System

Abstract

An object of a system according to an embodiment is to propose appropriate furniture and home appliances based on the lifestyle and preferences of a user.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs information on the lifestyle, preferences, and necessary functions of the user. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes appropriate furniture and home appliances based on the information analyzed by the analysis unit. The customization unit customizes the furniture or home appliance proposed by the proposal 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 technologies have not adequately proposed suitable furniture and home appliances based on a user's lifestyle and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to propose suitable furniture and home appliances based on the user's lifestyle and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs information about a user's lifestyle, preferences, and required functions. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes appropriate furniture and home appliances based on the information analyzed by the analysis unit. The customization unit customizes the furniture and home appliances proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest suitable furniture and home appliances based on the user's lifestyle and preferences. [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 proposal system according to an embodiment of the present invention allows a user to input information about their lifestyle, preferences, and required functions, and then AI analyzes this information to propose and customize optimal furniture and home appliances. The proposal system allows a user to input information about their lifestyle, preferences, and required functions, and then AI analyzes this information to propose optimal furniture and home appliances. The proposed furniture and home appliances are customized to suit the user's lifestyle. For example, a user inputs information such as their lifestyle, preferences, and required functions. For example, if a user inputs, "I'm too busy at work to spend time on housework," the AI ​​uses that information to propose home appliances that will streamline housework. Next, the proposal system analyzes the input information and proposes optimal furniture and home appliances for the user. For example, if a user inputs, "I want a space where I can relax," the AI ​​uses that information to propose relaxing furniture and home appliances. Furthermore, the proposal system customizes the proposed furniture and home appliances to suit the user's lifestyle. For example, if a user inputs, "I want furniture that is space-saving and has ample storage," the AI ​​uses that information to propose furniture that is space-saving and has ample storage. This allows even busy businesspeople to easily find furniture and home appliances that suit them. This allows the proposal system to suggest and customize the optimal furniture and home appliances based on the user's lifestyle and preferences. For example, users can intuitively find furniture and home appliances that suit them without having to perform complicated operations. This allows users to find furniture and home appliances that suit their lifestyle.

[0029] The proposal system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs information about a user's lifestyle, preferences, and required functions. For example, if a user inputs, "I'm too busy at work to spend time on housework," the reception unit receives the information. The reception unit can also receive information about a user inputting, "I want a space where I can relax." Furthermore, if a user inputs, "I want furniture that saves space and has plenty of storage space," the reception unit can also receive the information. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit analyzes information about the user's lifestyle, preferences, and required functions, and generates data for proposing optimal furniture and home appliances to the user. The analysis unit can also use AI to analyze information about the user's lifestyle, preferences, and required functions. Furthermore, the analysis unit can analyze user information using data mining, statistical analysis, and machine learning algorithms. The proposal unit proposes appropriate furniture and home appliances based on the information analyzed by the analysis unit. For example, if a user inputs "I want a space where I can relax," the suggestion unit can suggest relaxing furniture and home appliances based on the information. Also, if a user inputs "I want furniture that is space-saving and has storage capacity," the suggestion unit can suggest furniture that is space-saving and has storage capacity based on the information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that suit the user's lifestyle. The customization unit customizes the furniture and home appliances suggested by the suggestion unit. For example, the customization unit customizes the color, design, and functions of the suggested furniture and home appliances. Also, the customization unit can customize furniture and home appliances to suit the user's needs. Furthermore, the customization unit can customize the suggested furniture and home appliances using AI. As a result, the suggestion system according to the embodiment can suggest and customize optimal furniture and home appliances based on the user's lifestyle and preferences.

[0030] The proposal system includes a collection unit that collects information on the user's lifestyle, room size, and budget. The collection unit collects information on the user's lifestyle, room size, and budget. For example, the collection unit collects information on the user's lifestyle, such as the user's wake-up time, bedtime, and meal timing. The collection unit can also measure the user's room size in square meters, the room shape, and furniture layout. Furthermore, the collection unit can collect budget information, such as the user's monthly budget, annual budget, and available price range. By collecting information on the user's lifestyle, room size, budget, and so on, the collection unit can make more appropriate suggestions.

[0031] The customization unit can customize the color, design, and functions of the proposed furniture and home appliances. The customization unit customizes the color, design, and functions of the proposed furniture and home appliances. For example, the customization unit changes the color of the proposed furniture to suit the user's preferences. The customization unit can also change the design of the proposed home appliance to suit the user's preferences. Furthermore, the customization unit can add or delete functions of the proposed furniture and home appliances to suit the user's needs. In this way, the customization unit can provide products that meet the user's needs by customizing the color, design, and functions of the proposed furniture and home appliances.

[0032] The suggestion unit can suggest furniture and home appliances that suit the user's lifestyle. The suggestion unit suggests furniture and home appliances that suit the user's lifestyle. For example, if the user inputs "I want a space where I can relax," the suggestion unit can suggest furniture and home appliances that will help them relax based on that information. Also, if the user inputs "I want furniture that is space-saving and has a lot of storage space," the suggestion unit can suggest furniture that is space-saving and has a lot of storage space based on that information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that suit the user's lifestyle. In this way, the suggestion unit improves the user's life by suggesting furniture and home appliances that suit the user's lifestyle.

[0033] The customization unit can customize furniture and home appliances to suit the user's needs. The customization unit customizes furniture and home appliances to suit the user's needs. For example, if the user inputs, "I want furniture that is space-saving and has a lot of storage space," the customization unit will suggest and customize furniture that is space-saving and has a lot of storage space based on that information. Also, if the user inputs, "I want a space where I can relax," the customization unit can suggest and customize furniture and home appliances that are relaxing based on that information. Furthermore, the customization unit can also use AI to customize furniture and home appliances to suit the user's needs. In this way, the customization unit can improve user satisfaction by customizing furniture and home appliances to suit the user's needs.

[0034] The suggestion unit can suggest furniture and home appliances necessary to improve the user's life. The suggestion unit suggests furniture and home appliances that will improve the user's life. For example, if the user inputs, "I'm too busy at work to spend time on housework," the suggestion unit can suggest home appliances that will make housework more efficient based on that information. Also, if the user inputs, "I want a space where I can relax," the suggestion unit can suggest furniture and home appliances that will help people relax based on that information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that will improve the user's life. In this way, the suggestion unit improves the user's quality of life by suggesting furniture and home appliances that will improve the user's life.

[0035] The reception unit can analyze the user's previous input history and select the optimal input method. The reception unit analyzes the user's past input history and selects the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. In this way, the reception unit can provide the optimal input method by analyzing the user's past input history.

[0036] The reception unit can perform filtering based on the user's current living situation or areas of interest when inputting information. The reception unit performs filtering based on the user's current living situation or areas of interest when inputting information. For example, when the user inputs their current living situation, the reception unit preferentially displays related input items based on the information. The reception unit can also customize input items based on the user's areas of interest, allowing highly relevant information to be preferentially input. Furthermore, the reception unit can omit unnecessary input items based on the user's living situation or areas of interest, simplifying the input work. As a result, the reception unit can perform filtering based on the user's current living situation or areas of interest, allowing highly relevant information to be preferentially input.

[0037] The reception unit can select the optimal input means based on the user's input method when inputting information. The reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.) when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Also, if the user selects text input, the reception unit can input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. In this way, the reception unit selects the optimal input means according to the user's input method, thereby improving the efficiency of information input.

[0038] The reception unit can, when inputting information, preferentially input highly relevant information based on the user's geographical location information. When inputting information, the reception unit preferentially inputs highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially inputs information related to that area. Furthermore, when the user is traveling, the reception unit can also input relevant information based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially input information related to that location. In this way, the reception unit can preferentially input highly relevant information by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and input related information when information is input. The reception unit can analyze the user's social media activity and input related information when information is input. For example, the reception unit can input related information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and input related information. Furthermore, the reception unit can input related information by referring to the activity of the user's friends on social media. In this way, the reception unit can efficiently input related information by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's previous feedback when inputting information. The reception unit customizes the input method by reflecting the user's past feedback when inputting information. For example, the reception unit improves the input method based on feedback provided by the user in the past. The reception unit can also simplify the input procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the input interface based on the user's past feedback. In this way, the reception unit can optimize the input method by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis during analysis, taking into account the importance of the information. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis depending on the importance of the information. In this way, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of the information.

[0042] The analysis unit can apply different analysis algorithms based on the category of information during analysis. The analysis unit applies different analysis algorithms based on the category of information during analysis. For example, the analysis unit applies an analysis algorithm specifically for furniture to information about furniture. The analysis unit can also apply an analysis algorithm specifically for home appliances to information about home appliances. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm based on the category of information. In this way, the analysis unit can apply the most appropriate analysis algorithm based on the category of information, thereby improving the accuracy of the analysis.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's previous analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's previous analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's previous analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's previous analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the user's previous analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's previous analysis results.

[0044] The analysis unit can determine the analysis priority during analysis, taking into account the time when the information was submitted. The analysis unit determines the analysis priority based on the time when the information was submitted during analysis. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also postpone analyzing information that was submitted earlier. Furthermore, the analysis unit can gradually adjust the analysis priority based on the time when the information was submitted. In this way, the analysis unit can prioritize analyzing the most recent information by determining the analysis priority based on the time when the information was submitted.

[0045] The analysis unit can adjust the order of analysis during analysis, taking into account the relevance of information. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can gradually adjust the order of analysis based on the relevance of information. In this way, the analysis unit can prioritize analysis of highly relevant information by adjusting the order of analysis based on the relevance of information.

[0046] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis based on the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that use a lot of technical terms. Also, if the user does not have technical knowledge, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis based on the user's level of expertise. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis based on the user's level of expertise.

[0047] The suggestion unit can adjust the level of detail of the suggestion taking into consideration the importance of the furniture or home appliance when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the furniture or home appliance when making a suggestion. For example, the suggestion unit makes detailed suggestions for important furniture or home appliances. The suggestion unit can also make brief suggestions for furniture or home appliances with low importance. Furthermore, the suggestion unit can gradually adjust the level of detail of the suggestion depending on the importance of the furniture or home appliance. In this way, the suggestion unit can suggest important furniture or home appliances in detail by adjusting the level of detail of the suggestion based on the importance of the furniture or home appliance.

[0048] The suggestion unit can apply different suggestion algorithms based on the category of furniture or home appliance when making a suggestion. The suggestion unit applies different suggestion algorithms based on the category of furniture or home appliance when making a suggestion. For example, the suggestion unit applies a suggestion algorithm dedicated to furniture to suggestions related to furniture. The suggestion unit can also apply a suggestion algorithm dedicated to home appliances to suggestions related to home appliances. Furthermore, the suggestion unit can select and apply an optimal suggestion algorithm based on the category of furniture or home appliance. In this way, the suggestion unit can apply an optimal suggestion algorithm based on the category of furniture or home appliance, thereby improving the accuracy of suggestions.

[0049] 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. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the user's past suggestion results. In this way, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results.

[0050] The suggestion unit can determine the priority of the suggestions taking into consideration the time of submission of the furniture and home appliances when making suggestions. The suggestion unit determines the priority of the suggestions based on the time of submission of the furniture and home appliances when making suggestions. For example, the suggestion unit preferentially suggests the latest furniture and home appliances. The suggestion unit can also postpone the proposal of furniture and home appliances that were submitted earlier. Furthermore, the suggestion unit can gradually adjust the priority of the suggestions based on the time of submission of the furniture and home appliances. In this way, the suggestion unit can preferentially suggest the latest furniture and home appliances by determining the priority of the suggestions based on the time of submission of the furniture and home appliances.

[0051] The suggestion unit can adjust the order of suggestions taking into account the relevance of the furniture and home appliances when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the furniture and home appliances when making suggestions. For example, the suggestion unit preferentially suggests furniture and home appliances that are highly relevant. The suggestion unit can also postpone suggesting furniture and home appliances that are less relevant. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance of the furniture and home appliances. In this way, the suggestion unit can preferentially suggest furniture and home appliances that are highly relevant by adjusting the order of suggestions based on the relevance of the furniture and home appliances.

[0052] The suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. The suggestion unit adjusts the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a proposal that avoids technical terminology. Furthermore, the suggestion unit can gradually adjust the use of technical terminology in the proposal based on the user's level of expertise. In this way, the suggestion unit can provide a proposal that is easy for the user to understand by adjusting the use of technical terminology in the proposal based on the user's level of expertise.

[0053] During customization, the customization unit can analyze the user's previous customization history to select the optimal customization method. During customization, the customization unit analyzes the user's past customization history to select the optimal customization method. For example, the customization unit suggests the optimal customization method based on the user's past customization history. The customization unit can also improve the accuracy of customization by referring to the user's past customization history. Furthermore, the customization unit can adjust the level of detail of customization based on the user's past customization history. In this way, the customization unit can provide the optimal customization method by analyzing the user's past customization history.

[0054] The customization unit can customize the customization means taking into account the user's current living situation during customization. The customization unit customizes the customization means based on the user's current living situation during customization. For example, the customization unit suggests an optimal customization means based on the user's current living situation. The customization unit can also adjust the customization means according to the user's living situation. Furthermore, the customization unit can adjust the level of detail of the customization based on the user's living situation. In this way, the customization unit can provide optimal customization for the user by customizing the customization means based on the user's current living situation.

[0055] The customization unit can improve the customization method by taking user feedback into consideration during customization. The customization unit improves the customization method by reflecting user feedback during customization. For example, the customization unit improves the customization method based on user feedback. The customization unit can also improve the accuracy of customization by reflecting user feedback. Furthermore, the customization unit can adjust the level of detail of customization based on user feedback. In this way, the customization unit improves the accuracy of customization by reflecting user feedback.

[0056] The customization unit can select the optimal customization method based on the user's geographical location information during customization. The customization unit selects the optimal customization method taking into account the user's geographical location information during customization. For example, if the user is in a specific area, the customization unit can suggest a customization method related to that area. Also, if the user is traveling, the customization unit can suggest the optimal customization method based on the user's current location. Furthermore, if the user is in a specific location, the customization unit can suggest a customization method related to that location. In this way, the customization unit can provide the optimal customization method by taking into account the user's geographical location information.

[0057] The customization unit can analyze the user's social media activity and suggest customization methods during customization. The customization unit analyzes the user's social media activity and suggest customization methods during customization. For example, the customization unit can suggest optimal customization methods based on the user's social media activity. The customization unit can also analyze the content of the user's social media posts and suggest related customization methods. Furthermore, the customization unit can suggest optimal customization methods by taking into account the activity of the user's friends on social media. In this way, the customization unit can provide optimal customization methods by analyzing the user's social media activity.

[0058] The customization unit can customize the customization method by reflecting previous user feedback during customization. The customization unit customizes the customization method by reflecting past user feedback during customization. For example, the customization unit improves the customization method based on past user feedback. The customization unit can also improve the accuracy of the customization by reflecting past user feedback. Furthermore, the customization unit can adjust the level of detail of the customization based on past user feedback. In this way, the customization unit improves the accuracy of the customization by reflecting past user feedback.

[0059] The collection unit can analyze the user's past information collection history and select the optimal collection method. The collection unit analyzes the user's past information collection history and selects the optimal collection method. For example, the collection unit suggests the optimal collection method based on the user's past information collection history. The collection unit can also improve the accuracy of collection by referring to the user's past information collection history. Furthermore, the collection unit can adjust the level of detail of collection based on the user's past information collection history. In this way, the collection unit can provide the optimal collection method by analyzing the user's past information collection history.

[0060] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting information. The collection unit performs filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting relevant information based on the user's current living situation. The collection unit can also customize the information to be collected based on the user's areas of interest. Furthermore, the collection unit can omit unnecessary information based on the user's living situation and areas of interest, thereby simplifying the collection work. In this way, the collection unit can prioritize collecting highly relevant information by filtering based on the user's current living situation and areas of interest.

[0061] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. When collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. In addition, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting information related to that location. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0062] The collection unit can analyze the user's social media activities and collect related information when collecting information. The collection unit analyzes the user's social media activities and collects related information when collecting information. For example, the collection unit collects related information based on the user's social media activities. The collection unit can also analyze the content of the user's posts on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit can efficiently collect related information by analyzing the user's social media activities.

[0063] The collection unit can customize the collection method by reflecting the user's previous feedback when collecting information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit improves the collection method based on the user's past feedback. The collection unit can also improve the accuracy of the collection by reflecting the user's past feedback. Furthermore, the collection unit can adjust the level of detail of the collection based on the user's past feedback. In this way, the collection unit can optimize the collection method by reflecting the user's past feedback.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The recommendation system may further include a hobby collection unit that collects the user's hobbies and interests. The hobby collection unit collects information such as the user's favorite music, movies, and sports, and provides it to the analysis unit. For example, if the user enjoys music, the hobby collection unit may suggest audio equipment and music-related furniture based on that information. If the user likes watching movies, the hobby collection unit may also suggest a home theater system or a comfortable sofa. If the user enjoys sports, the hobby collection unit may also suggest exercise equipment and sports-related furniture. This allows the recommendation system to make optimal suggestions based on the user's hobbies and interests.

[0066] The proposal system may further include a family information collection unit that takes into account the user's family composition. The family information collection unit collects information about the user's family composition and family lifestyle, and provides it to the analysis unit. For example, if the user has children, the family information collection unit may suggest furniture and home appliances for children based on the children's ages and interests. Also, if the user lives with elderly people, the family information collection unit may suggest furniture and home appliances that are considerate of the elderly. Furthermore, if the user has pets, the family information collection unit may suggest furniture and home appliances that are suitable for pets. This allows the proposal system to make optimal proposals based on the user's family composition.

[0067] The proposal system may further include an environment monitoring unit that monitors the user's living environment. The environment monitoring unit collects data such as temperature, humidity, and lighting in the user's living environment and provides it to the analysis unit. For example, if the user's living environment is dry, the environment monitoring unit may suggest a humidifier. If the user's living environment is dark, the environment monitoring unit may also suggest lighting fixtures. Furthermore, if the user's living environment is noisy, the environment monitoring unit may also suggest soundproof furniture or home appliances. This allows the proposal system to make optimal proposals based on the user's living environment.

[0068] The proposal system may further include an energy monitoring unit that monitors the user's energy consumption. The energy monitoring unit collects energy consumption data within the user's home and provides it to the analysis unit. For example, if the user's energy consumption is high, the energy monitoring unit may suggest energy-saving home appliances. Also, if the user wants to reduce their energy consumption, the energy monitoring unit may suggest energy-efficient furniture and home appliances. Furthermore, the energy monitoring unit may suggest optimal energy management methods based on the user's energy consumption patterns. This allows the proposal system to make optimal suggestions based on the user's energy consumption.

[0069] The recommendation system can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes the user's past purchase history and provides the results to the analysis unit. For example, the purchase history analysis unit understands the user's preferences and trends based on data on furniture and home appliances purchased by the user in the past. The purchase history analysis unit can also analyze ratings and feedback on products purchased by the user in the past to improve the accuracy of recommendations. Furthermore, the purchase history analysis unit can suggest related products based on the user's purchase history. This allows the recommendation system to make optimal recommendations based on the user's purchase history.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit inputs information about the user's lifestyle, preferences, and required functions. For example, if the user inputs, "I'm too busy with work to spend time on housework," the reception unit receives that information. Also, if the user inputs, "I want a space where I can relax," that information can be received. Furthermore, if the user inputs, "I want furniture that is space-saving and has plenty of storage space," that information can be received. Step 2: The analysis unit analyzes the information input by the reception unit. For example, the analysis unit analyzes information on the user's lifestyle, preferences, and required functions, and generates data for proposing furniture and home appliances that are best suited to the user. The analysis unit can also use AI to analyze information on the user's lifestyle, preferences, and required functions. Furthermore, the analysis unit can also analyze user information using data mining, statistical analysis, and machine learning algorithms. Step 3: The suggestion unit suggests appropriate furniture and home appliances based on the information analyzed by the analysis unit. For example, if the user inputs "I want a space where I can relax," the suggestion unit will suggest relaxing furniture and home appliances based on that information. Also, if the user inputs "I want furniture that is space-saving and has storage capacity," the suggestion unit can suggest space-saving furniture with storage capacity based on that information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that suit the user's lifestyle. Step 4: The customization unit customizes the furniture and home appliances suggested by the suggestion unit. For example, the customization unit customizes the color, design, and functions of the suggested furniture and home appliances. The customization unit can also customize furniture and home appliances to meet the user's needs. Furthermore, the customization unit can also customize the suggested furniture and home appliances using AI.

[0072] (Example 2) A proposal system according to an embodiment of the present invention allows a user to input information about their lifestyle, preferences, and required functions, and then AI analyzes this information to propose and customize optimal furniture and home appliances. The proposal system allows a user to input information about their lifestyle, preferences, and required functions, and then AI analyzes this information to propose optimal furniture and home appliances. The proposed furniture and home appliances are customized to suit the user's lifestyle. For example, a user inputs information such as their lifestyle, preferences, and required functions. For example, if a user inputs, "I'm too busy at work to spend time on housework," the AI ​​uses that information to propose home appliances that will streamline housework. Next, the proposal system analyzes the input information and proposes optimal furniture and home appliances for the user. For example, if a user inputs, "I want a space where I can relax," the AI ​​uses that information to propose relaxing furniture and home appliances. Furthermore, the proposal system customizes the proposed furniture and home appliances to suit the user's lifestyle. For example, if a user inputs, "I want furniture that is space-saving and has ample storage," the AI ​​uses that information to propose furniture that is space-saving and has ample storage. This allows even busy businesspeople to easily find furniture and home appliances that suit them. This allows the proposal system to suggest and customize the optimal furniture and home appliances based on the user's lifestyle and preferences. For example, users can intuitively find furniture and home appliances that suit them without having to perform complicated operations. This allows users to find furniture and home appliances that suit their lifestyle.

[0073] The proposal system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs information about a user's lifestyle, preferences, and required functions. For example, if a user inputs, "I'm too busy at work to spend time on housework," the reception unit receives the information. The reception unit can also receive information about a user inputting, "I want a space where I can relax." Furthermore, if a user inputs, "I want furniture that saves space and has plenty of storage space," the reception unit can also receive the information. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit analyzes information about the user's lifestyle, preferences, and required functions, and generates data for proposing optimal furniture and home appliances to the user. The analysis unit can also use AI to analyze information about the user's lifestyle, preferences, and required functions. Furthermore, the analysis unit can analyze user information using data mining, statistical analysis, and machine learning algorithms. The proposal unit proposes appropriate furniture and home appliances based on the information analyzed by the analysis unit. For example, if a user inputs "I want a space where I can relax," the suggestion unit can suggest relaxing furniture and home appliances based on the information. Also, if a user inputs "I want furniture that is space-saving and has storage capacity," the suggestion unit can suggest furniture that is space-saving and has storage capacity based on the information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that suit the user's lifestyle. The customization unit customizes the furniture and home appliances suggested by the suggestion unit. For example, the customization unit customizes the color, design, and functions of the suggested furniture and home appliances. Also, the customization unit can customize furniture and home appliances to suit the user's needs. Furthermore, the customization unit can customize the suggested furniture and home appliances using AI. As a result, the suggestion system according to the embodiment can suggest and customize optimal furniture and home appliances based on the user's lifestyle and preferences.

[0074] The proposal system includes a collection unit that collects information on the user's lifestyle, room size, and budget. The collection unit collects information on the user's lifestyle, room size, and budget. For example, the collection unit collects information on the user's lifestyle, such as the user's wake-up time, bedtime, and meal timing. The collection unit can also measure the user's room size in square meters, the room shape, and furniture layout. Furthermore, the collection unit can collect budget information, such as the user's monthly budget, annual budget, and available price range. By collecting information on the user's lifestyle, room size, budget, and so on, the collection unit can make more appropriate suggestions.

[0075] The customization unit can customize the color, design, and functions of the proposed furniture and home appliances. The customization unit customizes the color, design, and functions of the proposed furniture and home appliances. For example, the customization unit changes the color of the proposed furniture to suit the user's preferences. The customization unit can also change the design of the proposed home appliance to suit the user's preferences. Furthermore, the customization unit can add or delete functions of the proposed furniture and home appliances to suit the user's needs. In this way, the customization unit can provide products that meet the user's needs by customizing the color, design, and functions of the proposed furniture and home appliances.

[0076] The suggestion unit can suggest furniture and home appliances that suit the user's lifestyle. The suggestion unit suggests furniture and home appliances that suit the user's lifestyle. For example, if the user inputs "I want a space where I can relax," the suggestion unit can suggest furniture and home appliances that will help them relax based on that information. Also, if the user inputs "I want furniture that is space-saving and has a lot of storage space," the suggestion unit can suggest furniture that is space-saving and has a lot of storage space based on that information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that suit the user's lifestyle. In this way, the suggestion unit improves the user's life by suggesting furniture and home appliances that suit the user's lifestyle.

[0077] The customization unit can customize furniture and home appliances to suit the user's needs. The customization unit customizes furniture and home appliances to suit the user's needs. For example, if the user inputs, "I want furniture that is space-saving and has a lot of storage space," the customization unit will suggest and customize furniture that is space-saving and has a lot of storage space based on that information. Also, if the user inputs, "I want a space where I can relax," the customization unit can suggest and customize furniture and home appliances that are relaxing based on that information. Furthermore, the customization unit can also use AI to customize furniture and home appliances to suit the user's needs. In this way, the customization unit can improve user satisfaction by customizing furniture and home appliances to suit the user's needs.

[0078] The suggestion unit can suggest furniture and home appliances necessary to improve the user's life. The suggestion unit suggests furniture and home appliances that will improve the user's life. For example, if the user inputs, "I'm too busy at work to spend time on housework," the suggestion unit can suggest home appliances that will make housework more efficient based on that information. Also, if the user inputs, "I want a space where I can relax," the suggestion unit can suggest furniture and home appliances that will help people relax based on that information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that will improve the user's life. In this way, the suggestion unit improves the user's quality of life by suggesting furniture and home appliances that will improve the user's life.

[0079] The reception unit can estimate the user's emotion and adjust the timing of information input based on the estimated user emotion. The reception unit can estimate the user's emotion and adjust the timing of information input based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can simplify the input procedure to allow the user to input information in a short time. If the user is relaxed, the reception unit can provide detailed input options to allow the user to input information carefully. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to input information quickly. In this way, the reception unit can adjust the timing of information input according to the user's emotion, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0080] The reception unit can analyze the user's previous input history and select the optimal input method. The reception unit analyzes the user's past input history and selects the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. In this way, the reception unit can provide the optimal input method by analyzing the user's past input history.

[0081] The reception unit can perform filtering based on the user's current living situation or areas of interest when inputting information. The reception unit performs filtering based on the user's current living situation or areas of interest when inputting information. For example, when the user inputs their current living situation, the reception unit preferentially displays related input items based on the information. The reception unit can also customize input items based on the user's areas of interest, allowing highly relevant information to be preferentially input. Furthermore, the reception unit can omit unnecessary input items based on the user's living situation or areas of interest, simplifying the input work. As a result, the reception unit can perform filtering based on the user's current living situation or areas of interest, allowing highly relevant information to be preferentially input.

[0082] The reception unit can select the optimal input means based on the user's input method when inputting information. The reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.) when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Also, if the user selects text input, the reception unit can input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. In this way, the reception unit selects the optimal input means according to the user's input method, thereby improving the efficiency of information input.

[0083] The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, the reception unit can prioritize input of important information when the user is stressed. The reception unit can also prioritize input of detailed information when the user is relaxed. Furthermore, the reception unit can prioritize input of the minimum necessary information when the user is in a hurry. In this way, the reception unit can prioritize input of important information by determining the priority of information to be input based on the user's emotion. Emotion estimation is realized using an emotion estimation function using, 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.

[0084] The reception unit can, when inputting information, preferentially input highly relevant information based on the user's geographical location information. When inputting information, the reception unit preferentially inputs highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially inputs information related to that area. Furthermore, when the user is traveling, the reception unit can also input relevant information based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially input information related to that location. In this way, the reception unit can preferentially input highly relevant information by taking into account the user's geographical location information.

[0085] The reception unit can analyze the user's social media activity and input related information when information is input. The reception unit can analyze the user's social media activity and input related information when information is input. For example, the reception unit can input related information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and input related information. Furthermore, the reception unit can input related information by referring to the activity of the user's friends on social media. In this way, the reception unit can efficiently input related information by analyzing the user's social media activity.

[0086] The reception unit can customize the input method by reflecting the user's previous feedback when inputting information. The reception unit customizes the input method by reflecting the user's past feedback when inputting information. For example, the reception unit improves the input method based on feedback provided by the user in the past. The reception unit can also simplify the input procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the input interface based on the user's past feedback. In this way, the reception unit can optimize the input method by reflecting the user's past feedback.

[0087] 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. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation method of the analysis based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0088] The analysis unit can adjust the level of detail of the analysis during analysis, taking into account the importance of the information. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis depending on the importance of the information. In this way, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of the information.

[0089] The analysis unit can apply different analysis algorithms based on the category of information during analysis. The analysis unit applies different analysis algorithms based on the category of information during analysis. For example, the analysis unit applies an analysis algorithm specifically for furniture to information about furniture. The analysis unit can also apply an analysis algorithm specifically for home appliances to information about home appliances. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm based on the category of information. In this way, the analysis unit can apply the most appropriate analysis algorithm based on the category of information, thereby improving the accuracy of the analysis.

[0090] The analysis unit can improve the accuracy of the analysis by referring to the user's previous analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's previous analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on the user's previous analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's previous analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the user's previous analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's previous analysis results.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, the analysis unit can adjust the length of the analysis based on the user's emotions and provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0092] The analysis unit can determine the analysis priority during analysis, taking into account the time when the information was submitted. The analysis unit determines the analysis priority based on the time when the information was submitted during analysis. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also postpone analyzing information that was submitted earlier. Furthermore, the analysis unit can gradually adjust the analysis priority based on the time when the information was submitted. In this way, the analysis unit can prioritize analyzing the most recent information by determining the analysis priority based on the time when the information was submitted.

[0093] The analysis unit can adjust the order of analysis during analysis, taking into account the relevance of information. The analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can gradually adjust the order of analysis based on the relevance of information. In this way, the analysis unit can prioritize analysis of highly relevant information by adjusting the order of analysis based on the relevance of information.

[0094] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis based on the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that use a lot of technical terms. Also, if the user does not have technical knowledge, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis based on the user's level of expertise. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis based on the user's level of expertise.

[0095] 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. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually stimulating suggestions when the user is excited. In this way, the suggestion unit can provide suggestions that are easy for the user to understand by adjusting the way the suggestion is expressed based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0096] The suggestion unit can adjust the level of detail of the suggestion taking into consideration the importance of the furniture or home appliance when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the furniture or home appliance when making a suggestion. For example, the suggestion unit makes detailed suggestions for important furniture or home appliances. The suggestion unit can also make brief suggestions for furniture or home appliances with low importance. Furthermore, the suggestion unit can gradually adjust the level of detail of the suggestion depending on the importance of the furniture or home appliance. In this way, the suggestion unit can suggest important furniture or home appliances in detail by adjusting the level of detail of the suggestion based on the importance of the furniture or home appliance.

[0097] The suggestion unit can apply different suggestion algorithms based on the category of furniture or home appliance when making a suggestion. The suggestion unit applies different suggestion algorithms based on the category of furniture or home appliance when making a suggestion. For example, the suggestion unit applies a suggestion algorithm dedicated to furniture to suggestions related to furniture. The suggestion unit can also apply a suggestion algorithm dedicated to home appliances to suggestions related to home appliances. Furthermore, the suggestion unit can select and apply an optimal suggestion algorithm based on the category of furniture or home appliance. In this way, the suggestion unit can apply an optimal suggestion algorithm based on the category of furniture or home appliance, thereby improving the accuracy of suggestions.

[0098] 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. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the user's past suggestion results. In this way, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results.

[0099] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can also provide a detailed suggestion. Furthermore, if the user is excited, the suggestion unit can also provide a visually stimulating suggestion. In this way, the suggestion unit can provide the optimal suggestion for the user by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0100] The suggestion unit can determine the priority of the suggestions taking into consideration the time of submission of the furniture and home appliances when making suggestions. The suggestion unit determines the priority of the suggestions based on the time of submission of the furniture and home appliances when making suggestions. For example, the suggestion unit preferentially suggests the latest furniture and home appliances. The suggestion unit can also postpone the proposal of furniture and home appliances that were submitted earlier. Furthermore, the suggestion unit can gradually adjust the priority of the suggestions based on the time of submission of the furniture and home appliances. In this way, the suggestion unit can preferentially suggest the latest furniture and home appliances by determining the priority of the suggestions based on the time of submission of the furniture and home appliances.

[0101] The suggestion unit can adjust the order of suggestions taking into account the relevance of the furniture and home appliances when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the furniture and home appliances when making suggestions. For example, the suggestion unit preferentially suggests furniture and home appliances that are highly relevant. The suggestion unit can also postpone suggesting furniture and home appliances that are less relevant. Furthermore, the suggestion unit can gradually adjust the order of suggestions based on the relevance of the furniture and home appliances. In this way, the suggestion unit can preferentially suggest furniture and home appliances that are highly relevant by adjusting the order of suggestions based on the relevance of the furniture and home appliances.

[0102] The suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. The suggestion unit adjusts the use of technical terminology in the proposal based on the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a proposal that avoids technical terminology. Furthermore, the suggestion unit can gradually adjust the use of technical terminology in the proposal based on the user's level of expertise. In this way, the suggestion unit can provide a proposal that is easy for the user to understand by adjusting the use of technical terminology in the proposal based on the user's level of expertise.

[0103] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. For example, the customization unit can provide detailed customization options when the user is relaxed. The customization unit can also provide concise customization options when the user is in a hurry. Furthermore, the customization unit can provide visually stimulating customization options when the user is excited. In this way, the customization unit can provide optimal customization for the user by adjusting the customization method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] During customization, the customization unit can analyze the user's previous customization history to select the optimal customization method. During customization, the customization unit analyzes the user's past customization history to select the optimal customization method. For example, the customization unit suggests the optimal customization method based on the user's past customization history. The customization unit can also improve the accuracy of customization by referring to the user's past customization history. Furthermore, the customization unit can adjust the level of detail of customization based on the user's past customization history. In this way, the customization unit can provide the optimal customization method by analyzing the user's past customization history.

[0105] The customization unit can customize the customization means taking into account the user's current living situation during customization. The customization unit customizes the customization means based on the user's current living situation during customization. For example, the customization unit suggests an optimal customization means based on the user's current living situation. The customization unit can also adjust the customization means according to the user's living situation. Furthermore, the customization unit can adjust the level of detail of the customization based on the user's living situation. In this way, the customization unit can provide optimal customization for the user by customizing the customization means based on the user's current living situation.

[0106] The customization unit can improve the customization method by taking user feedback into consideration during customization. The customization unit improves the customization method by reflecting user feedback during customization. For example, the customization unit improves the customization method based on user feedback. The customization unit can also improve the accuracy of customization by reflecting user feedback. Furthermore, the customization unit can adjust the level of detail of customization based on user feedback. In this way, the customization unit improves the accuracy of customization by reflecting user feedback.

[0107] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, when the user is feeling stressed, the customization unit can prioritize important customization. Furthermore, when the user is relaxed, the customization unit can also perform detailed customization. Furthermore, when the user is in a hurry, the customization unit can prioritize the minimum necessary customization. In this way, the customization unit can prioritize important customization by determining the priority of customization based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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.

[0108] The customization unit can select the optimal customization method based on the user's geographical location information during customization. The customization unit selects the optimal customization method taking into account the user's geographical location information during customization. For example, if the user is in a specific area, the customization unit can suggest a customization method related to that area. Also, if the user is traveling, the customization unit can suggest the optimal customization method based on the user's current location. Furthermore, if the user is in a specific location, the customization unit can suggest a customization method related to that location. In this way, the customization unit can provide the optimal customization method by taking into account the user's geographical location information.

[0109] The customization unit can analyze the user's social media activity and suggest customization methods during customization. The customization unit analyzes the user's social media activity and suggest customization methods during customization. For example, the customization unit can suggest optimal customization methods based on the user's social media activity. The customization unit can also analyze the content of the user's social media posts and suggest related customization methods. Furthermore, the customization unit can suggest optimal customization methods by taking into account the activity of the user's friends on social media. In this way, the customization unit can provide optimal customization methods by analyzing the user's social media activity.

[0110] The customization unit can customize the customization method by reflecting previous user feedback during customization. The customization unit customizes the customization method by reflecting past user feedback during customization. For example, the customization unit improves the customization method based on past user feedback. The customization unit can also improve the accuracy of the customization by reflecting past user feedback. Furthermore, the customization unit can adjust the level of detail of the customization based on past user feedback. In this way, the customization unit improves the accuracy of the customization by reflecting past user feedback.

[0111] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection, thereby reducing the user's burden. The collection unit can also collect detailed information when the user is relaxed. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting the minimum amount of information necessary. In this way, the collection unit adjusts the timing of information collection based on the user's emotions, thereby reducing the user's burden. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0112] The collection unit can analyze the user's past information collection history and select the optimal collection method. The collection unit analyzes the user's past information collection history and selects the optimal collection method. For example, the collection unit suggests the optimal collection method based on the user's past information collection history. The collection unit can also improve the accuracy of collection by referring to the user's past information collection history. Furthermore, the collection unit can adjust the level of detail of collection based on the user's past information collection history. In this way, the collection unit can provide the optimal collection method by analyzing the user's past information collection history.

[0113] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting information. The collection unit performs filtering based on the user's current living situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting relevant information based on the user's current living situation. The collection unit can also customize the information to be collected based on the user's areas of interest. Furthermore, the collection unit can omit unnecessary information based on the user's living situation and areas of interest, thereby simplifying the collection work. In this way, the collection unit can prioritize collecting highly relevant information by filtering based on the user's current living situation and areas of interest.

[0114] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit prioritizes collecting important information when the user is stressed. The collection unit can also collect detailed information when the user is relaxed. Furthermore, the collection unit can prioritize collecting the minimum amount of information necessary when the user is in a hurry. In this way, the collection unit can prioritize collecting important information by determining the priority of information to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0115] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. When collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. In addition, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting information related to that location. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0116] The collection unit can analyze the user's social media activities and collect related information when collecting information. The collection unit analyzes the user's social media activities and collects related information when collecting information. For example, the collection unit collects related information based on the user's social media activities. The collection unit can also analyze the content of the user's posts on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit can efficiently collect related information by analyzing the user's social media activities.

[0117] The collection unit can customize the collection method by reflecting the user's previous feedback when collecting information. The collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit improves the collection method based on the user's past feedback. The collection unit can also improve the accuracy of the collection by reflecting the user's past feedback. Furthermore, the collection unit can adjust the level of detail of the collection based on the user's past feedback. In this way, the collection unit can optimize the collection method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, suggestion unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs information on the user's lifestyle, preferences, and required functions. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate furniture and home appliances based on the analyzed information. For example, the customization unit is realized by the control unit 46A of the smart device 14 and customizes the suggested furniture and home appliances. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, suggestion unit, and customization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs information on the user's lifestyle, preferences, and required functions. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate furniture or home appliances based on the analyzed information. For example, the customization unit is realized by the control unit 46A of the smart glasses 214 and customizes the suggested furniture or home appliances. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, suggestion unit, and customization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and inputs information on the user's lifestyle, preferences, and required functions. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate furniture and home appliances based on the analyzed information. For example, the customization unit is realized by the control unit 46A of the headset-type terminal 314 and customizes the suggested furniture and home appliances. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs information on the user's lifestyle, preferences, and required functions. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate furniture or home appliances based on the analyzed information. For example, the customization unit is realized by the control unit 46A of the robot 414 and customizes the suggested furniture or home appliances.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The proposal system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects health data such as the user's heart rate, blood pressure, and sleep patterns, and provides it to the analysis unit. For example, if the user is feeling stressed, the health monitoring unit detects an increase in heart rate and sends the information to the analysis unit. Also, if the user is not getting enough sleep, the health monitoring unit provides the sleep data to the analysis unit, which can be used to suggest appropriate furniture and home appliances. Furthermore, the health monitoring unit can suggest furniture and home appliances that have a relaxing effect based on the user's health condition. This allows the proposal system to make optimal suggestions taking into account the user's health condition.

[0120] The recommendation system may further include a hobby collection unit that collects the user's hobbies and interests. The hobby collection unit collects information such as the user's favorite music, movies, and sports, and provides it to the analysis unit. For example, if the user enjoys music, the hobby collection unit may suggest audio equipment and music-related furniture based on that information. If the user likes watching movies, the hobby collection unit may also suggest a home theater system or a comfortable sofa. If the user enjoys sports, the hobby collection unit may also suggest exercise equipment and sports-related furniture. This allows the recommendation system to make optimal suggestions based on the user's hobbies and interests.

[0121] The proposed system can further estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the proposed system can reduce the frequency of suggestions to reduce the user's burden. If the user is relaxed, the proposed system can provide detailed suggestions to allow the user to carefully select. If the user is in a hurry, the proposed system can provide concise and quick suggestions. As a result, the proposed system can reduce the user's stress and provide optimal suggestions by adjusting the timing of suggestions according to the user's emotions.

[0122] The proposal system may further include a family information collection unit that takes into account the user's family composition. The family information collection unit collects information about the user's family composition and family lifestyle, and provides it to the analysis unit. For example, if the user has children, the family information collection unit may suggest furniture and home appliances for children based on the children's ages and interests. Also, if the user lives with elderly people, the family information collection unit may suggest furniture and home appliances that are considerate of the elderly. Furthermore, if the user has pets, the family information collection unit may suggest furniture and home appliances that are suitable for pets. This allows the proposal system to make optimal proposals based on the user's family composition.

[0123] The proposal system can further estimate the user's emotions and customize the content of the proposal based on the estimated emotions. For example, if the user feels like relaxing, the proposal system can suggest furniture and home appliances that have a relaxing effect. Also, if the user is feeling energetic, the proposal system can suggest furniture and home appliances that are suitable for an active lifestyle. Furthermore, if the user is feeling stressed, the proposal system can suggest furniture and home appliances that have a stress-reducing effect. In this way, the proposal system can customize the content of the proposal based on the user's emotions and make optimal suggestions that meet the user's needs.

[0124] The proposal system may further include an environment monitoring unit that monitors the user's living environment. The environment monitoring unit collects data such as temperature, humidity, and lighting in the user's living environment and provides it to the analysis unit. For example, if the user's living environment is dry, the environment monitoring unit may suggest a humidifier. If the user's living environment is dark, the environment monitoring unit may also suggest lighting fixtures. Furthermore, if the user's living environment is noisy, the environment monitoring unit may also suggest soundproof furniture or home appliances. This allows the proposal system to make optimal proposals based on the user's living environment.

[0125] The proposal system can further estimate the user's emotions and prioritize suggestions based on the estimated emotions. For example, if the user is feeling stressed, the proposal system can prioritize suggestions of furniture and home appliances that have a stress-reducing effect. Also, if the user is relaxed, the proposal system can prioritize suggestions of furniture and home appliances that have a relaxing effect. Furthermore, if the user is in a hurry, the proposal system can prioritize suggestions of the minimum necessary information. In this way, the proposal system can make optimal suggestions for the user by prioritizing suggestions based on the user's emotions.

[0126] The proposal system may further include an energy monitoring unit that monitors the user's energy consumption. The energy monitoring unit collects energy consumption data within the user's home and provides it to the analysis unit. For example, if the user's energy consumption is high, the energy monitoring unit may suggest energy-saving home appliances. Also, if the user wants to reduce their energy consumption, the energy monitoring unit may suggest energy-efficient furniture and home appliances. Furthermore, the energy monitoring unit may suggest optimal energy management methods based on the user's energy consumption patterns. This allows the proposal system to make optimal suggestions based on the user's energy consumption.

[0127] The suggestion system can further estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion system can provide detailed suggestions. If the user is in a hurry, the suggestion system can provide concise and to-the-point suggestions. If the user is excited, the suggestion system can provide visually stimulating suggestions. In this way, the suggestion system can provide suggestions that are easy for the user to understand by adjusting the way suggestions are presented based on the user's emotions.

[0128] The recommendation system can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes the user's past purchase history and provides the results to the analysis unit. For example, the purchase history analysis unit understands the user's preferences and trends based on data on furniture and home appliances purchased by the user in the past. The purchase history analysis unit can also analyze ratings and feedback on products purchased by the user in the past to improve the accuracy of recommendations. Furthermore, the purchase history analysis unit can suggest related products based on the user's purchase history. This allows the recommendation system to make optimal recommendations based on the user's purchase history.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit inputs information about the user's lifestyle, preferences, and required functions. For example, if the user inputs, "I'm too busy with work to spend time on housework," the reception unit receives that information. Also, if the user inputs, "I want a space where I can relax," that information can be received. Furthermore, if the user inputs, "I want furniture that is space-saving and has plenty of storage space," that information can be received. Step 2: The analysis unit analyzes the information input by the reception unit. For example, the analysis unit analyzes information on the user's lifestyle, preferences, and required functions, and generates data for proposing furniture and home appliances that are best suited to the user. The analysis unit can also use AI to analyze information on the user's lifestyle, preferences, and required functions. Furthermore, the analysis unit can also analyze user information using data mining, statistical analysis, and machine learning algorithms. Step 3: The suggestion unit suggests appropriate furniture and home appliances based on the information analyzed by the analysis unit. For example, if the user inputs "I want a space where I can relax," the suggestion unit will suggest relaxing furniture and home appliances based on that information. Also, if the user inputs "I want furniture that is space-saving and has storage capacity," the suggestion unit can suggest space-saving furniture with storage capacity based on that information. Furthermore, the suggestion unit can use AI to suggest furniture and home appliances that suit the user's lifestyle. Step 4: The customization unit customizes the furniture and home appliances suggested by the suggestion unit. For example, the customization unit customizes the color, design, and functions of the suggested furniture and home appliances. The customization unit can also customize furniture and home appliances to meet the user's needs. Furthermore, the customization unit can also customize the suggested furniture and home appliances using AI.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 AI 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 AI 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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 AI 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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, in order to avoid confusion and to 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 reception section for inputting information on the user's lifestyle, preferences, and required functions; an analysis unit that analyzes the information input by the reception unit; a suggestion unit that suggests appropriate furniture and home appliances based on the information analyzed by the analysis unit; a customization unit that customizes the furniture and home appliances proposed by the proposal unit. A system characterized by:

2. It has a collection section that collects information on the user's lifestyle, room size, and budget.

2. The system of claim 1.

3. The customization unit Customize the color, design, and functionality of proposed furniture and appliances 2. The system of claim 1.

4. The proposal unit Proposing furniture and home appliances that suit the user's lifestyle 2. The system of claim 1.

5. The customization unit Customize furniture and appliances to suit your needs 2. The system of claim 1.

6. The proposal unit Propose furniture and appliances that improve users' lives 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of information input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's previous input history and select the optimal input method 2. The system of claim 1.

9. The reception unit Filter information as it is entered based on the user's current life situation or interests 2. The system of claim 1.

Images