system

Abstract

The system according to this embodiment aims to analyze the user's lifestyle patterns and automatically optimize the home environment. [Solution] The system according to the embodiment comprises a learning unit, an analysis unit, an adjustment unit, and a scene adjustment unit. The learning unit learns the user's lifestyle patterns. The analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit. The adjustment unit automatically adjusts the home environment based on the data analyzed by the analysis unit. The scene adjustment unit optimizes the home environment adjusted by the adjustment unit according to the user's scenes.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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, the home environment has not been fully automatically optimized based on the user's living pattern, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's living pattern and automatically optimize the home environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an analysis unit, an adjustment unit, and a scene adjustment unit. The learning unit learns the user's lifestyle patterns. The analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit. The adjustment unit automatically adjusts the home environment based on the data analyzed by the analysis unit. The scene adjustment unit optimizes the home environment adjusted by the adjustment unit according to the user's scenes. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's lifestyle patterns and automatically optimize the home environment. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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] <0000​​​​​​​​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) An AI system according to an embodiment of the present invention is a system that analyzes and learns a user's personal preferences and lifestyle patterns, and automatically adjusts the settings of smart home devices such as lighting, temperature, and music. This AI system automatically adjusts the home environment according to various scenes in which the user is at home, out, or sleeping, aiming to improve comfort and optimize power consumption. For example, the AI ​​system learns the user's lifestyle patterns. Next, based on the learned data, it automatically adjusts the home environment according to scenes in which the user is at home, out, or sleeping. For example, when at home, it adjusts the lighting to an appropriate brightness, maintains the temperature within a comfortable range, and plays the user's preferred music. When going out, it turns off the lights and sets the temperature to energy-saving mode. When going to sleep, it dims the lights and adjusts the temperature to a comfortable sleeping environment. In this way, it aims to improve the user's comfort and reduce annual power consumption by 20%. Thus, the AI ​​system learns and analyzes the user's lifestyle patterns, automatically adjusts the home environment, and optimizes it according to the scene, enabling improved comfort and optimized power consumption.

[0029] The AI ​​system according to this embodiment comprises a learning unit, an analysis unit, an adjustment unit, and a scene adjustment unit. The learning unit learns the user's lifestyle patterns. For example, the learning unit learns the user's lifestyle patterns such as wake-up time, meal times, and times when the user goes out. The learning unit can collect data for analyzing the user's lifestyle patterns using AI. The analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit. For example, the analysis unit improves the accuracy of the analysis by considering fluctuations in the user's lifestyle patterns. The analysis unit can apply algorithms for analyzing the user's lifestyle patterns using AI. The adjustment unit automatically adjusts the home environment based on the data analyzed by the analysis unit. For example, the adjustment unit automatically adjusts the settings of smart home devices such as lighting, temperature, and music. The adjustment unit can apply control algorithms for automatically adjusting the home environment using AI. The scene adjustment unit optimizes the home environment adjusted by the adjustment unit according to the user's scenes. For example, the scene adjustment unit optimizes the home environment according to scenes such as when the user is at home, going out, or going to sleep. The scene adjustment unit can use AI to apply algorithms that provide the optimal home environment according to the user's scene. As a result, the AI ​​system according to the embodiment can learn and analyze the user's lifestyle patterns, automatically adjust the home environment, and optimize it according to the scene, thereby improving comfort and optimizing power consumption.

[0030] The learning unit learns the user's lifestyle patterns. For example, the learning unit learns the user's wake-up time, meal times, and times when they go out. Specifically, the learning unit utilizes data collected from the user's smartphone and wearable devices. These devices provide data such as the user's location, activity level, heart rate, and sleep patterns. The learning unit collects this data and analyzes the user's lifestyle patterns using AI. For example, it learns that the user wakes up at 7 am every morning, eats breakfast at 8 am, and goes out at 9 am. The learning unit accumulates this data and improves the accuracy of its analysis by considering fluctuations in the user's lifestyle patterns. Furthermore, the learning unit can also predict future lifestyle patterns based on the user's past data. For example, if the user tends to repeat a specific action on a particular day of the week, it will predict similar actions on that day. This allows the learning unit to gain a detailed understanding of the user's lifestyle patterns and generate data to provide to the analysis and adjustment units.

[0031] The analysis unit analyzes the user's lifestyle patterns based on data learned by the learning unit. The analysis unit improves the accuracy of the analysis by considering, for example, fluctuations in the user's lifestyle patterns. Specifically, the analysis unit uses AI to apply algorithms for analyzing the user's lifestyle patterns. This includes machine learning and deep learning techniques. For example, machine learning algorithms are used to cluster user behavior data and identify different behavior patterns. Furthermore, deep learning is used to extract complex patterns from user behavior data and predict future behavior. By applying these algorithms, the analysis unit analyzes the user's lifestyle patterns with high accuracy and generates data to provide to the adjustment unit and scene adjustment unit. In addition, the analysis unit can collect user feedback and continuously improve the accuracy of the analysis algorithm. For example, based on feedback provided by the user, the analysis results are modified and the algorithm parameters are adjusted. This allows the analysis unit to accurately analyze the user's lifestyle patterns and improve the overall system performance.

[0032] The adjustment unit automatically adjusts the home environment based on data analyzed by the analysis unit. For example, the adjustment unit automatically adjusts the settings of smart home devices such as lighting, temperature, and music. Specifically, the adjustment unit uses AI to apply control algorithms for automatically adjusting the home environment. For instance, it gradually brightens the lights and adjusts the room temperature to a comfortable level to match the user's wake-up time. It also turns on the lights and plays preferred music to coincide with the user's return home. The adjustment unit coordinates these devices to provide an optimal home environment according to the user's lifestyle. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy of its adjustment algorithms. For example, it adjusts lighting brightness and temperature settings based on user feedback. This allows the adjustment unit to provide an optimal home environment tailored to the user's lifestyle, thereby improving comfort.

[0033] The Scene Adjustment Unit optimizes the home environment, which has been adjusted by the Adjustment Unit, according to the user's situation. For example, the Scene Adjustment Unit optimizes the home environment according to the user's situation, such as being at home, going out, or going to sleep. Specifically, the Scene Adjustment Unit uses AI to apply algorithms to provide the optimal home environment according to the user's situation. For example, when the user is at home, it brightens the lights and maintains a comfortable room temperature. When the user goes out, it turns off the lights to minimize energy consumption. Furthermore, when the user goes to sleep, it dims the lights to provide a quiet environment. The Scene Adjustment Unit coordinates these devices to provide the optimal home environment according to the user's situation. In addition, the Scene Adjustment Unit can collect user feedback and continuously improve the accuracy of the adjustment algorithm. For example, it can adjust the brightness of the lights and the temperature settings based on the feedback provided by the user. In this way, the Scene Adjustment Unit can provide the optimal home environment according to the user's situation and improve comfort.

[0034] The learning unit can analyze the user's past behavior history and select the optimal learning algorithm. For example, the learning unit can analyze the user's past behavior patterns and select the optimal learning algorithm. The learning unit can also predict actions to be taken during specific time periods based on the user's past behavior history and select a learning algorithm. The learning unit can also detect changes in behavior patterns based on the user's past behavior history and select a learning algorithm. In this way, the optimal learning algorithm can be selected by analyzing past behavior history. 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 the user's behavior history data into a generating AI and have the generating AI perform the selection of the optimal learning algorithm.

[0035] The learning unit can detect changes in the user's daily rhythm in real time during learning and update the learning content. For example, the learning unit can detect changes in the user's daily rhythm in real time and update the learning content. The learning unit can also adjust the learning content as appropriate in response to changes in the user's daily rhythm. The learning unit can also detect changes in the user's daily rhythm and optimize the learning content. This enables more accurate learning of daily patterns by detecting changes in the daily rhythm in real time and updating the learning content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the update of the learning content.

[0036] The learning unit can learn lifestyle patterns while considering the user's geographical location information during the learning process. For example, the learning unit can learn lifestyle patterns in a specific region based on the user's geographical location information. The learning unit can also learn region-specific lifestyle patterns while considering the user's geographical location information. The learning unit can also learn lifestyle patterns that are appropriate to the climate and culture of a region based on the user's geographical location information. In this way, region-specific lifestyle patterns can be learned by considering geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the user's geographical location information into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0037] The learning unit can analyze the user's social media activity during the learning process and reflect this in the learning of lifestyle patterns. For example, the learning unit can analyze the user's social media activity and reflect this in the learning of lifestyle patterns. The learning unit can also predict actions taken at specific times of day based on the user's social media activity and reflect this in the learning process. The learning unit can also detect changes in behavioral patterns based on the user's social media activity and reflect this in the learning process. This makes it possible to learn lifestyle patterns more accurately by analyzing social media activity. 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 user's social media data into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0038] The analysis unit can improve the accuracy of the analysis by taking into account changes in the user's lifestyle patterns during the analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account changes in the user's lifestyle patterns. The analysis unit can also adjust the analysis method as appropriate according to changes in the user's lifestyle patterns. The analysis unit can also detect changes in the user's lifestyle patterns and optimize the accuracy of the analysis. As a result, the accuracy of the analysis is improved by taking into account changes in lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user lifestyle pattern data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0039] The analysis unit can detect the periodicity of the user's lifestyle patterns during analysis and reflect it in the analysis results. For example, the analysis unit can detect the periodicity of the user's lifestyle patterns and reflect it in the analysis results. The analysis unit can also adjust the analysis method as appropriate according to the periodicity of the user's lifestyle patterns. The analysis unit can also detect the periodicity of the user's lifestyle patterns and optimize the analysis results. This improves the accuracy of the analysis results by detecting the periodicity of lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform periodicity detection.

[0040] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can analyze lifestyle patterns in a specific region based on the user's geographical location information. The analysis unit can also analyze region-specific lifestyle patterns while considering the user's geographical location information. The analysis unit can also analyze lifestyle patterns that correspond to the climate and culture of a region based on the user's geographical location information. In this way, region-specific lifestyle patterns can be analyzed by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the lifestyle pattern analysis.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can refer to the user's relevant literature to improve the accuracy of the analysis. The analysis unit can also predict actions taken during specific time periods based on the user's relevant literature and reflect this in the analysis. The analysis unit can also detect changes in behavioral patterns based on the user's relevant literature and reflect this in the analysis. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0042] The adjustment unit can analyze the user's past settings history during adjustment to select the optimal adjustment method. For example, the adjustment unit analyzes the user's past settings history and selects the optimal adjustment method. The adjustment unit can also predict settings to be made during a specific time period based on the user's past settings history and select an adjustment method. The adjustment unit can also detect changes in setting patterns based on the user's past settings history and select an adjustment method. In this way, the optimal adjustment method can be selected by analyzing the past settings history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's settings history data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0043] The adjustment unit can customize the adjustment content based on the user's current living situation during the adjustment process. For example, the adjustment unit customizes the adjustment content based on the user's current living situation. The adjustment unit can also adjust the adjustment content as appropriate according to the user's current living situation. The adjustment unit can also detect the user's current living situation and optimize the adjustment content. This makes it possible to perform more appropriate adjustments by customizing the adjustment content based on the current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user living situation data into a generating AI and have the generating AI perform the customization of the adjustment content.

[0044] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, the adjustment unit can select an adjustment method for a specific region based on the user's geographical location information. The adjustment unit can also select a region-specific adjustment method, taking into account the user's geographical location information. The adjustment unit can also select an adjustment method that is appropriate for the region's climate and culture, based on the user's geographical location information. In this way, a region-specific adjustment method can be selected by taking into account geographical location information. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal adjustment method.

[0045] The adjustment unit can analyze the user's social media activity and propose adjustments during the adjustment process. For example, the adjustment unit can analyze the user's social media activity and propose adjustments. The adjustment unit can also predict and propose adjustments to be made at specific times based on the user's social media activity. The adjustment unit can also detect changes in behavioral patterns based on the user's social media activity and propose adjustments. In this way, by analyzing social media activity, it is possible to propose more appropriate adjustments. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of adjustments.

[0046] The scene adjustment unit can select the optimal adjustment method by referring to the user's past scene history during scene adjustment. For example, the scene adjustment unit can refer to the user's past scene history and select the optimal adjustment method. The scene adjustment unit can also predict scene adjustments to be performed at a specific time period from the user's past scene history and select an adjustment method. The scene adjustment unit can also detect changes in the scene adjustment pattern based on the user's past scene history and select an adjustment method. In this way, the optimal adjustment method can be selected by referring to past scene history. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input the user's scene history data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0047] The scene adjustment unit can customize the means of scene adjustment based on the user's current living situation during scene adjustment. For example, the scene adjustment unit customizes the means of scene adjustment based on the user's current living situation. The scene adjustment unit can also adjust the means of scene adjustment as appropriate according to the user's current living situation. The scene adjustment unit can also detect the user's current living situation and optimize the means of scene adjustment. This makes it possible to perform more appropriate scene adjustment by customizing the means of scene adjustment based on the current living situation. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of scene adjustment.

[0048] The scene adjustment unit can select the optimal scene adjustment method by considering the user's geographical location information during scene adjustment. For example, the scene adjustment unit can select a scene adjustment method for a specific region based on the user's geographical location information. The scene adjustment unit can also select a region-specific scene adjustment method by considering the user's geographical location information. The scene adjustment unit can also select a scene adjustment method that is appropriate for the region's climate and culture based on the user's geographical location information. In this way, a region-specific scene adjustment method can be selected by considering geographical location information. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal scene adjustment method.

[0049] The scene adjustment unit can analyze the user's social media activity and propose methods for scene adjustment during scene adjustment. For example, the scene adjustment unit can analyze the user's social media activity and propose methods for scene adjustment. The scene adjustment unit can also predict scene adjustments to be performed at specific times based on the user's social media activity and propose methods. The scene adjustment unit can also detect changes in behavioral patterns based on the user's social media activity and propose methods for scene adjustment. In this way, by analyzing social media activity, it is possible to propose more appropriate methods for scene adjustment. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without AI. For example, the scene adjustment unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of methods for scene adjustment.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The learning unit can also acquire user health data and incorporate it into learning lifestyle patterns. For example, it can acquire the user's heart rate and sleep data and learn lifestyle patterns according to their health status. If the user is fatigued, it can learn a lifestyle pattern that prioritizes rest. It can also acquire the user's exercise data and learn a lifestyle pattern that takes post-exercise recovery into consideration. This makes it possible to learn lifestyle patterns based on the user's health status.

[0052] The adjustment unit can customize the settings of the home environment based on the user's preferences. For example, if the user prefers a particular scent, it can control the device that provides that scent. If the user prefers a particular color of lighting, it can adjust the lighting to that color. If the user prefers a particular music genre, it can play music of that genre. This makes it possible to customize the home environment based on the user's preferences.

[0053] The learning unit can acquire user meal data and incorporate it into learning lifestyle patterns. For example, it can acquire the user's meal times and meal contents and learn lifestyle patterns corresponding to those meal patterns. If a user eats at a specific time, it can learn a lifestyle pattern that is tailored to that time. It can also learn a lifestyle pattern that takes nutritional balance into account based on the user's meal contents. This makes it possible to learn lifestyle patterns based on the user's meal data.

[0054] The adjustment unit can suggest adjustments to the home environment based on the user's hobbies and interests. For example, if the user enjoys watching movies, it can suggest lighting and sound environments suitable for movie watching. If the user enjoys reading, it can suggest lighting and a quiet environment suitable for reading. If the user enjoys cooking, it can suggest a kitchen environment suitable for cooking. This makes it possible to suggest adjustments to the home environment based on the user's hobbies and interests.

[0055] The learning unit can acquire user sleep data and incorporate it into learning lifestyle patterns. For example, it can acquire the user's sleep duration and quality, and learn lifestyle patterns corresponding to those sleep patterns. If a user goes to bed at a specific time, it can learn a lifestyle pattern that is appropriate for that time. It can also learn lifestyle patterns to provide a comfortable sleep environment based on the user's sleep quality. This makes it possible to learn lifestyle patterns based on the user's sleep data.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The learning unit learns the user's lifestyle patterns. For example, it learns the user's wake-up time, meal times, and time spent going out, and collects data using AI. Step 2: The analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit. For example, it improves the accuracy of the analysis by considering fluctuations in the user's lifestyle patterns and applies an algorithm using AI. Step 3: The adjustment unit automatically adjusts the home environment based on the data analyzed by the analysis unit. For example, it automatically adjusts the settings of smart home devices such as lighting, temperature, and music, and applies control algorithms using AI. Step 4: The scene adjustment unit optimizes the home environment adjusted by the adjustment unit according to the user's scenario. For example, it optimizes the home environment according to the user's scenarios of being at home, going out, or sleeping, and applies an algorithm using AI.

[0058] (Example of form 2) An AI system according to an embodiment of the present invention is a system that analyzes and learns a user's personal preferences and lifestyle patterns, and automatically adjusts the settings of smart home devices such as lighting, temperature, and music. This AI system automatically adjusts the home environment according to various scenes in which the user is at home, out, or sleeping, aiming to improve comfort and optimize power consumption. For example, the AI ​​system learns the user's lifestyle patterns. Next, based on the learned data, it automatically adjusts the home environment according to scenes in which the user is at home, out, or sleeping. For example, when at home, it adjusts the lighting to an appropriate brightness, maintains the temperature within a comfortable range, and plays the user's preferred music. When going out, it turns off the lights and sets the temperature to energy-saving mode. When going to sleep, it dims the lights and adjusts the temperature to a comfortable sleeping environment. In this way, it aims to improve the user's comfort and reduce annual power consumption by 20%. Thus, the AI ​​system learns and analyzes the user's lifestyle patterns, automatically adjusts the home environment, and optimizes it according to the scene, enabling improved comfort and optimized power consumption.

[0059] The AI ​​system according to this embodiment comprises a learning unit, an analysis unit, an adjustment unit, and a scene adjustment unit. The learning unit learns the user's lifestyle patterns. For example, the learning unit learns the user's lifestyle patterns such as wake-up time, meal times, and times when the user goes out. The learning unit can collect data for analyzing the user's lifestyle patterns using AI. The analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit. For example, the analysis unit improves the accuracy of the analysis by considering fluctuations in the user's lifestyle patterns. The analysis unit can apply algorithms for analyzing the user's lifestyle patterns using AI. The adjustment unit automatically adjusts the home environment based on the data analyzed by the analysis unit. For example, the adjustment unit automatically adjusts the settings of smart home devices such as lighting, temperature, and music. The adjustment unit can apply control algorithms for automatically adjusting the home environment using AI. The scene adjustment unit optimizes the home environment adjusted by the adjustment unit according to the user's scenes. For example, the scene adjustment unit optimizes the home environment according to scenes such as when the user is at home, going out, or going to sleep. The scene adjustment unit can use AI to apply algorithms that provide the optimal home environment according to the user's scene. As a result, the AI ​​system according to the embodiment can learn and analyze the user's lifestyle patterns, automatically adjust the home environment, and optimize it according to the scene, thereby improving comfort and optimizing power consumption.

[0060] The learning unit learns the user's lifestyle patterns. For example, the learning unit learns the user's wake-up time, meal times, and times when they go out. Specifically, the learning unit utilizes data collected from the user's smartphone and wearable devices. These devices provide data such as the user's location, activity level, heart rate, and sleep patterns. The learning unit collects this data and analyzes the user's lifestyle patterns using AI. For example, it learns that the user wakes up at 7 am every morning, eats breakfast at 8 am, and goes out at 9 am. The learning unit accumulates this data and improves the accuracy of its analysis by considering fluctuations in the user's lifestyle patterns. Furthermore, the learning unit can also predict future lifestyle patterns based on the user's past data. For example, if the user tends to repeat a specific action on a particular day of the week, it will predict similar actions on that day. This allows the learning unit to gain a detailed understanding of the user's lifestyle patterns and generate data to provide to the analysis and adjustment units.

[0061] The analysis unit analyzes the user's lifestyle patterns based on data learned by the learning unit. The analysis unit improves the accuracy of the analysis by considering, for example, fluctuations in the user's lifestyle patterns. Specifically, the analysis unit uses AI to apply algorithms for analyzing the user's lifestyle patterns. This includes machine learning and deep learning techniques. For example, machine learning algorithms are used to cluster user behavior data and identify different behavior patterns. Furthermore, deep learning is used to extract complex patterns from user behavior data and predict future behavior. By applying these algorithms, the analysis unit analyzes the user's lifestyle patterns with high accuracy and generates data to provide to the adjustment unit and scene adjustment unit. In addition, the analysis unit can collect user feedback and continuously improve the accuracy of the analysis algorithm. For example, based on feedback provided by the user, the analysis results are modified and the algorithm parameters are adjusted. This allows the analysis unit to accurately analyze the user's lifestyle patterns and improve the overall system performance.

[0062] The adjustment unit automatically adjusts the home environment based on data analyzed by the analysis unit. For example, the adjustment unit automatically adjusts the settings of smart home devices such as lighting, temperature, and music. Specifically, the adjustment unit uses AI to apply control algorithms for automatically adjusting the home environment. For instance, it gradually brightens the lights and adjusts the room temperature to a comfortable level to match the user's wake-up time. It also turns on the lights and plays preferred music to coincide with the user's return home. The adjustment unit coordinates these devices to provide an optimal home environment according to the user's lifestyle. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy of its adjustment algorithms. For example, it adjusts lighting brightness and temperature settings based on user feedback. This allows the adjustment unit to provide an optimal home environment tailored to the user's lifestyle, thereby improving comfort.

[0063] The Scene Adjustment Unit optimizes the home environment, which has been adjusted by the Adjustment Unit, according to the user's situation. For example, the Scene Adjustment Unit optimizes the home environment according to the user's situation, such as being at home, going out, or going to sleep. Specifically, the Scene Adjustment Unit uses AI to apply algorithms to provide the optimal home environment according to the user's situation. For example, when the user is at home, it brightens the lights and maintains a comfortable room temperature. When the user goes out, it turns off the lights to minimize energy consumption. Furthermore, when the user goes to sleep, it dims the lights to provide a quiet environment. The Scene Adjustment Unit coordinates these devices to provide the optimal home environment according to the user's situation. In addition, the Scene Adjustment Unit can collect user feedback and continuously improve the accuracy of the adjustment algorithm. For example, it can adjust the brightness of the lights and the temperature settings based on the feedback provided by the user. In this way, the Scene Adjustment Unit can provide the optimal home environment according to the user's situation and improve comfort.

[0064] The learning unit can estimate the user's emotions and adjust the learning method for lifestyle patterns based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can adjust the learning method to provide a relaxing environment. If the user is feeling happy, the learning unit can also adjust the learning method to maintain that state. If the user is tired, the learning unit can also adjust the learning method to prioritize rest. By adjusting the learning method based on the user's emotions, it becomes possible to learn more appropriate lifestyle patterns. 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, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The learning unit can analyze the user's past behavior history and select the optimal learning algorithm. For example, the learning unit can analyze the user's past behavior patterns and select the optimal learning algorithm. The learning unit can also predict actions to be taken during specific time periods based on the user's past behavior history and select a learning algorithm. The learning unit can also detect changes in behavior patterns based on the user's past behavior history and select a learning algorithm. In this way, the optimal learning algorithm can be selected by analyzing past behavior history. 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 the user's behavior history data into a generating AI and have the generating AI perform the selection of the optimal learning algorithm.

[0066] The learning unit can detect changes in the user's daily rhythm in real time during learning and update the learning content. For example, the learning unit can detect changes in the user's daily rhythm in real time and update the learning content. The learning unit can also adjust the learning content as appropriate in response to changes in the user's daily rhythm. The learning unit can also detect changes in the user's daily rhythm and optimize the learning content. This enables more accurate learning of daily patterns by detecting changes in the daily rhythm in real time and updating the learning content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the update of the learning content.

[0067] The learning unit can estimate the user's emotions and prioritize training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit will prioritize learning data related to relaxation. If the user is stressed, the learning unit can also prioritize learning data related to stress reduction. If the user is excited, the learning unit can also prioritize learning data to calm the excitement. This allows for more effective learning by prioritizing training data based on 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, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The learning unit can learn lifestyle patterns while considering the user's geographical location information during the learning process. For example, the learning unit can learn lifestyle patterns in a specific region based on the user's geographical location information. The learning unit can also learn region-specific lifestyle patterns while considering the user's geographical location information. The learning unit can also learn lifestyle patterns that are appropriate to the climate and culture of a region based on the user's geographical location information. In this way, region-specific lifestyle patterns can be learned by considering geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the user's geographical location information into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0069] The learning unit can analyze the user's social media activity during the learning process and reflect this in the learning of lifestyle patterns. For example, the learning unit can analyze the user's social media activity and reflect this in the learning of lifestyle patterns. The learning unit can also predict actions taken at specific times of day based on the user's social media activity and reflect this in the learning process. The learning unit can also detect changes in behavioral patterns based on the user's social media activity and reflect this in the learning process. This makes it possible to learn lifestyle patterns more accurately by analyzing social media activity. 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 user's social media data into a generating AI and have the generating AI perform the learning of lifestyle patterns.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize analyzing data related to relaxation. If the user is stressed, the analysis unit may also prioritize analyzing data related to stress reduction. If the user is agitated, the analysis unit may also prioritize analyzing data to calm the agitation. By adjusting the analysis method based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can improve the accuracy of the analysis by taking into account changes in the user's lifestyle patterns during the analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account changes in the user's lifestyle patterns. The analysis unit can also adjust the analysis method as appropriate according to changes in the user's lifestyle patterns. The analysis unit can also detect changes in the user's lifestyle patterns and optimize the accuracy of the analysis. As a result, the accuracy of the analysis is improved by taking into account changes in lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user lifestyle pattern data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0072] The analysis unit can detect the periodicity of the user's lifestyle patterns during analysis and reflect it in the analysis results. For example, the analysis unit can detect the periodicity of the user's lifestyle patterns and reflect it in the analysis results. The analysis unit can also adjust the analysis method as appropriate according to the periodicity of the user's lifestyle patterns. The analysis unit can also detect the periodicity of the user's lifestyle patterns and optimize the analysis results. This improves the accuracy of the analysis results by detecting the periodicity of lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform periodicity detection.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will prioritize displaying data related to relaxation. If the user is stressed, the analysis unit may also prioritize displaying data related to stress reduction. If the user is agitated, the analysis unit may also prioritize displaying data to calm agitation. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can analyze lifestyle patterns in a specific region based on the user's geographical location information. The analysis unit can also analyze region-specific lifestyle patterns while considering the user's geographical location information. The analysis unit can also analyze lifestyle patterns that correspond to the climate and culture of a region based on the user's geographical location information. In this way, region-specific lifestyle patterns can be analyzed by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the lifestyle pattern analysis.

[0075] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can refer to the user's relevant literature to improve the accuracy of the analysis. The analysis unit can also predict actions taken during specific time periods based on the user's relevant literature and reflect this in the analysis. The analysis unit can also detect changes in behavioral patterns based on the user's relevant literature and reflect this in the analysis. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0076] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit will prioritize adjusting data related to relaxation. If the user is stressed, the adjustment unit may also prioritize adjusting data related to stress reduction. If the user is excited, the adjustment unit may also prioritize adjusting data to calm the excitement. This allows for more appropriate adjustments by adjusting the adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The adjustment unit can analyze the user's past settings history during adjustment to select the optimal adjustment method. For example, the adjustment unit analyzes the user's past settings history and selects the optimal adjustment method. The adjustment unit can also predict settings to be made during a specific time period based on the user's past settings history and select an adjustment method. The adjustment unit can also detect changes in setting patterns based on the user's past settings history and select an adjustment method. In this way, the optimal adjustment method can be selected by analyzing the past settings history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's settings history data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0078] The adjustment unit can customize the adjustment content based on the user's current living situation during the adjustment process. For example, the adjustment unit customizes the adjustment content based on the user's current living situation. The adjustment unit can also adjust the adjustment content as appropriate according to the user's current living situation. The adjustment unit can also detect the user's current living situation and optimize the adjustment content. This makes it possible to perform more appropriate adjustments by customizing the adjustment content based on the current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user living situation data into a generating AI and have the generating AI perform the customization of the adjustment content.

[0079] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit will prioritize adjusting data related to relaxation. If the user is stressed, the adjustment unit can also prioritize adjusting data related to stress reduction. If the user is agitated, the adjustment unit can also prioritize adjusting data to calm agitation. This allows for more effective adjustments by determining the priority of adjustments based on 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, the adjustment unit can select an adjustment method for a specific region based on the user's geographical location information. The adjustment unit can also select a region-specific adjustment method, taking into account the user's geographical location information. The adjustment unit can also select an adjustment method that is appropriate for the region's climate and culture, based on the user's geographical location information. In this way, a region-specific adjustment method can be selected by taking into account geographical location information. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal adjustment method.

[0081] The adjustment unit can analyze the user's social media activity and propose adjustments during the adjustment process. For example, the adjustment unit can analyze the user's social media activity and propose adjustments. The adjustment unit can also predict and propose adjustments to be made at specific times based on the user's social media activity. The adjustment unit can also detect changes in behavioral patterns based on the user's social media activity and propose adjustments. In this way, by analyzing social media activity, it is possible to propose more appropriate adjustments. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of adjustments.

[0082] The scene adjustment unit can estimate the user's emotions and adjust the scene adjustment method based on the estimated user emotions. For example, if the user is relaxed, the scene adjustment unit will perform scene adjustments suitable for relaxation. If the user is stressed, the scene adjustment unit can also perform scene adjustments suitable for stress reduction. If the user is excited, the scene adjustment unit can also perform scene adjustments to calm the excitement. By adjusting the scene adjustment method based on the user's emotions, more appropriate scene adjustments become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The scene adjustment unit can select the optimal adjustment method by referring to the user's past scene history during scene adjustment. For example, the scene adjustment unit can refer to the user's past scene history and select the optimal adjustment method. The scene adjustment unit can also predict scene adjustments to be performed at a specific time period from the user's past scene history and select an adjustment method. The scene adjustment unit can also detect changes in the scene adjustment pattern based on the user's past scene history and select an adjustment method. In this way, the optimal adjustment method can be selected by referring to past scene history. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input the user's scene history data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0084] The scene adjustment unit can customize the means of scene adjustment based on the user's current living situation during scene adjustment. For example, the scene adjustment unit customizes the means of scene adjustment based on the user's current living situation. The scene adjustment unit can also adjust the means of scene adjustment as appropriate according to the user's current living situation. The scene adjustment unit can also detect the user's current living situation and optimize the means of scene adjustment. This makes it possible to perform more appropriate scene adjustment by customizing the means of scene adjustment based on the current living situation. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of scene adjustment.

[0085] The scene adjustment unit can estimate the user's emotions and determine the priority of scene adjustments based on the estimated user emotions. For example, if the user is relaxed, the scene adjustment unit will prioritize scene adjustments suitable for relaxation. If the user is stressed, the scene adjustment unit may also prioritize scene adjustments suitable for stress reduction. If the user is excited, the scene adjustment unit may also prioritize scene adjustments to calm the excitement. This allows for more effective scene adjustments by determining the priority of scene adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scene adjustment unit may be performed using AI, or not using AI. For example, the scene adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The scene adjustment unit can select the optimal scene adjustment method by considering the user's geographical location information during scene adjustment. For example, the scene adjustment unit can select a scene adjustment method for a specific region based on the user's geographical location information. The scene adjustment unit can also select a region-specific scene adjustment method by considering the user's geographical location information. The scene adjustment unit can also select a scene adjustment method that is appropriate for the region's climate and culture based on the user's geographical location information. In this way, a region-specific scene adjustment method can be selected by considering geographical location information. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without using AI. For example, the scene adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal scene adjustment method.

[0087] The scene adjustment unit can analyze the user's social media activity and propose methods for scene adjustment during scene adjustment. For example, the scene adjustment unit can analyze the user's social media activity and propose methods for scene adjustment. The scene adjustment unit can also predict scene adjustments to be performed at specific times based on the user's social media activity and propose methods. The scene adjustment unit can also detect changes in behavioral patterns based on the user's social media activity and propose methods for scene adjustment. In this way, by analyzing social media activity, it is possible to propose more appropriate methods for scene adjustment. Some or all of the above processing in the scene adjustment unit may be performed using AI, for example, or without AI. For example, the scene adjustment unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of methods for scene adjustment.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The learning unit can also acquire user health data and incorporate it into learning lifestyle patterns. For example, it can acquire the user's heart rate and sleep data and learn lifestyle patterns according to their health status. If the user is fatigued, it can learn a lifestyle pattern that prioritizes rest. It can also acquire the user's exercise data and learn a lifestyle pattern that takes post-exercise recovery into consideration. This makes it possible to learn lifestyle patterns based on the user's health status.

[0090] The analysis unit can estimate the user's emotions and adjust the timing of notification of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis results can be notified immediately. If the user is stressed, the notification can be sent when the stress has subsided. If the user is excited, the notification can be sent after the excitement has subsided. This makes it possible to notify the user of analysis results that take their emotions into consideration.

[0091] The adjustment unit can customize the settings of the home environment based on the user's preferences. For example, if the user prefers a particular scent, it can control the device that provides that scent. If the user prefers a particular color of lighting, it can adjust the lighting to that color. If the user prefers a particular music genre, it can play music of that genre. This makes it possible to customize the home environment based on the user's preferences.

[0092] The scene adjustment unit can estimate the user's emotions and adjust the frequency of scene adjustments based on those emotions. For example, if the user is relaxed, the frequency of scene adjustments can be reduced. If the user is stressed, scene adjustments can be performed more frequently. If the user is excited, scene adjustments can be withheld until the excitement subsides. This makes it possible to adjust the frequency of scene adjustments according to the user's emotions.

[0093] The learning unit can acquire user meal data and incorporate it into learning lifestyle patterns. For example, it can acquire the user's meal times and meal contents and learn lifestyle patterns corresponding to those meal patterns. If a user eats at a specific time, it can learn a lifestyle pattern that is tailored to that time. It can also learn a lifestyle pattern that takes nutritional balance into account based on the user's meal contents. This makes it possible to learn lifestyle patterns based on the user's meal data.

[0094] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on those emotions. For example, if the user is relaxed, it can prioritize displaying analysis results of low importance. If the user is stressed, it can prioritize displaying analysis results of high importance. If the user is excited, it can prioritize displaying analysis results of medium importance. This makes it possible to adjust the importance of analysis results according to the user's emotions.

[0095] The adjustment unit can suggest adjustments to the home environment based on the user's hobbies and interests. For example, if the user enjoys watching movies, it can suggest lighting and sound environments suitable for movie watching. If the user enjoys reading, it can suggest lighting and a quiet environment suitable for reading. If the user enjoys cooking, it can suggest a kitchen environment suitable for cooking. This makes it possible to suggest adjustments to the home environment based on the user's hobbies and interests.

[0096] The scene adjustment unit can estimate the user's emotions and customize the scene adjustments based on those emotions. For example, if the user is relaxed, it can provide lighting and music suitable for relaxation. If the user is stressed, it can provide an environment suitable for stress reduction. If the user is excited, it can provide an environment to calm them down. This makes it possible to customize scene adjustments according to the user's emotions.

[0097] The learning unit can acquire user sleep data and incorporate it into learning lifestyle patterns. For example, it can acquire the user's sleep duration and quality, and learn lifestyle patterns corresponding to those sleep patterns. If a user goes to bed at a specific time, it can learn a lifestyle pattern that is appropriate for that time. It can also learn lifestyle patterns to provide a comfortable sleep environment based on the user's sleep quality. This makes it possible to learn lifestyle patterns based on the user's sleep data.

[0098] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis results can be notified in a gentle voice. If the user is stressed, the analysis results can be notified visually. If the user is excited, the analysis results can be notified by vibration. This makes it possible to adjust the notification method of the analysis results according to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The learning unit learns the user's lifestyle patterns. For example, it learns the user's wake-up time, meal times, and time spent going out, and collects data using AI. Step 2: The analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit. For example, it improves the accuracy of the analysis by considering fluctuations in the user's lifestyle patterns and applies an algorithm using AI. Step 3: The adjustment unit automatically adjusts the home environment based on the data analyzed by the analysis unit. For example, it automatically adjusts the settings of smart home devices such as lighting, temperature, and music, and applies control algorithms using AI. Step 4: The scene adjustment unit optimizes the home environment adjusted by the adjustment unit according to the user's scenario. For example, it optimizes the home environment according to the user's scenarios of being at home, going out, or sleeping, and applies an algorithm using AI.

[0101] 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] 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.

[0104] Each of the multiple elements described above, including the learning unit, analysis unit, adjustment unit, and scene adjustment unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns the user's lifestyle patterns. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the user's lifestyle patterns based on the learned data. The adjustment unit is implemented by the control unit 46A of the smart device 14 and automatically adjusts the home environment. The scene adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes the home environment according to the user's scene. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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.

[0120] Each of the multiple elements described above, including the learning unit, analysis unit, adjustment unit, and scene adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the user's lifestyle patterns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle patterns based on the learned data. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and automatically adjusts the home environment. The scene adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the home environment according to the user's scene. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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.

[0136] Each of the multiple elements described above, including the learning unit, analysis unit, adjustment unit, and scene adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the user's lifestyle patterns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle patterns based on the learned data. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and automatically adjusts the home environment. The scene adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the home environment according to the user's scene. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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.

[0153] Each of the multiple elements described above, including the learning unit, analysis unit, adjustment unit, and scene adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns the user's lifestyle patterns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle patterns based on the learned data. The adjustment unit is implemented by the control unit 46A of the robot 414 and automatically adjusts the home environment. The scene adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the home environment according to the user's scene. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A learning unit that learns the user's lifestyle patterns, An analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit, An adjustment unit that automatically adjusts the home environment based on the data analyzed by the aforementioned analysis unit, The system includes a scene adjustment unit that optimizes the home environment adjusted by the adjustment unit according to the user's scene. A system characterized by the following features. (Note 2) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method of lifestyle patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Analyze the user's past behavior history and select the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, During learning, the system detects changes in the user's daily routine in real time and updates the learning content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It estimates the user's emotions and prioritizes the training data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, During learning, the system learns lifestyle patterns while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity and incorporates it into learning their lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by taking into account changes in the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the periodicity of the user's lifestyle patterns is detected and reflected in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, The system estimates the user's emotions and adjusts the adjustment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, During adjustment, the system analyzes the user's past settings history to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, During the adjustment process, the adjustments are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, During the adjustment process, we analyze the user's social media activity and propose adjustments accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scene adjustment unit, It estimates the user's emotions and adjusts the scene adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scene adjustment unit, During scene adjustment, the system references the user's past scene history to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scene adjustment unit, During scene adjustment, the method of scene adjustment is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned scene adjustment unit, It estimates the user's emotions and determines the priority of scene adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scene adjustment unit, When adjusting the scene, the optimal scene adjustment method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scene adjustment unit, During scene adjustment, we analyze the user's social media activity and suggest methods for scene adjustment. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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 learning unit that learns the user's lifestyle patterns, An analysis unit analyzes the user's lifestyle patterns based on the data learned by the learning unit, An adjustment unit that automatically adjusts the home environment based on the data analyzed by the aforementioned analysis unit, The system includes a scene adjustment unit that optimizes the home environment adjusted by the adjustment unit according to the user's scene. A system characterized by the following features.

2. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method of lifestyle patterns based on the estimated user emotions. The system according to feature 1.

3. The aforementioned learning unit, Analyze the user's past behavior history and select the optimal learning algorithm. The system according to feature 1.

4. The aforementioned learning unit, During learning, the system detects changes in the user's daily routine in real time and updates the learning content accordingly. The system according to feature 1.

5. The aforementioned learning unit, It estimates the user's emotions and prioritizes the training data based on the estimated user emotions. The system according to feature 1.

6. The aforementioned learning unit, During learning, the system learns lifestyle patterns while taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity and incorporates it into learning their lifestyle patterns. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by taking into account changes in the user's lifestyle patterns. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, the periodicity of the user's lifestyle patterns is detected and reflected in the analysis results. The system according to feature 1.

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