System

Abstract

An object of a system according to an embodiment is to propose an optimal house design based on a user's preference and lifestyle.SOLUTION: A system includes a reception unit, an analysis unit, a feedback unit, and a re-proposal unit. The receiving unit receives information on a user's preference and lifestyle. The analysis unit analyzes the information received by the reception unit and proposes a house design on the basis of the user's preference and lifestyle. The feedback unit allows the user to give feedback based on the house design proposed by the analysis unit. The re-proposal unit makes a proposal again by reflecting the feedback received by the feedback 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 do not adequately propose home designs based on users' preferences and lifestyles, and there is room for improvement.

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

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a feedback unit, and a re-proposal unit. The receiving unit receives information related to a user's preferences and lifestyle. The analysis unit analyzes the information received by the receiving unit and proposes a home design based on the user's preferences and lifestyle. The feedback unit allows the user to provide feedback based on the home design proposed by the analysis unit. The re-proposal unit makes a new proposal that reflects the feedback received by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal home design based on the user's preferences and lifestyle. [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 home design proposal system according to an embodiment of the present invention proposes an optimal home design based on a user's preferences and lifestyle, and re-proposes the optimal home design by reflecting the user's feedback. In the home design proposal system, a user inputs information about their preferences and lifestyle, and a generation AI analyzes the information to propose an optimal home design. The user provides feedback on the proposed home design, and the generation AI reflects the feedback and re-proposes the optimal home design. For example, in a home design proposal system, a user inputs detailed information such as their favorite design, color, and lifestyle. The generation AI proposes an optimal home design based on the user's preferences and lifestyle. The user provides feedback on the proposed plan, and the generation AI reflects the feedback and re-generates the optimal plan. This allows users to collaboratively create their own ideal living environment. This allows the home design proposal system to propose an optimal home design based on the user's preferences and lifestyle, and re-proposes the optimal home design by reflecting the user's feedback. For example, by designing a home that suits their lifestyle, users can make their daily lives more comfortable. Furthermore, enabling designs tailored to individual needs increases the diversity of homes and improves the living environment of society as a whole.

[0029] A home design proposal system according to an embodiment includes a reception unit, an analysis unit, a feedback unit, and a re-proposal unit. The reception unit receives information about a user's preferences and lifestyle. The user's preferences and lifestyle include, but are not limited to, interior design preferences, daily habits, and hobbies. The reception unit allows the user to input detailed information, such as their favorite designs, colors, and lifestyle habits. The analysis unit uses a generation AI to analyze the information received by the reception unit and propose a home design based on the user's preferences and lifestyle. For example, if the user prefers modern designs, the generation AI generates a home plan with a modern design. The analysis unit can also provide specific design ideas for making the living room larger and the kitchen more functional. The feedback unit receives feedback from the user regarding the proposed home design. The feedback includes specific requests, such as "I want this part to be a little wider" or "I want to change this color." The re-proposal unit uses the generation AI to re-propose a home design that incorporates the feedback received by the feedback unit. The generation AI regenerates an optimal plan based on the user's feedback. As a result, the home design proposal system according to the embodiment can propose optimal home designs based on the user's preferences and lifestyle, and can make re-proposals by reflecting feedback. For example, by allowing users to design homes that fit their lifestyles, daily life becomes more comfortable. Furthermore, because designs can be tailored to individual needs, the diversity of homes increases, improving the living environment of society as a whole.

[0030] The reception unit can receive information about the user's family structure, hobbies, and daily routine. Family structure includes, for example, but is not limited to, the number of family members, their ages, and their genders. Hobbies include, for example, but are not limited to, sports, music, and reading. Daily routines include, for example, but are not limited to, commuting time, meal times, and exercise habits. The reception unit, for example, allows the user to input information about the family structure, hobbies, and daily routines. By receiving information about the user's family structure, hobbies, and daily routines, the reception unit can provide more detailed home design proposals. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input information to a generation AI, which then analyzes the information.

[0031] The analysis unit can propose a home design based on the user's preferences and lifestyle. The analysis unit uses the generation AI to propose a home design based on the user's preferences and lifestyle. For example, if the user prefers modern designs, the generation AI generates a home plan with a modern design. The analysis unit can also provide specific design ideas for making the living room spacious and the kitchen functional. For example, if the user inputs a specific request such as "I want a spacious living room and a functional kitchen," the generation AI proposes an optimal design based on that request. This allows the analysis unit to propose an optimal home design based on the user's preferences and lifestyle, enabling a home design that suits the user. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's input information into the generation AI, which can then propose a home design.

[0032] The feedback unit can accept feedback provided by the user on the proposed home design. Examples of feedback include, but are not limited to, specific requests such as "I want this part to be a little wider" or "I want this color to be changed." For example, the feedback unit enables the user to input feedback provided by the user on the proposed home design. By accepting the user's feedback on the proposed home design, the feedback unit can create a home design that reflects the user's opinions. Some or all of the above-described processing in the feedback unit may be performed, for example, using AI or without AI. For example, the feedback unit can input the user's feedback information to a generation AI, which then analyzes the feedback.

[0033] The re-proposal unit can re-propose a home design by reflecting user feedback. The re-proposal unit uses the generation AI to re-propose a home design by reflecting user feedback. For example, if the user provides feedback such as "I want this part to be a little wider," the generation AI re-proposes an optimal design based on that feedback. Furthermore, if the user provides feedback such as "I want to change this color," the re-proposal unit can also re-propose a design that reflects the color change based on that feedback. In this way, the re-proposal unit can re-propose an optimal home design by reflecting user feedback, thereby enabling a home design that meets the user's needs. Some or all of the above-mentioned processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input user feedback information into the generation AI, and have the generation AI generate a re-proposal.

[0034] The reception unit can analyze the user's past input history and select the optimal information reception method. For example, the reception unit can automatically display 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. The reception unit can also 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 select the optimal information reception method by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI, which can select the optimal information reception method.

[0035] The reception unit can filter information based on the user's current living situation and areas of interest when receiving information. For example, when the user inputs their current living situation, the reception unit preferentially receives information that corresponds to that situation. The reception unit can also filter and receive relevant information based on the user's areas of interest. The reception unit can also exclude unnecessary information based on the user's living situation and areas of interest. This allows the reception unit to filter information based on the user's current living situation and areas of interest, thereby receiving highly relevant information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into a generation AI, and the generation AI can filter the information.

[0036] When accepting information, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs information by voice, the acceptance unit accepts the information using voice recognition technology. Furthermore, when the user inputs information by text, the acceptance unit can also provide a text input interface. Furthermore, when the user inputs information by image, the acceptance unit can also accept the information using image recognition technology. In this way, the acceptance unit selects the optimal acceptance means depending on the user's input method, thereby improving the efficiency of information acceptance. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input method data into a generation AI, which can select the optimal acceptance means.

[0037] When accepting information, the acceptance unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. For example, when the user inputs their current location, the acceptance unit prioritizes accepting information related to that area. The acceptance unit can also filter and accept relevant information based on the user's geographical location information. The acceptance unit can also exclude unnecessary information based on the user's current location. In this way, the acceptance unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI. For example, the acceptance unit can input the user's geographical location information data to a generation AI, which can select highly relevant information.

[0038] When receiving information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit can preferentially receive information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. In this way, the reception unit can receive related information by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can select related information.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. The reception unit can also exclude unnecessary information based on the user's past feedback. In this way, the reception unit can provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI, and the generation AI can customize the reception method.

[0040] The analysis unit can adjust the accuracy of the analysis based on the user's preferences and the level of detail of their lifestyle during analysis. The analysis unit uses the generation AI to adjust the accuracy of the analysis based on the user's preferences and the level of detail of their lifestyle during analysis. For example, if the user prefers detailed information, the analysis unit can provide highly accurate analysis results. Also, if the user prefers concise information, the analysis unit can provide analysis results that focus on the main points. The analysis unit can also select the optimal analysis accuracy based on the user's lifestyle. This allows the analysis unit to provide optimal analysis results by adjusting the analysis accuracy based on the user's preferences and the level of detail of their lifestyle. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input detailed data on the user's preferences and lifestyle into the generation AI, and the generation AI can adjust the analysis accuracy.

[0041] The analysis unit can apply different analysis algorithms depending on the user's category during analysis. The analysis unit uses the generation AI to apply different analysis algorithms depending on the user's category during analysis. For example, if the user desires a home for a family, the analysis unit applies an analysis algorithm for families. Furthermore, if the user desires a home for a single person, the analysis unit can apply an analysis algorithm for single people. Furthermore, if the user desires a home for the elderly, the analysis unit can apply an analysis algorithm for the elderly. In this way, the analysis unit can provide optimal analysis results by applying different analysis algorithms depending on the user's category. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's category data into the generation AI and have the generation AI apply different analysis algorithms.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit performs a highly accurate analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the analysis accuracy. The analysis unit can also select an optimal analysis algorithm based on the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority based on the time of user input during analysis. The analysis unit uses the generation AI to determine the analysis priority based on the time of user input during analysis. For example, the analysis unit can perform a quick analysis when the user is in a hurry. The analysis unit can also perform a detailed analysis when the user is relaxed. The analysis unit can also perform an analysis according to the time of user input when the user inputs during a specific time period. In this way, the analysis unit can determine the analysis priority based on the time of user input, thereby providing analysis results quickly. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input user input time data into the generation AI, and the generation AI can determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the user's relevance during analysis. The analysis unit uses the generation AI to adjust the order of analysis based on the user's relevance during analysis. For example, if the user inputs important information, the analysis unit prioritizes analyzing that information. Furthermore, if the user inputs highly relevant information, the analysis unit can also prioritize analyzing that information. Furthermore, if the user inputs less relevant information, the analysis unit can analyze that information later. In this way, the analysis unit can prioritize analyzing important information by adjusting the order of analysis based on the user's relevance. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's relevance data into the generation AI, and the generation AI can adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit uses the generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. If the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. The analysis unit can also provide optimal analysis results 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 according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0046] When receiving feedback, the feedback unit can analyze the user's past feedback history and select the optimal feedback reception method. For example, the feedback unit can automatically display feedback that the user has frequently provided in the past as candidates. The feedback unit can also preferentially suggest feedback methods (audio, text, etc.) that the user has used in the past. The feedback unit can also predict and suggest feedback to be provided in a specific time period based on the user's past feedback history. In this way, the feedback unit can select the optimal feedback reception method by analyzing the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI, which can select the optimal feedback reception method.

[0047] When receiving feedback, the feedback unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the feedback unit preferentially receives feedback that corresponds to that situation. The feedback unit can also filter and receive relevant feedback based on the user's areas of interest. The feedback unit can also exclude unnecessary feedback based on the user's living situation and areas of interest. In this way, the feedback unit can receive highly relevant feedback by filtering the feedback based on the user's current living situation and areas of interest. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's living situation and areas of interest into a generation AI, and the generation AI can filter the feedback.

[0048] When receiving feedback, the feedback unit can select the optimal receiving means depending on the user's input method. For example, when the user inputs feedback by voice, the feedback unit can receive the feedback using voice recognition technology. Furthermore, when the user inputs feedback by text, the feedback unit can also provide a text input interface. Furthermore, when the user inputs feedback by image, the feedback unit can also receive the feedback using image recognition technology. In this way, the feedback unit selects the optimal receiving means depending on the user's input method, thereby improving the efficiency of feedback reception. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user input method data into a generation AI, which can select the optimal receiving means.

[0049] When receiving feedback, the feedback unit can prioritize receiving highly relevant feedback by taking into account the user's geographical location information. For example, when the user inputs their current location, the feedback unit prioritizes receiving feedback related to that area. The feedback unit can also filter and receive relevant feedback based on the user's geographical location information. The feedback unit can also filter out unnecessary feedback based on the user's current location. In this way, the feedback unit can prioritize receiving highly relevant feedback by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information data to a generation AI, which can select highly relevant feedback.

[0050] When receiving feedback, the feedback unit can analyze the user's social media activity and receive relevant feedback. For example, the feedback unit can preferentially receive feedback regarding places where the user has checked in on social media. The feedback unit can also analyze the content of the user's social media posts and receive relevant feedback. The feedback unit can also receive relevant feedback by referring to the activities of the user's friends on social media. In this way, the feedback unit can receive relevant feedback by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI, which can select relevant feedback.

[0051] When receiving feedback, the feedback unit can customize the receiving method by reflecting the user's past feedback. The feedback unit can, for example, suggest an optimal receiving method based on feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback and customize the receiving method. The feedback unit can also exclude unnecessary feedback based on the user's past feedback. In this way, the feedback unit can provide an optimal feedback receiving method by reflecting the user's past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI, and the generation AI can customize the receiving method.

[0052] The re-suggestion unit can adjust the accuracy of the re-suggestion based on the level of detail of the user's feedback when making a re-suggestion. The re-suggestion unit uses the generation AI to adjust the accuracy of the re-suggestion based on the level of detail of the user's feedback when making a re-suggestion. For example, if the user provides detailed feedback, the re-suggestion unit can make a highly accurate re-suggestion. Also, if the user provides concise feedback, the re-suggestion unit can make a re-suggestion that focuses on the main points. Also, the re-suggestion unit can select an optimal re-suggestion based on the level of detail of the user's feedback. In this way, the re-suggestion unit can provide an optimal re-suggestion by adjusting the accuracy of the re-suggestion based on the level of detail of the user's feedback. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input level of detail data of the user's feedback to the generation AI, and the generation AI can adjust the accuracy of the re-suggestion.

[0053] The re-proposal unit can apply different re-proposal algorithms depending on the user's category when making a re-proposal. The re-proposal unit uses the generation AI to apply different re-proposal algorithms depending on the user's category when making a re-proposal. For example, if the user desires a home for families, the re-proposal unit can apply a re-proposal algorithm for families. Furthermore, if the user desires a home for single people, the re-proposal unit can also apply a re-proposal algorithm for single people. Furthermore, if the user desires a home for elderly people, the re-proposal unit can also apply a re-proposal algorithm for elderly people. In this way, the re-proposal unit can provide an optimal re-proposal by applying different re-proposal algorithms depending on the user's category. Some or all of the above-mentioned processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input the user's category data into the generation AI and apply different re-proposal algorithms using the generation AI.

[0054] The re-proposal unit can improve the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. The re-proposal unit uses the generation AI to improve the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. For example, the re-proposal unit makes a highly accurate re-proposal based on the user's past re-proposal results. The re-proposal unit can also analyze the user's past re-proposal results and improve the accuracy of the re-proposal. The re-proposal unit can also select an optimal re-proposal algorithm based on the user's past re-proposal results. In this way, the re-proposal unit can improve the accuracy of the re-proposal by referring to the user's past re-proposal results. Some or all of the above-mentioned processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input the user's past re-proposal result data into the generation AI, and the generation AI can improve the accuracy of the re-proposal.

[0055] The re-suggestion unit can determine the priority of re-suggestions based on the timing of the user's feedback when making a re-suggestion. The re-suggestion unit uses the generation AI to determine the priority of re-suggestions based on the timing of the user's feedback when making a re-suggestion. For example, if the user is in a hurry, the re-suggestion unit can quickly make a re-suggestion. The re-suggestion unit can also provide a detailed re-suggestion when the user is relaxed. The re-suggestion unit can also provide a re-suggestion according to the time period when the user provides feedback. In this way, the re-suggestion unit can quickly provide re-suggestions by determining the priority of re-suggestions based on the timing of the user's feedback. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input user feedback time data into the generation AI, and the generation AI can determine the priority of re-suggestions.

[0056] The re-proposal unit can adjust the order of re-proposals based on the user's relevance when re-proposals are made. The re-proposal unit uses the generation AI to adjust the order of re-proposals based on the user's relevance when re-proposals are made. For example, if the user provides important feedback, the re-proposal unit can prioritize reflect that feedback in the re-proposal. Furthermore, if the user provides highly relevant feedback, the re-proposal unit can also prioritize reflecting that feedback in the re-proposal when re-proposals are made. Furthermore, if the user provides less relevant feedback, the re-proposal unit can reflect that feedback in the re-proposal later. In this way, the re-proposal unit can prioritize reflecting important feedback in the re-proposal by adjusting the order of re-proposals based on the user's relevance. Some or all of the above-described processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input user relevance data into the generation AI, and the generation AI can adjust the order of re-proposals.

[0057] The re-suggestion unit can adjust the use of technical terminology in the re-suggestion according to the user's level of expertise when making a re-suggestion. The re-suggestion unit uses the generation AI to adjust the use of technical terminology in the re-suggestion according to the user's level of expertise when making a re-suggestion. For example, if the user has technical expertise, the re-suggestion unit can provide a re-suggestion that uses a lot of technical terminology. Also, if the user does not have technical expertise, the re-suggestion unit can provide a concise and easy-to-understand re-suggestion. The re-suggestion unit can also provide an optimal re-suggestion based on the user's level of expertise. In this way, the re-suggestion unit can provide a re-suggestion that is easy for the user to understand by adjusting the use of technical terminology in the re-suggestion according to the user's level of expertise. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terminology in the re-suggestion.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The home design proposal system can further include a health analysis unit that acquires the user's health data and proposes the optimal home design based on the user's health condition. For example, if the user has allergies, the system can propose a design that minimizes allergens. If the user has high blood pressure, the system can propose a design that emphasizes a relaxing space. Furthermore, if the user enjoys exercise, the system can suggest the installation of a home gym. In this way, the health analysis unit can support the user's health by proposing the optimal home design based on the user's health condition.

[0060] The home design proposal system can further include an energy analysis unit that acquires the user's energy consumption data and proposes a design that optimizes energy efficiency. For example, if the user wants to reduce energy consumption, the system can propose a design that uses materials with high thermal insulation performance. It can also propose the installation of a solar power generation system. It can also propose the placement of home appliances with high energy efficiency. In this way, the energy analysis unit can improve energy efficiency by proposing the optimal home design based on the user's energy consumption data.

[0061] The home design proposal system can further include a rhythm analysis unit that acquires the user's lifestyle rhythm data and proposes an optimal home design based on the user's lifestyle rhythm. For example, if the user is a nocturnal person, the system can propose a lighting design suitable for nighttime activities. If the user is active early in the morning, the system can also propose a window placement that allows the morning sun to easily enter. Furthermore, if the user spends a lot of time during the day, the system can propose a design that makes the most of natural light. In this way, the rhythm analysis unit can propose an optimal home design based on the user's lifestyle rhythm, thereby providing a design that suits the user's lifestyle.

[0062] The home design proposal system can further include a theme analysis unit that proposes home designs along specific themes based on the user's hobbies and interests. For example, if the user's hobby is gardening, the system can propose designs that emphasize garden and balcony designs. If the user's hobby is music, the system can propose a music room design equipped with soundproofing equipment. Furthermore, if the user's hobby is cooking, the system can propose a design equipped with a spacious kitchen and the latest cooking equipment. In this way, the theme analysis unit can propose optimal home designs based on the user's hobbies and interests, thereby providing a design that suits the user's lifestyle.

[0063] The home design proposal system can further include a pet analysis unit that acquires information about the user's pet and proposes a home design that takes the pet's comfort into consideration. For example, if the user has a dog, a design with a large yard and space for the dog can be proposed. If the user has a cat, a design with a catwalk or cat tower can be proposed. Furthermore, if the user has a small animal, a design with a dedicated space for keeping the animal can be proposed. In this way, the pet analysis unit can propose an optimal home design based on the information about the user's pet, thereby providing a living environment where the user can live comfortably with their pet.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives information about the user's preferences and lifestyle. The user's preferences and lifestyle include interior design preferences, daily habits, hobbies, etc. The reception unit allows the user to input detailed information such as their favorite designs, colors, and lifestyle habits. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose a home design based on the user's preferences and lifestyle. For example, if the user prefers modern designs, it will generate a home plan with a modern design. It will also provide specific design ideas for making the living room spacious and the kitchen functional. Step 3: The feedback section accepts feedback from the user on the proposed home design. Feedback includes specific requests such as "I want this part to be a little wider" or "I want this color to be different." Step 4: The re-proposal unit uses the generation AI to re-propose a new home design, reflecting the feedback received by the feedback unit. The generation AI re-generates the optimal plan based on the user's feedback.

[0066] (Example 2) A home design proposal system according to an embodiment of the present invention proposes an optimal home design based on a user's preferences and lifestyle, and re-proposes the optimal home design by reflecting the user's feedback. In the home design proposal system, a user inputs information about their preferences and lifestyle, and a generation AI analyzes the information to propose an optimal home design. The user provides feedback on the proposed home design, and the generation AI reflects the feedback and re-proposes the optimal home design. For example, in a home design proposal system, a user inputs detailed information such as their favorite design, color, and lifestyle. The generation AI proposes an optimal home design based on the user's preferences and lifestyle. The user provides feedback on the proposed plan, and the generation AI reflects the feedback and re-generates the optimal plan. This allows users to collaboratively create their own ideal living environment. This allows the home design proposal system to propose an optimal home design based on the user's preferences and lifestyle, and re-proposes the optimal home design by reflecting the user's feedback. For example, by designing a home that suits their lifestyle, users can make their daily lives more comfortable. Furthermore, enabling designs tailored to individual needs increases the diversity of homes and improves the living environment of society as a whole.

[0067] A home design proposal system according to an embodiment includes a reception unit, an analysis unit, a feedback unit, and a re-proposal unit. The reception unit receives information about a user's preferences and lifestyle. The user's preferences and lifestyle include, but are not limited to, interior design preferences, daily habits, and hobbies. The reception unit allows the user to input detailed information, such as their favorite designs, colors, and lifestyle habits. The analysis unit uses a generation AI to analyze the information received by the reception unit and propose a home design based on the user's preferences and lifestyle. For example, if the user prefers modern designs, the generation AI generates a home plan with a modern design. The analysis unit can also provide specific design ideas for making the living room larger and the kitchen more functional. The feedback unit receives feedback from the user regarding the proposed home design. The feedback includes specific requests, such as "I want this part to be a little wider" or "I want to change this color." The re-proposal unit uses the generation AI to re-propose a home design that incorporates the feedback received by the feedback unit. The generation AI regenerates an optimal plan based on the user's feedback. As a result, the home design proposal system according to the embodiment can propose optimal home designs based on the user's preferences and lifestyle, and can make re-proposals by reflecting feedback. For example, by allowing users to design homes that fit their lifestyles, daily life becomes more comfortable. Furthermore, because designs can be tailored to individual needs, the diversity of homes increases, improving the living environment of society as a whole.

[0068] The reception unit can receive information about the user's family structure, hobbies, and daily routine. Family structure includes, for example, but is not limited to, the number of family members, their ages, and their genders. Hobbies include, for example, but are not limited to, sports, music, and reading. Daily routines include, for example, but are not limited to, commuting time, meal times, and exercise habits. The reception unit, for example, allows the user to input information about the family structure, hobbies, and daily routines. By receiving information about the user's family structure, hobbies, and daily routines, the reception unit can provide more detailed home design proposals. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input information to a generation AI, which then analyzes the information.

[0069] The analysis unit can propose a home design based on the user's preferences and lifestyle. The analysis unit uses the generation AI to propose a home design based on the user's preferences and lifestyle. For example, if the user prefers modern designs, the generation AI generates a home plan with a modern design. The analysis unit can also provide specific design ideas for making the living room spacious and the kitchen functional. For example, if the user inputs a specific request such as "I want a spacious living room and a functional kitchen," the generation AI proposes an optimal design based on that request. This allows the analysis unit to propose an optimal home design based on the user's preferences and lifestyle, enabling a home design that suits the user. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's input information into the generation AI, which can then propose a home design.

[0070] The feedback unit can accept feedback provided by the user on the proposed home design. Examples of feedback include, but are not limited to, specific requests such as "I want this part to be a little wider" or "I want this color to be changed." For example, the feedback unit enables the user to input feedback provided by the user on the proposed home design. By accepting the user's feedback on the proposed home design, the feedback unit can create a home design that reflects the user's opinions. Some or all of the above-described processing in the feedback unit may be performed, for example, using AI or without AI. For example, the feedback unit can input the user's feedback information to a generation AI, which then analyzes the feedback.

[0071] The re-proposal unit can re-propose a home design by reflecting user feedback. The re-proposal unit uses the generation AI to re-propose a home design by reflecting user feedback. For example, if the user provides feedback such as "I want this part to be a little wider," the generation AI re-proposes an optimal design based on that feedback. Furthermore, if the user provides feedback such as "I want to change this color," the re-proposal unit can also re-propose a design that reflects the color change based on that feedback. In this way, the re-proposal unit can re-propose an optimal home design by reflecting user feedback, thereby enabling a home design that meets the user's needs. Some or all of the above-mentioned processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input user feedback information into the generation AI, and have the generation AI generate a re-proposal.

[0072] The reception unit can estimate the user's emotions and adjust the information reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This allows the reception unit to adjust the information reception method according to the user's emotions, thereby enabling optimal information reception for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into a generation AI, which then estimates the emotion.

[0073] The reception unit can analyze the user's past input history and select the optimal information reception method. For example, the reception unit can automatically display 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. The reception unit can also 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 select the optimal information reception method by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI, which can select the optimal information reception method.

[0074] The reception unit can filter information based on the user's current living situation and areas of interest when receiving information. For example, when the user inputs their current living situation, the reception unit preferentially receives information that corresponds to that situation. The reception unit can also filter and receive relevant information based on the user's areas of interest. The reception unit can also exclude unnecessary information based on the user's living situation and areas of interest. This allows the reception unit to filter information based on the user's current living situation and areas of interest, thereby receiving highly relevant information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into a generation AI, and the generation AI can filter the information.

[0075] When accepting information, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs information by voice, the acceptance unit accepts the information using voice recognition technology. Furthermore, when the user inputs information by text, the acceptance unit can also provide a text input interface. Furthermore, when the user inputs information by image, the acceptance unit can also accept the information using image recognition technology. In this way, the acceptance unit selects the optimal acceptance means depending on the user's input method, thereby improving the efficiency of information acceptance. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input method data into a generation AI, which can select the optimal acceptance means.

[0076] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving important information. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving information that requires quick processing. In this way, the reception unit can prioritize receiving important information by determining the priority of information to be received according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0077] When accepting information, the acceptance unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. For example, when the user inputs their current location, the acceptance unit prioritizes accepting information related to that area. The acceptance unit can also filter and accept relevant information based on the user's geographical location information. The acceptance unit can also exclude unnecessary information based on the user's current location. In this way, the acceptance unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI. For example, the acceptance unit can input the user's geographical location information data to a generation AI, which can select highly relevant information.

[0078] When receiving information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit can preferentially receive information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. In this way, the reception unit can receive related information by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can select related information.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. The reception unit can also exclude unnecessary information based on the user's past feedback. In this way, the reception unit can provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI, and the generation AI can customize the reception method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. The analysis unit can use a generation AI to estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. 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. The analysis unit can also provide visually stimulating analysis results when the user is excited. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions and provide optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0081] The analysis unit can adjust the accuracy of the analysis based on the user's preferences and the level of detail of their lifestyle during analysis. The analysis unit uses the generation AI to adjust the accuracy of the analysis based on the user's preferences and the level of detail of their lifestyle during analysis. For example, if the user prefers detailed information, the analysis unit can provide highly accurate analysis results. Also, if the user prefers concise information, the analysis unit can provide analysis results that focus on the main points. The analysis unit can also select the optimal analysis accuracy based on the user's lifestyle. This allows the analysis unit to provide optimal analysis results by adjusting the analysis accuracy based on the user's preferences and the level of detail of their lifestyle. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input detailed data on the user's preferences and lifestyle into the generation AI, and the generation AI can adjust the analysis accuracy.

[0082] The analysis unit can apply different analysis algorithms depending on the user's category during analysis. The analysis unit uses the generation AI to apply different analysis algorithms depending on the user's category during analysis. For example, if the user desires a home for a family, the analysis unit applies an analysis algorithm for families. Furthermore, if the user desires a home for a single person, the analysis unit can apply an analysis algorithm for single people. Furthermore, if the user desires a home for the elderly, the analysis unit can apply an analysis algorithm for the elderly. In this way, the analysis unit can provide optimal analysis results by applying different analysis algorithms depending on the user's category. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's category data into the generation AI and have the generation AI apply different analysis algorithms.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit performs a highly accurate analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the analysis accuracy. The analysis unit can also select an optimal analysis algorithm based on the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can use a generation AI to estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can adjust the length of the analysis.

[0085] The analysis unit can determine the analysis priority based on the time of user input during analysis. The analysis unit uses the generation AI to determine the analysis priority based on the time of user input during analysis. For example, the analysis unit can perform a quick analysis when the user is in a hurry. The analysis unit can also perform a detailed analysis when the user is relaxed. The analysis unit can also perform an analysis according to the time of user input when the user inputs during a specific time period. In this way, the analysis unit can determine the analysis priority based on the time of user input, thereby providing analysis results quickly. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input user input time data into the generation AI, and the generation AI can determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the user's relevance during analysis. The analysis unit uses the generation AI to adjust the order of analysis based on the user's relevance during analysis. For example, if the user inputs important information, the analysis unit prioritizes analyzing that information. Furthermore, if the user inputs highly relevant information, the analysis unit can also prioritize analyzing that information. Furthermore, if the user inputs less relevant information, the analysis unit can analyze that information later. In this way, the analysis unit can prioritize analyzing important information by adjusting the order of analysis based on the user's relevance. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's relevance data into the generation AI, and the generation AI can adjust the order of analysis.

[0087] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit uses the generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. If the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. The analysis unit can also provide optimal analysis results 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 according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0088] The feedback unit can estimate the user's emotions and adjust the feedback acceptance method based on the estimated user emotions. For example, if the user is feeling stressed, the feedback unit can provide a simple interface and minimize the feedback procedure. Furthermore, if the user is relaxed, the feedback unit can provide detailed feedback options and suggest a customizable feedback method. Furthermore, if the user is in a hurry, the feedback unit can prioritize voice input and quickly accept feedback. This allows the feedback unit to adjust the feedback acceptance method according to the user's emotions, thereby enabling optimal feedback acceptance for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0089] When receiving feedback, the feedback unit can analyze the user's past feedback history and select the optimal feedback reception method. For example, the feedback unit can automatically display feedback that the user has frequently provided in the past as candidates. The feedback unit can also preferentially suggest feedback methods (audio, text, etc.) that the user has used in the past. The feedback unit can also predict and suggest feedback to be provided in a specific time period based on the user's past feedback history. In this way, the feedback unit can select the optimal feedback reception method by analyzing the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI, which can select the optimal feedback reception method.

[0090] When receiving feedback, the feedback unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the feedback unit preferentially receives feedback that corresponds to that situation. The feedback unit can also filter and receive relevant feedback based on the user's areas of interest. The feedback unit can also exclude unnecessary feedback based on the user's living situation and areas of interest. In this way, the feedback unit can receive highly relevant feedback by filtering the feedback based on the user's current living situation and areas of interest. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's living situation and areas of interest into a generation AI, and the generation AI can filter the feedback.

[0091] When receiving feedback, the feedback unit can select the optimal receiving means depending on the user's input method. For example, when the user inputs feedback by voice, the feedback unit can receive the feedback using voice recognition technology. Furthermore, when the user inputs feedback by text, the feedback unit can also provide a text input interface. Furthermore, when the user inputs feedback by image, the feedback unit can also receive the feedback using image recognition technology. In this way, the feedback unit selects the optimal receiving means depending on the user's input method, thereby improving the efficiency of feedback reception. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user input method data into a generation AI, which can select the optimal receiving means.

[0092] The feedback unit can estimate the user's emotions and determine the priority of feedback to be received based on the estimated user's emotions. For example, when the user is stressed, the feedback unit can prioritize receiving important feedback. Furthermore, when the user is relaxed, the feedback unit can prioritize receiving detailed feedback. Furthermore, when the user is in a hurry, the feedback unit can prioritize receiving feedback that requires quick processing. In this way, the feedback unit can prioritize receiving important feedback by determining the priority of feedback to be received according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit may be performed using an AI, for example, or without an AI. For example, the feedback unit can input user emotion data into the generation AI, and the generation AI can estimate the emotion.

[0093] When receiving feedback, the feedback unit can prioritize receiving highly relevant feedback by taking into account the user's geographical location information. For example, when the user inputs their current location, the feedback unit prioritizes receiving feedback related to that area. The feedback unit can also filter and receive relevant feedback based on the user's geographical location information. The feedback unit can also filter out unnecessary feedback based on the user's current location. In this way, the feedback unit can prioritize receiving highly relevant feedback by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information data to a generation AI, which can select highly relevant feedback.

[0094] When receiving feedback, the feedback unit can analyze the user's social media activity and receive relevant feedback. For example, the feedback unit can preferentially receive feedback regarding places where the user has checked in on social media. The feedback unit can also analyze the content of the user's social media posts and receive relevant feedback. The feedback unit can also receive relevant feedback by referring to the activities of the user's friends on social media. In this way, the feedback unit can receive relevant feedback by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI, which can select relevant feedback.

[0095] When receiving feedback, the feedback unit can customize the receiving method by reflecting the user's past feedback. The feedback unit can, for example, suggest an optimal receiving method based on feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback and customize the receiving method. The feedback unit can also exclude unnecessary feedback based on the user's past feedback. In this way, the feedback unit can provide an optimal feedback receiving method by reflecting the user's past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI, and the generation AI can customize the receiving method.

[0096] The re-suggestion unit can estimate the user's emotions and adjust the expression method of the re-suggestion based on the estimated user's emotions. The re-suggestion unit can estimate the user's emotions using a generation AI and adjust the expression method of the re-suggestion based on the estimated user's emotions. For example, if the user is relaxed, the re-suggestion unit can provide a detailed re-suggestion. If the user is in a hurry, the re-suggestion unit can provide a concise re-suggestion that focuses on the main points. If the user is excited, the re-suggestion unit can provide a visually stimulating re-suggestion. In this way, the re-suggestion unit can adjust the expression method of the re-suggestion according to the user's emotions, thereby providing an optimal re-suggestion for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the expression method of the re-suggestion.

[0097] The re-suggestion unit can adjust the accuracy of the re-suggestion based on the level of detail of the user's feedback when making a re-suggestion. The re-suggestion unit uses the generation AI to adjust the accuracy of the re-suggestion based on the level of detail of the user's feedback when making a re-suggestion. For example, if the user provides detailed feedback, the re-suggestion unit can make a highly accurate re-suggestion. Also, if the user provides concise feedback, the re-suggestion unit can make a re-suggestion that focuses on the main points. Also, the re-suggestion unit can select an optimal re-suggestion based on the level of detail of the user's feedback. In this way, the re-suggestion unit can provide an optimal re-suggestion by adjusting the accuracy of the re-suggestion based on the level of detail of the user's feedback. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input level of detail data of the user's feedback to the generation AI, and the generation AI can adjust the accuracy of the re-suggestion.

[0098] The re-proposal unit can apply different re-proposal algorithms depending on the user's category when making a re-proposal. The re-proposal unit uses the generation AI to apply different re-proposal algorithms depending on the user's category when making a re-proposal. For example, if the user desires a home for families, the re-proposal unit can apply a re-proposal algorithm for families. Furthermore, if the user desires a home for single people, the re-proposal unit can also apply a re-proposal algorithm for single people. Furthermore, if the user desires a home for elderly people, the re-proposal unit can also apply a re-proposal algorithm for elderly people. In this way, the re-proposal unit can provide an optimal re-proposal by applying different re-proposal algorithms depending on the user's category. Some or all of the above-mentioned processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input the user's category data into the generation AI and apply different re-proposal algorithms using the generation AI.

[0099] The re-proposal unit can improve the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. The re-proposal unit uses the generation AI to improve the accuracy of the re-proposal by referring to the user's past re-proposal results when making a re-proposal. For example, the re-proposal unit makes a highly accurate re-proposal based on the user's past re-proposal results. The re-proposal unit can also analyze the user's past re-proposal results and improve the accuracy of the re-proposal. The re-proposal unit can also select an optimal re-proposal algorithm based on the user's past re-proposal results. In this way, the re-proposal unit can improve the accuracy of the re-proposal by referring to the user's past re-proposal results. Some or all of the above-mentioned processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input the user's past re-proposal result data into the generation AI, and the generation AI can improve the accuracy of the re-proposal.

[0100] The re-suggestion unit can estimate the user's emotion and adjust the length of the re-suggestion based on the estimated user's emotion. The re-suggestion unit can estimate the user's emotion using a generation AI and adjust the length of the re-suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the re-suggestion unit can provide a short and to-the-point re-suggestion. Also, if the user is relaxed, the re-suggestion unit can provide a detailed re-suggestion. Also, if the user is excited, the re-suggestion unit can provide a visually stimulating re-suggestion. Thus, the re-suggestion unit can adjust the length of the re-suggestion according to the user's emotion, thereby providing an optimal re-suggestion for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the length of the re-suggestion.

[0101] The re-suggestion unit can determine the priority of re-suggestions based on the timing of the user's feedback when making a re-suggestion. The re-suggestion unit uses the generation AI to determine the priority of re-suggestions based on the timing of the user's feedback when making a re-suggestion. For example, if the user is in a hurry, the re-suggestion unit can quickly make a re-suggestion. The re-suggestion unit can also provide a detailed re-suggestion when the user is relaxed. The re-suggestion unit can also provide a re-suggestion according to the time period when the user provides feedback. In this way, the re-suggestion unit can quickly provide re-suggestions by determining the priority of re-suggestions based on the timing of the user's feedback. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input user feedback time data into the generation AI, and the generation AI can determine the priority of re-suggestions.

[0102] The re-proposal unit can adjust the order of re-proposals based on the user's relevance when re-proposals are made. The re-proposal unit uses the generation AI to adjust the order of re-proposals based on the user's relevance when re-proposals are made. For example, if the user provides important feedback, the re-proposal unit can prioritize reflect that feedback in the re-proposal. Furthermore, if the user provides highly relevant feedback, the re-proposal unit can also prioritize reflecting that feedback in the re-proposal when re-proposals are made. Furthermore, if the user provides less relevant feedback, the re-proposal unit can reflect that feedback in the re-proposal later. In this way, the re-proposal unit can prioritize reflecting important feedback in the re-proposal by adjusting the order of re-proposals based on the user's relevance. Some or all of the above-described processing in the re-proposal unit is performed using the generation AI. For example, the re-proposal unit can input user relevance data into the generation AI, and the generation AI can adjust the order of re-proposals.

[0103] The re-suggestion unit can adjust the use of technical terminology in the re-suggestion according to the user's level of expertise when making a re-suggestion. The re-suggestion unit uses the generation AI to adjust the use of technical terminology in the re-suggestion according to the user's level of expertise when making a re-suggestion. For example, if the user has technical expertise, the re-suggestion unit can provide a re-suggestion that uses a lot of technical terminology. Also, if the user does not have technical expertise, the re-suggestion unit can provide a concise and easy-to-understand re-suggestion. The re-suggestion unit can also provide an optimal re-suggestion based on the user's level of expertise. In this way, the re-suggestion unit can provide a re-suggestion that is easy for the user to understand by adjusting the use of technical terminology in the re-suggestion according to the user's level of expertise. Some or all of the above-mentioned processing in the re-suggestion unit is performed using the generation AI. For example, the re-suggestion unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terminology in the re-suggestion. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, feedback unit, and re-proposal 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 allows the user to input information about their preferences and lifestyle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's information using a generative AI and proposes an optimal home design. The feedback unit is realized by the reception device 38 of the smart device 14, and allows the user to provide feedback on the proposed home design. The re-proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a new home design reflecting the feedback. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, feedback unit, and re-proposal 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, allowing the user to voice-input information related to their preferences and lifestyle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzing the user's information using a generative AI and proposing an optimal home design. The feedback unit is realized by the microphone 238 of the smart glasses 214, allowing the user to provide voice feedback on the proposed home design. The re-proposal unit is realized by the specific processing unit 290 of the data processing device 12, and re-proposing a home design reflecting the feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, feedback unit, and re-proposal 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 allows the user to input information about their preferences and lifestyle by voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's information using a generative AI and proposes an optimal home design. The feedback unit is realized by the microphone 238 of the headset-type terminal 314, and allows the user to provide voice feedback on the proposed home design. The re-proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a new home design reflecting the feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, feedback unit, and re-proposal 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 allows the user to voice-input information about their preferences and lifestyle. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's information using a generative AI and proposes an optimal home design. The feedback unit is realized by the microphone 238 of the robot 414, and allows the user to provide voice feedback on the proposed home design. The re-proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a new home design reflecting the feedback.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The home design proposal system can further include a health analysis unit that acquires the user's health data and proposes the optimal home design based on the user's health condition. For example, if the user has allergies, the system can propose a design that minimizes allergens. If the user has high blood pressure, the system can propose a design that emphasizes a relaxing space. Furthermore, if the user enjoys exercise, the system can suggest the installation of a home gym. In this way, the health analysis unit can support the user's health by proposing the optimal home design based on the user's health condition.

[0106] The home design proposal system can further include an emotion analysis unit that estimates the user's emotions and adjusts the home design proposal based on the estimated emotions. For example, if the user is feeling stressed, a design that emphasizes a relaxing space can be proposed. If the user is excited, a lively design can be proposed. Furthermore, if the user is sad, a design with calm colors can be proposed. In this way, the emotion analysis unit can propose the optimal home design based on the user's emotions, thereby providing a design that is in line with the user's emotions.

[0107] The home design proposal system can further include an energy analysis unit that acquires the user's energy consumption data and proposes a design that optimizes energy efficiency. For example, if the user wants to reduce energy consumption, the system can propose a design that uses materials with high thermal insulation performance. It can also propose the installation of a solar power generation system. It can also propose the placement of home appliances with high energy efficiency. In this way, the energy analysis unit can improve energy efficiency by proposing the optimal home design based on the user's energy consumption data.

[0108] The home design proposal system may further include an emotion feedback unit that estimates the user's emotion and adjusts the feedback acceptance method based on the estimated emotion. For example, if the user is feeling stressed, a simple interface may be provided to minimize the feedback procedure. Alternatively, if the user is relaxed, detailed feedback options may be provided. Furthermore, if the user is in a hurry, voice input may be prioritized to quickly accept feedback. In this way, the emotion feedback unit adjusts the feedback acceptance method according to the user's emotion, thereby enabling optimal feedback acceptance for the user.

[0109] The home design proposal system can further include a rhythm analysis unit that acquires the user's lifestyle rhythm data and proposes an optimal home design based on the user's lifestyle rhythm. For example, if the user is a nocturnal person, the system can propose a lighting design suitable for nighttime activities. If the user is active early in the morning, the system can also propose a window placement that allows the morning sun to easily enter. Furthermore, if the user spends a lot of time during the day, the system can propose a design that makes the most of natural light. In this way, the rhythm analysis unit can propose an optimal home design based on the user's lifestyle rhythm, thereby providing a design that suits the user's lifestyle.

[0110] The home design proposal system may further include an emotion re-proposal unit that estimates the user's emotion and adjusts the way in which the re-proposal is expressed based on the estimated emotion. For example, if the user is relaxed, a detailed re-proposal may be provided. If the user is in a hurry, a concise re-proposal that focuses on the main points may be provided. Furthermore, if the user is excited, a visually stimulating re-proposal may be provided. In this way, the emotion re-proposal unit can adjust the way in which the re-proposal is expressed according to the user's emotion, thereby providing the most suitable re-proposal for the user.

[0111] The home design proposal system can further include a theme analysis unit that proposes home designs along specific themes based on the user's hobbies and interests. For example, if the user's hobby is gardening, the system can propose designs that emphasize garden and balcony designs. If the user's hobby is music, the system can propose a music room design equipped with soundproofing equipment. Furthermore, if the user's hobby is cooking, the system can propose a design equipped with a spacious kitchen and the latest cooking equipment. In this way, the theme analysis unit can propose optimal home designs based on the user's hobbies and interests, thereby providing a design that suits the user's lifestyle.

[0112] The home design proposal system may further include an emotional color unit that estimates the user's emotions and adjusts the colors of the home design based on the estimated emotions. For example, if the user is relaxed, a design using subdued colors may be proposed. If the user is excited, a design using vivid colors may be proposed. Furthermore, if the user is sad, a design using warm colors may be proposed. In this way, the emotional color unit can propose an optimal color design based on the user's emotions, thereby providing a design that is in line with the user's emotions.

[0113] The home design proposal system can further include a pet analysis unit that acquires information about the user's pet and proposes a home design that takes the pet's comfort into consideration. For example, if the user has a dog, a design with a large yard and space for the dog can be proposed. If the user has a cat, a design with a catwalk or cat tower can be proposed. Furthermore, if the user has a small animal, a design with a dedicated space for keeping the animal can be proposed. In this way, the pet analysis unit can propose an optimal home design based on the information about the user's pet, thereby providing a living environment where the user can live comfortably with their pet.

[0114] The home design proposal system can further include an emotion function unit that estimates the user's emotions and adjusts the functionality of the home design based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-use design can be proposed. Alternatively, if the user is relaxed, a design that pays attention to detail can be proposed. Furthermore, if the user is in a hurry, a layout that allows quick access can be proposed. In this way, the emotion function unit can provide functionality that is in line with the user's emotions by proposing an optimal functional design based on the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives information about the user's preferences and lifestyle. The user's preferences and lifestyle include interior design preferences, daily habits, hobbies, etc. The reception unit allows the user to input detailed information such as their favorite designs, colors, and lifestyle habits. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose a home design based on the user's preferences and lifestyle. For example, if the user prefers modern designs, it will generate a home plan with a modern design. It will also provide specific design ideas for making the living room spacious and the kitchen functional. Step 3: The feedback section accepts feedback from the user on the proposed home design. Feedback includes specific requests such as "I want this part to be a little wider" or "I want this color to be different." Step 4: The re-proposal unit uses the generation AI to re-propose a new home design, reflecting the feedback received by the feedback unit. The generation AI re-generates the optimal plan based on the user's feedback.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 unit that receives information about a user's preferences and lifestyle; an analysis unit that analyzes the information received by the reception unit and proposes a home design based on the user's preferences and lifestyle; a feedback unit that allows a user to provide feedback based on the house design proposed by the analysis unit; a re-proposal unit that makes a new proposal by reflecting the feedback received by the feedback unit. A system characterized by:

2. The reception unit Accepts information about the user's family, hobbies, and daily routine 2. The system of claim 1.

3. The analysis unit Proposing home designs based on the user's preferences and lifestyle 2. The system of claim 1.

4. The feedback unit Accept user feedback on proposed home designs 2. The system of claim 1.

5. The re-proposal unit Propose new home designs based on user feedback 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the way information is received based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the optimal method of receiving information 2. The system of claim 1.

8. The reception unit When receiving information, it filters it based on the user's current life situation and areas of interest.

2. The system of claim 1.

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