system

Abstract

The system according to this embodiment aims to realize a smart home that operates autonomously without user instructions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a decision unit, and an execution unit. The data collection unit collects information about the user. The analysis unit analyzes the data collected by the data collection unit. The decision unit determines an appropriate action based on the analysis results obtained by the analysis unit. The execution unit executes the action determined by the decision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that in the operation of a smart home, a user needs to give instructions to a smartphone or a smart speaker, and may feel troublesome.

[0005] The system according to the embodiment aims to realize a smart home that operates autonomously without a user giving instructions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a decision unit, and an execution unit. The data collection unit collects information about the user's behavior. The analysis unit analyzes the data collected by the data collection unit. The decision unit determines an appropriate action based on the analysis results obtained by the analysis unit. The execution unit executes the action determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can realize a smart home that operates autonomously without user instructions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart home system according to an embodiment of the present invention is a smart home system that does not require instructions from a smart speaker equipped with a camera and a generative AI. This smart home system collects the user's movements with a camera mounted on the smart speaker, and the generative AI analyzes the data to recognize the user's actions. Based on the analysis results, the generative AI determines the optimal action, such as playing music, and performs it autonomously. As a result, the user can enjoy a comfortable environment without having to operate a smartphone or speak to the speaker. Furthermore, it also has a function to learn the user's behavior patterns and provide the optimal environment according to the time of day and situation. In addition, to protect the privacy of the camera, it includes a mechanism for encrypting data and obtaining user consent. For example, the user's movements are collected with a camera mounted on the smart speaker. For example, the camera captures the user relaxing in the living room. This data is sent to the generative AI. Next, the generative AI analyzes the collected data and recognizes the user's actions. For example, if it determines that the user is relaxed, the generative AI selects music appropriate for that situation. Based on the analysis results, the generative AI determines the optimal action. For example, it decides to play relaxing music for a relaxed user. The determined action is executed. For example, the smart speaker plays music. In this way, users can enjoy a comfortable environment without any operation required. Furthermore, the generating AI learns the user's behavior patterns and provides the optimal environment according to the time of day and situation. For example, it plays relaxing music at night and music that helps wake you up in the morning. In addition, mechanisms for encrypting data and obtaining user consent are included to protect camera privacy. For example, collected data is encrypted and analyzed with the user's consent. As a result, the smart home system can provide a smart home that requires no operation by automatically collecting, analyzing, deciding, and acting on the user's behavior.

[0029] The smart home system according to the embodiment comprises a data collection unit, an analysis unit, a decision unit, and an execution unit. The data collection unit collects information about the user. This information includes, but is not limited to, actions, facial expressions, and voice. The data collection unit may, for example, use a camera to collect the user's actions. The data collection unit may also collect the user's voice using a microphone. Furthermore, the data collection unit may also collect the user's facial expressions using sensors. For example, the data collection unit may collect the user's actions in real time using a camera and transmit them to a generation AI. The data collection unit may also collect the user's voice using a microphone and transmit it to a generation AI. Furthermore, the data collection unit may also collect the user's facial expressions using sensors and transmit them to a generation AI. The analysis unit uses a generation AI to analyze the data collected by the data collection unit. The analysis is performed based on, for example, a data analysis algorithm and the accuracy of the analysis, but is not limited to such examples. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data. The analysis unit may also use a multimodal generation AI to analyze the data. Furthermore, the analysis unit can also use generative AI to extract and analyze important parts of the data. For example, text generation AI has learned from large amounts of data and possesses advanced natural language processing capabilities. Multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI uses keyword extraction technology to pick out particularly important information from the data and performs analysis based on it. The decision unit determines the optimal action based on the analysis results obtained by the analysis unit. The action is determined based on, for example, operating home appliances or sending notifications, but is not limited to such examples. For example, the decision unit may decide to play relaxing music based on the analysis results. The decision unit may also adjust the lighting based on the analysis results. The decision unit may also adjust the temperature based on the analysis results. For example, the decision unit may decide to play relaxing music based on the analysis results. The decision unit may also adjust the lighting based on the analysis results. The decision unit may also adjust the temperature based on the analysis results. The execution unit executes the action determined by the decision unit.Execution is based on, for example, music playback or lighting adjustment, but is not limited to such examples. For example, the execution unit plays relaxing music determined by the decision unit. The execution unit can also adjust the lighting determined by the decision unit. The execution unit can also adjust the temperature determined by the decision unit. For example, the execution unit plays relaxing music determined by the decision unit. The execution unit can also adjust the lighting determined by the decision unit. The execution unit can also adjust the temperature determined by the decision unit. As a result, the smart home system according to the embodiment can provide a smart home that requires no operation by automatically collecting, analyzing, deciding, and executing on the user's behavior.

[0030] The analysis unit may include a learning unit that learns the user's behavior patterns. The learning unit, for example, learns the user's behavior patterns. Behavior patterns include, for example, daily routines or specific events, but are not limited to such examples. The learning unit may, for example, learn the user's behavior patterns and send them to the generative AI. The learning unit may also learn the user's behavior patterns and send them to the generative AI. Furthermore, the learning unit may also learn the user's behavior patterns and send them to the generative AI. For example, the learning unit learns the user's behavior patterns and sends them to the generative AI. Furthermore, the learning unit may also learn the user's behavior patterns and send them to the generative AI. Furthermore, the learning unit may also learn the user's behavior patterns and send them to the generative AI. This allows for more appropriate actions to be provided by learning the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using, for example, an AI, or not using an AI. For example, the learning unit may input the user's behavior patterns into the generative AI and cause the generative AI to learn the behavior patterns.

[0031] The data collection unit may include an encryption unit that encrypts data collected by the camera. The encryption unit encrypts data collected by the camera, for example. Encryption includes, but is not limited to, the encryption algorithm used and the strength of the encryption. The encryption unit encrypts data collected by the camera and sends it to a generation AI. The encryption unit can also encrypt data collected by the camera and send it to a generation AI. Furthermore, the encryption unit can also encrypt data collected by the camera and send it to a generation AI. For example, the encryption unit encrypts data collected by the camera and sends it to a generation AI. The encryption unit can also encrypt data collected by the camera and send it to a generation AI. Furthermore, the encryption unit can also encrypt data collected by the camera and send it to a generation AI. This allows for the protection of privacy by encrypting the collected data. Some or all of the above processing in the encryption unit may be performed using, for example, AI, or not using AI. For example, the encryption unit can input data collected by the camera into a generation AI and have the generation AI encrypt the data.

[0032] The data collection unit may include a consent acquisition unit for obtaining user consent. The consent acquisition unit, for example, obtains user consent. Consent includes, but is not limited to, the format and scope of consent. The consent acquisition unit may, for example, obtain user consent and transmit it to the generating AI. The consent acquisition unit may also obtain user consent and transmit it to the generating AI. Furthermore, the consent acquisition unit may also obtain user consent and transmit it to the generating AI. For example, the consent acquisition unit may obtain user consent and transmit it to the generating AI. Furthermore, the consent acquisition unit may also obtain user consent and transmit it to the generating AI. This ensures transparency in data collection by obtaining user consent. Some or all of the above processing in the consent acquisition unit may be performed using, for example, AI, or not using AI. For example, the consent acquisition unit may input user consent into the generating AI and have the generating AI perform the consent acquisition.

[0033] The execution unit can play music. Music playback includes, but is not limited to, the type of music to play and the timing of playback. For example, the execution unit can play relaxing music. The execution unit can also play active music. Furthermore, the execution unit can also play ambient sounds. In this way, by playing music, a comfortable environment can be provided to the user. Some or all of the above processing in the execution unit may be performed using, for example, AI, or without AI. For example, the execution unit can input music playback to a generation AI and have the generation AI perform music playback.

[0034] The execution unit can adjust the lighting. Lighting adjustments include, but are not limited to, adjusting brightness or changing color temperature. For example, the execution unit can adjust the brightness of the lighting. It can also change the color temperature of the lighting. Furthermore, the execution unit can control the on / off state of the lighting. For example, the execution unit can adjust the brightness of the lighting to provide a relaxing environment. It can also change the color temperature of the lighting to provide an active environment. Furthermore, the execution unit can control the on / off state of the lighting to provide an appropriate lighting environment. This allows the user to be provided with an optimal environment by adjusting the lighting. Some or all of the above-described processes in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the lighting adjustments to a generating AI and have the generating AI perform the lighting adjustments.

[0035] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can analyze the times when the user has previously relaxed in the living room and concentrate data collection during those times. It can also analyze patterns of activity in specific rooms in the user's past and prioritize data collection in those rooms. Furthermore, the data collection unit can achieve efficient data collection by collecting data on specific days of the week or times of day based on the user's past behavior history. This allows for efficient data collection by analyzing past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, and have the generating AI perform an analysis of that behavioral history.

[0036] The data collection unit can filter data based on the user's current activities and environment. For example, if the user is watching television in the living room, the data collection unit may refrain from collecting audio data and prioritize collecting video data. Alternatively, if the user is cooking in the kitchen, the data collection unit may prioritize collecting audio data and wait for user instructions. Furthermore, if the user is resting in the bedroom, the data collection unit may minimize data collection to protect privacy. This allows for appropriate data collection by filtering data based on the user's current activities and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity and environment data into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in the living room, the data collection unit will prioritize the collection of living room environmental data. Similarly, if the user is in the kitchen, the data collection unit can prioritize the collection of kitchen temperature and humidity data. Furthermore, if the user is in the bedroom, the data collection unit can prioritize the collection of bedroom lighting and music data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media that they are relaxed, the data collection unit can collect data appropriate to that situation. Similarly, if a user posts on social media that they are stressed, the data collection unit can also collect data appropriate to that situation. Furthermore, if a user posts on social media that they are active, the data collection unit can also collect data appropriate to that situation. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI collect relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data and generate specific suggestions. Alternatively, it can perform a simplified analysis on less important data and provide only an overview. Furthermore, it can perform an analysis with a moderate level of detail on moderately important data to provide balanced information. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for music data, the analysis unit performs analysis based on the genre and tempo of the music. Furthermore, for lighting data, the analysis unit can perform analysis based on the color temperature and brightness of the lighting. In addition, for environmental data, the analysis unit can perform analysis based on temperature and humidity. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data, enabling real-time responses. The analysis unit can also refer to past data to perform analyses to understand long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during specific time periods and take appropriate actions for that time period. For example, the analysis unit can prioritize the analysis of the latest data, enabling real-time responses. The analysis unit can also refer to past data to perform analyses to understand long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during specific time periods and take appropriate actions for that time period. This enables real-time responses by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to enable a quick response. It can also postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data to perform efficient analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to enable a quick response. It can also postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0043] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships of the analysis results when making action decisions. For example, the decision-making unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the decision-making unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. For example, the decision-making unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the decision-making unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. This improves the accuracy of decisions by considering the interrelationships of the analysis results. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the interrelationships of the analysis results into a generating AI and have the generating AI perform the improvement of decision accuracy.

[0044] The decision unit can determine an appropriate action by considering the user's attribute information when making an action decision. For example, the decision unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the decision unit can play appropriate ambient sounds according to the user's health condition. For example, the decision unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the decision unit can play appropriate ambient sounds according to the user's health condition. In this way, by considering the user's attribute information, the system can provide the optimal action. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's attribute information into a generating AI and have the generating AI perform the action decision.

[0045] The decision-making unit can determine an action by considering the geographical distribution of the data. For example, if the user is in the living room, the decision-making unit will determine an action appropriate for the living room. It can also determine an action appropriate for the kitchen if the user is in the kitchen. Furthermore, it can determine an action appropriate for the bedroom if the user is in the bedroom. This allows the system to provide the optimal action by considering the geographical distribution of the data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the action decision.

[0046] The decision-making unit can improve the accuracy of its decisions by referring to relevant literature when making decisions. For example, the decision-making unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustments to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. For example, the decision-making unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustments to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. This improves the accuracy of decisions by referring to relevant literature. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input relevant literature into a generating AI and have the generating AI perform the improvement of decision accuracy.

[0047] The execution unit can improve the accuracy of its execution by considering the interrelationships of actions during execution. For example, the execution unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the execution unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. For example, the execution unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the execution unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. This improves the accuracy of execution by considering the interrelationships of actions. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the interrelationships of actions into a generating AI and have the generating AI perform the improvement of execution accuracy.

[0048] The execution unit can customize its operation at runtime by considering the user's attribute information. For example, the execution unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the execution unit can play appropriate ambient sounds according to the user's health condition. For example, the execution unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the execution unit can play appropriate ambient sounds according to the user's health condition. This allows for more appropriate operation by considering the user's attribute information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's attribute information into a generating AI and have the generating AI perform the operation customization.

[0049] The execution unit can perform actions at runtime while considering the geographical distribution of the data. For example, if the user is in the living room, the execution unit will perform actions appropriate for the living room. It can also perform actions appropriate for the kitchen if the user is in the kitchen. Furthermore, it can perform actions appropriate for the bedroom if the user is in the bedroom. This allows for the provision of optimal actions by considering the geographical distribution of the data. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the actions.

[0050] The execution unit can improve the accuracy of its execution by referring to relevant literature during execution. For example, the execution unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustment to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. For example, the execution unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustment to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. This improves the accuracy of the execution by referring to relevant literature. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input relevant literature into a generation AI and have the generation AI perform the improvement of execution accuracy.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0052] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign a high weight to the latest data to enable real-time responses. Alternatively, it can assign a low weight to past data to learn about long-term trends. Furthermore, the learning unit can assign appropriate weights to data collected during specific time periods to provide responses suitable for that time period. This enables real-time responses by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data collection timing into a generating AI and have the generating AI perform the weighting of the training data.

[0053] The encryption unit can adjust the level of encryption detail based on the importance of the data during encryption. For example, the encryption unit can perform detailed encryption for highly important data to ensure data security. It can also perform simplified encryption for less important data to improve processing speed. Furthermore, it can encrypt data of moderate importance with an appropriate level of detail to achieve balanced data protection. This allows for efficient data protection by adjusting the level of encryption detail based on the importance of the data. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input the importance of the data into a generating AI and have the generating AI adjust the level of encryption detail.

[0054] The encryption unit can adjust the encryption order based on the data collection timing during encryption. For example, the encryption unit can prioritize the encryption of the most recent data, enabling real-time response. Alternatively, the encryption unit can prioritize the encryption of important data, delaying the encryption of older data. Furthermore, the encryption unit can prioritize the encryption of data collected during a specific time period, providing appropriate responses for that time period. This allows for real-time response by adjusting the encryption order based on the data collection timing. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input the data collection timing into a generating AI and have the generating AI adjust the encryption order.

[0055] The consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history when acquiring consent. For example, the consent acquisition unit can select the optimal consent acquisition method based on methods the user has previously consented to. The consent acquisition unit can also analyze the user's past consent history and optimize the consent acquisition procedure. Furthermore, the consent acquisition unit can refer to the user's past consent history and adjust the frequency of consent acquisition. For example, the consent acquisition unit can select the optimal consent acquisition method based on methods the user has previously consented to. Furthermore, the consent acquisition unit can analyze the user's past consent history and optimize the consent acquisition procedure. Furthermore, the consent acquisition unit can refer to the user's past consent history and adjust the frequency of consent acquisition. This allows the optimal consent acquisition method to be selected by referring to the user's past consent history. Some or all of the above processing in the consent acquisition unit may be performed using AI, for example, or without AI. For example, the consent acquisition unit can input the user's past consent history into a generating AI and have the generating AI select the consent acquisition method.

[0056] The consent acquisition unit can select an appropriate consent acquisition method when acquiring consent, taking into account the user's device information. For example, if the user is using a smartphone, the consent acquisition unit can provide a consent acquisition method that is adapted to the screen size. Furthermore, if the user is using a tablet, the consent acquisition unit can provide a consent acquisition method optimized for a larger screen. Additionally, if the user is using a smartwatch, the consent acquisition unit can provide a concise and highly visible consent acquisition method. This allows for the selection of the optimal consent acquisition method by considering the user's device information. Some or all of the above-described processes in the consent acquisition unit may be performed using AI, for example, or without AI. For example, the consent acquisition unit can input the user's device information into a generating AI and have the generating AI select a consent acquisition method.

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

[0058] The data collection unit may include a health data collection unit that collects user health data. The health data collection unit may, for example, collect vital signs such as the user's heart rate, blood pressure, and body temperature. The health data collection unit may also collect the user's exercise level and sleep patterns. Furthermore, the health data collection unit may also collect the user's diet and calorie intake. This allows the system to understand the user's health status based on the collected health data and take appropriate action. Some or all of the above-described processing in the health data collection unit may be performed using AI, for example, or without AI. For example, the health data collection unit may input the collected health data into a generating AI and have the generating AI perform an analysis of the health status.

[0059] The analysis unit may include a hobby and preference learning unit that learns the user's hobbies and preferences. For example, the hobby and preference learning unit can learn the user's preferred music genres and movie types. It can also learn the user's preferred foods and drinks. Furthermore, it can learn the user's preferred travel destinations and activities. This allows for the provision of more personalized services based on the user's hobbies and preferences. Some or all of the above-described processing in the hobby and preference learning unit may be performed using AI, for example, or without AI. For example, the hobby and preference learning unit can input the user's hobby and preference data into a generating AI and have the generating AI perform hobby and preference learning.

[0060] The encryption unit can apply different encryption algorithms depending on the type of data. For example, a high-level encryption algorithm can be applied to personal information data, and a lightweight encryption algorithm can be applied to general environmental data. The encryption unit can also apply an encryption algorithm specifically for audio data, and an encryption algorithm specifically for video data. Furthermore, the encryption unit can apply a encryption algorithm specifically for text data. This enables efficient data protection by performing optimal encryption according to the type of data. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input the type of data into a generating AI and have the generating AI execute the application of the encryption algorithm.

[0061] The consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history when obtaining user consent. For example, it can select the optimal consent acquisition method based on methods the user has previously consented to. The consent acquisition unit can also analyze the user's past consent history and optimize the consent acquisition procedure. Furthermore, the consent acquisition unit can refer to the user's past consent history and adjust the frequency of consent acquisition. This allows the unit to select the optimal consent acquisition method by referring to the user's past consent history. Some or all of the above processing in the consent acquisition unit may be performed using AI, for example, or without AI. For example, the consent acquisition unit can input the user's past consent history into a generating AI and have the generating AI select the consent acquisition method.

[0062] The execution unit can perform actions while taking the user's health condition into consideration. For example, if the user's heart rate is high, it can play relaxing music. It can also lower the room temperature if the user's body temperature is high. Furthermore, it can provide appropriate lighting and music based on the user's sleep pattern. This allows the system to provide optimal actions according to the user's health condition. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's health data into a generating AI and have the generating AI perform the actions.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects information about the user. This includes actions, facial expressions, and voice. The data collection unit can collect user actions using a camera, voice using a microphone, and facial expressions using sensors. The collected data is sent to the generating AI. Step 2: The analysis unit analyzes the data collected by the collection unit using a generation AI. The analysis is performed based on the data analysis algorithm and the accuracy of the analysis. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI, and extracts and analyzes the important parts. Step 3: The decision unit determines the optimal operation based on the analysis results obtained by the analysis unit. The operation is determined based on factors such as controlling home appliances and sending notifications. For example, this may include playing relaxing music, adjusting lighting, or adjusting the temperature. Step 4: The execution unit carries out the actions determined by the decision unit. Execution is based on actions such as playing music, adjusting lighting, or adjusting the temperature. For example, this could include playing relaxing music, adjusting lighting, or adjusting the temperature.

[0065] (Example of form 2) The smart home system according to an embodiment of the present invention is a smart home system that does not require instructions from a smart speaker equipped with a camera and a generative AI. This smart home system collects the user's movements with a camera mounted on the smart speaker, and the generative AI analyzes the data to recognize the user's actions. Based on the analysis results, the generative AI determines the optimal action, such as playing music, and performs it autonomously. As a result, the user can enjoy a comfortable environment without having to operate a smartphone or speak to the speaker. Furthermore, it also has a function to learn the user's behavior patterns and provide the optimal environment according to the time of day and situation. In addition, to protect the privacy of the camera, it includes a mechanism for encrypting data and obtaining user consent. For example, the user's movements are collected with a camera mounted on the smart speaker. For example, the camera captures the user relaxing in the living room. This data is sent to the generative AI. Next, the generative AI analyzes the collected data and recognizes the user's actions. For example, if it determines that the user is relaxed, the generative AI selects music appropriate for that situation. Based on the analysis results, the generative AI determines the optimal action. For example, it decides to play relaxing music for a relaxed user. The determined action is executed. For example, the smart speaker plays music. In this way, users can enjoy a comfortable environment without any operation required. Furthermore, the generating AI learns the user's behavior patterns and provides the optimal environment according to the time of day and situation. For example, it plays relaxing music at night and music that helps wake you up in the morning. In addition, mechanisms for encrypting data and obtaining user consent are included to protect camera privacy. For example, collected data is encrypted and analyzed with the user's consent. As a result, the smart home system can provide a smart home that requires no operation by automatically collecting, analyzing, deciding, and acting on the user's behavior.

[0066] The smart home system according to the embodiment comprises a data collection unit, an analysis unit, a decision unit, and an execution unit. The data collection unit collects information about the user. This information includes, but is not limited to, actions, facial expressions, and voice. The data collection unit may, for example, use a camera to collect the user's actions. The data collection unit may also collect the user's voice using a microphone. Furthermore, the data collection unit may also collect the user's facial expressions using sensors. For example, the data collection unit may collect the user's actions in real time using a camera and transmit them to a generation AI. The data collection unit may also collect the user's voice using a microphone and transmit it to a generation AI. Furthermore, the data collection unit may also collect the user's facial expressions using sensors and transmit them to a generation AI. The analysis unit uses a generation AI to analyze the data collected by the data collection unit. The analysis is performed based on, for example, a data analysis algorithm and the accuracy of the analysis, but is not limited to such examples. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data. The analysis unit may also use a multimodal generation AI to analyze the data. Furthermore, the analysis unit can also use generative AI to extract and analyze important parts of the data. For example, text generation AI has learned from large amounts of data and possesses advanced natural language processing capabilities. Multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI uses keyword extraction technology to pick out particularly important information from the data and performs analysis based on it. The decision unit determines the optimal action based on the analysis results obtained by the analysis unit. The action is determined based on, for example, operating home appliances or sending notifications, but is not limited to such examples. For example, the decision unit may decide to play relaxing music based on the analysis results. The decision unit may also adjust the lighting based on the analysis results. The decision unit may also adjust the temperature based on the analysis results. For example, the decision unit may decide to play relaxing music based on the analysis results. The decision unit may also adjust the lighting based on the analysis results. The decision unit may also adjust the temperature based on the analysis results. The execution unit executes the action determined by the decision unit.Execution is based on, for example, music playback or lighting adjustment, but is not limited to such examples. For example, the execution unit plays relaxing music determined by the decision unit. The execution unit can also adjust the lighting determined by the decision unit. The execution unit can also adjust the temperature determined by the decision unit. For example, the execution unit plays relaxing music determined by the decision unit. The execution unit can also adjust the lighting determined by the decision unit. The execution unit can also adjust the temperature determined by the decision unit. As a result, the smart home system according to the embodiment can provide a smart home that requires no operation by automatically collecting, analyzing, deciding, and executing on the user's behavior.

[0067] The analysis unit may include a learning unit that learns the user's behavior patterns. The learning unit, for example, learns the user's behavior patterns. Behavior patterns include, for example, daily routines or specific events, but are not limited to such examples. The learning unit may, for example, learn the user's behavior patterns and send them to the generative AI. The learning unit may also learn the user's behavior patterns and send them to the generative AI. Furthermore, the learning unit may also learn the user's behavior patterns and send them to the generative AI. For example, the learning unit learns the user's behavior patterns and sends them to the generative AI. Furthermore, the learning unit may also learn the user's behavior patterns and send them to the generative AI. Furthermore, the learning unit may also learn the user's behavior patterns and send them to the generative AI. This allows for more appropriate actions to be provided by learning the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using, for example, an AI, or not using an AI. For example, the learning unit may input the user's behavior patterns into the generative AI and cause the generative AI to learn the behavior patterns.

[0068] The data collection unit may include an encryption unit that encrypts data collected by the camera. The encryption unit encrypts data collected by the camera, for example. Encryption includes, but is not limited to, the encryption algorithm used and the strength of the encryption. The encryption unit encrypts data collected by the camera and sends it to a generation AI. The encryption unit can also encrypt data collected by the camera and send it to a generation AI. Furthermore, the encryption unit can also encrypt data collected by the camera and send it to a generation AI. For example, the encryption unit encrypts data collected by the camera and sends it to a generation AI. The encryption unit can also encrypt data collected by the camera and send it to a generation AI. Furthermore, the encryption unit can also encrypt data collected by the camera and send it to a generation AI. This allows for the protection of privacy by encrypting the collected data. Some or all of the above processing in the encryption unit may be performed using, for example, AI, or not using AI. For example, the encryption unit can input data collected by the camera into a generation AI and have the generation AI encrypt the data.

[0069] The data collection unit may include a consent acquisition unit for obtaining user consent. The consent acquisition unit, for example, obtains user consent. Consent includes, but is not limited to, the format and scope of consent. The consent acquisition unit may, for example, obtain user consent and transmit it to the generating AI. The consent acquisition unit may also obtain user consent and transmit it to the generating AI. Furthermore, the consent acquisition unit may also obtain user consent and transmit it to the generating AI. For example, the consent acquisition unit may obtain user consent and transmit it to the generating AI. Furthermore, the consent acquisition unit may also obtain user consent and transmit it to the generating AI. This ensures transparency in data collection by obtaining user consent. Some or all of the above processing in the consent acquisition unit may be performed using, for example, AI, or not using AI. For example, the consent acquisition unit may input user consent into the generating AI and have the generating AI perform the consent acquisition.

[0070] The execution unit can play music. Music playback includes, but is not limited to, the type of music to play and the timing of playback. For example, the execution unit can play relaxing music. The execution unit can also play active music. Furthermore, the execution unit can also play ambient sounds. In this way, by playing music, a comfortable environment can be provided to the user. Some or all of the above processing in the execution unit may be performed using, for example, AI, or without AI. For example, the execution unit can input music playback to a generation AI and have the generation AI perform music playback.

[0071] The execution unit can adjust the lighting. Lighting adjustments include, but are not limited to, adjusting brightness or changing color temperature. For example, the execution unit can adjust the brightness of the lighting. It can also change the color temperature of the lighting. Furthermore, the execution unit can control the on / off state of the lighting. For example, the execution unit can adjust the brightness of the lighting to provide a relaxing environment. It can also change the color temperature of the lighting to provide an active environment. Furthermore, the execution unit can control the on / off state of the lighting to provide an appropriate lighting environment. This allows the user to be provided with an optimal environment by adjusting the lighting. Some or all of the above-described processes in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the lighting adjustments to a generating AI and have the generating AI perform the lighting adjustments.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the data collection unit estimates that the user is relaxed, it can reduce the frequency of data collection to respect the user's privacy. Conversely, if the data collection unit estimates that the user is stressed, it can increase the frequency of data collection to take appropriate action quickly. Furthermore, if the data collection unit estimates that the user is active, it can shorten the timing of data collection to enable real-time responses. In this way, by adjusting the timing of data collection according to the user's emotions, appropriate data collection can be performed while respecting privacy. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0073] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can analyze the times when the user has previously relaxed in the living room and concentrate data collection during those times. It can also analyze patterns of activity in specific rooms in the user's past and prioritize data collection in those rooms. Furthermore, the data collection unit can achieve efficient data collection by collecting data on specific days of the week or times of day based on the user's past behavior history. This allows for efficient data collection by analyzing past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, and have the generating AI perform an analysis of that behavioral history.

[0074] The data collection unit can filter data based on the user's current activities and environment. For example, if the user is watching television in the living room, the data collection unit may refrain from collecting audio data and prioritize collecting video data. Alternatively, if the user is cooking in the kitchen, the data collection unit may prioritize collecting audio data and wait for user instructions. Furthermore, if the user is resting in the bedroom, the data collection unit may minimize data collection to protect privacy. This allows for appropriate data collection by filtering data based on the user's current activities and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity and environment data into a generating AI and have the generating AI perform data filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the data collection unit estimates that the user is relaxed, it will prioritize collecting data on music and lighting. It can also prioritize collecting data on ambient sounds and temperature if the user is estimated to be stressed. Furthermore, if the data collection unit estimates that the user is active, it can prioritize collecting data on exercise level and heart rate. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in the living room, the data collection unit will prioritize the collection of living room environmental data. Similarly, if the user is in the kitchen, the data collection unit can prioritize the collection of kitchen temperature and humidity data. Furthermore, if the user is in the bedroom, the data collection unit can prioritize the collection of bedroom lighting and music data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media that they are relaxed, the data collection unit can collect data appropriate to that situation. Similarly, if a user posts on social media that they are stressed, the data collection unit can also collect data appropriate to that situation. Furthermore, if a user posts on social media that they are active, the data collection unit can also collect data appropriate to that situation. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the analysis unit estimates that the user is relaxed, it will display the analysis results in visually calming colors. If the analysis unit estimates that the user is stressed, it can also display the analysis results in a simple and highly visible format. Furthermore, if the analysis unit estimates that the user is active, it can display the analysis results in dynamic graphs or animations. This allows for more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data and generate specific suggestions. Alternatively, it can perform a simplified analysis on less important data and provide only an overview. Furthermore, it can perform an analysis with a moderate level of detail on moderately important data to provide balanced information. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for music data, the analysis unit performs analysis based on the genre and tempo of the music. Furthermore, for lighting data, the analysis unit can perform analysis based on the color temperature and brightness of the lighting. In addition, for environmental data, the analysis unit can perform analysis based on temperature and humidity. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the analysis unit estimates that the user is relaxed, it will provide detailed analysis results. If the analysis unit estimates that the user is stressed, it can also provide concise analysis results. Furthermore, if the analysis unit estimates that the user is active, it can provide a summary analysis result. For example, if the analysis unit estimates that the user is relaxed, it will provide detailed analysis results. If the analysis unit estimates that the user is stressed, it can also provide concise analysis results. Furthermore, if the analysis unit estimates that the user is active, it can also provide a summary analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data, enabling real-time responses. The analysis unit can also refer to past data to perform analyses to understand long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during specific time periods and take appropriate actions for that time period. For example, the analysis unit can prioritize the analysis of the latest data, enabling real-time responses. The analysis unit can also refer to past data to perform analyses to understand long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during specific time periods and take appropriate actions for that time period. This enables real-time responses by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to enable a quick response. It can also postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data to perform efficient analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to enable a quick response. It can also postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0084] The decision-making unit can estimate the user's emotions and adjust the criteria for action decisions based on the estimated emotions. For example, if the decision-making unit estimates that the user is relaxed, it can play relaxing music. If the decision-making unit estimates that the user is stressed, it can also play ambient sounds to reduce stress. Furthermore, if the decision-making unit estimates that the user is active, it can also play upbeat music to support the activity. For example, if the decision-making unit estimates that the user is relaxed, it can play relaxing music. If the decision-making unit estimates that the user is stressed, it can also play ambient sounds to reduce stress. Furthermore, if the decision-making unit estimates that the user is active, it can also play upbeat music to support the activity. This allows for more appropriate actions to be provided by adjusting the criteria for action decisions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships of the analysis results when making action decisions. For example, the decision-making unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the decision-making unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. For example, the decision-making unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the decision-making unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. This improves the accuracy of decisions by considering the interrelationships of the analysis results. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the interrelationships of the analysis results into a generating AI and have the generating AI perform the improvement of decision accuracy.

[0086] The decision unit can determine an appropriate action by considering the user's attribute information when making an action decision. For example, the decision unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the decision unit can play appropriate ambient sounds according to the user's health condition. For example, the decision unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the decision unit can play appropriate ambient sounds according to the user's health condition. In this way, by considering the user's attribute information, the system can provide the optimal action. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the user's attribute information into a generating AI and have the generating AI perform the action decision.

[0087] The decision-making unit can estimate the user's emotions and adjust the order in which it displays the results of action decisions based on the estimated emotions. For example, if the decision-making unit estimates that the user is relaxed, it will prioritize displaying actions that promote relaxation. It can also prioritize displaying actions that reduce stress if the user is stressed. Furthermore, if the decision-making unit estimates that the user is active, it can prioritize displaying actions that support that activity. This allows for more appropriate actions to be provided by adjusting the order in which action decisions are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The decision-making unit can determine an action by considering the geographical distribution of the data. For example, if the user is in the living room, the decision-making unit will determine an action appropriate for the living room. It can also determine an action appropriate for the kitchen if the user is in the kitchen. Furthermore, it can determine an action appropriate for the bedroom if the user is in the bedroom. This allows the system to provide the optimal action by considering the geographical distribution of the data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the action decision.

[0089] The decision-making unit can improve the accuracy of its decisions by referring to relevant literature when making decisions. For example, the decision-making unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustments to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. For example, the decision-making unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustments to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. This improves the accuracy of decisions by referring to relevant literature. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input relevant literature into a generating AI and have the generating AI perform the improvement of decision accuracy.

[0090] The execution unit can estimate the user's emotions and determine the priority of actions to perform based on the estimated emotions. For example, if the execution unit estimates that the user is relaxed, it will prioritize playing relaxing music. It can also prioritize playing stress-reducing ambient sounds if the user is stressed. Furthermore, if the execution unit estimates that the user is active, it can prioritize playing music that supports that activity. This allows for more appropriate actions to be provided by prioritizing actions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The execution unit can improve the accuracy of its execution by considering the interrelationships of actions during execution. For example, the execution unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the execution unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. For example, the execution unit can link music playback and lighting adjustment to provide an environment that is optimal for the user's emotions. It can also link ambient sound and temperature adjustment to maximize user comfort. Furthermore, the execution unit can link the color temperature of the lighting and the genre of music to provide an environment that suits the user's mood. This improves the accuracy of execution by considering the interrelationships of actions. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the interrelationships of actions into a generating AI and have the generating AI perform the improvement of execution accuracy.

[0092] The execution unit can customize its operation at runtime by considering the user's attribute information. For example, the execution unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the execution unit can play appropriate ambient sounds according to the user's health condition. For example, the execution unit can select an appropriate music genre according to the user's age. It can also adjust the preferred color temperature of the lighting according to the user's gender. Furthermore, the execution unit can play appropriate ambient sounds according to the user's health condition. This allows for more appropriate operation by considering the user's attribute information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's attribute information into a generating AI and have the generating AI perform the operation customization.

[0093] The execution unit can estimate the user's emotions and adjust how actions are displayed based on the estimated emotions. For example, if the execution unit estimates that the user is relaxed, it can display actions in calming colors. It can also display actions in a simple and highly visible format if the user is estimated to be stressed. Furthermore, if the execution unit estimates that the user is active, it can display actions using dynamic graphs or animations. This allows for more appropriate actions to be provided by adjusting how actions are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The execution unit can perform actions at runtime while considering the geographical distribution of the data. For example, if the user is in the living room, the execution unit will perform actions appropriate for the living room. It can also perform actions appropriate for the kitchen if the user is in the kitchen. Furthermore, it can perform actions appropriate for the bedroom if the user is in the bedroom. This allows for the provision of optimal actions by considering the geographical distribution of the data. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the actions.

[0095] The execution unit can improve the accuracy of its execution by referring to relevant literature during execution. For example, the execution unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustment to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. For example, the execution unit can refer to the latest research papers to select the optimal music genre. It can also refer to literature on lighting adjustment to determine the optimal color temperature. Furthermore, it can refer to literature on the effects of ambient sound to select the optimal ambient sound. This improves the accuracy of the execution by referring to relevant literature. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input relevant literature into a generation AI and have the generation AI perform the improvement of execution accuracy.

[0096] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the learning unit estimates that the user is relaxed, it will select training data appropriate to that situation. It can also select training data that helps reduce stress if the user is estimated to be stressed. Furthermore, if the learning unit estimates that the user is active, it can select training data that supports that activity. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0097] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0098] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the learning unit estimates that the user is relaxed, it can lower the learning frequency to respect the user's privacy. Conversely, if the learning unit estimates that the user is stressed, it can increase the learning frequency to provide a quick and appropriate response. Furthermore, if the learning unit estimates that the user is active, it can shorten the learning frequency to enable a real-time response. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0099] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign a high weight to the latest data to enable real-time responses. Alternatively, it can assign a low weight to past data to learn about long-term trends. Furthermore, the learning unit can assign appropriate weights to data collected during specific time periods to provide responses suitable for that time period. This enables real-time responses by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data collection timing into a generating AI and have the generating AI perform the weighting of the training data.

[0100] The encryption unit can estimate the user's emotions and adjust the encryption strength based on the estimated emotions. For example, if the encryption unit estimates the user to be relaxed, it will use standard encryption strength. If the encryption unit estimates the user to be stressed, it can also use high encryption strength to ensure data security. Furthermore, if the encryption unit estimates the user to be active, it can use moderate encryption strength to maintain data processing speed. This ensures data security by adjusting the encryption strength according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0101] The encryption unit can adjust the level of encryption detail based on the importance of the data during encryption. For example, the encryption unit can perform detailed encryption for highly important data to ensure data security. It can also perform simplified encryption for less important data to improve processing speed. Furthermore, it can encrypt data of moderate importance with an appropriate level of detail to achieve balanced data protection. This allows for efficient data protection by adjusting the level of encryption detail based on the importance of the data. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input the importance of the data into a generating AI and have the generating AI adjust the level of encryption detail.

[0102] The encryption unit can estimate the user's emotions and determine encryption priorities based on the estimated emotions. For example, if the encryption unit estimates the user to be relaxed, it will prioritize standard encryption. It can also prioritize high encryption strength if the user is estimated to be stressed. Furthermore, if the encryption unit estimates the user to be active, it can prioritize moderate encryption strength. This allows for efficient data protection by determining encryption priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0103] The encryption unit can adjust the encryption order based on the data collection timing during encryption. For example, the encryption unit can prioritize the encryption of the most recent data, enabling real-time response. Alternatively, the encryption unit can prioritize the encryption of important data, delaying the encryption of older data. Furthermore, the encryption unit can prioritize the encryption of data collected during a specific time period, providing appropriate responses for that time period. This allows for real-time response by adjusting the encryption order based on the data collection timing. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input the data collection timing into a generating AI and have the generating AI adjust the encryption order.

[0104] The consent acquisition unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user emotions. For example, if the consent acquisition unit estimates that the user is relaxed, it can provide a consent acquisition method that includes a detailed explanation. It can also provide a concise and easy-to-understand consent acquisition method if the user is estimated to be stressed. Furthermore, if the consent acquisition unit estimates that the user is active, it can provide a method for obtaining consent quickly. This allows for more appropriate consent acquisition by adjusting the consent acquisition method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the consent acquisition unit may be performed using AI, for example, or without using AI. For example, the consent acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history when acquiring consent. For example, the consent acquisition unit can select the optimal consent acquisition method based on methods the user has previously consented to. The consent acquisition unit can also analyze the user's past consent history and optimize the consent acquisition procedure. Furthermore, the consent acquisition unit can refer to the user's past consent history and adjust the frequency of consent acquisition. For example, the consent acquisition unit can select the optimal consent acquisition method based on methods the user has previously consented to. Furthermore, the consent acquisition unit can analyze the user's past consent history and optimize the consent acquisition procedure. Furthermore, the consent acquisition unit can refer to the user's past consent history and adjust the frequency of consent acquisition. This allows the optimal consent acquisition method to be selected by referring to the user's past consent history. Some or all of the above processing in the consent acquisition unit may be performed using AI, for example, or without AI. For example, the consent acquisition unit can input the user's past consent history into a generating AI and have the generating AI select the consent acquisition method.

[0106] The consent acquisition unit can estimate the user's emotions and determine the priority of consent acquisition based on the estimated emotions. For example, if the consent acquisition unit estimates that the user is relaxed, it will prioritize obtaining consent that includes a detailed explanation. Alternatively, if the consent acquisition unit estimates that the user is stressed, it may prioritize obtaining consent in a concise and easy-to-understand manner. Furthermore, if the consent acquisition unit estimates that the user is active, it may prioritize methods that allow for quick consent acquisition. This allows for more appropriate consent acquisition by determining the priority of consent acquisition according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the consent acquisition unit may be performed using AI, for example, or without using AI. For example, the consent acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0107] The consent acquisition unit can select an appropriate consent acquisition method when acquiring consent, taking into account the user's device information. For example, if the user is using a smartphone, the consent acquisition unit can provide a consent acquisition method that is adapted to the screen size. Furthermore, if the user is using a tablet, the consent acquisition unit can provide a consent acquisition method optimized for a larger screen. Additionally, if the user is using a smartwatch, the consent acquisition unit can provide a concise and highly visible consent acquisition method. This allows for the selection of the optimal consent acquisition method by considering the user's device information. Some or all of the above-described processes in the consent acquisition unit may be performed using AI, for example, or without AI. For example, the consent acquisition unit can input the user's device information into a generating AI and have the generating AI select a consent acquisition method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and execution unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's behavior using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the optimal operation based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the smart device 14 and executes the determined operation. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's condition using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the optimal operation based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the smart glasses 214 and executes the determined operation. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and execution unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's condition using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the optimal operation based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the headset terminal 314 and executes the determined operation. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, and execution unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's condition using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the optimal operation based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the robot 414 and executes the determined operation.

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

[0109] The data collection unit may include a health data collection unit that collects user health data. The health data collection unit may, for example, collect vital signs such as the user's heart rate, blood pressure, and body temperature. The health data collection unit may also collect the user's exercise level and sleep patterns. Furthermore, the health data collection unit may also collect the user's diet and calorie intake. This allows the system to understand the user's health status based on the collected health data and take appropriate action. Some or all of the above-described processing in the health data collection unit may be performed using AI, for example, or without AI. For example, the health data collection unit may input the collected health data into a generating AI and have the generating AI perform an analysis of the health status.

[0110] The analysis unit may include a hobby and preference learning unit that learns the user's hobbies and preferences. For example, the hobby and preference learning unit can learn the user's preferred music genres and movie types. It can also learn the user's preferred foods and drinks. Furthermore, it can learn the user's preferred travel destinations and activities. This allows for the provision of more personalized services based on the user's hobbies and preferences. Some or all of the above-described processing in the hobby and preference learning unit may be performed using AI, for example, or without AI. For example, the hobby and preference learning unit can input the user's hobby and preference data into a generating AI and have the generating AI perform hobby and preference learning.

[0111] The encryption unit can apply different encryption algorithms depending on the type of data. For example, a high-level encryption algorithm can be applied to personal information data, and a lightweight encryption algorithm can be applied to general environmental data. The encryption unit can also apply an encryption algorithm specifically for audio data, and an encryption algorithm specifically for video data. Furthermore, the encryption unit can apply a encryption algorithm specifically for text data. This enables efficient data protection by performing optimal encryption according to the type of data. Some or all of the above processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit can input the type of data into a generating AI and have the generating AI execute the application of the encryption algorithm.

[0112] The consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history when obtaining user consent. For example, it can select the optimal consent acquisition method based on methods the user has previously consented to. The consent acquisition unit can also analyze the user's past consent history and optimize the consent acquisition procedure. Furthermore, the consent acquisition unit can refer to the user's past consent history and adjust the frequency of consent acquisition. This allows the unit to select the optimal consent acquisition method by referring to the user's past consent history. Some or all of the above processing in the consent acquisition unit may be performed using AI, for example, or without AI. For example, the consent acquisition unit can input the user's past consent history into a generating AI and have the generating AI select the consent acquisition method.

[0113] The execution unit can perform actions while taking the user's health condition into consideration. For example, if the user's heart rate is high, it can play relaxing music. It can also lower the room temperature if the user's body temperature is high. Furthermore, it can provide appropriate lighting and music based on the user's sleep pattern. This allows the system to provide optimal actions according to the user's health condition. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's health data into a generating AI and have the generating AI perform the actions.

[0114] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is estimated to be relaxed, the frequency of data collection can be reduced to respect the user's privacy. If the user is estimated to be stressed, the frequency of data collection can be increased to enable a quick and appropriate response. Furthermore, if the user is estimated to be active, the timing of data collection can be shortened to enable a real-time response. In this way, by adjusting the timing of data collection according to the user's emotions, appropriate data collection can be performed while respecting privacy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is estimated to be relaxed, the analysis results can be displayed in visually calming colors. If the user is estimated to be stressed, the analysis results can be displayed in a simple and highly visible format. Furthermore, if the user is estimated to be active, the analysis results can be displayed in dynamic graphs or animations. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The decision-making unit can estimate the user's emotions and adjust the criteria for action decisions based on the estimated emotions. For example, if the user is estimated to be relaxed, relaxing music can be played. If the user is estimated to be stressed, ambient sounds to reduce stress can be played. Furthermore, if the user is estimated to be active, upbeat music to support their activity can be played. This allows for more appropriate actions to be provided by adjusting the criteria for action decisions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, or not using AI. For example, the decision-making unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The execution unit can estimate the user's emotions and determine the priority of actions to perform based on the estimated emotions. For example, if the user is estimated to be relaxed, relaxing music can be played with the highest priority. If the user is estimated to be stressed, ambient sounds to reduce stress can be played with the highest priority. Furthermore, if the user is estimated to be active, music that supports their activity can be played with the highest priority. In this way, by determining the priority of actions according to the user's emotions, more appropriate actions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is estimated to be relaxed, it can select training data appropriate for that situation. If the user is estimated to be stressed, it can also select training data that helps reduce stress. Furthermore, if the user is estimated to be active, it can select training data that supports that activity. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

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

[0120] Step 1: The data collection unit collects information about the user. This includes actions, facial expressions, and voice. The data collection unit can collect user actions using a camera, voice using a microphone, and facial expressions using sensors. The collected data is sent to the generating AI. Step 2: The analysis unit analyzes the data collected by the collection unit using a generation AI. The analysis is performed based on the data analysis algorithm and the accuracy of the analysis. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI, and extracts and analyzes the important parts. Step 3: The decision unit determines the optimal operation based on the analysis results obtained by the analysis unit. The operation is determined based on factors such as controlling home appliances and sending notifications. For example, this may include playing relaxing music, adjusting lighting, or adjusting the temperature. Step 4: The execution unit carries out the actions determined by the decision unit. Execution is based on actions such as playing music, adjusting lighting, or adjusting the temperature. For example, this could include playing relaxing music, adjusting lighting, or adjusting the temperature.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] [Explanation of symbols]

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

Claims

1. A collection unit that collects user activity, An analysis unit analyzes the data collected by the aforementioned collection unit, A decision unit that determines an appropriate operation based on the analysis results obtained by the analysis unit, The system comprises an execution unit that performs the operation determined by the decision unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a learning unit that learns user behavior patterns. The system according to feature 1.

3. The aforementioned collection unit is It is equipped with an encryption unit that encrypts the data collected by the camera. The system according to feature 1.

4. The aforementioned collection unit is It includes a consent acquisition unit to obtain user consent. The system according to feature 1.

5. The execution unit is, Play music The system according to feature 1.

6. The execution unit is, Adjust the lighting. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the appropriate data collection method. The system according to feature 1.

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