system

Abstract

The system according to this embodiment aims to comprehensively support the user's life. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, an advice unit, a control unit, and a dialogue unit. The acquisition unit acquires information. The analysis unit analyzes the information acquired by the acquisition unit. The advice unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. The control unit controls the home appliance based on the analysis results obtained by the analysis unit. The dialogue unit interacts with the user interactively.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system for comprehensively supporting a user's life has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support a user's life.

Means for Solving the Problems

[0007] The system according to this embodiment can comprehensively support the user's life. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The GeniusMate system according to an embodiment of the present invention is an AI partner designed to support the user's life and provides personalized services. The GeniusMate system collaborates with various devices and cloud services to improve user efficiency and enhance the user experience. Furthermore, it contributes to solving problems in various fields, such as reducing power consumption in home appliances, improving environmental issues, and providing health advice to users. This allows users to live better lives. First, the GeniusMate system collaborates with home appliances such as air conditioners and lighting, terminals such as smartphones and wearable devices, and various cloud services such as webmail and e-commerce sites. This seamlessly connects information obtained from connected devices and services, supporting the user from every perspective. For example, it considers weather forecasts and traffic information to provide proactive support such as schedule-based reminders and advice. It controls home appliances (such as air conditioners) based on the user's health status and weather, ensuring optimal operation. It also collaborates with wearable devices to provide health advice and support. Furthermore, it interacts with the user, reading and replying to received emails. It also performs automated purchase processing on e-commerce sites such as Amazon. The GeniusMate system comprises an information acquisition unit, an analysis unit that analyzes the information acquired by the acquisition unit, an advice unit that provides advice or reminders to the user based on the analysis results obtained by the analysis unit, a control unit that controls home appliances based on the analysis results obtained by the analysis unit, and a dialogue unit that interacts with the user. Through this system, the GeniusMate system can support the user's life and contribute to improving work efficiency and user experience.

[0029] The GeniusMate system according to this embodiment comprises an acquisition unit, an analysis unit, an advisory unit, a control unit, and a dialogue unit. The acquisition unit acquires information. The acquisition unit acquires information from, for example, home appliances such as air conditioners and lighting, terminals such as smartphones and wearable devices, and various cloud services such as webmail and e-commerce sites. For example, the acquisition unit can acquire temperature setting information from an air conditioner. The acquisition unit can also acquire notification information from a smartphone. Furthermore, the acquisition unit can acquire sensor data from a wearable device. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit analyzes the information using data mining techniques. Furthermore, the analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. The advisory unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. For example, the advisory unit provides reminders based on the user's schedule. Furthermore, the advisory unit can provide advice based on the user's health condition. Furthermore, the advisory unit can provide advice based on weather forecasts. The control unit controls home appliances based on the analysis results obtained by the analysis unit. For example, the control unit adjusts the temperature of an air conditioner. The control unit can also adjust the brightness of lighting. Furthermore, the control unit can control the on / off state of home appliances. The dialogue unit interacts with the user. For example, the dialogue unit reads out received emails. The dialogue unit can also reply to received emails. Furthermore, the dialogue unit can perform automated purchase processing on e-commerce sites. As a result, the GeniusMate system according to this embodiment can support the user's life and contribute to improving work efficiency and user experience.

[0030] The data acquisition unit acquires information. For example, it acquires information from home appliances such as air conditioners and lighting, terminals such as smartphones and wearable devices, and various cloud services such as webmail and e-commerce sites. Specifically, when acquiring temperature setting information for an air conditioner, it collects data directly from the air conditioner's internal sensors and control system. When acquiring notification information from a smartphone, it uses the smartphone's notification API to obtain the content and timing of notifications from applications. When acquiring sensor data from wearable devices, it collects detailed data such as heart rate, steps, and sleep patterns to understand the user's health status. Furthermore, when acquiring information from cloud services, the data acquisition unit uses APIs and web scraping technology to collect data such as webmail inboxes and purchase history from e-commerce sites. As a result, the data acquisition unit can efficiently collect necessary data from a wide range of information sources and build an information infrastructure for the entire system.

[0031] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit uses data mining techniques to analyze the information. Data mining techniques are used to extract patterns and trends from the acquired data and predict user behavior and preferences. The analysis unit can also analyze the information using statistical analysis techniques. Statistical analysis techniques are used to clarify the distribution and correlation of data and to quantitatively evaluate changes in user behavior and the environment. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. Machine learning algorithms are used to build models based on the acquired data and perform predictions and classifications. For example, it can predict the most likely next action based on the user's past behavior data. The analysis unit can also analyze data in real time and provide results immediately. This allows the analysis unit to analyze the acquired information from multiple perspectives and respond quickly to user needs and environmental changes.

[0032] The advisory unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. For example, the advisory unit can provide reminders based on the user's schedule. Specifically, it can link with the user's calendar application and send reminder notifications based on the time and location of appointments. It can also provide advice based on the user's health status. It analyzes heart rate and sleep data obtained from wearable devices to advise on appropriate timing for exercise and rest. Furthermore, the advisory unit can provide advice based on weather forecasts. It obtains weather forecast data and provides advice on clothing and items to bring when going out. In this way, the advisory unit can support the user's life and provide appropriate advice in various everyday situations.

[0033] The control unit controls home appliances based on the analysis results obtained by the analysis unit. For example, the control unit adjusts the temperature of an air conditioner. Specifically, it analyzes the user's presence at home, the outside temperature, and the indoor temperature, and automatically sets the optimal temperature. The control unit can also adjust the brightness of lighting. It adjusts the brightness and color temperature of the lighting according to the user's activity level and time of day, providing a comfortable environment. Furthermore, the control unit can control the on / off of home appliances. For example, it saves energy by automatically turning off unnecessary appliances when the user goes out. In this way, the control unit can optimize the user's living environment and support a comfortable and efficient life.

[0034] The dialogue unit interacts with the user. For example, it can read aloud received emails. Specifically, it uses speech synthesis technology to read the contents of received emails aloud. The dialogue unit can also reply to received emails. Using speech recognition technology, it converts the user's voice instructions into text and creates the email reply. Furthermore, the dialogue unit can also process automated purchases on e-commerce sites. It analyzes the user's purchase history and preferences, suggests appropriate products, and completes the purchase process based on the user's instructions. In this way, the dialogue unit can efficiently process various tasks through natural dialogue with the user, contributing to an improved user experience.

[0035] The data acquisition unit can acquire information from home appliances such as air conditioners and lighting, smartphones and wearable devices, and cloud services such as webmail and e-commerce sites. For example, the data acquisition unit can acquire temperature setting information from an air conditioner. It can also acquire notification information from a smartphone. Furthermore, it can acquire sensor data from wearable devices. This allows for multifaceted support of the user's life by acquiring information from diverse sources. Some or all of the processing described above in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input air conditioner temperature setting information into an AI, which can then output the analysis results.

[0036] The analysis unit can analyze the acquired information and provide reminders and advice based on the user's schedule. For example, the analysis unit can analyze the information using data mining techniques. It can also analyze the information using statistical analysis techniques. Furthermore, it can analyze the information using machine learning algorithms. This allows for the provision of appropriate reminders and advice based on the user's schedule. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the acquired information into an AI, which can then output the analysis results.

[0037] The control unit can control home appliances based on the user's health status and the weather, thereby enabling their operation. For example, the control unit can adjust the temperature of an air conditioner. It can also adjust the brightness of lighting. Furthermore, the control unit can control the on / off function of home appliances. This enables optimal operation of home appliances according to the user's health status and the weather. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's health status and weather information into the AI, which can then output a method for controlling the home appliances.

[0038] The dialogue unit can interact with the user and perform tasks such as reading aloud and replying to received emails. For example, the dialogue unit can read aloud received emails. It can also reply to received emails. Furthermore, the dialogue unit can perform automated purchase processing on e-commerce sites. This improves user convenience by allowing interactive interaction with the user and performing tasks such as reading aloud and replying to received emails. Some or all of the above-mentioned processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the content of a received email into AI, and the AI ​​can generate a reply.

[0039] The dialogue unit can perform automated purchase processing on Amazon's e-commerce site. For example, the dialogue unit can automatically purchase products specified by the user. It can also suggest recommended products based on the user's purchase history. Furthermore, the dialogue unit can automate the purchase process, saving the user time and effort. This improves the user's purchasing experience by enabling automated purchase processing on the e-commerce site. Some or all of the above-described processes in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's purchase history into AI, which can then suggest recommended products.

[0040] The data acquisition unit can analyze the user's past behavior history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring information sources that the user has frequently accessed in the past. The data acquisition unit can also select the optimal information acquisition method for a specific time period based on the user's past behavior patterns. Furthermore, the data acquisition unit can propose the most efficient information acquisition method based on the user's past behavior history. In this way, by analyzing the user's past behavior history, the optimal information acquisition method can be provided. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past behavior history data into AI, and the AI ​​can output the optimal information acquisition method.

[0041] The data acquisition unit can filter information based on the user's current activity status and areas of interest when acquiring it. For example, if the user is working, the data acquisition unit will acquire only work-related information. Furthermore, if the user is spending time on a hobby, the data acquisition unit can prioritize acquiring information related to that hobby. Additionally, if the user is taking a break, the data acquisition unit can acquire information that promotes relaxation. By filtering information based on the user's activity status and areas of interest, the data acquisition unit can provide highly relevant information. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's current activity data into the AI, which can then output the filtered results.

[0042] The information acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring information. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring information related to that region. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring tourist information for their travel destination. Additionally, if the user is at home, the acquisition unit can prioritize acquiring information about nearby events. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's geographical location information into the AI, which can then output highly relevant information.

[0043] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring data. For example, the data acquisition unit can acquire information related to topics the user has shown interest in on social media. The data acquisition unit can also acquire information based on the content of posts from accounts the user follows. Furthermore, the data acquisition unit can acquire information related to the activities of groups and communities the user participates in. This allows the system to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into an AI, which can then output relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. It can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail according to the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into the AI, which can then adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a health-related analysis algorithm to health information. It can also apply a traffic-related analysis algorithm to traffic information. Furthermore, it can apply a weather-related analysis algorithm to weather information. By applying an analysis algorithm appropriate to the category of information, highly accurate analysis can be achieved. 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 information category data into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the information was acquired. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. Furthermore, the analysis unit can adjust the level of detail of the analysis according to when the information was acquired. This allows the analysis to prioritize the analysis of the most recent information by determining the priority of analysis based on when the information was acquired. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information acquisition time data into the AI, and the AI ​​can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into the AI, and the AI ​​can adjust the order of analysis.

[0048] The advisory unit can adjust the level of detail of its advice based on the importance of the information it provides. For example, it can provide detailed advice for highly important information, and simplified advice for less important information. Furthermore, the advisory unit can prioritize advice according to its importance. This allows for efficient advice by adjusting the level of detail according to the importance of the information. Some or all of the above processes in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information importance data into the AI, which can then adjust the level of detail of the advice.

[0049] The advisory unit can apply different advisory algorithms depending on the category of information when providing advice. For example, the advisory unit can apply a health-related advisory algorithm to health information. It can also apply a traffic-related advisory algorithm to traffic information. Furthermore, it can apply a weather-related advisory algorithm to weather information. By applying an advisory algorithm according to the category of information, highly accurate advice can be achieved. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information category data into AI, and the AI ​​can apply an appropriate advisory algorithm.

[0050] The advisory unit can determine the priority of advice based on when the information was acquired. For example, the advisory unit will prioritize advice based on the most recent information. It can also provide advice on older information as needed. Furthermore, the advisory unit can adjust the level of detail of the advice according to when the information was acquired. This allows the advisory unit to prioritize advice based on when the information was acquired, thereby prioritizing advice based on the most recent information. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input information acquisition time data into AI, and the AI ​​can determine the priority of advice.

[0051] The advisory unit can adjust the order of advice based on the relevance of the information when providing advice. For example, the advisory unit will prioritize advice on highly relevant information. It can also postpone advice on less relevant information. Furthermore, the advisory unit can adjust the level of detail of the advice according to the relevance of the information. This allows for efficient advice by adjusting the order of advice based on the relevance of the information. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information relevance data into the AI, and the AI ​​can adjust the order of advice.

[0052] The control unit can analyze the user's past usage history and select the optimal control method during control. For example, the control unit can select the optimal control method based on the settings the user has previously preferred. The control unit can also select the optimal control method for a specific time period based on the user's past usage history. Furthermore, the control unit can propose the most efficient control method based on the user's past usage history. In this way, by analyzing the user's past usage history, the control unit can provide the optimal control method for home appliances. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's past usage history data into AI, and the AI ​​can output the optimal control method.

[0053] The control unit can customize the control means of the home appliances based on the user's current living situation during control. For example, the control unit can optimize the operation of the home appliances when the user is at home. It can also minimize the operation of the home appliances when the user is out. Furthermore, the control unit can switch the operation of the home appliances to silent mode when the user is sleeping. In this way, efficient control of the home appliances can be achieved by customizing the control means of the home appliances according to the user's living situation. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user living situation data into the AI, and the AI ​​can output the optimal control means.

[0054] The control unit can select the optimal control method during control, taking into account the user's geographical location information. For example, if the user is at home, the control unit can optimize the operation of home appliances. Furthermore, if the user is at an office, the control unit can control home appliances in a way that is appropriate for the office environment. Additionally, if the user is traveling, the control unit can control home appliances in a way that is appropriate for the environment of the travel destination. This allows the control unit to provide the optimal home appliance control method by considering the user's geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's geographical location information into the AI, which can then output the optimal control method.

[0055] The control unit can analyze the user's social media activity during control and propose control methods for home appliances. For example, the control unit can propose control methods for home appliances based on information shared by the user on social media. The control unit can also propose control methods for home appliances based on the content of posts from accounts the user follows on social media. Furthermore, the control unit can propose control methods for home appliances based on the activities of groups and communities the user participates in on social media. In this way, by analyzing the user's social media activity, the control unit can provide the optimal control methods for home appliances. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media activity data into an AI, and the AI ​​can output the optimal control methods for home appliances.

[0056] The dialogue unit can select the optimal dialogue method during a conversation by referring to the user's past dialogue history. For example, the dialogue unit can select the optimal dialogue method based on the dialogue style the user has preferred in the past. The dialogue unit can also select the optimal dialogue method for a specific time period based on the user's past dialogue history. Furthermore, the dialogue unit can propose the most efficient dialogue method based on the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history data into AI, and the AI ​​can output the optimal dialogue method.

[0057] The dialogue unit can customize the content of the conversation based on the user's current activity status. For example, if the user is working, the dialogue unit will provide work-related content. It can also provide content related to the user's hobby if the user is enjoying a hobby. Furthermore, if the user is taking a break, it can provide relaxing content. This allows for highly relevant conversations by customizing the content according to the user's activity status. Some or all of the above processing in the dialogue unit may be performed using AI, or without AI. For example, the dialogue unit can input the user's current activity data into the AI, which can then output the most appropriate dialogue content.

[0058] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location information. For example, if the user is at home, the dialogue unit can provide a dialogue method suitable for the home environment. It can also provide a dialogue method suitable for the office environment if the user is at the office. Furthermore, if the user is traveling, the dialogue unit can provide a dialogue method suitable for the environment of the travel destination. This allows the dialogue unit to provide the optimal dialogue method by considering the user's geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location information into the AI, which can then output the optimal dialogue method.

[0059] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can provide dialogue content related to topics the user has shown interest in on social media. It can also provide dialogue content based on the posts of accounts the user follows. Furthermore, the dialogue unit can provide dialogue content related to the activities of groups and communities the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not. For example, the dialogue unit can input the user's social media activity data into an AI, which can then output the most appropriate dialogue content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The GeniusMate system can also include a mobility management unit that optimizes user movement by considering the user's geographical location. For example, if the user is at home, the mobility management unit can suggest the optimal commute route. If the user is traveling, it can also suggest tourist attractions at their destination. Furthermore, the mobility management unit can analyze the user's travel history and suggest the most suitable mode of transportation. This allows for efficient travel by considering the user's geographical location.

[0062] The GeniusMate system can also include a recommendation unit that analyzes the user's past behavior history and makes recommendations based on the user's hobbies and interests. For example, the recommendation unit can suggest new movies based on movies the user has watched in the past. It can also suggest new restaurants based on restaurants the user has visited in the past. Furthermore, the recommendation unit can analyze the user's past purchase history and suggest related products. In this way, by analyzing the user's past behavior history, it is possible to provide recommendations based on the user's hobbies and interests.

[0063] The GeniusMate system can also include a social media analytics unit that analyzes users' social media activity and provides information based on user interests. For example, the social media analytics unit can provide relevant news based on the content of posts from accounts the user follows. It can also provide event information based on the activities of groups and communities the user participates in. Furthermore, the social media analytics unit can suggest relevant articles based on content the user shares. This allows the system to provide information based on user interests by analyzing users' social media activity.

[0064] The GeniusMate system can also include a task management unit that optimizes user task management by considering the user's current activity status. For example, if the user is working, the task management unit will prioritize managing work-related tasks. It can also suggest relaxing tasks if the user is on a break. Furthermore, the task management unit can analyze the user's schedule and suggest the optimal order of tasks. This allows for efficient task management by considering the user's current activity status.

[0065] The GeniusMate system can also include a health advice unit that analyzes the user's health data and provides health advice based on the user's health status. For example, the health advice unit can monitor the user's heart rate and blood pressure and issue an alert if an abnormality is detected. It can also analyze the user's diet and exercise history and suggest balanced lifestyle habits. Furthermore, it can analyze the user's sleep data and provide advice to improve sleep quality. In this way, the system can support the user's health by analyzing their health data.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit acquires information. The acquisition unit acquires information from various sources, such as home appliances like air conditioners and lighting, devices like smartphones and wearable devices, and various cloud services like webmail and e-commerce sites. For example, the acquisition unit can acquire temperature setting information from an air conditioner. It can also acquire notification information from a smartphone. Furthermore, the acquisition unit can acquire sensor data from wearable devices. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit analyzes the information using, for example, data mining techniques. The analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. Step 3: The advisory unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. For example, the advisory unit may provide reminders based on the user's schedule. It can also provide advice based on the user's health condition. Furthermore, it can provide advice based on weather forecasts. Step 4: The control unit controls the appliance based on the analysis results obtained by the analysis unit. For example, the control unit adjusts the temperature of the air conditioner. The control unit can also adjust the brightness of the lighting. Furthermore, the control unit can also control the on / off function of the appliance. Step 5: The dialogue unit interacts with the user. For example, the dialogue unit can read out incoming emails. It can also reply to incoming emails. Furthermore, the dialogue unit can process automated purchases on e-commerce sites.

[0068] (Example of form 2) The GeniusMate system according to an embodiment of the present invention is an AI partner designed to support the user's life and provides personalized services. The GeniusMate system collaborates with various devices and cloud services to improve user efficiency and enhance the user experience. Furthermore, it contributes to solving problems in various fields, such as reducing power consumption in home appliances, improving environmental issues, and providing health advice to users. This allows users to live better lives. First, the GeniusMate system collaborates with home appliances such as air conditioners and lighting, terminals such as smartphones and wearable devices, and various cloud services such as webmail and e-commerce sites. This seamlessly connects information obtained from connected devices and services, supporting the user from every perspective. For example, it considers weather forecasts and traffic information to provide proactive support such as schedule-based reminders and advice. It controls home appliances (such as air conditioners) based on the user's health status and weather, ensuring optimal operation. It also collaborates with wearable devices to provide health advice and support. Furthermore, it interacts with the user, reading and replying to received emails. It also performs automated purchase processing on e-commerce sites such as Amazon. The GeniusMate system comprises an information acquisition unit, an analysis unit that analyzes the information acquired by the acquisition unit, an advice unit that provides advice or reminders to the user based on the analysis results obtained by the analysis unit, a control unit that controls home appliances based on the analysis results obtained by the analysis unit, and a dialogue unit that interacts with the user. Through this system, the GeniusMate system can support the user's life and contribute to improving work efficiency and user experience.

[0069] The GeniusMate system according to this embodiment comprises an acquisition unit, an analysis unit, an advisory unit, a control unit, and a dialogue unit. The acquisition unit acquires information. The acquisition unit acquires information from, for example, home appliances such as air conditioners and lighting, terminals such as smartphones and wearable devices, and various cloud services such as webmail and e-commerce sites. For example, the acquisition unit can acquire temperature setting information from an air conditioner. The acquisition unit can also acquire notification information from a smartphone. Furthermore, the acquisition unit can acquire sensor data from a wearable device. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit analyzes the information using data mining techniques. Furthermore, the analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. The advisory unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. For example, the advisory unit provides reminders based on the user's schedule. Furthermore, the advisory unit can provide advice based on the user's health condition. Furthermore, the advisory unit can provide advice based on weather forecasts. The control unit controls home appliances based on the analysis results obtained by the analysis unit. For example, the control unit adjusts the temperature of an air conditioner. The control unit can also adjust the brightness of lighting. Furthermore, the control unit can control the on / off state of home appliances. The dialogue unit interacts with the user. For example, the dialogue unit reads out received emails. The dialogue unit can also reply to received emails. Furthermore, the dialogue unit can perform automated purchase processing on e-commerce sites. As a result, the GeniusMate system according to this embodiment can support the user's life and contribute to improving work efficiency and user experience.

[0070] The data acquisition unit acquires information. For example, it acquires information from home appliances such as air conditioners and lighting, terminals such as smartphones and wearable devices, and various cloud services such as webmail and e-commerce sites. Specifically, when acquiring temperature setting information for an air conditioner, it collects data directly from the air conditioner's internal sensors and control system. When acquiring notification information from a smartphone, it uses the smartphone's notification API to obtain the content and timing of notifications from applications. When acquiring sensor data from wearable devices, it collects detailed data such as heart rate, steps, and sleep patterns to understand the user's health status. Furthermore, when acquiring information from cloud services, the data acquisition unit uses APIs and web scraping technology to collect data such as webmail inboxes and purchase history from e-commerce sites. As a result, the data acquisition unit can efficiently collect necessary data from a wide range of information sources and build an information infrastructure for the entire system.

[0071] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit uses data mining techniques to analyze the information. Data mining techniques are used to extract patterns and trends from the acquired data and predict user behavior and preferences. The analysis unit can also analyze the information using statistical analysis techniques. Statistical analysis techniques are used to clarify the distribution and correlation of data and to quantitatively evaluate changes in user behavior and the environment. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. Machine learning algorithms are used to build models based on the acquired data and perform predictions and classifications. For example, it can predict the most likely next action based on the user's past behavior data. The analysis unit can also analyze data in real time and provide results immediately. This allows the analysis unit to analyze the acquired information from multiple perspectives and respond quickly to user needs and environmental changes.

[0072] The advisory unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. For example, the advisory unit can provide reminders based on the user's schedule. Specifically, it can link with the user's calendar application and send reminder notifications based on the time and location of appointments. It can also provide advice based on the user's health status. It analyzes heart rate and sleep data obtained from wearable devices to advise on appropriate timing for exercise and rest. Furthermore, the advisory unit can provide advice based on weather forecasts. It obtains weather forecast data and provides advice on clothing and items to bring when going out. In this way, the advisory unit can support the user's life and provide appropriate advice in various everyday situations.

[0073] The control unit controls home appliances based on the analysis results obtained by the analysis unit. For example, the control unit adjusts the temperature of an air conditioner. Specifically, it analyzes the user's presence at home, the outside temperature, and the indoor temperature, and automatically sets the optimal temperature. The control unit can also adjust the brightness of lighting. It adjusts the brightness and color temperature of the lighting according to the user's activity level and time of day, providing a comfortable environment. Furthermore, the control unit can control the on / off of home appliances. For example, it saves energy by automatically turning off unnecessary appliances when the user goes out. In this way, the control unit can optimize the user's living environment and support a comfortable and efficient life.

[0074] The dialogue unit interacts with the user. For example, it can read aloud received emails. Specifically, it uses speech synthesis technology to read the contents of received emails aloud. The dialogue unit can also reply to received emails. Using speech recognition technology, it converts the user's voice instructions into text and creates the email reply. Furthermore, the dialogue unit can also process automated purchases on e-commerce sites. It analyzes the user's purchase history and preferences, suggests appropriate products, and completes the purchase process based on the user's instructions. In this way, the dialogue unit can efficiently process various tasks through natural dialogue with the user, contributing to an improved user experience.

[0075] The data acquisition unit can acquire information from home appliances such as air conditioners and lighting, smartphones and wearable devices, and cloud services such as webmail and e-commerce sites. For example, the data acquisition unit can acquire temperature setting information from an air conditioner. It can also acquire notification information from a smartphone. Furthermore, it can acquire sensor data from wearable devices. This allows for multifaceted support of the user's life by acquiring information from diverse sources. Some or all of the processing described above in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input air conditioner temperature setting information into an AI, which can then output the analysis results.

[0076] The analysis unit can analyze the acquired information and provide reminders and advice based on the user's schedule. For example, the analysis unit can analyze the information using data mining techniques. It can also analyze the information using statistical analysis techniques. Furthermore, it can analyze the information using machine learning algorithms. This allows for the provision of appropriate reminders and advice based on the user's schedule. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the acquired information into an AI, which can then output the analysis results.

[0077] The control unit can control home appliances based on the user's health status and the weather, thereby enabling their operation. For example, the control unit can adjust the temperature of an air conditioner. It can also adjust the brightness of lighting. Furthermore, the control unit can control the on / off function of home appliances. This enables optimal operation of home appliances according to the user's health status and the weather. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's health status and weather information into the AI, which can then output a method for controlling the home appliances.

[0078] The dialogue unit can interact with the user and perform tasks such as reading aloud and replying to received emails. For example, the dialogue unit can read aloud received emails. It can also reply to received emails. Furthermore, the dialogue unit can perform automated purchase processing on e-commerce sites. This improves user convenience by allowing interactive interaction with the user and performing tasks such as reading aloud and replying to received emails. Some or all of the above-mentioned processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the content of a received email into AI, and the AI ​​can generate a reply.

[0079] The dialogue unit can perform automated purchase processing on Amazon's e-commerce site. For example, the dialogue unit can automatically purchase products specified by the user. It can also suggest recommended products based on the user's purchase history. Furthermore, the dialogue unit can automate the purchase process, saving the user time and effort. This improves the user's purchasing experience by enabling automated purchase processing on the e-commerce site. Some or all of the above-described processes in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's purchase history into AI, which can then suggest recommended products.

[0080] The acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can delay information acquisition and acquire it when the user is relaxed. It can also refrain from acquiring information when the user is concentrating and acquire it when the user has finished a task. Furthermore, if the user is relaxed, the acquisition unit can acquire information in real time and provide it immediately. This allows for adjustment of the information acquisition timing according to the user's emotions, thereby reducing user stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input user facial expression data into an AI, which can then estimate the emotions.

[0081] The data acquisition unit can analyze the user's past behavior history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring information sources that the user has frequently accessed in the past. The data acquisition unit can also select the optimal information acquisition method for a specific time period based on the user's past behavior patterns. Furthermore, the data acquisition unit can propose the most efficient information acquisition method based on the user's past behavior history. In this way, by analyzing the user's past behavior history, the optimal information acquisition method can be provided. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past behavior history data into AI, and the AI ​​can output the optimal information acquisition method.

[0082] The data acquisition unit can filter information based on the user's current activity status and areas of interest when acquiring it. For example, if the user is working, the data acquisition unit will acquire only work-related information. Furthermore, if the user is spending time on a hobby, the data acquisition unit can prioritize acquiring information related to that hobby. Additionally, if the user is taking a break, the data acquisition unit can acquire information that promotes relaxation. By filtering information based on the user's activity status and areas of interest, the data acquisition unit can provide highly relevant information. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's current activity data into the AI, which can then output the filtered results.

[0083] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring information that helps them relax. It can also prioritize acquiring work-related information if the user is focused. Furthermore, if the user is relaxed, the data acquisition unit can prioritize acquiring information of interest. This allows the system to provide the user with optimal information by prioritizing information according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user facial expression data into an AI, which can estimate emotions and determine the priority of information.

[0084] The information acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring information. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring information related to that region. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring tourist information for their travel destination. Additionally, if the user is at home, the acquisition unit can prioritize acquiring information about nearby events. This allows the system to provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's geographical location information into the AI, which can then output highly relevant information.

[0085] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring data. For example, the data acquisition unit can acquire information related to topics the user has shown interest in on social media. The data acquisition unit can also acquire information based on the content of posts from accounts the user follows. Furthermore, the data acquisition unit can acquire information related to the activities of groups and communities the user participates in. This allows the system to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into an AI, which can then output relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the AI, which can estimate emotions and adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. It can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail according to the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into the AI, which can then adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a health-related analysis algorithm to health information. It can also apply a traffic-related analysis algorithm to traffic information. Furthermore, it can apply a weather-related analysis algorithm to weather information. By applying an analysis algorithm appropriate to the category of information, highly accurate analysis can be achieved. 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 information category data into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into an AI, which can estimate emotions and adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on when the information was acquired. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. Furthermore, the analysis unit can adjust the level of detail of the analysis according to when the information was acquired. This allows the analysis to prioritize the analysis of the most recent information by determining the priority of analysis based on when the information was acquired. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information acquisition time data into the AI, and the AI ​​can determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into the AI, and the AI ​​can adjust the order of analysis.

[0092] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is nervous, the advice unit can provide simple and easy-to-understand advice. If the user is relaxed, the advice unit can also provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide concise advice. In this way, by adjusting the way advice is expressed according to the user's emotions, it is possible to provide advice that is easy for the user to understand. 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input the user's facial expression data into AI, which can estimate emotions and adjust the way advice is expressed.

[0093] The advisory unit can adjust the level of detail of its advice based on the importance of the information it provides. For example, it can provide detailed advice for highly important information, and simplified advice for less important information. Furthermore, the advisory unit can prioritize advice according to its importance. This allows for efficient advice by adjusting the level of detail according to the importance of the information. Some or all of the above processes in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information importance data into the AI, which can then adjust the level of detail of the advice.

[0094] The advisory unit can apply different advisory algorithms depending on the category of information when providing advice. For example, the advisory unit can apply a health-related advisory algorithm to health information. It can also apply a traffic-related advisory algorithm to traffic information. Furthermore, it can apply a weather-related advisory algorithm to weather information. By applying an advisory algorithm according to the category of information, highly accurate advice can be achieved. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information category data into AI, and the AI ​​can apply an appropriate advisory algorithm.

[0095] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is excited, the advice unit can provide visually stimulating advice. By adjusting the length of the advice according to the user's emotions, the advice unit can provide the most appropriate advice for the user. 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user facial expression data into an AI, which can estimate emotions and adjust the length of the advice.

[0096] The advisory unit can determine the priority of advice based on when the information was acquired. For example, the advisory unit will prioritize advice based on the most recent information. It can also provide advice on older information as needed. Furthermore, the advisory unit can adjust the level of detail of the advice according to when the information was acquired. This allows the advisory unit to prioritize advice based on when the information was acquired, thereby prioritizing advice based on the most recent information. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input information acquisition time data into AI, and the AI ​​can determine the priority of advice.

[0097] The advisory unit can adjust the order of advice based on the relevance of the information when providing advice. For example, the advisory unit will prioritize advice on highly relevant information. It can also postpone advice on less relevant information. Furthermore, the advisory unit can adjust the level of detail of the advice according to the relevance of the information. This allows for efficient advice by adjusting the order of advice based on the relevance of the information. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input information relevance data into the AI, and the AI ​​can adjust the order of advice.

[0098] The control unit can estimate the user's emotions and adjust the control method of home appliances based on the estimated emotions. For example, if the user is relaxed, the control unit can adjust the lighting to a warmer color. It can also adjust the air conditioner temperature to a comfortable range if the user is concentrating. Furthermore, if the user is tired, the control unit can play music to help them relax. This allows for the provision of an optimal environment for the user by adjusting the control method of home appliances according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, or not. For example, the control unit can input user facial expression data into an AI, which can estimate emotions and adjust the control method of home appliances.

[0099] The control unit can analyze the user's past usage history and select the optimal control method during control. For example, the control unit can select the optimal control method based on the settings the user has previously preferred. The control unit can also select the optimal control method for a specific time period based on the user's past usage history. Furthermore, the control unit can propose the most efficient control method based on the user's past usage history. In this way, by analyzing the user's past usage history, the control unit can provide the optimal control method for home appliances. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's past usage history data into AI, and the AI ​​can output the optimal control method.

[0100] The control unit can customize the control means of the home appliances based on the user's current living situation during control. For example, the control unit can optimize the operation of the home appliances when the user is at home. It can also minimize the operation of the home appliances when the user is out. Furthermore, the control unit can switch the operation of the home appliances to silent mode when the user is sleeping. In this way, efficient control of the home appliances can be achieved by customizing the control means of the home appliances according to the user's living situation. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user living situation data into the AI, and the AI ​​can output the optimal control means.

[0101] The control unit can estimate the user's emotions and determine the control priority of home appliances based on the estimated emotions. For example, if the user is relaxed, the control unit may prioritize adjusting the lighting. It may also prioritize adjusting the air conditioner temperature if the user is focused. Furthermore, if the user is tired, the control unit may prioritize playing music. This allows for the provision of an optimal environment for the user by determining the control priority of home appliances according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using AI, or not. For example, the control unit can input user facial expression data into an AI, which can estimate emotions and determine the control priority of home appliances.

[0102] The control unit can select the optimal control method during control, taking into account the user's geographical location information. For example, if the user is at home, the control unit can optimize the operation of home appliances. Furthermore, if the user is at an office, the control unit can control home appliances in a way that is appropriate for the office environment. Additionally, if the user is traveling, the control unit can control home appliances in a way that is appropriate for the environment of the travel destination. This allows the control unit to provide the optimal home appliance control method by considering the user's geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's geographical location information into the AI, which can then output the optimal control method.

[0103] The control unit can analyze the user's social media activity during control and propose control methods for home appliances. For example, the control unit can propose control methods for home appliances based on information shared by the user on social media. The control unit can also propose control methods for home appliances based on the content of posts from accounts the user follows on social media. Furthermore, the control unit can propose control methods for home appliances based on the activities of groups and communities the user participates in on social media. In this way, by analyzing the user's social media activity, the control unit can provide the optimal control methods for home appliances. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media activity data into an AI, and the AI ​​can output the optimal control methods for home appliances.

[0104] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is expressed based on those emotions. For example, if the user is nervous, the dialogue unit can use a calm tone. If the user is relaxed, the dialogue unit can use a bright tone. Furthermore, if the user is in a hurry, the dialogue unit can use a quick and concise tone. By adjusting the way the dialogue is expressed according to the user's emotions, it is possible to provide a dialogue that is easy for the user to understand. 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 dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user facial expression data into an AI, which can estimate emotions and adjust the way the dialogue is expressed.

[0105] The dialogue unit can select the optimal dialogue method during a conversation by referring to the user's past dialogue history. For example, the dialogue unit can select the optimal dialogue method based on the dialogue style the user has preferred in the past. The dialogue unit can also select the optimal dialogue method for a specific time period based on the user's past dialogue history. Furthermore, the dialogue unit can propose the most efficient dialogue method based on the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history data into AI, and the AI ​​can output the optimal dialogue method.

[0106] The dialogue unit can customize the content of the conversation based on the user's current activity status. For example, if the user is working, the dialogue unit will provide work-related content. It can also provide content related to the user's hobby if the user is enjoying a hobby. Furthermore, if the user is taking a break, it can provide relaxing content. This allows for highly relevant conversations by customizing the content according to the user's activity status. Some or all of the above processing in the dialogue unit may be performed using AI, or without AI. For example, the dialogue unit can input the user's current activity data into the AI, which can then output the most appropriate dialogue content.

[0107] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will prioritize relaxing dialogue. It can also prioritize dialogue related to the user's work if the user is focused. Furthermore, if the user is tired, it can prioritize relaxing dialogue. This allows the system to provide the user with the most optimal dialogue by prioritizing dialogue according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input user facial expression data into an AI, which can estimate emotions and determine the priority of the dialogue.

[0108] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location information. For example, if the user is at home, the dialogue unit can provide a dialogue method suitable for the home environment. It can also provide a dialogue method suitable for the office environment if the user is at the office. Furthermore, if the user is traveling, the dialogue unit can provide a dialogue method suitable for the environment of the travel destination. This allows the dialogue unit to provide the optimal dialogue method by considering the user's geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location information into the AI, which can then output the optimal dialogue method.

[0109] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can provide dialogue content related to topics the user has shown interest in on social media. It can also provide dialogue content based on the posts of accounts the user follows. Furthermore, the dialogue unit can provide dialogue content related to the activities of groups and communities the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not. For example, the dialogue unit can input the user's social media activity data into an AI, which can then output the most appropriate dialogue content.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The GeniusMate system can also include a stress monitoring unit that estimates the user's emotions and monitors the user's stress level based on those emotions. For example, the stress monitoring unit analyzes the user's facial expression data and voice tone to estimate the stress level. Furthermore, the stress monitoring unit can acquire biometric data such as the user's heart rate and blood pressure to evaluate the stress level. In addition, the stress monitoring unit can identify the cause of stress by considering the user's schedule and task progress. This allows for real-time monitoring of the user's stress level and the implementation of appropriate countermeasures.

[0112] The GeniusMate system can also include a motivation enhancement unit that estimates the user's emotions and improves their motivation based on those emotions. For example, if the user is feeling down, the motivation enhancement unit can provide encouraging messages. It can also suggest a break to refresh the user if they are feeling tired. Furthermore, it can send messages of praise when the user achieves their goals. This allows the system to maximize user performance by improving motivation in accordance with the user's emotions.

[0113] The GeniusMate system can also include a sleep management unit that estimates the user's emotions and improves the user's sleep quality based on those emotions. For example, if the user is feeling stressed, the sleep management unit might play relaxing music. It can also suggest an optimal sleep duration if the user is feeling tired. Furthermore, the sleep management unit can analyze the user's sleep patterns and advise on areas for improvement. This allows the system to support the user's health by improving sleep quality in accordance with their emotions.

[0114] The GeniusMate system can also include a meal management unit that estimates the user's emotions and manages their meals based on those emotions. For example, if the user is feeling stressed, the meal management unit can suggest a relaxing meal. It can also suggest a nutritious meal if the user is feeling tired. Furthermore, the meal management unit can analyze the user's eating history and provide a balanced meal plan. This allows the system to support the user's health by managing their meals according to their emotions.

[0115] The GeniusMate system can also include an exercise management unit that estimates the user's emotions and manages their exercise based on those emotions. For example, if the user is feeling stressed, the exercise management unit can suggest relaxing exercises. It can also suggest light stretching if the user is feeling tired. Furthermore, the exercise management unit can analyze the user's exercise history and provide an optimal exercise plan. This allows the system to support the user's health by managing their exercise according to their emotions.

[0116] The GeniusMate system can also include a mobility management unit that optimizes user movement by considering the user's geographical location. For example, if the user is at home, the mobility management unit can suggest the optimal commute route. If the user is traveling, it can also suggest tourist attractions at their destination. Furthermore, the mobility management unit can analyze the user's travel history and suggest the most suitable mode of transportation. This allows for efficient travel by considering the user's geographical location.

[0117] The GeniusMate system can also include a recommendation unit that analyzes the user's past behavior history and makes recommendations based on the user's hobbies and interests. For example, the recommendation unit can suggest new movies based on movies the user has watched in the past. It can also suggest new restaurants based on restaurants the user has visited in the past. Furthermore, the recommendation unit can analyze the user's past purchase history and suggest related products. In this way, by analyzing the user's past behavior history, it is possible to provide recommendations based on the user's hobbies and interests.

[0118] The GeniusMate system can also include a social media analytics unit that analyzes users' social media activity and provides information based on user interests. For example, the social media analytics unit can provide relevant news based on the content of posts from accounts the user follows. It can also provide event information based on the activities of groups and communities the user participates in. Furthermore, the social media analytics unit can suggest relevant articles based on content the user shares. This allows the system to provide information based on user interests by analyzing users' social media activity.

[0119] The GeniusMate system can also include a task management unit that optimizes user task management by considering the user's current activity status. For example, if the user is working, the task management unit will prioritize managing work-related tasks. It can also suggest relaxing tasks if the user is on a break. Furthermore, the task management unit can analyze the user's schedule and suggest the optimal order of tasks. This allows for efficient task management by considering the user's current activity status.

[0120] The GeniusMate system can also include a health advice unit that analyzes the user's health data and provides health advice based on the user's health status. For example, the health advice unit can monitor the user's heart rate and blood pressure and issue an alert if an abnormality is detected. It can also analyze the user's diet and exercise history and suggest balanced lifestyle habits. Furthermore, it can analyze the user's sleep data and provide advice to improve sleep quality. In this way, the system can support the user's health by analyzing their health data.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The acquisition unit acquires information. The acquisition unit acquires information from various sources, such as home appliances like air conditioners and lighting, devices like smartphones and wearable devices, and various cloud services like webmail and e-commerce sites. For example, the acquisition unit can acquire temperature setting information from an air conditioner. It can also acquire notification information from a smartphone. Furthermore, the acquisition unit can acquire sensor data from wearable devices. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit analyzes the information using, for example, data mining techniques. The analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. Step 3: The advisory unit provides advice or reminders to the user based on the analysis results obtained by the analysis unit. For example, the advisory unit may provide reminders based on the user's schedule. It can also provide advice based on the user's health condition. Furthermore, it can provide advice based on weather forecasts. Step 4: The control unit controls the appliance based on the analysis results obtained by the analysis unit. For example, the control unit adjusts the temperature of the air conditioner. The control unit can also adjust the brightness of the lighting. Furthermore, the control unit can also control the on / off function of the appliance. Step 5: The dialogue unit interacts with the user. For example, the dialogue unit can read out incoming emails. It can also reply to incoming emails. Furthermore, the dialogue unit can process automated purchases on e-commerce sites.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] For example, the acquisition unit can acquire information using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The advice unit is implemented by the specific processing unit 290 of the data processing device 12 and provides advice and reminders to the user based on the analysis results. The control unit is implemented by the control unit 46A of the smart device 14 and controls the home appliance. The dialogue unit is implemented by the control unit 46A of the smart device 14 and interacts with the user interactively. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] For example, the acquisition unit can acquire information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The advice unit is implemented by the specific processing unit 290 of the data processing device 12 and provides advice and reminders to the user based on the analysis results. The control unit is implemented by the control unit 46A of the smart glasses 214 and controls the home appliance. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and interacts interactively with the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] For example, the acquisition unit can acquire information using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The advice unit is implemented by the specific processing unit 290 of the data processing device 12 and provides advice and reminders to the user based on the analysis results. The control unit is implemented by the control unit 46A of the headset terminal 314 and controls the home appliance. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and interacts with the user interactively. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] For example, the acquisition unit can acquire information using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information. The advice unit is implemented by the specific processing unit 290 of the data processing device 12 and provides advice and reminders to the user based on the analysis results. The control unit is implemented by the control unit 46A of the robot 414 and controls the home appliance. The dialogue unit is implemented by the control unit 46A of the robot 414 and interacts with the user interactively. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) Information acquisition unit, An analysis unit analyzes the information acquired by the acquisition unit, Based on the analysis results obtained by the aforementioned analysis unit, an advisory unit provides advice or reminders to the user. Based on the analysis results obtained by the analysis unit, a control unit controls the home appliance, It includes a dialogue unit that interacts with the user. A system characterized by the following features. (Note 2) The acquisition unit is, Information is obtained from home appliances such as air conditioners and lighting, smartphones and wearable devices, and cloud services such as webmail and e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to provide reminders and advice based on the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, Controlling home appliances based on the user's health status and weather conditions to ensure their operation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, It interacts with the user, reading out incoming emails and sending replies, among other things. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, The Amazon e-commerce site will process the purchase automatically. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past behavior history and select the optimal method for acquiring it. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving information, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advisory unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advisory unit, When providing advice, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advisory unit, When providing advice, different advisory algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advisory unit, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advisory unit, When providing advice, prioritize the advice based on when the information was obtained. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advisory unit, When providing advice, adjust the order of advice based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, It estimates the user's emotions and adjusts the control method of home appliances based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, During control, the system analyzes the user's past usage history to select the optimal control method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The control unit, During control, the control methods for home appliances are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The control unit, It estimates the user's emotions and determines the control priority of home appliances based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The control unit, During control, the optimal control method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The control unit, During control, the system analyzes the user's social media activity and proposes control methods for home appliances. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, During conversations, the content of the conversation is customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. Information acquisition unit, An analysis unit analyzes the information acquired by the acquisition unit, Based on the analysis results obtained by the aforementioned analysis unit, an advisory unit provides advice or reminders to the user. Based on the analysis results obtained by the analysis unit, a control unit controls the home appliance, It includes a dialogue unit that interacts with the user. A system characterized by the following features.

2. The acquisition unit is, Information is obtained from home appliances such as air conditioners and lighting, smartphones and wearable devices, and cloud services such as webmail and e-commerce sites. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to provide reminders and advice based on the user's schedule. The system according to feature 1.

4. The control unit, Controlling home appliances based on the user's health status and weather conditions to ensure their operation. The system according to feature 1.

5. The aforementioned dialogue unit, It interacts with the user, reading out incoming emails and sending replies, among other things. The system according to feature 1.

6. The aforementioned dialogue unit, The Amazon e-commerce site will process the purchase automatically. The system according to feature 1.

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

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

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