The lighting unit can also customize lighting settings by reflecting user feedback. For example, the lighting unit adjusts the lighting schedule based on user feedback. In this way, optimal lighting settings can be provided by reflecting user feedback.

[0083] The lighting unit can estimate a user's emotions and adjust the timing of turning the lights on and off based on the estimated user emotions. For example, the lighting unit delays the timing of turning the lights on when the user is relaxed. For example, the lighting unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The lighting unit can also advance the timing of turning the lights on when the user is concentrating. For example, the lighting unit records the user's voice and estimates the user's emotions using voice analysis technology. The lighting unit can also adjust the timing of turning the lights off when the user is feeling drowsy. For example, the lighting unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for a comfortable lighting environment by adjusting the timing of turning the lights on and off based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] When adjusting the lighting, the lighting unit can select optimal lighting settings by taking into account the user's geographical location information. For example, when the user is in a specific location, the lighting unit provides lighting settings appropriate for that location. For example, the lighting unit obtains the user's geographical location information from GPS data and selects optimal lighting settings based on that information. The lighting unit can also provide optimal lighting settings based on the user's geographical location information. For example, when the user is traveling, the lighting unit provides lighting settings appropriate for the destination. The lighting unit can also adjust the lighting schedule based on the user's geographical location information. In this way, optimal lighting settings can be provided by taking into account the user's geographical location information.

[0085] The lighting unit can analyze the user's social media activity and suggest relevant lighting settings when adjusting the lighting. For example, the lighting unit can analyze the content of the user's social media posts and suggest relevant lighting settings. For example, the lighting unit can suggest relevant lighting settings by referring to the activity of the user's friends on social media. The lighting unit can also suggest relevant lighting settings based on the user's social media check-in information. For example, the lighting unit can collect the user's social media data and suggest lighting settings based on that information. In this way, by analyzing the user's social media activity, relevant lighting settings can be suggested, providing a comfortable lighting environment.

[0086] The lighting unit can customize lighting settings by reflecting the user's past feedback when adjusting the lighting. For example, the lighting unit adjusts the brightness and color of the lighting based on the user's past feedback. For example, the lighting unit collects the user's opinions and usage history and customizes the lighting settings based on that information. The lighting unit can also analyze the user's past feedback to provide optimal lighting settings. For example, the lighting unit optimizes lighting setting parameters based on the user's feedback. The lighting unit can also customize lighting settings by reflecting the user's past feedback. For example, the lighting unit adjusts the lighting schedule based on the user's feedback. In this way, optimal lighting settings can be provided by reflecting the user's past feedback.

[0087] The temperature management unit can estimate the user's emotions and adjust the temperature setting based on the estimated user emotions. For example, the temperature management unit provides a comfortable temperature when the user is relaxed. For example, the temperature management unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The temperature management unit can also provide a moderately cool temperature when the user is concentrating. For example, the temperature management unit records the user's voice and estimates the emotion using voice analysis technology. The temperature management unit can also provide a warm temperature when the user is feeling drowsy. For example, the temperature management unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the temperature setting to be adjusted based on the user's emotions, providing a comfortable temperature environment. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When managing the temperature, the temperature management unit can select the optimal setting by referring to the user's past temperature setting history. The temperature management unit provides the optimal setting, for example, based on the user's past preferred temperature settings. For example, the temperature management unit analyzes the user's past temperature setting history and suggests the optimal temperature pattern. The temperature management unit can also adjust the temperature based on the user's past temperature setting history. For example, the temperature management unit collects the user's temperature setting change history and selects the optimal setting based on that information. In this way, the optimal temperature setting can be provided by referring to the past temperature setting history.

[0089] During temperature management, the temperature management unit can customize the temperature setting according to the user's current health condition and activity. The temperature management unit, for example, monitors the user's health condition and adjusts the temperature if the user is not feeling well. For example, the temperature management unit monitors the user's body temperature and heart rate and adjusts the temperature based on that information. The temperature management unit can also provide an optimal temperature according to the user's activity. For example, the temperature management unit obtains the user's activity from sensor information and customizes the temperature setting based on that information. The temperature management unit can also provide an optimal temperature setting based on the user's health data. For example, the temperature management unit customizes the temperature setting according to the user's health condition. In this way, a comfortable temperature environment can be provided by customizing the temperature setting according to the user's current health condition and activity.

[0090] The temperature management unit can improve the temperature setting by reflecting user feedback during temperature management. The temperature management unit, for example, adjusts the temperature based on user feedback. For example, the temperature management unit collects user opinions and usage history and improves the temperature setting based on that information. The temperature management unit can also analyze user feedback and provide optimal temperature settings. For example, the temperature management unit optimizes temperature setting parameters based on user feedback. The temperature management unit can also customize the temperature setting by reflecting user feedback. For example, the temperature management unit adjusts the temperature schedule based on user feedback. In this way, optimal temperature settings can be provided by reflecting user feedback.

[0091] The temperature management unit can estimate the user's emotions and adjust the timing of temperature management based on the estimated user emotions. For example, the temperature management unit delays the timing of temperature management when the user is relaxed. For example, the temperature management unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The temperature management unit can also advance the timing of temperature management when the user is concentrating. For example, the temperature management unit records the user's voice and estimates the emotion using voice analysis technology. The temperature management unit can also adjust the timing of temperature management when the user is feeling drowsy. For example, the temperature management unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for adjusting the timing of temperature management based on the user's emotions, thereby providing a comfortable temperature environment. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The temperature management unit can select the optimal temperature setting by taking into account the user's geographical location information when managing the temperature. For example, when the user is in a specific location, the temperature management unit provides a temperature setting appropriate for that location. For example, the temperature management unit obtains the user's geographical location information from GPS data and selects the optimal temperature setting based on that information. The temperature management unit can also provide the optimal temperature setting based on the user's geographical location information. For example, when the user is traveling, the temperature management unit provides a temperature setting appropriate for the destination. The temperature management unit can also adjust the temperature schedule based on the user's geographical location information. In this way, the optimal temperature setting can be provided by taking into account the user's geographical location information.

[0093] The temperature control unit can analyze the user's social media activity and suggest relevant temperature settings when controlling the temperature. The temperature control unit, for example, analyzes the content of the user's social media posts and suggests relevant temperature settings. For example, the temperature control unit can refer to the activity of the user's friends on social media to suggest relevant temperature settings. The temperature control unit can also suggest relevant temperature settings based on the user's social media check-in information. For example, the temperature control unit collects the user's social media data and suggests temperature settings based on that information. In this way, by analyzing the user's social media activity, it is possible to suggest relevant temperature settings and provide a comfortable temperature environment.

[0094] The temperature management unit can customize the temperature setting by reflecting the user's past feedback when managing the temperature. The temperature management unit, for example, adjusts the temperature based on the user's past feedback. For example, the temperature management unit collects the user's opinions and usage history and customizes the temperature setting based on that information. The temperature management unit can also analyze the user's past feedback and provide the optimal temperature setting. For example, the temperature management unit optimizes the temperature setting parameters based on the user's feedback. The temperature management unit can also customize the temperature setting by reflecting the user's past feedback. For example, the temperature management unit adjusts the temperature schedule based on the user's feedback. In this way, the optimal temperature setting can be provided by reflecting the user's past feedback.

[0095] The security unit can estimate the user's emotions and adjust the frequency of security alerts based on the estimated user emotions. For example, the security unit can reduce the frequency of security alerts when the user is relaxed. For example, the security unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The security unit can also increase the frequency of security alerts when the user is nervous. For example, the security unit can record the user's voice and estimate the user's emotions using voice analysis technology. The security unit can also adjust the frequency of security alerts when the user is feeling drowsy. For example, the security unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This can reduce the burden on the user by adjusting the frequency of security alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When adjusting security, the security unit can select optimal settings by referring to the user's past security history. The security unit provides optimal settings, for example, based on security settings that the user has preferred in the past. For example, the security unit analyzes the user's past security history and proposes an optimal security pattern. The security unit can also adjust security settings based on the user's past security history. For example, the security unit collects a history of changes to the user's security settings and selects optimal settings based on that information. In this way, optimal security settings can be provided by referring to the past security history.

[0097] When adjusting security, the security unit can customize security settings according to the user's current living situation and activities. The security unit, for example, monitors the user's living situation and adjusts security settings according to the situation. For example, the security unit obtains the user's daily routines and the frequency of specific actions from sensor information and customizes security settings based on that information. The security unit can also provide optimal security settings according to the user's activities. For example, the security unit obtains the user's activities from user input and customizes security settings based on that information. The security unit can also provide optimal security settings based on the user's living data. For example, the security unit customizes security settings according to the user's living situation. In this way, by customizing security settings according to the user's current living situation and activities, an optimal security environment can be provided.

[0098] The security unit can improve security settings by reflecting user feedback when adjusting security. The security unit, for example, adjusts security settings based on user feedback. For example, the security unit collects user opinions and usage history and improves security settings based on that information. The security unit can also analyze user feedback and provide optimal security settings. For example, the security unit optimizes security setting parameters based on user feedback. The security unit can also customize security settings by reflecting user feedback. For example, the security unit adjusts a security schedule based on user feedback. In this way, optimal security settings can be provided by reflecting user feedback.

[0099] The security unit can estimate the user's emotions and prioritize security alerts based on the estimated user emotions. For example, if the user is relaxed, the security unit can lower the priority of less important alerts. For example, the security unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is nervous, the security unit can also increase the priority of more important alerts. For example, the security unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is feeling drowsy, the security unit can also increase the priority of more important alerts. For example, the security unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the system to prioritize security alerts based on the user's emotions, allowing more important alerts to be processed first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When adjusting security, the security unit can select optimal security settings by taking into account the user's geographical location information. For example, when the user is in a specific location, the security unit provides security settings appropriate for that location. For example, the security unit obtains the user's geographical location information from GPS data and selects optimal security settings based on that information. The security unit can also provide optimal security settings based on the user's geographical location information. For example, when the user is traveling, the security unit provides security settings appropriate for the user's destination. The security unit can also adjust the security schedule based on the user's geographical location information. In this way, optimal security settings can be provided by taking the user's geographical location information into account.

[0101] When adjusting security, the security unit can analyze the user's social media activity and suggest relevant security settings. For example, the security unit can analyze the content of the user's social media posts and suggest relevant security settings. For example, the security unit can refer to the activities of the user's friends on social media to suggest relevant security settings. The security unit can also suggest relevant security settings based on the user's social media check-in information. For example, the security unit can collect the user's social media data and suggest security settings based on that information. In this way, by analyzing the user's social media activity, relevant security settings can be suggested and an optimal security environment can be provided.

[0102] When adjusting security settings, the security unit can customize the security settings by reflecting the user's past feedback. The security unit adjusts the security settings based on, for example, the user's past feedback. For example, the security unit collects the user's opinions and usage history and customizes the security settings based on that information. The security unit can also analyze the user's past feedback to provide optimal security settings. For example, the security unit optimizes the security setting parameters based on the user's feedback. The security unit can also customize the security settings by reflecting the user's past feedback. For example, the security unit adjusts the security schedule based on the user's feedback. In this way, optimal security settings can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the learning unit, lighting unit, temperature control unit, and security unit, described above, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the learning unit is realized by the control unit 46A of the smart device 14 and learns the user's behavioral patterns. The lighting unit is realized by the output device 40 of the smart device 14 and automatically adjusts the lighting based on the learned behavioral patterns. The temperature control unit is realized by the control unit 46A of the smart device 14 and automatically adjusts the temperature according to the user's preferences and health condition. The security unit is realized by the camera 42 and communication I / F 44 of the smart device 14 and monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. === Hard Collateral 1-2 === Each of the multiple elements, including the learning unit, lighting unit, temperature control unit, and security unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit is realized by the control unit 46A of the smart glasses 214 and learns the user's behavioral patterns. The lighting unit is realized by the speaker 240 of the smart glasses 214 and automatically adjusts the lighting based on the learned behavioral patterns. The temperature control unit is realized by the control unit 46A of the smart glasses 214 and automatically adjusts the temperature according to the user's preferences and health condition. The security unit is realized by the camera 42 and communication I / F 44 of the smart glasses 214 and monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. === Hard Collateral 1-3 === Each of the multiple elements including the learning unit, lighting unit, temperature control unit, and security unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the learning unit is realized by the control unit 46A of the headset terminal 314 and learns the user's behavioral patterns. The lighting unit is realized by the display 343 of the headset terminal 314 and automatically adjusts the lighting based on the learned behavioral patterns. The temperature control unit is realized by the control unit 46A of the headset terminal 314 and automatically adjusts the temperature according to the user's preferences and health condition. The security unit is realized by the camera 42 and communication I / F 44 of the headset terminal 314 and monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. === Hard Collateral 1-4 === Each of the multiple elements including the learning unit, lighting unit, temperature control unit, and security unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the learning unit is realized by the control unit 46A of the robot 414 and learns the user's behavioral patterns. The lighting unit is realized by LEDs in the eyes of the robot 414 and automatically adjusts the lighting based on the learned behavioral patterns. The temperature control unit is realized by the control unit 46A of the robot 414 and automatically adjusts the temperature according to the user's preferences and health condition. The security unit is realized by the camera 42 and communication I / F 44 of the robot 414 and monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The learning unit can automatically diffuse a scent that the user prefers based on the user's behavioral patterns. For example, the learning unit can diffuse a lavender scent when the user wants to relax. The learning unit can also diffuse a mint scent when the user wants to concentrate. Furthermore, the learning unit can adjust the strength of the scent based on the user's behavioral patterns. This allows the user's comfort to be improved by diffusing a scent based on the user's behavioral patterns.

[0105] The learning unit can automatically play video content that the user likes based on the user's behavioral patterns. For example, the learning unit can play videos of natural scenery when the user wants to relax. The learning unit can also play quiet background videos when the user wants to concentrate. Furthermore, the learning unit can automatically set a video playback schedule based on the user's behavioral patterns. This can improve the user's comfort by playing video content based on the user's behavioral patterns.

[0106] The learning unit can estimate the user's emotions and automatically select a music genre that the user prefers based on the estimated user emotions. For example, the learning unit can play classical music when the user is relaxed. Alternatively, the learning unit can play pop music when the user wants to cheer up. Furthermore, the learning unit can automatically set a music playback schedule based on the user's emotions. This can improve the user's comfort by selecting a music genre based on the user's emotions.

[0107] The learning unit can automatically provide a drink that the user prefers based on the user's behavioral patterns. For example, the learning unit can provide herbal tea when the user wants to relax. The learning unit can also provide coffee when the user wants to concentrate. Furthermore, the learning unit can automatically set a drink serving schedule based on the user's behavioral patterns. This can improve the user's comfort by providing drinks based on the user's behavioral patterns.

[0108] The learning unit can estimate the user's emotions and suggest activities that the user prefers based on the estimated user emotions. For example, the learning unit can suggest reading if the user is relaxed. The learning unit can also suggest taking a walk if the user wants to cheer up. Furthermore, the learning unit can automatically set a suggested activity schedule based on the user's emotions. This can improve the user's comfort by suggesting activities based on the user's emotions.

[0109] The learning unit can automatically select a news genre that the user prefers based on the user's behavioral patterns. For example, the learning unit can provide entertainment news when the user wants to relax. The learning unit can also provide business news when the user wants to concentrate. Furthermore, the learning unit can automatically set a news delivery schedule based on the user's behavioral patterns. This can improve the user's comfort by selecting a news genre based on the user's behavioral patterns.

[0110] The learning unit can estimate the user's emotions and automatically change the interior decoration to the user's preference based on the estimated user's emotions. For example, the learning unit can provide warm-colored decorations when the user is relaxed. The learning unit can also provide simple decorations when the user is concentrating. Furthermore, the learning unit can automatically set a schedule for changing the interior decorations based on the user's emotions. This can improve the user's comfort by changing the interior decorations based on the user's emotions.

[0111] The learning unit can automatically suggest exercise programs that the user prefers based on the user's behavioral patterns. For example, the learning unit can suggest yoga when the user wants to relax. The learning unit can also suggest aerobics when the user wants to energize. Furthermore, the learning unit can automatically set a suggested schedule for exercise programs based on the user's behavioral patterns. This makes it possible to support the user's health by suggesting exercise programs based on the user's behavioral patterns.

[0112] The learning unit can estimate the user's emotions and automatically select a reading genre that the user prefers based on the estimated user's emotions. For example, the learning unit can suggest fiction when the user is relaxed. The learning unit can also suggest non-fiction when the user is concentrating. Furthermore, the learning unit can automatically set a reading suggestion schedule based on the user's emotions. This can improve the user's comfort by selecting a reading genre based on the user's emotions.

[0113] The learning unit can automatically suggest a relaxation method that the user prefers based on the user's behavioral patterns. For example, the learning unit can suggest meditation when the user wants to relax. The learning unit can also suggest stretching when the user wants to cheer up. Furthermore, the learning unit can automatically set a relaxation method suggestion schedule based on the user's behavioral patterns. This can improve the user's comfort by suggesting relaxation methods based on the user's behavioral patterns.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The learning unit learns the user's behavioral patterns. For example, the learning unit learns the user's pattern of going to the bedroom at a specific time each day, or their preference for a specific temperature. The learning unit also collects data based on the user's behavioral patterns to provide an optimal living environment. Step 2: The lighting unit automatically adjusts the lighting based on the behavioral patterns learned by the learning unit. For example, the lighting unit automatically turns the lights on and off when the user goes to the bedroom, and adjusts the brightness and color of the lights according to the user's preferences. The lighting unit also automatically sets a lighting schedule based on the user's behavioral patterns. Step 3: The temperature control unit automatically adjusts the temperature based on the behavioral patterns learned by the learning unit. For example, if the user prefers a specific temperature, the temperature control unit adjusts the temperature to maintain that temperature, and adjusts the temperature according to the user's health condition. The temperature control unit also automatically sets a temperature schedule based on the user's behavioral patterns. Step 4: The security unit automatically adjusts security based on the behavioral patterns learned by the learning unit. For example, the security unit monitors the opening and closing of doors and windows and issues an alert if an abnormality is detected. The security unit also automatically sets a security schedule based on the user's behavioral patterns and customizes security settings according to the user's preferences.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a 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.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a learning unit that learns user behavior patterns; a lighting unit that automatically adjusts lighting based on the behavioral patterns learned by the learning unit; a temperature control unit that automatically adjusts the temperature based on the behavioral patterns learned by the learning unit; a security unit that automatically adjusts security based on the behavioral patterns learned by the learning unit; Equipped with A system characterized by:

2. The learning unit Adjusting the temperature control system based on user preferences and health conditions 2. The system of claim 1.

3. The learning unit Playing music based on user behavior patterns 2. The system of claim 1.

4. The learning unit Providing meal suggestions based on user behavior patterns 2. The system of claim 1.

5. The learning unit The system estimates the user's emotions and adjusts the frequency of learning behavioral patterns based on the estimated user emotions.

2. The system of claim 1.

6. The learning unit Analyze the user's past behavioral patterns and select the optimal learning algorithm 2. The system of claim 1.

7. The learning unit When learning behavioral patterns, the learning content is customized taking into account the user's health condition and lifestyle.

2. The system of claim 1.

8. The learning unit When learning behavioral patterns, improve the learning algorithm by incorporating user feedback.

2. The system of claim 1.

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