System

Abstract

The system according to the embodiment aims to propose an optimal smart home automation plan to a user.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects camera information. The analysis unit analyzes the camera information collected by the collection unit. The collection unit collects daily activity records. The analysis unit analyzes daily activity records. The collection unit collects floor plan information of a home. The analysis unit analyzes the floor plan information of the home. The proposal unit proposes an automation proposal suitable for the user based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not established a way to make the most of smart home devices, which means users are unable to fully enjoy their convenience.

[0005] The system according to the embodiment aims to propose optimal smart home automation plans to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects camera information. The analysis unit analyzes the camera information collected by the collection unit. The collection unit collects daily activity records. The analysis unit analyzes the daily activity records. The collection unit collects home floor plan information. The analysis unit analyzes the home floor plan information. The proposal unit proposes appropriate automation plans to the user based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal smart home automation plans to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A smart home automation system according to an embodiment of the present invention collects and analyzes camera information, daily activity records, and home floor plan information to propose optimal automation solutions to a user. The smart home automation system collects and analyzes camera information to detect family members' movements. It also collects and analyzes daily activity records to propose automation settings tailored to the user's lifestyle. It also collects and analyzes home floor plan information to propose optimal device placement and settings. For example, the smart home automation system can detect family members' movements based on camera information and automatically control lighting and air conditioning. It also analyzes daily activity records to propose automation settings tailored to the user's lifestyle. For example, if a user wakes up at 7:00 a.m. every morning, the system can automatically turn on the air conditioner to maintain a comfortable room temperature. It also utilizes home floor plan information to propose optimal device placement and settings. For example, a smart speaker can be placed in the living room and home appliances can be controlled with voice commands. This allows the smart home automation system to propose optimal automation solutions tailored to the user's lifestyle. For example, the smart home automation system can reduce the need to manually operate home appliances, resulting in a more convenient and comfortable lifestyle. The smart home automation system can also receive user feedback and improve its suggestions, allowing it to provide optimal automation solutions tailored to the user's needs.

[0029] A smart home automation system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects camera information. The camera information includes, for example, still images, videos, resolution, and collection frequency, but is not limited to these examples. The collection unit collects the camera information to detect, for example, the movements of family members. The collection unit also collects daily activity records. The daily activity records include, for example, the number of steps, distance traveled, and applications used, but are not limited to these examples. The collection unit also collects, for example, a user's daily activity records. The collection unit also collects home floor plan information. The home floor plan information includes, for example, room layout, furniture layout, and area, but is not limited to these examples. The collection unit also collects, for example, the home floor plan information. The analysis unit analyzes the camera information collected by the collection unit. For example, the analysis unit analyzes the camera information to detect the movements of family members. The analysis unit also analyzes the daily activity records collected by the collection unit. For example, the analysis unit analyzes the daily activity records to understand the user's lifestyle patterns. The analysis unit also analyzes the home floor plan information collected by the collection unit. The analysis unit, for example, analyzes the home floor plan information and proposes optimal device placement and settings. The proposal unit proposes optimal automation plans to the user based on the information analyzed by the analysis unit. The proposal unit, for example, comprehensively analyzes camera information, daily activity records, and home floor plan information and proposes automation settings that suit the user's lifestyle. As a result, the smart home automation system according to the embodiment can propose optimal automation plans that suit the user's lifestyle.

[0030] The suggestion unit includes a unit for receiving user feedback and improving the proposal content. The suggestion unit receives user feedback and improves the proposal content. The feedback includes, for example, user opinions, ratings, usage history, etc., but is not limited to these examples. The suggestion unit, for example, collects user opinions and improves the proposal content. The suggestion unit can also collect user ratings and improve the proposal content. The suggestion unit can also collect user usage history and improve the proposal content. In this way, the suggestion unit can improve the proposal content based on user feedback. In this way, it is possible to provide optimal automation suggestions that meet the user needs.

[0031] The suggestion unit includes a control unit that cooperates with the user's smart device and performs voice commands or automatic control. The suggestion unit cooperates with the user's smart device and performs voice commands or automatic control. Smart devices include, but are not limited to, smartphones, smart speakers, smart lights, etc. For example, the suggestion unit cooperates with a smartphone and accepts voice commands. The suggestion unit can also cooperate with a smart speaker and accept voice commands. The suggestion unit can also cooperate with a smart light and perform automatic control. This allows the suggestion unit to cooperate with the user's smart device and perform voice commands or automatic control. This saves the user the trouble of manually operating home appliances, allowing for a more convenient and comfortable lifestyle.

[0032] The collection unit can collect camera information to detect the movements of family members. The collection unit collects camera information to detect the movements of family members. The camera information includes, for example, still images, videos, resolution, and collection frequency, but is not limited to these examples. The collection unit collects camera information to detect movements in, for example, the living room. The collection unit can also collect camera information to detect movements in the bedroom. The collection unit can also collect camera information to detect movements in the kitchen. In this way, the collection unit can collect camera information to detect the movements of family members. This saves the user the trouble of manually operating home appliances, allowing for a more convenient and comfortable lifestyle.

[0033] The analysis unit can analyze the camera information and detect the movements of family members. The analysis unit analyzes the camera information and detects the movements of family members. Methods for detecting movements include, but are not limited to, a motion detection algorithm and a type of sensor. The analysis unit can analyze the camera information using, for example, a motion detection algorithm and detect the movements of family members. The analysis unit can also analyze the camera information using a sensor and detect the movements of family members. The analysis unit can also analyze the camera information using AI and detect the movements of family members. This allows the analysis unit to analyze the camera information and detect the movements of family members. This saves users the trouble of manually operating home appliances, allowing them to live a more convenient and comfortable life.

[0034] The collection unit can collect a daily activity record of the user. The collection unit collects the daily activity record of the user. The daily activity record includes, for example, the number of steps, the distance traveled, and the applications used, but is not limited to these examples. The collection unit, for example, collects the number of steps taken by the user. The collection unit can also collect the distance traveled by the user. The collection unit can also collect the applications used by the user. This allows the collection unit to collect the daily activity record of the user. This makes it possible to understand the user's lifestyle patterns and propose optimal automation settings.

[0035] The analysis unit can analyze the daily activity records to understand the user's lifestyle patterns. The analysis unit analyzes the daily activity records to understand the user's lifestyle patterns. Examples of information to understand the lifestyle patterns include, but are not limited to, daily routines, activity time periods, and frequency. The analysis unit can, for example, analyze the daily routines to understand the user's lifestyle patterns. The analysis unit can also analyze activity time periods to understand the user's lifestyle patterns. The analysis unit can also analyze frequency to understand the user's lifestyle patterns. This allows the analysis unit to analyze the daily activity records to understand the user's lifestyle patterns. This makes it possible to propose optimal automation settings that are tailored to the user's lifestyle patterns.

[0036] The collection unit can collect floor plan information about the home. The collection unit collects the floor plan information about the home. The floor plan information includes, for example, room layout, furniture layout, area, etc., but is not limited to these examples. The collection unit, for example, collects the room layout. The collection unit can also collect the furniture layout. The collection unit can also collect the area of ​​the room. This allows the collection unit to collect the floor plan information about the home. This makes it possible to suggest optimal device layouts and settings.

[0037] The analysis unit can analyze the floor plan information of the home and propose optimal device placement and settings. The analysis unit can analyze the floor plan information of the home and propose optimal device placement and settings. Proposing device placement and settings can include, for example, optimal placement locations and setting parameters, but is not limited to these examples. The analysis unit can, for example, propose optimal placement locations. The analysis unit can also propose setting parameters. The analysis unit can also analyze the floor plan information of the home using AI and propose optimal device placement and settings. This allows the analysis unit to analyze the floor plan information of the home and propose optimal device placement and settings. This saves users the trouble of manually operating home appliances, allowing them to live a more convenient and comfortable life.

[0038] The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The importance of information includes, but is not limited to, for example, frequency, impact, and user interest. The suggestion unit can, for example, make a detailed proposal for important information. The suggestion unit can also make a simplified proposal for information with low importance. The suggestion unit can also make a proposal with emphasis on specific information. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of each piece of information. This allows the user to receive the information they need with an appropriate level of detail, thereby realizing more effective automation proposals.

[0039] The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. Information categories include, for example, text information, image information, sensor data, etc., but are not limited to these examples. For example, the suggestion unit can apply a proposal algorithm dedicated to lighting control for lighting control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to air conditioner control for air conditioner control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to security control for security control. This allows the suggestion unit to apply different proposal algorithms depending on the category of information. This allows the user to receive suggestions that are optimal for each category, making it possible to realize more effective automation proposals.

[0040] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, suggestion history, user reactions, and success rates. The suggestion unit can improve the accuracy of the suggestion based on, for example, suggestion results that the user considered important in the past. The suggestion unit can also omit unnecessary suggestions based on suggestion results that the user ignored in the past. The suggestion unit can also adjust the suggestion algorithm by reflecting the user's past feedback. This allows the suggestion unit to improve the accuracy of the suggestion by referring to the user's past suggestion results. This allows the user to receive more accurate suggestions, and more effective automation suggestions can be realized.

[0041] The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The time when the information was generated includes, for example, a timestamp, the time when an event occurred, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes proposing recent information. The suggestion unit can also postpone proposing older information. The suggestion unit can also prioritize proposing information from a specific time period. This allows the suggestion unit to determine the priority of the suggestion based on the time when the information was generated. This allows the user to receive the latest information preferentially, and more effective automation suggestions can be realized.

[0042] The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The relevance of the information includes, for example, common keywords, related topics, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes suggesting highly relevant information. The suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also make suggestions with emphasis on specific information. This allows the suggestion unit to adjust the order of the suggestions based on the relevance of the information. This allows the user to receive highly relevant information preferentially, and more effective automation suggestions can be realized.

[0043] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the suggestion unit may use a lot of technical terms if the user has specialized knowledge. Furthermore, the suggestion unit may avoid technical terms if the user does not have specialized knowledge. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise. This allows the user to receive suggestions that are appropriate for their own level of expertise, thereby achieving more effective automation proposals.

[0044] The collection unit can learn patterns of family movement when collecting camera information and select an optimal collection method. The collection unit can learn patterns of family movement when collecting camera information and select an optimal collection method. Movement patterns include, but are not limited to, movement routes, activity times, and frequency. For example, the collection unit can learn patterns of family movement and concentrate collection of camera information during specific time periods. The collection unit can also set a low frequency of camera information collection during time periods when family movement is low. The collection unit can also collect camera information with a focus on areas where family movement is active. This allows the collection unit to learn patterns of family movement and select an optimal collection method. This allows the user to collect necessary information at the appropriate time, resulting in more effective automation.

[0045] The collection unit can filter the camera information based on a specific area or time period when collecting the camera information. The collection unit can filter the camera information based on a specific area or time period when collecting the camera information. The specific area or time period includes, but is not limited to, the location of a room or a time period. For example, the collection unit can prioritize collecting camera information from the living room and filter information from other areas. The collection unit can also collect camera information from the bedroom at night and from the living room during the day. The collection unit can also prioritize collecting camera information from areas where families gather on weekends. This allows the collection unit to filter the camera information based on a specific area or time period. This allows the user to collect the information they need at the appropriate time, thereby realizing more effective automation plans.

[0046] The collection unit can select the optimal collection means according to the user's input method when collecting camera information. The collection unit can select the optimal collection means according to the user's input method when collecting camera information. Examples of input methods include, but are not limited to, voice input, touch input, and gesture input. For example, when a user vocally commands, "Turn on the living room camera," the collection unit collects camera information based on the voice input. Furthermore, when a user operates the camera using a smartphone app, the collection unit can also collect camera information based on text input. Furthermore, when a user operates the camera using gestures, the collection unit can also collect camera information based on gesture input. This allows the collection unit to select the optimal collection means according to the user's input method. This allows the user to use the optimal collection means according to their input method, thereby realizing more effective automation solutions.

[0047] When collecting camera information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting camera information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is at home, the collection unit prioritizes collecting indoor camera information. Furthermore, when the user is out, the collection unit can prioritize collecting camera information from the entrance or garden. Furthermore, when the user is traveling, the collection unit can collect all camera information to enhance security. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation plans.

[0048] The collection unit can analyze the user's social media activities and collect related information when collecting the camera information. The collection unit can analyze the user's social media activities and collect related information when collecting the camera information. Social media activities include, but are not limited to, for example, post content, the number of likes, and comments. For example, the collection unit can prioritize collecting camera information for locations where the user checked in on social media. The collection unit can also analyze the user's social media posts and collect related camera information. The collection unit can also refer to the activities of the user's friends on social media to collect related camera information. In this way, the collection unit can analyze the user's social media activities and collect related information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation solutions.

[0049] The collection unit can customize the collection method by reflecting the user's past feedback when collecting camera information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting camera information. Past feedback includes, but is not limited to, the user's opinions, ratings, and usage history. For example, if the user has prioritized camera information for the living room in the past, the collection unit can prioritize collecting information for the living room. Furthermore, if the user has prioritized camera information for the bedroom in the past, the collection unit can prioritize collecting information for the bedroom. Furthermore, if the user has prioritized camera information for the entrance in the past, the collection unit can prioritize collecting information for the entrance. This allows the collection unit to customize the collection method by reflecting the user's past feedback. This allows the user to use the optimal collection method according to their needs, thereby realizing more effective automation plans.

[0050] When analyzing the camera information, the analysis unit can adjust the level of detail of the analysis based on the importance of the family members' movements. When analyzing the camera information, the analysis unit can adjust the level of detail of the analysis based on the importance of the family members' movements. The importance of the movements includes, but is not limited to, for example, frequency, impact, and user interest. For example, the analysis unit can perform a detailed analysis when the family members are actively moving. Furthermore, the analysis unit can also perform a simplified analysis when the family members are not moving much. Furthermore, the analysis unit can perform an analysis that focuses on the movements of specific family members. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the family members' movements. This allows the user to receive necessary information with an appropriate level of detail, thereby realizing more effective automation proposals.

[0051] The analysis unit can apply different analysis algorithms depending on the category of movement when analyzing the camera information. The analysis unit can apply different analysis algorithms depending on the category of movement when analyzing the camera information. Movement categories include, but are not limited to, walking, sitting, and exercise, for example. The analysis unit can apply a face recognition algorithm to human movement, for example. The analysis unit can also apply an animal recognition algorithm to pet movement, or an object recognition algorithm to object movement. This allows the analysis unit to apply different analysis algorithms depending on the category of movement. This allows the user to receive optimal analysis results for each category, and more effective automation solutions can be realized.

[0052] When analyzing the camera information, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. When analyzing the camera information, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, user reactions, and success rates. The analysis unit can improve the accuracy of the analysis, for example, based on analysis results that the user considered important in the past. The analysis unit can also omit unnecessary analysis based on analysis results that the user ignored in the past. The analysis unit can also adjust the analysis algorithm by reflecting the user's past feedback. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. This allows the user to receive more accurate analysis results and realize more effective automation plans.

[0053] When analyzing the camera information, the analysis unit can determine the priority of analysis based on the time when the movement occurred. When analyzing the camera information, the analysis unit can determine the priority of analysis based on the time when the movement occurred. The time when the movement occurred includes, but is not limited to, for example, a timestamp or the time when an event occurred. For example, the analysis unit can prioritize analysis of recent movement. The analysis unit can also postpone analysis of past movement. The analysis unit can also prioritize analysis of movement in a specific time period. This allows the analysis unit to determine the priority of analysis based on the time when the movement occurred. This allows the user to receive analysis results based on the most recent movement, making it possible to realize more effective automation plans.

[0054] The analysis unit can adjust the order of analysis based on the relevance of movements when analyzing camera information. The analysis unit can adjust the order of analysis based on the relevance of movements when analyzing camera information. Examples of relevance of movements include, but are not limited to, common keywords, related topics, etc. For example, the analysis unit can prioritize analysis of related family movements. Furthermore, the analysis unit can analyze related pet movements next. Furthermore, the analysis unit can analyze related object movements last. This allows the analysis unit to adjust the order of analysis based on the relevance of movements. This allows the user to receive analysis results based on highly related movements, thereby realizing more effective automation solutions.

[0055] When analyzing the camera information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. When analyzing the camera information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the analysis unit uses a lot of technical terms when the user has technical knowledge. Furthermore, the analysis unit can avoid technical terms when the user does not have technical knowledge. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to receive analysis results that correspond to their own level of expertise, thereby achieving more effective automation solutions.

[0056] The collection unit can learn the user's lifestyle patterns and select an optimal collection method when collecting daily activity records. The collection unit can learn the user's lifestyle patterns and select an optimal collection method when collecting daily activity records. Lifestyle patterns include, but are not limited to, daily routines, activity time periods, and frequencies. For example, the collection unit can learn the user's lifestyle patterns and concentrate collection of activity records during specific time periods. The collection unit can also automatically adjust the collection method when the user's lifestyle patterns change. The collection unit can also adjust the collection frequency based on the user's lifestyle patterns. This allows the collection unit to learn the user's lifestyle patterns and select an optimal collection method. This allows the user to collect necessary information at the appropriate time, thereby achieving more effective automation.

[0057] The collection unit can filter the daily activity records based on specific activities and time periods when collecting the daily activity records. The collection unit can filter the daily activity records based on specific activities and time periods when collecting the daily activity records. The specific activities and time periods include, but are not limited to, for example, types of activities and time periods. For example, the collection unit can preferentially collect activity records for time periods when the user is exercising. The collection unit can also filter activity records for time periods when the user is resting. The collection unit can also preferentially collect activity records for time periods when the user is working. This allows the collection unit to filter the daily activity records based on specific activities and time periods. This allows the user to collect necessary information at an appropriate time, thereby realizing more effective automation plans.

[0058] The collection unit can select an optimal collection means according to a user's input method when collecting a daily activity record. The collection unit selects an optimal collection means according to a user's input method when collecting a daily activity record. Examples of input methods include, but are not limited to, voice input, touch input, and gesture input. For example, when a user gives a voice command such as "Start recording today's exercise," the collection unit collects the activity record based on the voice input. Furthermore, when a user operates the activity record using a smartphone app, the collection unit can also collect the activity record based on text input. Furthermore, when a user operates the activity record using gestures, the collection unit can also collect the activity record based on gesture input. This allows the collection unit to select an optimal collection means according to the user's input method. This allows the user to use the optimal collection means according to their input method, thereby achieving more effective automation.

[0059] When collecting daily activity records, the collection unit can prioritize collecting highly relevant records by taking into account the user's geographical location information. When collecting daily activity records, the collection unit prioritizes collecting highly relevant records by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is at home, the collection unit prioritizes collecting indoor activity records. Furthermore, when the user is out, the collection unit can prioritize collecting activity records while away from home. Furthermore, when the user is traveling, the collection unit can prioritize collecting activity records while traveling. This allows the collection unit to prioritize collecting highly relevant records by taking into account the user's geographical location information. This allows the user to collect necessary information at an appropriate time, thereby realizing more effective automation plans.

[0060] The collection unit can analyze the user's social media activities and collect related records when collecting daily activity records. The collection unit can analyze the user's social media activities and collect related records when collecting daily activity records. Social media activities include, but are not limited to, post content, number of likes, comments, etc. For example, the collection unit can prioritize collecting activity records of places where the user checked in on social media. The collection unit can also analyze the user's social media posts and collect related activity records. The collection unit can also collect related activity records by referring to the activities of the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect related records. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation solutions.

[0061] The collection unit can customize the collection method by reflecting the user's past feedback when collecting the daily activity record. The collection unit customizes the collection method by reflecting the user's past feedback when collecting the daily activity record. Past feedback includes, but is not limited to, the user's opinions, ratings, and usage history, for example. For example, if the user placed importance on exercise records in the past, the collection unit can prioritize collecting exercise records. Furthermore, if the user placed importance on rest records in the past, the collection unit can prioritize collecting rest records. Furthermore, if the user placed importance on meal records in the past, the collection unit can prioritize collecting meal records. This allows the collection unit to customize the collection method by reflecting the user's past feedback. This allows the user to use the optimal collection method according to their needs, and more effective automation plans can be realized.

[0062] When analyzing the daily activity log, the analysis unit can adjust the level of detail of the analysis based on the importance of the activity. When analyzing the daily activity log, the analysis unit adjusts the level of detail of the analysis based on the importance of the activity. The importance of an activity includes, but is not limited to, for example, frequency, impact, and user interest. For example, the analysis unit performs a detailed analysis on important activities. The analysis unit can also perform a simplified analysis on less important activities. The analysis unit can also focus on specific activities during analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the activity. This allows the user to receive necessary information at an appropriate level of detail, thereby enabling more effective automation proposals to be realized.

[0063] When analyzing the daily activity record, the analysis unit can apply different analysis algorithms depending on the activity category. When analyzing the daily activity record, the analysis unit applies different analysis algorithms depending on the activity category. Activity categories include, for example, exercise, rest, and meals, but are not limited to these examples. For example, the analysis unit applies an exercise analysis algorithm to exercise records. The analysis unit can also apply a rest analysis algorithm to rest records. The analysis unit can also apply a meal analysis algorithm to meal records. This allows the analysis unit to apply different analysis algorithms depending on the activity category. This allows the user to receive analysis results that are optimal for each category, and more effective automation plans can be realized.

[0064] When analyzing the daily activity log, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. When analyzing the daily activity log, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, user reactions, and success rates. The analysis unit can improve the accuracy of the analysis, for example, based on analysis results that the user considered important in the past. The analysis unit can also omit unnecessary analysis based on analysis results that the user ignored in the past. The analysis unit can also adjust the analysis algorithm by reflecting the user's past feedback. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. This allows the user to receive more accurate analysis results and realize more effective automation plans.

[0065] When analyzing the daily activity log, the analysis unit can determine the priority of analysis based on the time when the activity occurred. When analyzing the daily activity log, the analysis unit can determine the priority of analysis based on the time when the activity occurred. The time when the activity occurred includes, but is not limited to, for example, a timestamp, the time when an event occurred, etc. For example, the analysis unit can prioritize analysis of recent activities. The analysis unit can also postpone analysis of past activities. The analysis unit can also prioritize analysis of activities in a specific time period. This allows the analysis unit to determine the priority of analysis based on the time when the activity occurred. This allows the user to receive analysis results based on the most recent activities, making it possible to realize more effective automation proposals.

[0066] The analysis unit can adjust the order of analysis based on the relevance of activities when analyzing the daily activity log. The analysis unit can adjust the order of analysis based on the relevance of activities when analyzing the daily activity log. Examples of the relevance of activities include, but are not limited to, common keywords and related topics. For example, the analysis unit can prioritize the analysis of highly relevant activities. The analysis unit can also postpone the analysis of less relevant activities. The analysis unit can also focus on specific activities during analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of activities. This allows the user to receive analysis results based on highly relevant activities, making it possible to achieve more effective automation proposals.

[0067] When analyzing the daily activity log, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. When analyzing the daily activity log, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the analysis unit uses a lot of technical terms when the user has technical expertise. Furthermore, the analysis unit can avoid technical terms when the user does not have technical expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to receive analysis results that correspond to their own level of expertise, thereby achieving more effective automation solutions.

[0068] When collecting floor plan information of the home, the collection unit can learn the frequency of use of each room and select the optimal collection method. When collecting floor plan information of the home, the collection unit can learn the frequency of use of each room and select the optimal collection method. The frequency of use includes, for example, the number of times a room is used and the time period during which it is used, but is not limited to these examples. For example, the collection unit can learn the frequency of use of each room and prioritize collecting floor plan information of rooms that are used more frequently. The collection unit can also collect floor plan information of rooms that are used less frequently at a later date. The collection unit can also adjust the collection method based on the frequency of use of a specific room. This allows the collection unit to learn the frequency of use of each room and select the optimal collection method. This allows the user to collect necessary information at the appropriate time, making it possible to realize more effective automation plans.

[0069] The collection unit can filter the home floor plan information based on a specific area or time period when collecting the home floor plan information. The collection unit can filter the home floor plan information based on a specific area or time period when collecting the home floor plan information. Examples of specific areas and time periods include, but are not limited to, the location of rooms and time period categories. For example, the collection unit can prioritize collecting floor plan information for the living room and filter information for other areas. The collection unit can also collect floor plan information for bedrooms at night and living room information during the day. The collection unit can also prioritize collecting floor plan information for areas where families gather on weekends. This allows the collection unit to filter the home floor plan information based on a specific area or time period. This allows the user to collect necessary information at an appropriate time, making it possible to realize more effective automation plans.

[0070] The collection unit can select the optimal collection means according to the user's input method when collecting home floor plan information. The collection unit selects the optimal collection means according to the user's input method when collecting home floor plan information. Input methods include, but are not limited to, voice input, touch input, and gesture input. For example, when a user vocally instructs, "Collect living room floor plan information," the collection unit collects floor plan information based on the voice input. Furthermore, when a user operates floor plan information using a smartphone app, the collection unit can also collect floor plan information based on text input. Furthermore, when a user operates floor plan information using gestures, the collection unit can also collect floor plan information based on gesture input. This allows the collection unit to select the optimal collection means according to the user's input method. This allows the user to use the optimal collection means according to their input method, thereby realizing more effective automation solutions.

[0071] When collecting floor plan information of a home, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting floor plan information of a home, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is at home, the collection unit prioritizes collecting indoor floor plan information. Furthermore, when the user is out, the collection unit can prioritize collecting floor plan information of the entrance and garden. Furthermore, when the user is traveling, the collection unit can collect all floor plan information and enhance security. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation plans.

[0072] The collection unit can analyze the user's social media activity and collect related information when collecting home floor plan information. The collection unit can analyze the user's social media activity and collect related information when collecting home floor plan information. Social media activity includes, but is not limited to, post content, number of likes, comments, and the like. For example, the collection unit prioritizes collecting floor plan information for places where the user has checked in on social media. The collection unit can also analyze the user's social media posts and collect related floor plan information. The collection unit can also collect related floor plan information by referring to the activity of the user's friends on social media. In this way, the collection unit can analyze the user's social media activity and collect related information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation solutions.

[0073] The collection unit can customize the collection method by reflecting the user's past feedback when collecting home floor plan information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting home floor plan information. Past feedback includes, but is not limited to, the user's opinions, ratings, and usage history, for example. For example, if the user placed importance on living room floor plan information in the past, the collection unit can prioritize collecting living room information. Furthermore, if the user placed importance on bedroom floor plan information in the past, the collection unit can prioritize collecting bedroom information. Furthermore, if the user placed importance on entrance floor plan information in the past, the collection unit can prioritize collecting entrance information. This allows the collection unit to customize the collection method by reflecting the user's past feedback. This allows the user to use the optimal collection method according to their needs, making it possible to realize more effective automation plans.

[0074] When analyzing the floor plan information of the home, the analysis unit can adjust the level of detail of the analysis based on the frequency of use of each room. When analyzing the floor plan information of the home, the analysis unit adjusts the level of detail of the analysis based on the frequency of use of each room. The frequency of use includes, for example, the number of times a room is used and the time period during which it is used, but is not limited to these examples. For example, the analysis unit analyzes the floor plan information of rooms that are used frequently in detail. The analysis unit can also analyze the floor plan information of rooms that are used less frequently in a simplified manner. The analysis unit can also adjust the level of detail of the analysis based on the frequency of use of a specific room. This allows the analysis unit to adjust the level of detail of the analysis based on the frequency of use of each room. This allows the user to receive the necessary information with an appropriate level of detail, making it possible to realize more effective automation plans.

[0075] When analyzing the floor plan information of a home, the analysis unit can apply different analysis algorithms depending on the room category. When analyzing the floor plan information of a home, the analysis unit applies different analysis algorithms depending on the room category. Room categories include, for example, living rooms, kitchens, bedrooms, etc., but are not limited to these examples. For example, the analysis unit applies an analysis algorithm dedicated to living rooms to living rooms. The analysis unit can also apply an analysis algorithm dedicated to bedrooms to bedrooms. The analysis unit can also apply an analysis algorithm dedicated to kitchens to kitchens. This allows the analysis unit to apply different analysis algorithms depending on the room category. This allows the user to receive analysis results that are optimal for each category, and more effective automation suggestions can be realized.

[0076] When analyzing the floor plan information of a home, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. When analyzing the floor plan information of a home, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, user reactions, and success rates. The analysis unit improves the accuracy of the analysis, for example, based on analysis results that the user considered important in the past. The analysis unit can also omit unnecessary analysis based on analysis results that the user ignored in the past. The analysis unit can also adjust the analysis algorithm by reflecting the user's past feedback. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. This allows the user to receive more accurate analysis results and realize more effective automation proposals.

[0077] When analyzing the floor plan information of a home, the analysis unit can determine the priority of analysis based on the time when the room was used. When analyzing the floor plan information of a home, the analysis unit can determine the priority of analysis based on the time when the room was used. The time when the room was used includes, but is not limited to, for example, a timestamp, the time when an event occurred, etc. For example, the analysis unit prioritizes analyzing the floor plan information of a room that was used most recently. The analysis unit can also postpone analyzing the floor plan information of a room that was used in the past. The analysis unit can also prioritize analyzing the floor plan information of a room that was used during a specific time period. This allows the analysis unit to determine the priority of analysis based on the time when the room was used. This allows the user to receive analysis results based on the most recent usage status, making it possible to realize more effective automation plans.

[0078] When analyzing the floor plan information of a home, the analysis unit can adjust the order of analysis based on the relevance of the rooms. When analyzing the floor plan information of a home, the analysis unit adjusts the order of analysis based on the relevance of the rooms. Room relevance includes, for example, common uses, related activities, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of floor plan information of highly relevant rooms. The analysis unit can also postpone analysis of floor plan information of less relevant rooms. The analysis unit can also focus analysis on specific rooms. This allows the analysis unit to adjust the order of analysis based on the relevance of the rooms. This allows the user to receive analysis results based on highly relevant rooms, and more effective automation suggestions can be realized.

[0079] When analyzing the home floor plan information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. When analyzing the home floor plan information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Expertise level includes, but is not limited to, the user's occupation, past learning history, etc. For example, the analysis unit uses a lot of technical terms if the user has expertise. The analysis unit can also avoid technical terms if the user does not have expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to receive analysis results that correspond to their own level of expertise, making it possible to realize more effective automation solutions.

[0080] The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The importance of information includes, but is not limited to, for example, frequency, impact, and user interest. The suggestion unit can, for example, make a detailed proposal for important information. The suggestion unit can also make a simplified proposal for information with low importance. The suggestion unit can also make a proposal with emphasis on specific information. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of each piece of information. This allows the user to receive the information they need with an appropriate level of detail, thereby realizing more effective automation proposals.

[0081] The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. Information categories include, for example, text information, image information, sensor data, etc., but are not limited to these examples. For example, the suggestion unit can apply a proposal algorithm dedicated to lighting control for lighting control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to air conditioner control for air conditioner control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to security control for security control. This allows the suggestion unit to apply different proposal algorithms depending on the category of information. This allows the user to receive suggestions that are optimal for each category, making it possible to realize more effective automation proposals.

[0082] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, suggestion history, user reactions, and success rates. The suggestion unit can improve the accuracy of the suggestion based on, for example, suggestion results that the user considered important in the past. The suggestion unit can also omit unnecessary suggestions based on suggestion results that the user ignored in the past. The suggestion unit can also adjust the suggestion algorithm by reflecting the user's past feedback. This allows the suggestion unit to improve the accuracy of the suggestion by referring to the user's past suggestion results. This allows the user to receive more accurate suggestions, and more effective automation suggestions can be realized.

[0083] The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The time when the information was generated includes, for example, a timestamp, the time when an event occurred, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes proposing recent information. The suggestion unit can also postpone proposing older information. The suggestion unit can also prioritize proposing information from a specific time period. This allows the suggestion unit to determine the priority of the suggestion based on the time when the information was generated. This allows the user to receive the latest information preferentially, and more effective automation suggestions can be realized.

[0084] The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The relevance of the information includes, for example, common keywords, related topics, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes suggesting highly relevant information. The suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also make suggestions with emphasis on specific information. This allows the suggestion unit to adjust the order of the suggestions based on the relevance of the information. This allows the user to receive highly relevant information preferentially, and more effective automation suggestions can be realized.

[0085] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the suggestion unit may use a lot of technical terms if the user has specialized knowledge. Furthermore, the suggestion unit may avoid technical terms if the user does not have specialized knowledge. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise. This allows the user to receive suggestions that are appropriate for their own level of expertise, thereby achieving more effective automation proposals.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The suggestion unit can determine the priority of suggestions based on the user's past behavior history. For example, if the user has accepted a specific suggestion in the past, that suggestion can be given priority. Also, if the user has rejected a specific suggestion in the past, that suggestion can be postponed. Furthermore, the suggestion unit can analyze the user's behavior history to make the most effective suggestion. This allows the suggestion unit to determine the priority of suggestions based on the user's past behavior history.

[0088] The collection unit can adjust the type of data to be collected based on the user's geographical location information. For example, when the user is at home, data within the home can be collected with priority. When the user is out, data about the user's location can be collected with priority. When the user is traveling, data about the user's location can be collected with priority. This allows the collection unit to adjust the type of data to be collected based on the user's geographical location information.

[0089] The analysis unit can adjust the analysis algorithm based on the user's past feedback. For example, if the user has evaluated a particular analysis result in the past, that algorithm can be used preferentially. Also, if the user has not evaluated a particular analysis result in the past, that algorithm can be avoided. Furthermore, the user's feedback can be analyzed to select the most effective analysis algorithm. This allows the analysis unit to adjust the analysis algorithm based on the user's past feedback.

[0090] The suggestion unit can customize the content of the suggestion according to the user's level of expertise. For example, if the user has expertise, the suggestion unit can provide a suggestion that includes detailed technical information. Alternatively, if the user does not have expertise, the suggestion unit can provide a concise and easy-to-understand suggestion. Furthermore, the suggestion unit can adjust the way the suggestion is expressed according to the user's level of expertise. This allows the suggestion unit to customize the content of the suggestion according to the user's level of expertise.

[0091] The collection unit can analyze the user's social media activities and collect related data. For example, if the user posts about a specific topic on social media, data related to that topic can be preferentially collected. Also, related data can be collected by referring to the activities of the user's friends on social media. Furthermore, the user's reactions on social media can be analyzed to collect the most relevant data. This allows the collection unit to analyze the user's social media activities and collect related data.

[0092] The processing flow of the first embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects camera information, daily activity records, and home layout information. Camera information includes still images, videos, resolution, and collection frequency, and is used to detect family movements. Daily activity records include the number of steps taken, distance traveled, and applications used, and are used to record the user's daily activities. Home layout information includes room layout, furniture layout, area, and is used to understand the home layout. Step 2: The analysis unit analyzes the camera information, daily activity records, and home layout information collected by the collection unit. It analyzes the camera information to detect family members' movements, analyzes the daily activity records to understand the user's lifestyle patterns, and analyzes the home layout information to suggest optimal device placement and settings. Step 3: The proposal unit proposes optimal automation plans to the user based on the information analyzed by the analysis unit. It comprehensively analyzes camera information, daily activity records, and home layout information to propose automation settings that suit the user's lifestyle.

[0094] (Example 2) A smart home automation system according to an embodiment of the present invention collects and analyzes camera information, daily activity records, and home floor plan information to propose optimal automation solutions to a user. The smart home automation system collects and analyzes camera information to detect family members' movements. It also collects and analyzes daily activity records to propose automation settings tailored to the user's lifestyle. It also collects and analyzes home floor plan information to propose optimal device placement and settings. For example, the smart home automation system can detect family members' movements based on camera information and automatically control lighting and air conditioning. It also analyzes daily activity records to propose automation settings tailored to the user's lifestyle. For example, if a user wakes up at 7:00 a.m. every morning, the system can automatically turn on the air conditioner to maintain a comfortable room temperature. It also utilizes home floor plan information to propose optimal device placement and settings. For example, a smart speaker can be placed in the living room and home appliances can be controlled with voice commands. This allows the smart home automation system to propose optimal automation solutions tailored to the user's lifestyle. For example, the smart home automation system can reduce the need to manually operate home appliances, resulting in a more convenient and comfortable lifestyle. The smart home automation system can also receive user feedback and improve its suggestions, allowing it to provide optimal automation solutions tailored to the user's needs.

[0095] A smart home automation system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects camera information. The camera information includes, for example, still images, videos, resolution, and collection frequency, but is not limited to these examples. The collection unit collects the camera information to detect, for example, the movements of family members. The collection unit also collects daily activity records. The daily activity records include, for example, the number of steps, distance traveled, and applications used, but are not limited to these examples. The collection unit also collects, for example, a user's daily activity records. The collection unit also collects home floor plan information. The home floor plan information includes, for example, room layout, furniture layout, and area, but is not limited to these examples. The collection unit also collects, for example, the home floor plan information. The analysis unit analyzes the camera information collected by the collection unit. For example, the analysis unit analyzes the camera information to detect the movements of family members. The analysis unit also analyzes the daily activity records collected by the collection unit. For example, the analysis unit analyzes the daily activity records to understand the user's lifestyle patterns. The analysis unit also analyzes the home floor plan information collected by the collection unit. The analysis unit, for example, analyzes the home floor plan information and proposes optimal device placement and settings. The proposal unit proposes optimal automation plans to the user based on the information analyzed by the analysis unit. The proposal unit, for example, comprehensively analyzes camera information, daily activity records, and home floor plan information and proposes automation settings that suit the user's lifestyle. As a result, the smart home automation system according to the embodiment can propose optimal automation plans that suit the user's lifestyle.

[0096] The suggestion unit includes a unit for receiving user feedback and improving the proposal content. The suggestion unit receives user feedback and improves the proposal content. The feedback includes, for example, user opinions, ratings, usage history, etc., but is not limited to these examples. The suggestion unit, for example, collects user opinions and improves the proposal content. The suggestion unit can also collect user ratings and improve the proposal content. The suggestion unit can also collect user usage history and improve the proposal content. In this way, the suggestion unit can improve the proposal content based on user feedback. In this way, it is possible to provide optimal automation suggestions that meet the user needs.

[0097] The suggestion unit includes a control unit that cooperates with the user's smart device and performs voice commands or automatic control. The suggestion unit cooperates with the user's smart device and performs voice commands or automatic control. Smart devices include, but are not limited to, smartphones, smart speakers, smart lights, etc. For example, the suggestion unit cooperates with a smartphone and accepts voice commands. The suggestion unit can also cooperate with a smart speaker and accept voice commands. The suggestion unit can also cooperate with a smart light and perform automatic control. This allows the suggestion unit to cooperate with the user's smart device and perform voice commands or automatic control. This saves the user the trouble of manually operating home appliances, allowing for a more convenient and comfortable lifestyle.

[0098] The collection unit can collect camera information to detect the movements of family members. The collection unit collects camera information to detect the movements of family members. The camera information includes, for example, still images, videos, resolution, and collection frequency, but is not limited to these examples. The collection unit collects camera information to detect movements in, for example, the living room. The collection unit can also collect camera information to detect movements in the bedroom. The collection unit can also collect camera information to detect movements in the kitchen. In this way, the collection unit can collect camera information to detect the movements of family members. This saves the user the trouble of manually operating home appliances, allowing for a more convenient and comfortable lifestyle.

[0099] The analysis unit can analyze the camera information and detect the movements of family members. The analysis unit analyzes the camera information and detects the movements of family members. Methods for detecting movements include, but are not limited to, a motion detection algorithm and a type of sensor. The analysis unit can analyze the camera information using, for example, a motion detection algorithm and detect the movements of family members. The analysis unit can also analyze the camera information using a sensor and detect the movements of family members. The analysis unit can also analyze the camera information using AI and detect the movements of family members. This allows the analysis unit to analyze the camera information and detect the movements of family members. This saves users the trouble of manually operating home appliances, allowing them to live a more convenient and comfortable life.

[0100] The collection unit can collect a daily activity record of the user. The collection unit collects the daily activity record of the user. The daily activity record includes, for example, the number of steps, the distance traveled, and the applications used, but is not limited to these examples. The collection unit, for example, collects the number of steps taken by the user. The collection unit can also collect the distance traveled by the user. The collection unit can also collect the applications used by the user. This allows the collection unit to collect the daily activity record of the user. This makes it possible to understand the user's lifestyle patterns and propose optimal automation settings.

[0101] The analysis unit can analyze the daily activity records to understand the user's lifestyle patterns. The analysis unit analyzes the daily activity records to understand the user's lifestyle patterns. Examples of information to understand the lifestyle patterns include, but are not limited to, daily routines, activity time periods, and frequency. The analysis unit can, for example, analyze the daily routines to understand the user's lifestyle patterns. The analysis unit can also analyze activity time periods to understand the user's lifestyle patterns. The analysis unit can also analyze frequency to understand the user's lifestyle patterns. This allows the analysis unit to analyze the daily activity records to understand the user's lifestyle patterns. This makes it possible to propose optimal automation settings that are tailored to the user's lifestyle patterns.

[0102] The collection unit can collect floor plan information about the home. The collection unit collects the floor plan information about the home. The floor plan information includes, for example, room layout, furniture layout, area, etc., but is not limited to these examples. The collection unit, for example, collects the room layout. The collection unit can also collect the furniture layout. The collection unit can also collect the area of ​​the room. This allows the collection unit to collect the floor plan information about the home. This makes it possible to suggest optimal device layouts and settings.

[0103] The analysis unit can analyze the floor plan information of the home and propose optimal device placement and settings. The analysis unit can analyze the floor plan information of the home and propose optimal device placement and settings. Proposing device placement and settings can include, for example, optimal placement locations and setting parameters, but is not limited to these examples. The analysis unit can, for example, propose optimal placement locations. The analysis unit can also propose setting parameters. The analysis unit can also analyze the floor plan information of the home using AI and propose optimal device placement and settings. This allows the analysis unit to analyze the floor plan information of the home and propose optimal device placement and settings. This saves users the trouble of manually operating home appliances, allowing them to live a more convenient and comfortable life.

[0104] The suggestion unit includes a unit that estimates a user's emotion and adjusts the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion and adjusts the way in which suggestions are expressed based on the estimated user's emotion. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The suggestion unit estimates the user's emotion using, for example, facial expression recognition. The suggestion unit can also estimate the user's emotion using voice analysis. The suggestion unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the suggestion unit to adjust the way in which suggestions are expressed based on the user's emotion. For example, if the user is relaxed, the suggestion content can be displayed in a visually easy-to-understand manner. If the user is stressed, the suggestion content can be displayed in a concise manner. If the user is excited, the suggestion content can be displayed in detail. This allows the way in which suggestions are expressed to be adjusted based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the way suggestions are presented to be tailored based on the user's feelings.

[0105] The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The importance of information includes, but is not limited to, for example, frequency, impact, and user interest. The suggestion unit can, for example, make a detailed proposal for important information. The suggestion unit can also make a simplified proposal for information with low importance. The suggestion unit can also make a proposal with emphasis on specific information. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of each piece of information. This allows the user to receive the information they need with an appropriate level of detail, thereby realizing more effective automation proposals.

[0106] The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. Information categories include, for example, text information, image information, sensor data, etc., but are not limited to these examples. For example, the suggestion unit can apply a proposal algorithm dedicated to lighting control for lighting control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to air conditioner control for air conditioner control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to security control for security control. This allows the suggestion unit to apply different proposal algorithms depending on the category of information. This allows the user to receive suggestions that are optimal for each category, making it possible to realize more effective automation proposals.

[0107] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, suggestion history, user reactions, and success rates. The suggestion unit can improve the accuracy of the suggestion based on, for example, suggestion results that the user considered important in the past. The suggestion unit can also omit unnecessary suggestions based on suggestion results that the user ignored in the past. The suggestion unit can also adjust the suggestion algorithm by reflecting the user's past feedback. This allows the suggestion unit to improve the accuracy of the suggestion by referring to the user's past suggestion results. This allows the user to receive more accurate suggestions, and more effective automation suggestions can be realized.

[0108] The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The time when the information was generated includes, for example, a timestamp, the time when an event occurred, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes proposing recent information. The suggestion unit can also postpone proposing older information. The suggestion unit can also prioritize proposing information from a specific time period. This allows the suggestion unit to determine the priority of the suggestion based on the time when the information was generated. This allows the user to receive the latest information preferentially, and more effective automation suggestions can be realized.

[0109] The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The relevance of the information includes, for example, common keywords, related topics, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes suggesting highly relevant information. The suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also make suggestions with emphasis on specific information. This allows the suggestion unit to adjust the order of the suggestions based on the relevance of the information. This allows the user to receive highly relevant information preferentially, and more effective automation suggestions can be realized.

[0110] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the suggestion unit may use a lot of technical terms if the user has specialized knowledge. Furthermore, the suggestion unit may avoid technical terms if the user does not have specialized knowledge. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise. This allows the user to receive suggestions that are appropriate for their own level of expertise, thereby achieving more effective automation proposals.

[0111] The collection unit can estimate the user's emotions and adjust the timing of collecting camera information based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting camera information based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The collection unit can estimate the user's emotions using, for example, facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows the collection unit to adjust the timing of collecting camera information based on the user's emotions. For example, if the user is relaxed, the frequency of collecting camera information can be set low to prioritize privacy. Also, if the user is stressed, the frequency of collecting camera information can be set high to detect abnormalities early. Also, if the user is absent, the collection of camera information can be stopped to reduce energy consumption. This allows the collection unit to adjust the timing of collecting camera information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI, which can adjust the timing of camera information collection based on the user's emotions.

[0112] The collection unit can learn patterns of family movement when collecting camera information and select an optimal collection method. The collection unit can learn patterns of family movement when collecting camera information and select an optimal collection method. Movement patterns include, but are not limited to, movement routes, activity times, and frequency. For example, the collection unit can learn patterns of family movement and concentrate collection of camera information during specific time periods. The collection unit can also set a low frequency of camera information collection during time periods when family movement is low. The collection unit can also collect camera information with a focus on areas where family movement is active. This allows the collection unit to learn patterns of family movement and select an optimal collection method. This allows the user to collect necessary information at the appropriate time, resulting in more effective automation.

[0113] The collection unit can filter the camera information based on a specific area or time period when collecting the camera information. The collection unit can filter the camera information based on a specific area or time period when collecting the camera information. The specific area or time period includes, but is not limited to, the location of a room or a time period. For example, the collection unit can prioritize collecting camera information from the living room and filter information from other areas. The collection unit can also collect camera information from the bedroom at night and from the living room during the day. The collection unit can also prioritize collecting camera information from areas where families gather on weekends. This allows the collection unit to filter the camera information based on a specific area or time period. This allows the user to collect the information they need at the appropriate time, thereby realizing more effective automation plans.

[0114] The collection unit can select the optimal collection means according to the user's input method when collecting camera information. The collection unit can select the optimal collection means according to the user's input method when collecting camera information. Examples of input methods include, but are not limited to, voice input, touch input, and gesture input. For example, when a user vocally commands, "Turn on the living room camera," the collection unit collects camera information based on the voice input. Furthermore, when a user operates the camera using a smartphone app, the collection unit can also collect camera information based on text input. Furthermore, when a user operates the camera using gestures, the collection unit can also collect camera information based on gesture input. This allows the collection unit to select the optimal collection means according to the user's input method. This allows the user to use the optimal collection means according to their input method, thereby realizing more effective automation solutions.

[0115] The collection unit can estimate the user's emotions and determine the priority of the camera information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of the camera information to be collected based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The collection unit can estimate the user's emotions using, for example, facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows the collection unit to determine the priority of the camera information to be collected based on the user's emotions. For example, if the user is relaxed, camera information from the living room can be collected preferentially. Also, if the user is stressed, camera information from the bedroom can be collected preferentially. Also, if the user is absent, camera information from the entrance can be collected preferentially. This allows the collection unit to determine the priority of the camera information to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI, which can prioritize camera information to be collected based on the user's emotions.

[0116] When collecting camera information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting camera information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is at home, the collection unit prioritizes collecting indoor camera information. Furthermore, when the user is out, the collection unit can prioritize collecting camera information from the entrance or garden. Furthermore, when the user is traveling, the collection unit can collect all camera information to enhance security. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation plans.

[0117] The collection unit can analyze the user's social media activities and collect related information when collecting the camera information. The collection unit can analyze the user's social media activities and collect related information when collecting the camera information. Social media activities include, but are not limited to, for example, post content, the number of likes, and comments. For example, the collection unit can prioritize collecting camera information for locations where the user checked in on social media. The collection unit can also analyze the user's social media posts and collect related camera information. The collection unit can also refer to the activities of the user's friends on social media to collect related camera information. In this way, the collection unit can analyze the user's social media activities and collect related information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation solutions.

[0118] The collection unit can customize the collection method by reflecting the user's past feedback when collecting camera information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting camera information. Past feedback includes, but is not limited to, the user's opinions, ratings, and usage history. For example, if the user has prioritized camera information for the living room in the past, the collection unit can prioritize collecting information for the living room. Furthermore, if the user has prioritized camera information for the bedroom in the past, the collection unit can prioritize collecting information for the bedroom. Furthermore, if the user has prioritized camera information for the entrance in the past, the collection unit can prioritize collecting information for the entrance. This allows the collection unit to customize the collection method by reflecting the user's past feedback. This allows the user to use the optimal collection method according to their needs, thereby realizing more effective automation plans.

[0119] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotion. For example, if the user is relaxed, the analysis results can be displayed visually in an easy-to-understand manner. If the user is stressed, the analysis results can be displayed concisely. If the user is excited, the analysis results can be displayed in detail. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotion. Emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text-generative AI (e.g., LLM) or a multimodal generative AI, which can adjust the way the analysis is presented based on the user's sentiment.

[0120] When analyzing the camera information, the analysis unit can adjust the level of detail of the analysis based on the importance of the family members' movements. When analyzing the camera information, the analysis unit can adjust the level of detail of the analysis based on the importance of the family members' movements. The importance of the movements includes, but is not limited to, for example, frequency, impact, and user interest. For example, the analysis unit can perform a detailed analysis when the family members are actively moving. Furthermore, the analysis unit can also perform a simplified analysis when the family members are not moving much. Furthermore, the analysis unit can perform an analysis that focuses on the movements of specific family members. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the family members' movements. This allows the user to receive necessary information with an appropriate level of detail, thereby realizing more effective automation proposals.

[0121] The analysis unit can apply different analysis algorithms depending on the category of movement when analyzing the camera information. The analysis unit can apply different analysis algorithms depending on the category of movement when analyzing the camera information. Movement categories include, but are not limited to, walking, sitting, and exercise, for example. The analysis unit can apply a face recognition algorithm to human movement, for example. The analysis unit can also apply an animal recognition algorithm to pet movement, or an object recognition algorithm to object movement. This allows the analysis unit to apply different analysis algorithms depending on the category of movement. This allows the user to receive optimal analysis results for each category, and more effective automation solutions can be realized.

[0122] When analyzing the camera information, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. When analyzing the camera information, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, user reactions, and success rates. The analysis unit can improve the accuracy of the analysis, for example, based on analysis results that the user considered important in the past. The analysis unit can also omit unnecessary analysis based on analysis results that the user ignored in the past. The analysis unit can also adjust the analysis algorithm by reflecting the user's past feedback. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. This allows the user to receive more accurate analysis results and realize more effective automation plans.

[0123] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. For example, if the user is relaxed, a detailed analysis can be performed. Also, if the user is stressed, a brief analysis can be performed. Also, if the user is excited, a detailed analysis can be performed. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the analysis to be adjusted based on the user's emotion.

[0124] When analyzing the camera information, the analysis unit can determine the priority of analysis based on the time when the movement occurred. When analyzing the camera information, the analysis unit can determine the priority of analysis based on the time when the movement occurred. The time when the movement occurred includes, but is not limited to, for example, a timestamp or the time when an event occurred. For example, the analysis unit can prioritize analysis of recent movement. The analysis unit can also postpone analysis of past movement. The analysis unit can also prioritize analysis of movement in a specific time period. This allows the analysis unit to determine the priority of analysis based on the time when the movement occurred. This allows the user to receive analysis results based on the most recent movement, making it possible to realize more effective automation plans.

[0125] The analysis unit can adjust the order of analysis based on the relevance of movements when analyzing camera information. The analysis unit can adjust the order of analysis based on the relevance of movements when analyzing camera information. Examples of relevance of movements include, but are not limited to, common keywords, related topics, etc. For example, the analysis unit can prioritize analysis of related family movements. Furthermore, the analysis unit can analyze related pet movements next. Furthermore, the analysis unit can analyze related object movements last. This allows the analysis unit to adjust the order of analysis based on the relevance of movements. This allows the user to receive analysis results based on highly related movements, thereby realizing more effective automation solutions.

[0126] When analyzing the camera information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. When analyzing the camera information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the analysis unit uses a lot of technical terms when the user has technical knowledge. Furthermore, the analysis unit can avoid technical terms when the user does not have technical knowledge. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to receive analysis results that correspond to their own level of expertise, thereby achieving more effective automation solutions.

[0127] The collection unit can estimate the user's emotions and adjust the timing of collecting activity records based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting activity records based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The collection unit can estimate the user's emotions using, for example, facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows the collection unit to adjust the timing of collecting activity records based on the user's emotions. For example, if the user is relaxed, the frequency of collecting activity records can be set low. Also, if the user is stressed, the frequency of collecting activity records can be set high. Also, if the user is absent, the collection of activity records can be stopped. This allows the collection unit to adjust the timing of collecting activity records based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI, which allows the timing of activity record collection to be adjusted based on the user's emotions.

[0128] The collection unit can learn the user's lifestyle patterns and select an optimal collection method when collecting daily activity records. The collection unit can learn the user's lifestyle patterns and select an optimal collection method when collecting daily activity records. Lifestyle patterns include, but are not limited to, daily routines, activity time periods, and frequencies. For example, the collection unit can learn the user's lifestyle patterns and concentrate collection of activity records during specific time periods. The collection unit can also automatically adjust the collection method when the user's lifestyle patterns change. The collection unit can also adjust the collection frequency based on the user's lifestyle patterns. This allows the collection unit to learn the user's lifestyle patterns and select an optimal collection method. This allows the user to collect necessary information at the appropriate time, thereby achieving more effective automation.

[0129] The collection unit can filter the daily activity records based on specific activities and time periods when collecting the daily activity records. The collection unit can filter the daily activity records based on specific activities and time periods when collecting the daily activity records. The specific activities and time periods include, but are not limited to, for example, types of activities and time periods. For example, the collection unit can preferentially collect activity records for time periods when the user is exercising. The collection unit can also filter activity records for time periods when the user is resting. The collection unit can also preferentially collect activity records for time periods when the user is working. This allows the collection unit to filter the daily activity records based on specific activities and time periods. This allows the user to collect necessary information at an appropriate time, thereby realizing more effective automation plans.

[0130] The collection unit can select an optimal collection means according to a user's input method when collecting a daily activity record. The collection unit selects an optimal collection means according to a user's input method when collecting a daily activity record. Examples of input methods include, but are not limited to, voice input, touch input, and gesture input. For example, when a user gives a voice command such as "Start recording today's exercise," the collection unit collects the activity record based on the voice input. Furthermore, when a user operates the activity record using a smartphone app, the collection unit can also collect the activity record based on text input. Furthermore, when a user operates the activity record using gestures, the collection unit can also collect the activity record based on gesture input. This allows the collection unit to select an optimal collection means according to the user's input method. This allows the user to use the optimal collection means according to their input method, thereby achieving more effective automation.

[0131] The collection unit can estimate the user's emotions and determine the priority of activity records to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of activity records to be collected based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The collection unit can estimate the user's emotions using, for example, facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows the collection unit to determine the priority of activity records to be collected based on the user's emotions. For example, if the user is relaxed, exercise records can be collected with priority. Also, if the user is stressed, rest records can be collected with priority. Also, if the user is absent, collection of activity records can be stopped. This allows the collection unit to determine the priority of activity records to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI, which can determine the priority of activity records to be collected based on the user's emotions.

[0132] When collecting daily activity records, the collection unit can prioritize collecting highly relevant records by taking into account the user's geographical location information. When collecting daily activity records, the collection unit prioritizes collecting highly relevant records by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is at home, the collection unit prioritizes collecting indoor activity records. Furthermore, when the user is out, the collection unit can prioritize collecting activity records while away from home. Furthermore, when the user is traveling, the collection unit can prioritize collecting activity records while traveling. This allows the collection unit to prioritize collecting highly relevant records by taking into account the user's geographical location information. This allows the user to collect necessary information at an appropriate time, thereby realizing more effective automation plans.

[0133] The collection unit can analyze the user's social media activities and collect related records when collecting daily activity records. The collection unit can analyze the user's social media activities and collect related records when collecting daily activity records. Social media activities include, but are not limited to, post content, number of likes, comments, etc. For example, the collection unit can prioritize collecting activity records of places where the user checked in on social media. The collection unit can also analyze the user's social media posts and collect related activity records. The collection unit can also collect related activity records by referring to the activities of the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect related records. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation solutions.

[0134] The collection unit can customize the collection method by reflecting the user's past feedback when collecting the daily activity record. The collection unit customizes the collection method by reflecting the user's past feedback when collecting the daily activity record. Past feedback includes, but is not limited to, the user's opinions, ratings, and usage history, for example. For example, if the user placed importance on exercise records in the past, the collection unit can prioritize collecting exercise records. Furthermore, if the user placed importance on rest records in the past, the collection unit can prioritize collecting rest records. Furthermore, if the user placed importance on meal records in the past, the collection unit can prioritize collecting meal records. This allows the collection unit to customize the collection method by reflecting the user's past feedback. This allows the user to use the optimal collection method according to their needs, and more effective automation plans can be realized.

[0135] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotion. For example, if the user is relaxed, the analysis results can be displayed visually in an easy-to-understand manner. If the user is stressed, the analysis results can be displayed concisely. If the user is excited, the analysis results can be displayed in detail. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotion. Emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text-generative AI (e.g., LLM) or a multimodal generative AI, which can adjust the way the analysis is presented based on the user's sentiment.

[0136] When analyzing the daily activity log, the analysis unit can adjust the level of detail of the analysis based on the importance of the activity. When analyzing the daily activity log, the analysis unit adjusts the level of detail of the analysis based on the importance of the activity. The importance of an activity includes, but is not limited to, for example, frequency, impact, and user interest. For example, the analysis unit performs a detailed analysis on important activities. The analysis unit can also perform a simplified analysis on less important activities. The analysis unit can also focus on specific activities during analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the activity. This allows the user to receive necessary information at an appropriate level of detail, thereby enabling more effective automation proposals to be realized.

[0137] When analyzing the daily activity record, the analysis unit can apply different analysis algorithms depending on the activity category. When analyzing the daily activity record, the analysis unit applies different analysis algorithms depending on the activity category. Activity categories include, for example, exercise, rest, and meals, but are not limited to these examples. For example, the analysis unit applies an exercise analysis algorithm to exercise records. The analysis unit can also apply a rest analysis algorithm to rest records. The analysis unit can also apply a meal analysis algorithm to meal records. This allows the analysis unit to apply different analysis algorithms depending on the activity category. This allows the user to receive analysis results that are optimal for each category, and more effective automation plans can be realized.

[0138] When analyzing the daily activity log, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. When analyzing the daily activity log, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, user reactions, and success rates. The analysis unit can improve the accuracy of the analysis, for example, based on analysis results that the user considered important in the past. The analysis unit can also omit unnecessary analysis based on analysis results that the user ignored in the past. The analysis unit can also adjust the analysis algorithm by reflecting the user's past feedback. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. This allows the user to receive more accurate analysis results and realize more effective automation plans.

[0139] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. For example, if the user is relaxed, a detailed analysis can be performed. Also, if the user is stressed, a brief analysis can be performed. Also, if the user is excited, a detailed analysis can be performed. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the analysis to be adjusted based on the user's emotion.

[0140] When analyzing the daily activity log, the analysis unit can determine the priority of analysis based on the time when the activity occurred. When analyzing the daily activity log, the analysis unit can determine the priority of analysis based on the time when the activity occurred. The time when the activity occurred includes, but is not limited to, for example, a timestamp, the time when an event occurred, etc. For example, the analysis unit can prioritize analysis of recent activities. The analysis unit can also postpone analysis of past activities. The analysis unit can also prioritize analysis of activities in a specific time period. This allows the analysis unit to determine the priority of analysis based on the time when the activity occurred. This allows the user to receive analysis results based on the most recent activities, making it possible to realize more effective automation proposals.

[0141] The analysis unit can adjust the order of analysis based on the relevance of activities when analyzing the daily activity log. The analysis unit can adjust the order of analysis based on the relevance of activities when analyzing the daily activity log. Examples of the relevance of activities include, but are not limited to, common keywords and related topics. For example, the analysis unit can prioritize the analysis of highly relevant activities. The analysis unit can also postpone the analysis of less relevant activities. The analysis unit can also focus on specific activities during analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of activities. This allows the user to receive analysis results based on highly relevant activities, making it possible to achieve more effective automation proposals.

[0142] When analyzing the daily activity log, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. When analyzing the daily activity log, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the analysis unit uses a lot of technical terms when the user has technical expertise. Furthermore, the analysis unit can avoid technical terms when the user does not have technical expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to receive analysis results that correspond to their own level of expertise, thereby achieving more effective automation solutions.

[0143] The collection unit can estimate the user's emotions and adjust the timing of collecting floor plan information based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting floor plan information based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The collection unit can estimate the user's emotions using, for example, facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows the collection unit to adjust the timing of collecting floor plan information based on the user's emotions. For example, if the user is relaxed, the frequency of collecting floor plan information can be set low. Also, if the user is stressed, the frequency of collecting floor plan information can be set high. Also, if the user is absent, the collection of floor plan information can be stopped. This allows the collection unit to adjust the timing of collecting floor plan information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI, which allows the timing of collecting floor plan information to be adjusted based on the user's emotions.

[0144] When collecting floor plan information of the home, the collection unit can learn the frequency of use of each room and select the optimal collection method. When collecting floor plan information of the home, the collection unit can learn the frequency of use of each room and select the optimal collection method. The frequency of use includes, for example, the number of times a room is used and the time period during which it is used, but is not limited to these examples. For example, the collection unit can learn the frequency of use of each room and prioritize collecting floor plan information of rooms that are used more frequently. The collection unit can also collect floor plan information of rooms that are used less frequently at a later date. The collection unit can also adjust the collection method based on the frequency of use of a specific room. This allows the collection unit to learn the frequency of use of each room and select the optimal collection method. This allows the user to collect necessary information at the appropriate time, making it possible to realize more effective automation plans.

[0145] The collection unit can filter the home floor plan information based on a specific area or time period when collecting the home floor plan information. The collection unit can filter the home floor plan information based on a specific area or time period when collecting the home floor plan information. Examples of specific areas and time periods include, but are not limited to, the location of rooms and time period categories. For example, the collection unit can prioritize collecting floor plan information for the living room and filter information for other areas. The collection unit can also collect floor plan information for bedrooms at night and living room information during the day. The collection unit can also prioritize collecting floor plan information for areas where families gather on weekends. This allows the collection unit to filter the home floor plan information based on a specific area or time period. This allows the user to collect necessary information at an appropriate time, making it possible to realize more effective automation plans.

[0146] The collection unit can select the optimal collection means according to the user's input method when collecting home floor plan information. The collection unit selects the optimal collection means according to the user's input method when collecting home floor plan information. Input methods include, but are not limited to, voice input, touch input, and gesture input. For example, when a user vocally instructs, "Collect living room floor plan information," the collection unit collects floor plan information based on the voice input. Furthermore, when a user operates floor plan information using a smartphone app, the collection unit can also collect floor plan information based on text input. Furthermore, when a user operates floor plan information using gestures, the collection unit can also collect floor plan information based on gesture input. This allows the collection unit to select the optimal collection means according to the user's input method. This allows the user to use the optimal collection means according to their input method, thereby realizing more effective automation solutions.

[0147] The collection unit can estimate the user's emotions and determine the priority of floor plan information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of floor plan information to be collected based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The collection unit can estimate the user's emotions using, for example, facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows the collection unit to determine the priority of floor plan information to be collected based on the user's emotions. For example, if the user is relaxed, floor plan information for the living room can be collected preferentially. Also, if the user is stressed, floor plan information for the bedroom can be collected preferentially. Also, if the user is absent, floor plan information for the entrance can be collected preferentially. This allows the collection unit to determine the priority of floor plan information to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI, which allows the prioritization of floor plan information to be collected based on the user's emotions.

[0148] When collecting floor plan information of a home, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting floor plan information of a home, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is at home, the collection unit prioritizes collecting indoor floor plan information. Furthermore, when the user is out, the collection unit can prioritize collecting floor plan information of the entrance and garden. Furthermore, when the user is traveling, the collection unit can collect all floor plan information and enhance security. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation plans.

[0149] The collection unit can analyze the user's social media activity and collect related information when collecting home floor plan information. The collection unit can analyze the user's social media activity and collect related information when collecting home floor plan information. Social media activity includes, but is not limited to, post content, number of likes, comments, and the like. For example, the collection unit prioritizes collecting floor plan information for places where the user has checked in on social media. The collection unit can also analyze the user's social media posts and collect related floor plan information. The collection unit can also collect related floor plan information by referring to the activity of the user's friends on social media. In this way, the collection unit can analyze the user's social media activity and collect related information. This allows the user to collect necessary information at the appropriate time, thereby realizing more effective automation solutions.

[0150] The collection unit can customize the collection method by reflecting the user's past feedback when collecting home floor plan information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting home floor plan information. Past feedback includes, but is not limited to, the user's opinions, ratings, and usage history, for example. For example, if the user placed importance on living room floor plan information in the past, the collection unit can prioritize collecting living room information. Furthermore, if the user placed importance on bedroom floor plan information in the past, the collection unit can prioritize collecting bedroom information. Furthermore, if the user placed importance on entrance floor plan information in the past, the collection unit can prioritize collecting entrance information. This allows the collection unit to customize the collection method by reflecting the user's past feedback. This allows the user to use the optimal collection method according to their needs, making it possible to realize more effective automation plans.

[0151] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotion. For example, if the user is relaxed, the analysis results can be displayed visually in an easy-to-understand manner. If the user is stressed, the analysis results can be displayed concisely. If the user is excited, the analysis results can be displayed in detail. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotion. Emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text-generative AI (e.g., LLM) or a multimodal generative AI, which can adjust the way the analysis is presented based on the user's sentiment.

[0152] When analyzing the floor plan information of the home, the analysis unit can adjust the level of detail of the analysis based on the frequency of use of each room. When analyzing the floor plan information of the home, the analysis unit adjusts the level of detail of the analysis based on the frequency of use of each room. The frequency of use includes, for example, the number of times a room is used and the time period during which it is used, but is not limited to these examples. For example, the analysis unit analyzes the floor plan information of rooms that are used frequently in detail. The analysis unit can also analyze the floor plan information of rooms that are used less frequently in a simplified manner. The analysis unit can also adjust the level of detail of the analysis based on the frequency of use of a specific room. This allows the analysis unit to adjust the level of detail of the analysis based on the frequency of use of each room. This allows the user to receive the necessary information with an appropriate level of detail, making it possible to realize more effective automation plans.

[0153] When analyzing the floor plan information of a home, the analysis unit can apply different analysis algorithms depending on the room category. When analyzing the floor plan information of a home, the analysis unit applies different analysis algorithms depending on the room category. Room categories include, for example, living rooms, kitchens, bedrooms, etc., but are not limited to these examples. For example, the analysis unit applies an analysis algorithm dedicated to living rooms to living rooms. The analysis unit can also apply an analysis algorithm dedicated to bedrooms to bedrooms. The analysis unit can also apply an analysis algorithm dedicated to kitchens to kitchens. This allows the analysis unit to apply different analysis algorithms depending on the room category. This allows the user to receive analysis results that are optimal for each category, and more effective automation suggestions can be realized.

[0154] When analyzing the floor plan information of a home, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. When analyzing the floor plan information of a home, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, user reactions, and success rates. The analysis unit improves the accuracy of the analysis, for example, based on analysis results that the user considered important in the past. The analysis unit can also omit unnecessary analysis based on analysis results that the user ignored in the past. The analysis unit can also adjust the analysis algorithm by reflecting the user's past feedback. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results. This allows the user to receive more accurate analysis results and realize more effective automation proposals.

[0155] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. For example, if the user is relaxed, a detailed analysis can be performed. Also, if the user is stressed, a brief analysis can be performed. Also, if the user is excited, a detailed analysis can be performed. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the analysis to be adjusted based on the user's emotion.

[0156] When analyzing the floor plan information of a home, the analysis unit can determine the priority of analysis based on the time when the room was used. When analyzing the floor plan information of a home, the analysis unit can determine the priority of analysis based on the time when the room was used. The time when the room was used includes, but is not limited to, for example, a timestamp, the time when an event occurred, etc. For example, the analysis unit prioritizes analyzing the floor plan information of a room that was used most recently. The analysis unit can also postpone analyzing the floor plan information of a room that was used in the past. The analysis unit can also prioritize analyzing the floor plan information of a room that was used during a specific time period. This allows the analysis unit to determine the priority of analysis based on the time when the room was used. This allows the user to receive analysis results based on the most recent usage status, making it possible to realize more effective automation plans.

[0157] When analyzing the floor plan information of a home, the analysis unit can adjust the order of analysis based on the relevance of the rooms. When analyzing the floor plan information of a home, the analysis unit adjusts the order of analysis based on the relevance of the rooms. Room relevance includes, for example, common uses, related activities, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of floor plan information of highly relevant rooms. The analysis unit can also postpone analysis of floor plan information of less relevant rooms. The analysis unit can also focus analysis on specific rooms. This allows the analysis unit to adjust the order of analysis based on the relevance of the rooms. This allows the user to receive analysis results based on highly relevant rooms, and more effective automation suggestions can be realized.

[0158] When analyzing the home floor plan information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. When analyzing the home floor plan information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Expertise level includes, but is not limited to, the user's occupation, past learning history, etc. For example, the analysis unit uses a lot of technical terms if the user has expertise. The analysis unit can also avoid technical terms if the user does not have expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to receive analysis results that correspond to their own level of expertise, making it possible to realize more effective automation solutions.

[0159] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral patterns. The suggestion unit can estimate the user's emotion using, for example, facial expression recognition. The suggestion unit can also estimate the user's emotion using voice analysis. The suggestion unit can also estimate the user's emotion by analyzing behavioral patterns. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion. For example, if the user is relaxed, the suggestion content can be displayed in a visually easy-to-understand manner. Also, if the user is stressed, the suggestion content can be displayed briefly. Also, if the user is excited, the suggestion content can be displayed in detail. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text-generative AI (e.g., LLM) or a multimodal generative AI, which can tailor the way suggestions are presented based on the user's sentiment.

[0160] The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of each piece of information when making a proposal. The importance of information includes, but is not limited to, for example, frequency, impact, and user interest. The suggestion unit can, for example, make a detailed proposal for important information. The suggestion unit can also make a simplified proposal for information with low importance. The suggestion unit can also make a proposal with emphasis on specific information. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of each piece of information. This allows the user to receive the information they need with an appropriate level of detail, thereby realizing more effective automation proposals.

[0161] The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. The suggestion unit can apply different proposal algorithms depending on the category of information when making a proposal. Information categories include, for example, text information, image information, sensor data, etc., but are not limited to these examples. For example, the suggestion unit can apply a proposal algorithm dedicated to lighting control for lighting control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to air conditioner control for air conditioner control. Furthermore, the suggestion unit can also apply a proposal algorithm dedicated to security control for security control. This allows the suggestion unit to apply different proposal algorithms depending on the category of information. This allows the user to receive suggestions that are optimal for each category, making it possible to realize more effective automation proposals.

[0162] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, suggestion history, user reactions, and success rates. The suggestion unit can improve the accuracy of the suggestion based on, for example, suggestion results that the user considered important in the past. The suggestion unit can also omit unnecessary suggestions based on suggestion results that the user ignored in the past. The suggestion unit can also adjust the suggestion algorithm by reflecting the user's past feedback. This allows the suggestion unit to improve the accuracy of the suggestion by referring to the user's past suggestion results. This allows the user to receive more accurate suggestions, and more effective automation suggestions can be realized.

[0163] The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The suggestion unit, when making a suggestion, can determine the priority of the suggestion based on the time when the information was generated. The time when the information was generated includes, for example, a timestamp, the time when an event occurred, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes proposing recent information. The suggestion unit can also postpone proposing older information. The suggestion unit can also prioritize proposing information from a specific time period. This allows the suggestion unit to determine the priority of the suggestion based on the time when the information was generated. This allows the user to receive the latest information preferentially, and more effective automation suggestions can be realized.

[0164] The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The suggestion unit, when making a suggestion, can adjust the order of the suggestions based on the relevance of the information. The relevance of the information includes, for example, common keywords, related topics, etc., but is not limited to these examples. The suggestion unit, for example, prioritizes suggesting highly relevant information. The suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also make suggestions with emphasis on specific information. This allows the suggestion unit to adjust the order of the suggestions based on the relevance of the information. This allows the user to receive highly relevant information preferentially, and more effective automation suggestions can be realized.

[0165] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, the suggestion unit may use a lot of technical terms if the user has specialized knowledge. Furthermore, the suggestion unit may avoid technical terms if the user does not have specialized knowledge. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise. This allows the user to receive suggestions that are appropriate for their own level of expertise, thereby achieving more effective automation proposals. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects camera information and daily activity records using the camera 42 and microphone 38B of the smart device 14, and collects home floor plan information using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and understands the family's movements and the user's lifestyle patterns. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes optimal automation proposals based on the analysis results. The proposal unit, realized, for example, by the control unit 46A of the smart device 14, receives user feedback and improves the proposal content. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects camera information and daily activity records using the camera 42 and microphone 238 of the smart glasses 214, and collects home floor plan information via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and understands the family's movements and the user's lifestyle patterns. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes optimal automation proposals based on the analysis results. The suggestion unit, realized, for example, by the control unit 46A of the smart glasses 214, receives user feedback, and improves the proposals. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects camera information and daily activity records using the camera 42 and microphone 238 of the headset-type terminal 314, and collects home floor plan information using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and understands the family's movements and the user's lifestyle patterns. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes optimal automation proposals based on the analysis results. The proposal unit, realized, for example, by the control unit 46A of the headset-type terminal 314, receives user feedback and improves the proposal content. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects camera information and daily activity records using the camera 42 and microphone 238 of the robot 414, and collects home floor plan information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to understand the family's movements and the user's lifestyle patterns. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal automation proposals based on the analysis results. The proposal unit is also realized, for example, by the control unit 46A of the robot 414, and receives user feedback and improves the proposal content.

[0166] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0167] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestion based on the estimated user's emotions. For example, if the user is relaxed, the timing of making the suggestion can be delayed. Also, if the user is feeling stressed, the timing of making the suggestion can be advanced. Furthermore, if the user is excited, the timing of making the suggestion can be adjusted so that the suggestion is made after the user has calmed down. In this way, the suggestion unit can adjust the timing of the suggestion based on the user's emotions.

[0168] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is relaxed, entertainment-related data can be collected with priority. Also, if the user is stressed, relaxation-related data can be collected with priority. Furthermore, if the user is excited, activity-related data can be collected with priority. This allows the collection unit to adjust the type of data to be collected based on the user's emotions.

[0169] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is stressed, a simple analysis can be performed. Furthermore, if the user is excited, a detailed analysis can be performed. This allows the analysis unit to adjust the accuracy of the analysis based on the user's emotions.

[0170] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make relaxation-related suggestions. If the user is stressed, the suggestion unit can make suggestions that are useful for stress reduction. If the user is excited, the suggestion unit can make activity-related suggestions. This allows the suggestion unit to adjust the content of the suggestions based on the user's emotions.

[0171] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, if the user is relaxed, the data collection frequency can be set low. Also, if the user is feeling stressed, the data collection frequency can be set high. Furthermore, if the user is excited, the data collection frequency can be adjusted to collect data according to the user's emotions. This allows the collection unit to adjust the frequency of data collection based on the user's emotions.

[0172] The suggestion unit can determine the priority of suggestions based on the user's past behavior history. For example, if the user has accepted a specific suggestion in the past, that suggestion can be given priority. Also, if the user has rejected a specific suggestion in the past, that suggestion can be postponed. Furthermore, the suggestion unit can analyze the user's behavior history to make the most effective suggestion. This allows the suggestion unit to determine the priority of suggestions based on the user's past behavior history.

[0173] The collection unit can adjust the type of data to be collected based on the user's geographical location information. For example, when the user is at home, data within the home can be collected with priority. When the user is out, data about the user's location can be collected with priority. When the user is traveling, data about the user's location can be collected with priority. This allows the collection unit to adjust the type of data to be collected based on the user's geographical location information.

[0174] The analysis unit can adjust the analysis algorithm based on the user's past feedback. For example, if the user has evaluated a particular analysis result in the past, that algorithm can be used preferentially. Also, if the user has not evaluated a particular analysis result in the past, that algorithm can be avoided. Furthermore, the user's feedback can be analyzed to select the most effective analysis algorithm. This allows the analysis unit to adjust the analysis algorithm based on the user's past feedback.

[0175] The suggestion unit can customize the content of the suggestion according to the user's level of expertise. For example, if the user has expertise, the suggestion unit can provide a suggestion that includes detailed technical information. Alternatively, if the user does not have expertise, the suggestion unit can provide a concise and easy-to-understand suggestion. Furthermore, the suggestion unit can adjust the way the suggestion is expressed according to the user's level of expertise. This allows the suggestion unit to customize the content of the suggestion according to the user's level of expertise.

[0176] The collection unit can analyze the user's social media activities and collect related data. For example, if the user posts about a specific topic on social media, data related to that topic can be preferentially collected. Also, related data can be collected by referring to the activities of the user's friends on social media. Furthermore, the user's reactions on social media can be analyzed to collect the most relevant data. This allows the collection unit to analyze the user's social media activities and collect related data.

[0177] The processing flow of the second embodiment will be briefly explained below.

[0178] Step 1: The collection unit collects camera information, daily activity records, and home layout information. Camera information includes still images, videos, resolution, and collection frequency, and is used to detect family movements. Daily activity records include the number of steps taken, distance traveled, and applications used, and are used to record the user's daily activities. Home layout information includes room layout, furniture layout, area, and is used to understand the home layout. Step 2: The analysis unit analyzes the camera information, daily activity records, and home layout information collected by the collection unit. It analyzes the camera information to detect family members' movements, analyzes the daily activity records to understand the user's lifestyle patterns, and analyzes the home layout information to suggest optimal device placement and settings. Step 3: The proposal unit proposes optimal automation plans to the user based on the information analyzed by the analysis unit. It comprehensively analyzes camera information, daily activity records, and home layout information to propose automation settings that suit the user's lifestyle.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0184] 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.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0200] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0207] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0208] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0209] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0210] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0211] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0213] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0214] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0215] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0216] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0217] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0218] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0219] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0221] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0222] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0223] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0224] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0226] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0227] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0228] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0229] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0230] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0231] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0232] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0233] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0234] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0235] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0236] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0237] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0238] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0239] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0240] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0241] 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.

[0242] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0243] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0244] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0245] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0246] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0247] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0248] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0249] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0250] [Explanation of symbols]

[0251] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects camera information; an analysis unit that analyzes the camera information collected by the collection unit; A collection department that collects daily activity records; an analysis unit that analyzes the daily activity record; A collection department that collects information on home floor plans; an analysis unit that analyzes floor plan information of the home; a proposal unit that proposes an appropriate automation plan to a user based on the information analyzed by the analysis unit. A system characterized by:

2. The proposal unit Have a department that receives user feedback and improves the proposals 2. The system of claim 1.

3. The proposal unit Equipped with a control unit that works in conjunction with the user's smart device to execute voice commands or automatic control 2. The system of claim 1.

4. The collecting unit Equipped with a unit that collects camera information to detect family movements 2. The system of claim 1.

5. The analysis unit Equipped with a unit that analyzes camera information and detects family movements 2. The system of claim 1.

6. The collecting unit Equipped with a section that collects the user's daily activity records 2. The system of claim 1.

7. The analysis unit Equipped with a section that analyzes daily activity records and understands the user's lifestyle patterns 2. The system of claim 1.

8. The collecting unit Have a department that collects information on home floor plans 2. The system of claim 1.

9. The analysis unit Analyzes home layout information and suggests appropriate device placement and settings 2. The system of claim 1.

10. The proposal unit A unit that estimates the user's emotions and adjusts the way suggestions are presented based on the estimated user emotions.

2. The system of claim 1.

Images