system

Abstract

The system according to the embodiment aims to provide suggestions for specifically realizing the interior design ideas desired by the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a supply unit. The reception unit receives input from the user regarding their desired interior design. The analysis unit analyzes the ideas input by the reception unit and makes proposals that take into account furniture placement, ventilation for air conditioning, and passageway width for traffic flow. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The supply unit presents products that are recommended for purchase based on the content proposed by the proposal unit.

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 provide sufficient proposals for specifically realizing users' desired interior ideas, and there is room for improvement.

[0005] The system according to the embodiment aims to provide suggestions for specifically realizing the interior design ideas desired by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs ideas for interior design desired by the user. The analysis unit analyzes the ideas input by the reception unit and makes proposals that take into consideration furniture placement, ventilation for air conditioning, and aisle width for traffic flow. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The provision unit presents recommended products for purchase based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can make suggestions for concretely realizing the interior design ideas desired by the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An interior design proposal system according to an embodiment of the present invention is a system that makes interior design proposals using a generative AI. This system allows users to input their desired interior design ideas, and the generative AI analyzes these ideas and makes proposals that take into account necessary factors such as furniture placement, airflow, and aisle width. Furthermore, the generative AI suggests products that the user should purchase based on the proposal. This system allows users to easily realize their ideal interior. For example, a user might input a specific request such as, "I want to place a modern sofa in the living room and create a layout that considers airflow." This information is input into the generative AI. Next, the generative AI analyzes the input idea. Based on the user's request, the generative AI makes proposals that take into account necessary factors such as furniture placement, airflow, and aisle width. For example, it might determine the placement of the sofa and propose a layout that ensures airflow. It might also propose a layout that ensures appropriate aisle width. Furthermore, the generative AI suggests products that the user should purchase based on the proposal. For example, it might suggest the specific product name and purchase location of the proposed sofa. The system also suggests recommended air conditioners and fans to ensure proper airflow. This allows users to easily purchase necessary items based on the suggestions. This system makes it easy for users to realize their ideal interior. For example, they can place a modern sofa in their living room and create a layout that considers proper airflow. By ensuring appropriate aisle widths, a comfortable living space can be provided. Furthermore, by suggesting products to purchase based on the suggestions, users can easily buy the necessary items. In this way, the interior design suggestion system can efficiently input, analyze, suggest, and provide users with their desired interior design ideas.

[0029] An interior design proposal system according to an embodiment includes a receiving unit, an analysis unit, a proposal unit, and a providing unit. The receiving unit inputs interior design ideas desired by a user. The interior design ideas desired by the user include, but are not limited to, room layouts, furniture types, and color combinations. The receiving unit can receive interior design ideas via, for example, text input, voice input, or image input. The analysis unit uses a generation AI to analyze the ideas input by the receiving unit. The analysis is performed based on, for example, but not limited to, the algorithm used, the accuracy of the analysis, and factors considered. For example, the analysis unit uses a text generation AI (e.g., LLM) to analyze the user's requests. The analysis unit can also use a multimodal generation AI to analyze multiple modalities such as text, images, and voice. The analysis unit also uses the generation AI to make proposals that take into account necessary factors such as furniture placement, ease of air conditioning, and aisle width for traffic flow. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The proposal is made based on, for example, the type of furniture to be proposed, the layout pattern, the basis for the proposal, etc., but is not limited to such examples. For example, the proposal unit uses a generation AI to determine where to place furniture and propose a layout that ensures easy air circulation. The proposal unit can also propose a layout that ensures appropriate aisle width for traffic flow. The provision unit presents products recommended for purchase based on the content proposed by the proposal unit. The provided products include, for example, furniture, interior accessories, lighting fixtures, etc., but are not limited to such examples. For example, the provision unit presents the specific product name and purchasing location of the proposed sofa. The provision unit can also present recommended products such as air conditioners and fans that ensure easy air circulation. As a result, the interior proposal system according to the embodiment can efficiently input, analyze, propose, and provide interior ideas desired by the user.

[0030] The reception desk can analyze the user's past input history of interior design ideas and select an appropriate input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used at specific times based on the user's past input history. Furthermore, the reception desk can analyze trends in the ideas the user has entered in the past and suggest the optimal input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0031] The reception unit can filter interior design ideas based on the user's current living situation and areas of interest when they are entered. For example, the reception unit can prioritize displaying interior design ideas relevant to the user's current living situation. The reception unit can also filter and display relevant interior design ideas based on the user's areas of interest. Furthermore, the reception unit can suggest optimal interior design ideas based on the user's living situation and areas of interest. This allows the reception unit to prioritize displaying relevant interior design ideas based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's living situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0032] When inputting interior ideas, the reception unit can prioritize inputting highly relevant ideas taking into account the user's geographical location information. The reception unit can, for example, prioritize displaying relevant interior ideas based on the user's current location. The reception unit can also suggest optimal interior ideas based on the user's geographical location information. Furthermore, the reception unit can prioritize inputting highly relevant interior ideas taking into account the user's geographical location information. This allows highly relevant interior ideas to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant ideas.

[0033] When inputting interior ideas, the reception unit can analyze the user's social media activities and input related ideas. For example, the reception unit extracts and inputs related interior ideas from the user's social media activities. The reception unit can also suggest optimal ideas based on interior ideas shared by the user on social media. Furthermore, the reception unit can analyze the user's social media activities and preferentially input related interior ideas. In this way, by analyzing the user's social media activities, related interior ideas can be preferentially input. Some or all of the above-described processing by 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 data to the generation AI and cause the generation AI to extract related ideas.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the interior design ideas. For example, the analysis unit can perform a detailed analysis on interior design ideas with high importance. It can also perform a simplified analysis on interior design ideas with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the interior design ideas. By adjusting the level of detail of the analysis based on the importance of the interior design ideas, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the interior design ideas into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of the interior design idea during the analysis. For example, the analysis unit can apply the optimal placement algorithm to an interior design idea related to furniture placement. It can also apply the optimal air conditioning analysis algorithm to an interior design idea related to the ease of air conditioning circulation. Furthermore, it can apply the optimal traffic flow analysis algorithm to an interior design idea related to the width of passageways. By applying the optimal analysis algorithm according to the category of the interior design idea, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the interior design idea into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the interior ideas. For example, the analysis unit prioritizes analysis of interior ideas submitted earlier. The analysis unit can also postpone analysis of interior ideas submitted later. Furthermore, the analysis unit can determine the priority of analysis based on the submission date of the interior ideas. This enables more efficient analysis by determining the priority of analysis based on the submission date of the interior ideas. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the interior ideas into the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the interior ideas. For example, the analysis unit prioritizes analysis of highly relevant interior ideas. The analysis unit can also postpone analysis of less relevant interior ideas. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the interior ideas. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the interior ideas. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the interior ideas into the generation AI and cause the generation AI to adjust the order of analysis.

[0038] The proposal unit can adjust the level of detail in its proposals based on the importance of the interior design ideas. For example, it can provide detailed proposals for highly important interior design ideas, and concise proposals for less important ones. Furthermore, it can adjust the level of detail in its proposals based on the importance of the interior design ideas. This allows for the provision of more appropriate proposals by adjusting the level of detail based on the importance of the interior design ideas. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input importance data for interior design ideas into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0039] The proposal unit can apply different proposal algorithms depending on the category of the interior design idea when making a proposal. For example, the proposal unit can apply the optimal placement algorithm to an interior design idea related to furniture placement. It can also apply the optimal air conditioning proposal algorithm to an interior design idea related to air conditioning efficiency. Furthermore, it can apply the optimal traffic flow proposal algorithm to an interior design idea related to the width of passageways. By applying the optimal proposal algorithm according to the category of the interior design idea, it can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input interior design idea category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0040] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the submission date of the interior ideas. For example, the suggestion unit prioritizes the suggestions of interior ideas submitted earlier. The suggestion unit can also postpone the suggestions of interior ideas submitted later. Furthermore, the suggestion unit can also determine the priority of the suggestions based on the submission date of the interior ideas. This enables more efficient suggestions by determining the priority of the suggestions based on the submission date of the interior ideas. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the submission date of the interior ideas into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0041] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the interior ideas. For example, the suggestion unit prioritizes suggesting highly relevant interior ideas. The suggestion unit can also postpone suggesting less relevant interior ideas. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the interior ideas. This allows for more efficient suggestions by adjusting the order of suggestions based on the relevance of the interior ideas. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the interior ideas into the generation AI and cause the generation AI to adjust the order of suggestions.

[0042] The service provider can improve the accuracy of its recommendations by considering the interrelationships of interior design ideas during the recommendation process. For example, the service provider can propose the most suitable products by considering the interrelationship between furniture placement and air conditioning efficiency. It can also propose the most suitable products by considering the interrelationship between aisle width and furniture placement. Furthermore, the service provider can analyze the interrelationships of interior design ideas and propose the most suitable products. This allows for the provision of more appropriate products by considering the interrelationships of interior design ideas. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the interrelationships of interior design ideas into a generating AI and have the generating AI perform the task of improving the accuracy of its recommendations.

[0043] The service provider can provide interior design ideas while considering the attribute information of the submitter. For example, the service provider can suggest the most suitable products based on the submitter's age and gender. It can also suggest the most suitable products based on the submitter's lifestyle. Furthermore, it can suggest the most suitable products based on the submitter's hobbies and interests. This allows for the provision of more appropriate products by considering the attribute information of the interior design idea submitter. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the submitter's attribute information data into a generating AI and have the generating AI perform adjustments to the provision.

[0044] The providing unit can provide the interior ideas taking into consideration the geographical distribution of the ideas. For example, the providing unit can suggest optimal products based on the user's region. The providing unit can also suggest related products based on the geographical distribution. Furthermore, the providing unit can suggest optimal products taking into consideration the geographical distribution of the interior ideas. This allows for more appropriate products to be provided by taking into consideration the geographical distribution of the interior ideas. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical distribution data of the interior ideas into the generating AI and cause the generating AI to adjust the provision.

[0045] The providing unit can improve the accuracy of the provision by referring to literature related to the interior idea when providing the ideas. The providing unit, for example, can refer to related literature to suggest the most suitable product. The providing unit can also suggest the most suitable product based on literature related to the interior idea. Furthermore, the providing unit can improve the accuracy of the provision by referring to related literature when providing the ideas. This makes it possible to provide more suitable products by referring to literature related to the interior idea. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input related literature data into the generating AI and cause the generating AI to improve the accuracy of the provision.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's past purchase history and automatically suggest related products when the user inputs an interior design idea. For example, it can suggest new products of the same brand or style based on data on furniture and interior accessories purchased in the past by the user. The reception unit can also analyze the usage of products purchased in the past by the user and suggest complementary products. Furthermore, the reception unit can suggest products related to specific seasons or events based on the user's past purchase history. This makes it possible to suggest more personalized interior design ideas by utilizing the user's past purchase history.

[0048] The suggestion unit can collect feedback on the user's interior ideas and improve the proposed content based on that feedback. For example, it can provide a function for users to evaluate the proposed ideas and adjust the proposed content based on the evaluation results. The suggestion unit can also analyze the user's feedback, extract common areas for improvement, and optimize the proposal algorithm. Furthermore, the suggestion unit can reflect the user's feedback in real time and instantly update the proposed content. This makes it possible to utilize user feedback to make more accurate interior proposals.

[0049] When a user inputs interior ideas, the reception unit can adjust the proposed content taking into account the user's health condition. For example, if the user has allergies, furniture made from allergen-free materials can be proposed. Also, if the user is elderly, interior ideas with a barrier-free design can be proposed. Furthermore, if the user is not getting enough exercise, a layout that encourages exercise can be proposed. This allows the system to provide more appropriate interior ideas according to the user's health condition.

[0050] The suggestion unit can analyze the user's past proposal history for interior ideas and make optimal suggestions. For example, it can prioritize and suggest ideas that have received high ratings from the user's past proposals. The suggestion unit can also extract specific trends and patterns from the user's past proposal history and make new suggestions based on them. Furthermore, the suggestion unit can customize and provide suggestions based on the user's past proposal history. This makes it possible to make more personalized interior suggestions by utilizing the user's past proposal history.

[0051] When the user inputs interior ideas, the reception unit can adjust the proposed content taking into account the user's family composition. For example, if the user has small children, the reception unit can propose interior ideas that emphasize safety. Also, if the user has pets, the reception unit can propose pet-friendly interior ideas. Furthermore, if the user lives with multiple generations, the reception unit can propose interior ideas that take into account the needs of each generation. This allows the reception unit to provide more appropriate interior ideas according to the user's family composition.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception unit inputs the user's desired interior design ideas. The user's ideas include room layout, furniture types, color combinations, etc. The reception unit can receive interior design ideas by text input, voice input, image input, etc. Step 2: The analysis unit uses generation AI to analyze the ideas entered by the reception unit. The analysis is performed based on the algorithm used, the accuracy of the analysis, the factors taken into consideration, etc. The analysis unit uses text generation AI (e.g., LLM) and multimodal generation AI to analyze multiple modalities such as text, images, and audio, and makes suggestions that take into account necessary factors such as furniture placement, ease of air conditioning circulation, and the width of aisles for traffic flow. Step 3: The proposal team makes specific proposals based on the analysis results obtained by the analysis team. The proposals are based on the types of furniture to be proposed, the arrangement patterns, and the rationale for the proposals. The proposal team uses a generation AI to determine the placement of furniture and proposes layouts that ensure good airflow and appropriate aisle widths for traffic flow. Step 4: The supply department presents recommended products based on the proposals made by the suggestion department. These products may include furniture, interior accessories, and lighting fixtures. The supply department will provide specific product names and purchase locations for the suggested sofas, as well as recommended air conditioners and fans to ensure proper ventilation.

[0054] (Example 2) An interior design proposal system according to an embodiment of the present invention is a system that makes interior design proposals using a generative AI. This system allows users to input their desired interior design ideas, and the generative AI analyzes these ideas and makes proposals that take into account necessary factors such as furniture placement, airflow, and aisle width. Furthermore, the generative AI suggests products that the user should purchase based on the proposal. This system allows users to easily realize their ideal interior. For example, a user might input a specific request such as, "I want to place a modern sofa in the living room and create a layout that considers airflow." This information is input into the generative AI. Next, the generative AI analyzes the input idea. Based on the user's request, the generative AI makes proposals that take into account necessary factors such as furniture placement, airflow, and aisle width. For example, it might determine the placement of the sofa and propose a layout that ensures airflow. It might also propose a layout that ensures appropriate aisle width. Furthermore, the generative AI suggests products that the user should purchase based on the proposal. For example, it might suggest the specific product name and purchase location of the proposed sofa. The system also suggests recommended air conditioners and fans to ensure proper airflow. This allows users to easily purchase necessary items based on the suggestions. This system makes it easy for users to realize their ideal interior. For example, they can place a modern sofa in their living room and create a layout that considers proper airflow. By ensuring appropriate aisle widths, a comfortable living space can be provided. Furthermore, by suggesting products to purchase based on the suggestions, users can easily buy the necessary items. In this way, the interior design suggestion system can efficiently input, analyze, suggest, and provide users with their desired interior design ideas.

[0055] An interior design proposal system according to an embodiment includes a receiving unit, an analysis unit, a proposal unit, and a providing unit. The receiving unit inputs interior design ideas desired by a user. The interior design ideas desired by the user include, but are not limited to, room layouts, furniture types, and color combinations. The receiving unit can receive interior design ideas via, for example, text input, voice input, or image input. The analysis unit uses a generation AI to analyze the ideas input by the receiving unit. The analysis is performed based on, for example, but not limited to, the algorithm used, the accuracy of the analysis, and factors considered. For example, the analysis unit uses a text generation AI (e.g., LLM) to analyze the user's requests. The analysis unit can also use a multimodal generation AI to analyze multiple modalities such as text, images, and voice. The analysis unit also uses the generation AI to make proposals that take into account necessary factors such as furniture placement, ease of air conditioning, and aisle width for traffic flow. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The proposal is made based on, for example, the type of furniture to be proposed, the layout pattern, the basis for the proposal, etc., but is not limited to such examples. For example, the proposal unit uses a generation AI to determine where to place furniture and propose a layout that ensures easy air circulation. The proposal unit can also propose a layout that ensures appropriate aisle width for traffic flow. The provision unit presents products recommended for purchase based on the content proposed by the proposal unit. The provided products include, for example, furniture, interior accessories, lighting fixtures, etc., but are not limited to such examples. For example, the provision unit presents the specific product name and purchasing location of the proposed sofa. The provision unit can also present recommended products such as air conditioners and fans that ensure easy air circulation. As a result, the interior proposal system according to the embodiment can efficiently input, analyze, propose, and provide interior ideas desired by the user.

[0056] The reception unit can estimate the user's emotions and adjust the timing of inputting interior ideas based on the estimated user emotions. For example, if the user is relaxed, the reception unit can send a notification prompting the user to input interior ideas. Furthermore, if the user is feeling stressed, the reception unit can refrain from sending a notification prompting the user to input ideas and wait until the user is able to relax. Furthermore, if the user is concentrating, the reception unit can send a notification prompting the user to input ideas to encourage the user to input ideas. This allows the user to input ideas at a more appropriate time by adjusting the timing of inputting interior ideas according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0057] The reception desk can analyze the user's past input history of interior design ideas and select an appropriate input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used at specific times based on the user's past input history. Furthermore, the reception desk can analyze trends in the ideas the user has entered in the past and suggest the optimal input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0058] The reception unit can filter interior design ideas based on the user's current living situation and areas of interest when they are entered. For example, the reception unit can prioritize displaying interior design ideas relevant to the user's current living situation. The reception unit can also filter and display relevant interior design ideas based on the user's areas of interest. Furthermore, the reception unit can suggest optimal interior design ideas based on the user's living situation and areas of interest. This allows the reception unit to prioritize displaying relevant interior design ideas based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's living situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0059] The reception unit can estimate the user's emotions and determine the order in which to input interior design ideas based on the estimated emotions. For example, if the user is relaxed, the reception unit may prioritize inputting detailed interior design ideas. If the user is stressed, the reception unit may also prioritize inputting simple interior design ideas. Furthermore, if the user is focused, the reception unit may also prioritize inputting complex interior design ideas. This allows for the input of more appropriate ideas by prioritizing interior design ideas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0060] When inputting interior ideas, the reception unit can prioritize inputting highly relevant ideas taking into account the user's geographical location information. The reception unit can, for example, prioritize displaying relevant interior ideas based on the user's current location. The reception unit can also suggest optimal interior ideas based on the user's geographical location information. Furthermore, the reception unit can prioritize inputting highly relevant interior ideas taking into account the user's geographical location information. This allows highly relevant interior ideas to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant ideas.

[0061] When inputting interior ideas, the reception unit can analyze the user's social media activities and input related ideas. For example, the reception unit extracts and inputs related interior ideas from the user's social media activities. The reception unit can also suggest optimal ideas based on interior ideas shared by the user on social media. Furthermore, the reception unit can analyze the user's social media activities and preferentially input related interior ideas. In this way, by analyzing the user's social media activities, related interior ideas can be preferentially input. Some or all of the above-described processing by 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 data to the generation AI and cause the generation AI to extract related ideas.

[0062] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is focused, the analysis unit can provide complex analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the interior design ideas. For example, the analysis unit can perform a detailed analysis on interior design ideas with high importance. It can also perform a simplified analysis on interior design ideas with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the interior design ideas. By adjusting the level of detail of the analysis based on the importance of the interior design ideas, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the interior design ideas into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0064] The analysis unit can apply different analysis algorithms depending on the category of the interior design idea during the analysis. For example, the analysis unit can apply the optimal placement algorithm to an interior design idea related to furniture placement. It can also apply the optimal air conditioning analysis algorithm to an interior design idea related to the ease of air conditioning circulation. Furthermore, it can apply the optimal traffic flow analysis algorithm to an interior design idea related to the width of passageways. By applying the optimal analysis algorithm according to the category of the interior design idea, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the interior design idea into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0065] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is focused, the analysis unit can provide complex analysis results. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0066] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the interior ideas. For example, the analysis unit prioritizes analysis of interior ideas submitted earlier. The analysis unit can also postpone analysis of interior ideas submitted later. Furthermore, the analysis unit can determine the priority of analysis based on the submission date of the interior ideas. This enables more efficient analysis by determining the priority of analysis based on the submission date of the interior ideas. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the interior ideas into the generation AI and have the generation AI determine the priority of analysis.

[0067] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the interior ideas. For example, the analysis unit prioritizes analysis of highly relevant interior ideas. The analysis unit can also postpone analysis of less relevant interior ideas. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the interior ideas. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the interior ideas. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the interior ideas into the generation AI and cause the generation AI to adjust the order of analysis.

[0068] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions when the user is stressed. Furthermore, the suggestion unit can provide complex suggestions when the user is concentrating. This allows for more appropriate suggestions to be provided by adjusting the way the suggestion is expressed based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0069] The proposal unit can adjust the level of detail in its proposals based on the importance of the interior design ideas. For example, it can provide detailed proposals for highly important interior design ideas, and concise proposals for less important ones. Furthermore, it can adjust the level of detail in its proposals based on the importance of the interior design ideas. This allows for the provision of more appropriate proposals by adjusting the level of detail based on the importance of the interior design ideas. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input importance data for interior design ideas into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0070] The proposal unit can apply different proposal algorithms depending on the category of the interior design idea when making a proposal. For example, the proposal unit can apply the optimal placement algorithm to an interior design idea related to furniture placement. It can also apply the optimal air conditioning proposal algorithm to an interior design idea related to air conditioning efficiency. Furthermore, it can apply the optimal traffic flow proposal algorithm to an interior design idea related to the width of passageways. By applying the optimal proposal algorithm according to the category of the interior design idea, it can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input interior design idea category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0071] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide concise suggestions. Furthermore, if the user is focused, the suggestion unit can provide complex suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the submission date of the interior ideas. For example, the suggestion unit prioritizes the suggestions of interior ideas submitted earlier. The suggestion unit can also postpone the suggestions of interior ideas submitted later. Furthermore, the suggestion unit can also determine the priority of the suggestions based on the submission date of the interior ideas. This enables more efficient suggestions by determining the priority of the suggestions based on the submission date of the interior ideas. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the submission date of the interior ideas into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0073] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the interior ideas. For example, the suggestion unit prioritizes suggesting highly relevant interior ideas. The suggestion unit can also postpone suggesting less relevant interior ideas. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the interior ideas. This allows for more efficient suggestions by adjusting the order of suggestions based on the relevance of the interior ideas. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the interior ideas into the generation AI and cause the generation AI to adjust the order of suggestions.

[0074] The service provider can estimate the user's emotions and determine the priority of products to offer based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed product information. If the user is stressed, the service provider can also provide concise product information. Furthermore, if the user is focused, the service provider can provide complex product information. This allows for the provision of more appropriate products by prioritizing products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The service provider can improve the accuracy of its recommendations by considering the interrelationships of interior design ideas during the recommendation process. For example, the service provider can propose the most suitable products by considering the interrelationship between furniture placement and air conditioning efficiency. It can also propose the most suitable products by considering the interrelationship between aisle width and furniture placement. Furthermore, the service provider can analyze the interrelationships of interior design ideas and propose the most suitable products. This allows for the provision of more appropriate products by considering the interrelationships of interior design ideas. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the interrelationships of interior design ideas into a generating AI and have the generating AI perform the task of improving the accuracy of its recommendations.

[0076] The service provider can provide interior design ideas while considering the attribute information of the submitter. For example, the service provider can suggest the most suitable products based on the submitter's age and gender. It can also suggest the most suitable products based on the submitter's lifestyle. Furthermore, it can suggest the most suitable products based on the submitter's hobbies and interests. This allows for the provision of more appropriate products by considering the attribute information of the interior design idea submitter. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the submitter's attribute information data into a generating AI and have the generating AI perform adjustments to the provision.

[0077] The providing unit can estimate the user's emotions and adjust the display method of the products to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can display detailed product information. Furthermore, when the user is stressed, the providing unit can display concise product information. Furthermore, when the user is concentrating, the providing unit can display complex product information. This allows for adjusting the display method of the products to be provided according to the user's emotions, thereby providing more appropriate products. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0078] The providing unit can provide the interior ideas taking into consideration the geographical distribution of the ideas. For example, the providing unit can suggest optimal products based on the user's region. The providing unit can also suggest related products based on the geographical distribution. Furthermore, the providing unit can suggest optimal products taking into consideration the geographical distribution of the interior ideas. This allows for more appropriate products to be provided by taking into consideration the geographical distribution of the interior ideas. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical distribution data of the interior ideas into the generating AI and cause the generating AI to adjust the provision.

[0079] The providing unit can improve the accuracy of the provision by referring to literature related to the interior idea when providing the ideas. The providing unit, for example, can refer to related literature to suggest the most suitable product. The providing unit can also suggest the most suitable product based on literature related to the interior idea. Furthermore, the providing unit can improve the accuracy of the provision by referring to related literature when providing the ideas. This makes it possible to provide more suitable products by referring to literature related to the interior idea. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input related literature data into the generating AI and cause the generating AI to improve the accuracy of the provision. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, and the user inputs their desired interior design ideas. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the input ideas using a generation AI. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and makes specific suggestions based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and presents products that are recommended for purchase based on the suggestions. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting interior design ideas based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, and the user inputs their desired interior design ideas. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the input ideas using a generation AI. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and makes specific suggestions based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and presents products that are recommended for purchase based on the suggestions. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting interior design ideas based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision 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 control unit 46A of the headset-type terminal 314 and inputs interior ideas desired by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input ideas using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes specific suggestions based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and presents products recommended for purchase based on the suggestions. The reception unit can, for example, estimate the user's emotions and adjust the timing of input of interior ideas based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision 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 control unit 46A of the robot 414 and inputs interior ideas desired by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input ideas using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes specific suggestions based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and presents products recommended for purchase based on the suggestions. The reception unit can, for example, estimate the user's emotions and adjust the timing of input of interior ideas based on the estimated emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the user's past purchase history and automatically suggest related products when the user inputs an interior design idea. For example, it can suggest new products of the same brand or style based on data on furniture and interior accessories purchased in the past by the user. The reception unit can also analyze the usage of products purchased in the past by the user and suggest complementary products. Furthermore, the reception unit can suggest products related to specific seasons or events based on the user's past purchase history. This makes it possible to suggest more personalized interior design ideas by utilizing the user's past purchase history.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis speed based on the estimated emotions. For example, if the user is relaxed, it can take more time to perform a detailed analysis. Conversely, if the user is stressed, it can provide analysis results quickly. Furthermore, if the user is concentrating, the analysis speed can be adjusted to maintain the user's concentration while the analysis progresses. In this way, by adjusting the analysis speed according to the user's emotions, more appropriate analysis results can be provided.

[0083] The suggestion unit can collect feedback on the user's interior ideas and improve the proposed content based on that feedback. For example, it can provide a function for users to evaluate the proposed ideas and adjust the proposed content based on the evaluation results. The suggestion unit can also analyze the user's feedback, extract common areas for improvement, and optimize the proposal algorithm. Furthermore, the suggestion unit can reflect the user's feedback in real time and instantly update the proposed content. This makes it possible to utilize user feedback to make more accurate interior proposals.

[0084] The providing unit can estimate the user's emotions and adjust the price range of the products to be provided based on the estimated user's emotions. For example, if the user is relaxed, it can suggest products in a high price range. If the user is stressed, it can suggest products in a low price range. Furthermore, if the user is concentrating, it can suggest products in a medium price range. In this way, by adjusting the price range of the products to be provided according to the user's emotions, it is possible to provide more appropriate products.

[0085] When a user inputs interior ideas, the reception unit can adjust the proposed content taking into account the user's health condition. For example, if the user has allergies, furniture made from allergen-free materials can be proposed. Also, if the user is elderly, interior ideas with a barrier-free design can be proposed. Furthermore, if the user is not getting enough exercise, a layout that encourages exercise can be proposed. This allows the system to provide more appropriate interior ideas according to the user's health condition.

[0086] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis results can be displayed using detailed graphs and charts. If the user is stressed, the analysis results can be displayed using simple icons and symbols. Furthermore, if the user is concentrating, the analysis results can be displayed using an interactive dashboard. In this way, by adjusting the visual presentation of the analysis results according to the user's emotions, it is possible to provide analysis results that are easier to understand.

[0087] The suggestion unit can analyze the user's past proposal history for interior ideas and make optimal suggestions. For example, it can prioritize and suggest ideas that have received high ratings from the user's past proposals. The suggestion unit can also extract specific trends and patterns from the user's past proposal history and make new suggestions based on them. Furthermore, the suggestion unit can customize and provide suggestions based on the user's past proposal history. This makes it possible to make more personalized interior suggestions by utilizing the user's past proposal history.

[0088] The providing unit can estimate the user's emotions and adjust the product explanation method to be provided based on the estimated user's emotions. For example, if the user is relaxed, a detailed product explanation can be provided. If the user is stressed, a concise product explanation can be provided. Furthermore, if the user is concentrating, a product explanation including technical details can be provided. In this way, by adjusting the product explanation method to be provided according to the user's emotions, more appropriate product information can be provided.

[0089] When the user inputs interior ideas, the reception unit can adjust the proposed content taking into account the user's family composition. For example, if the user has small children, the reception unit can propose interior ideas that emphasize safety. Also, if the user has pets, the reception unit can propose pet-friendly interior ideas. Furthermore, if the user lives with multiple generations, the reception unit can propose interior ideas that take into account the needs of each generation. This allows the reception unit to provide more appropriate interior ideas according to the user's family composition.

[0090] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the timing of suggestions can be delayed. Conversely, if the user is stressed, suggestions can be made quickly. Furthermore, if the user is concentrating, the timing of suggestions can be adjusted to maintain the user's concentration. In this way, by adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be provided.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit inputs the user's desired interior design ideas. The user's ideas include room layout, furniture types, color combinations, etc. The reception unit can receive interior design ideas by text input, voice input, image input, etc. Step 2: The analysis unit uses generation AI to analyze the ideas entered by the reception unit. The analysis is performed based on the algorithm used, the accuracy of the analysis, the factors taken into consideration, etc. The analysis unit uses text generation AI (e.g., LLM) and multimodal generation AI to analyze multiple modalities such as text, images, and audio, and makes suggestions that take into account necessary factors such as furniture placement, ease of air conditioning circulation, and the width of aisles for traffic flow. Step 3: The proposal team makes specific proposals based on the analysis results obtained by the analysis team. The proposals are based on the types of furniture to be proposed, the arrangement patterns, and the rationale for the proposals. The proposal team uses a generation AI to determine the placement of furniture and proposes layouts that ensure good airflow and appropriate aisle widths for traffic flow. Step 4: The supply department presents recommended products based on the proposals made by the suggestion department. These products may include furniture, interior accessories, and lighting fixtures. The supply department will provide specific product names and purchase locations for the suggested sofas, as well as recommended air conditioners and fans to ensure proper ventilation.

[0093] 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.

[0094] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 area where users input their desired interior design ideas, The analysis unit analyzes the ideas entered by the reception unit and makes suggestions considering furniture placement, air conditioning ventilation, and passageway width for traffic flow. A proposal unit makes specific proposals based on the analysis results obtained by the aforementioned analysis unit, A supply unit presents products that are recommended for purchase based on the content proposed by the aforementioned proposal unit, Equipped with A system characterized by:

2. The reception unit It estimates the user's emotions and adjusts the timing of inputting interior design ideas based on those estimated emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past input history of interior design ideas and select the appropriate input method.

2. The system of claim 1.

4. The reception unit When users input interior design ideas, the system filters them based on their current living situation and areas of interest.

2. The system of claim 1.

5. The reception unit It estimates the user's emotions and determines the order of interior design ideas to input based on those estimated emotions.

2. The system of claim 1.

6. The reception unit When users input interior design ideas, the system prioritizes inputting ideas that are highly relevant to their geographical location.

2. The system of claim 1.

7. The reception unit When users input interior design ideas, the system analyzes their social media activity and inputs relevant ideas.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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