System

Abstract

An object of a system according to an embodiment is to propose optimum interior coordination on the basis of user's preference and budget.SOLUTION: A system includes a preference and budget grasping part, a coordination proposal part, and a selection part. A preference and budget grasping part grasps the preference and budget of the user by using the generation AI. A coordination proposal part proposes optimum interior coordination on the basis of the taste and the budget. The selection part selects concrete furniture and ornaments on the basis of the interior coordination.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to propose optimal interior coordination based on a user's preferences and budget, making it difficult to create a space with a unified feel.

[0005] The system according to the embodiment aims to propose optimal interior coordination based on the user's preferences and budget. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference and budget understanding unit, a coordination suggestion unit, and a selection unit. The preference and budget understanding unit understands the user's preferences and budget using a generation AI. The coordination suggestion unit proposes an optimal interior coordination based on the preferences and budget. The selection unit selects specific furniture and decorative items based on the interior coordination. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal interior coordination based on the user's preferences and budget. [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) The interior concierge system according to an embodiment of the present invention is a system that proposes optimal interior coordination based on the user's preferences and budget, and supports the selection and placement of furniture and decorations. As a result, the interior concierge system can solve the user's taste and budget problems and create a stylish and beautiful space.

[0029] An interior concierge system according to an embodiment includes a preference and budget understanding unit, a coordination suggestion unit, and a selection unit. The preference and budget understanding unit uses a generation AI to understand a user's preferences and budget. For example, the generation AI may ask the user questions such as, "What style do you like?" or "What is your budget?" and analyze the user's needs based on the answers. The generation AI may also analyze the user's past purchase history and social media posts to estimate preferences and budget. For example, the generation AI may understand the user's interior style trends based on data on furniture and decorative items purchased in the past. The coordination suggestion unit proposes optimal interior coordination based on preferences and budget. For example, the generation AI may generate a list of furniture and decorative items and propose a specific layout plan for a given condition, such as "a simple Scandinavian design with a budget of less than 100,000 yen." The generation AI can make more accurate suggestions by referring to related background information and topic models and understanding the context. The selection unit selects specific furniture and decorative items based on the interior coordination. For example, the generation AI may suggest suitable items to the user, such as, "This sofa is within budget and matches the style." The generative AI also works with online shopping sites to help users easily make purchases, suggesting optimal interior coordination based on the user's preferences and budget, and selecting specific furniture and decorations.

[0030] The preference and budget understanding unit can estimate preferences and budget by analyzing a user's past purchase history and social media posts. For example, the preference and budget understanding unit uses a generation AI to analyze a user's past online shopping history and estimate preferences and budget from the category and price range of purchased products. For example, the generation AI identifies the user's interior style trends based on data on furniture and decorative items purchased in the past. The generation AI also analyzes the user's social media posts and estimates preferences and budget from the post content and images. For example, the generation AI identifies the user's preferred style and budget based on interior-related posts and the number of "likes." The generation AI also analyzes the user's past reviews and ratings to estimate preferences and budget. For example, the generation AI identifies the user's satisfaction level and budget based on the review content and rating scores of purchased products. This allows for a more accurate understanding of a user's preferences and budget by analyzing a user's past purchase history and social media posts.

[0031] The preference and budget understanding unit can propose the optimal interior style by taking into account the user's family composition and lifestyle. For example, the preference and budget understanding unit uses a generation AI to consider the user's family composition and propose an interior style for a home with children or pets. For example, it provides a layout plan that emphasizes durable furniture and safety. The generation AI also analyzes the user's lifestyle and proposes an interior style that suits their work or hobbies. For example, it proposes coordination that includes a comfortable workspace for a user who often works from home. The generation AI also considers the user's daily rhythm and proposes an interior style that suits a morning or night-type lifestyle. For example, it provides a layout plan that incorporates natural light for a morning-type user. In this way, it is possible to propose a more suitable interior style by taking into account the user's family composition and lifestyle.

[0032] The preference and budget understanding unit can use the voice assistant to engage in dialogue and understand the user's preferences and budget. For example, the generation AI asks the user questions about interior design through the voice assistant to understand their preferences and budget. For example, it asks questions such as, "What colors do you like?" or "What is your budget?" The voice assistant then analyzes the user's answers in real time, and the generation AI estimates the user's preferences and budget based on that information. For example, it converts the user's answers into text data and analyzes them. The voice assistant also collects detailed information about the interior design through dialogue with the user, and the generation AI makes optimal suggestions based on that information. For example, it collects information about the user's lifestyle and family composition. As a result, the voice assistant can be used to understand the user's preferences and budget in an interactive format.

[0033] The preference and budget understanding unit can analyze images and videos selected by the user to understand the user's preferences and budget. For example, the generation AI analyzes interior images selected by the user to understand the user's preferences and budget. For example, it analyzes the colors and design elements in the images to identify the user's preferences. The generation AI also analyzes interior-related videos watched by the user to understand the user's preferences and budget. For example, it analyzes the style of furniture and decorations in the videos to identify the user's preferences. The generation AI also analyzes the metadata of interior images and videos saved by the user to understand the user's preferences and budget. For example, it identifies the user's preferences based on the tags and descriptions of the images and videos. In this way, the user's preferences and budget can be understood by analyzing the images and videos selected by the user.

[0034] The coordination suggestion unit can suggest interior coordination that takes into account the season and trends, based on the user's preferences and budget. For example, the generation AI in the coordination suggestion unit suggests interior coordination for each season based on the user's preferences and budget. For example, in spring, it suggests furniture and decorations in bright colors. The generation AI also analyzes the latest interior trends and suggests coordination that incorporates the trends based on the user's preferences and budget. For example, it makes suggestions using popular designs and materials. The generation AI also suggests interior coordination that takes into account the season and trends, based on the user's preferences and budget. For example, it suggests furniture and decorations with a cool design in summer. This makes it possible to suggest interior coordination that takes into account the season and trends.

[0035] The coordination suggestion unit can suggest eco-friendly interior items based on the user's preferences and budget. In the coordination suggestion unit, for example, the generation AI suggests furniture and decorations made with eco-friendly materials based on the user's preferences and budget. For example, it suggests items made with reclaimed wood or recycled materials. The generation AI also suggests energy-efficient interior items. For example, it suggests coordinations that include LED lighting and energy-saving home appliances. The generation AI also suggests environmentally friendly interior items based on the user's preferences and budget. For example, it suggests furniture and decorations made with low-VOC paint. This makes it possible to suggest eco-friendly interior items.

[0036] The coordination suggestion unit can suggest interior styles from different cultures and regions based on the user's preferences and budget. For example, the generation AI of the coordination suggestion unit suggests interior styles from different cultures, such as Nordic or Japanese, based on the user's preferences and budget. For example, it suggests a simple Nordic design or a calm Japanese design. The generation AI also suggests interior styles specific to a region, such as Mediterranean or Asian, based on the user's preferences and budget. For example, it suggests bright Mediterranean colors or Asian styles using natural materials. The generation AI also suggests a fusion of interior styles from different cultures and regions based on the user's preferences and budget. For example, it suggests a modern design that combines Nordic and Japanese styles. This makes it possible to suggest interior styles from different cultures and regions.

[0037] The coordination suggestion unit can suggest interior coordination including DIY ideas based on the user's preferences and budget. In the coordination suggestion unit, for example, the generation AI suggests interior items that can be created by DIY based on the user's preferences and budget. For example, it suggests how to make a handmade shelf or decorative item. The generation AI also suggests interior coordination including DIY projects based on the user's preferences and budget. For example, it suggests ways to remake old furniture or ideas for painting walls. The generation AI also suggests interior coordination that incorporates DIY ideas based on the user's preferences and budget. For example, it makes suggestions including handmade cushion covers and art pieces. In this way, it is possible to suggest interior coordination including DIY ideas.

[0038] The selection unit can select furniture and decorative items from recycle shops and second-hand markets based on the user's preferences and budget. For example, the generation AI searches a database of recycle shops based on the user's preferences and budget to suggest suitable furniture and decorative items. For example, it lists used furniture that can be purchased within the budget. The generation AI also analyzes online platforms for second-hand markets based on the user's preferences and budget to select suitable items. For example, it makes suggestions based on information from auction sites and second-hand goods sales sites. The generation AI also selects furniture and decorative items from recycle shops and second-hand markets based on the user's preferences and budget and provides a purchase link. For example, it provides the user with a link to the purchase page for the selected item. This allows the selection of furniture and decorative items from recycle shops and second-hand markets.

[0039] The selection unit can suggest customizable furniture and decorative items based on the user's preferences and budget. For example, the generation AI suggests customizable furniture based on the user's preferences and budget. For example, it suggests sofas and tables with selectable colors and materials. The generation AI also suggests customizable decorative items based on the user's preferences and budget. For example, it suggests artwork and cushion covers with selectable designs and sizes. The generation AI also suggests customizable furniture and decorative items based on the user's preferences and budget and provides online customization options. For example, it provides the user with a link to a customization page. This allows the selection of customizable furniture and decorative items.

[0040] The selection unit can suggest works by local craftsmen and artists based on the user's preferences and budget. For example, the generation AI can suggest furniture handmade by local craftsmen based on the user's preferences and budget. For example, it can suggest custom-made tables and chairs. The generation AI can also suggest decorative items created by local artists based on the user's preferences and budget. For example, it can suggest unique artworks and handmade cushion covers. The generation AI can also suggest works by local craftsmen and artists based on the user's preferences and budget and provide purchase links. For example, it can provide the user with links to local galleries and online shops. This allows the selection unit to suggest works by local craftsmen and artists.

[0041] The selection unit can suggest furniture and decoration rental services based on the user's preferences and budget. For example, the generation AI of the selection unit suggests furniture rental services based on the user's preferences and budget. For example, it suggests sofas and tables that can be rented for a short period of time. The generation AI also suggests decoration rental services based on the user's preferences and budget. For example, it suggests artworks and cushion covers that can be changed with the seasons. The generation AI also suggests furniture and decoration rental services based on the user's preferences and budget and provides rental links. For example, it provides the user with links to rental service websites. This makes it possible to suggest furniture and decoration rental services.

[0042] The system creates a 3D model of the user's room and simulates an optimal furniture layout plan. For example, the system uses a generation AI to create a 3D model based on the dimensions and layout of the user's room and simulate an optimal furniture layout plan. For example, the furniture layout can be visually confirmed on the 3D model. The generation AI also creates a 3D model of the user's room and simulates and compares different layout plans. For example, it proposes multiple layout plans and allows the user to select. The generation AI also takes into account traffic flow and visual balance when creating a 3D model of the user's room and simulating furniture layout. For example, it proposes layout plans that emphasize ease of use and aesthetic beauty. This allows the system to create a 3D model of the user's room and simulate an optimal layout plan.

[0043] The system is able to propose optimal furniture layout plans by taking into account the user's movement lines and lifestyle patterns. For example, the generation AI analyzes the user's movement lines and proposes optimal furniture layout plans. For example, it arranges furniture so that there are no obstacles in areas that are frequently passed through. The generation AI also analyzes the user's lifestyle patterns and proposes optimal furniture layout plans. For example, if there is a lot of activity in the living room, it proposes comfortable sofa and table placements. The generation AI also considers the user's movement lines and lifestyle patterns and proposes optimal layout plans. For example, it proposes a layout that smooths the movement lines between the kitchen and dining room. This makes it possible to propose optimal layout plans by taking into account the user's movement lines and lifestyle patterns.

[0044] The system can dynamically adjust the user's room layout plan according to the season or event. In the system, for example, the generation AI adjusts the user's room layout plan according to the season. For example, it proposes a cool layout in the summer and a warm layout in the winter. The generation AI also adjusts the user's room layout plan according to events. For example, it proposes a layout suitable for a party or family gathering. The generation AI also dynamically adjusts the user's room layout plan according to the season or event. For example, it proposes a layout that matches seasonal events such as Christmas or Halloween. This makes it possible to dynamically adjust the layout plan according to the season or event.

[0045] The system can propose layout plans for the user's room from the perspective of feng shui and fortune telling. For example, the generation AI of the system proposes layout plans for the user's room based on the principles of feng shui. For example, it proposes a layout that takes into account the flow of good energy in feng shui. The generation AI also proposes layout plans for the user's room based on the results of fortune telling. For example, it proposes a layout that will bring good luck in fortune telling. The generation AI also proposes layout plans for the user's room from the perspective of feng shui and fortune telling. For example, it proposes a layout that takes both feng shui and fortune telling into account. This makes it possible to propose layout plans from the perspective of feng shui and fortune telling.

[0046] The system can prioritize the selection of eco-friendly materials and items when coordinating a total interior based on the user's preferences and budget. For example, the generation AI may prioritize the selection of furniture and decorations made with eco-friendly materials based on the user's preferences and budget. For example, it may suggest items made with reclaimed wood or recycled materials. The generation AI may also prioritize the selection of energy-efficient interior items. For example, it may suggest coordination that includes LED lighting and energy-saving home appliances. The generation AI may also prioritize the selection of environmentally friendly interior items based on the user's preferences and budget. For example, it may suggest furniture and decorations made with low-VOC paint. This allows the system to prioritize the selection of eco-friendly materials and items.

[0047] The system can take into consideration integration with smart home devices when coordinating a total interior design based on the user's preferences and budget. For example, the generation AI of the system proposes interior coordination that is integrated with smart home devices based on the user's preferences and budget. For example, it proposes coordination that includes smart lighting and a smart thermostat. The generation AI also proposes interior coordination that incorporates smart home devices based on the user's preferences and budget. For example, it proposes coordination that includes a voice assistant and a smart lock. The generation AI also proposes interior coordination that takes into consideration integration with smart home devices based on the user's preferences and budget. For example, it proposes coordination that includes smart home appliances and a security system. This enables total coordination that takes into consideration integration with smart home devices.

[0048] The system can blend styles from different cultures and regions when creating a total interior coordination based on the user's preferences and budget. For example, the generation AI can propose a total coordination that blends interior styles from different cultures, such as Scandinavian and Japanese, based on the user's preferences and budget. For example, it can combine a simple Scandinavian design with a calm Japanese design. The generation AI can also propose a total coordination that blends regional interior styles, such as Mediterranean and Asian, based on the user's preferences and budget. For example, it can combine the bright colors of the Mediterranean style with the natural materials of Asian style. The generation AI can also propose a total coordination that blends interior styles from different cultures and regions based on the user's preferences and budget. For example, it can propose a modern design that combines Scandinavian and Japanese styles. This allows for a total coordination that blends styles from different cultures and regions.

[0049] The system can make suggestions including DIY ideas when coordinating total interior decor based on the user's preferences and budget. For example, the generative AI can propose total coordination including interior items that can be created by DIY based on the user's preferences and budget. For example, it can suggest how to make a handmade shelf or decorative item. The generative AI can also propose total coordination including DIY projects based on the user's preferences and budget. For example, it can suggest ways to remake old furniture or ideas for painting walls. The generative AI can also propose total coordination incorporating DIY ideas based on the user's preferences and budget. For example, it can make suggestions including handmade cushion covers or artwork. This makes it possible to propose total coordination including DIY ideas.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The interior concierge system can also suggest interior coordination that takes the user's health condition into consideration. For example, for a user with allergies, it can suggest furniture and decorations made from hypoallergenic materials. The generative AI can also analyze the user's health data and suggest interior coordination that will provide a comfortable sleeping environment. For example, it can suggest an appropriate mattress or blackout curtains. The generative AI can also consider the user's exercise habits and suggest interior coordination that includes setting up a home gym. For example, it can suggest the placement of exercise equipment and the necessary space. This makes it possible to suggest interior coordination that takes the user's health condition into consideration.

[0052] The interior concierge system can also suggest interior coordination that reflects the user's hobbies and interests. For example, for a user whose hobby is reading, it can suggest a comfortable reading space. For example, it can suggest appropriate lighting and bookcase placement. The generation AI can also suggest decorations and furniture related to the user's hobbies based on the user's hobbies. For example, for a user who likes music, it can suggest interior coordination that takes acoustics into consideration. The generation AI can also analyze the user's interests and suggest interior coordination based on those interests. For example, for a user who likes traveling, it can suggest a space to display photos and souvenirs from their travels. In this way, it can suggest interior coordination that reflects the user's hobbies and interests.

[0053] The interior concierge system can also suggest interior coordination according to the user's life events. For example, for a newlywed household, it will suggest interior design that creates a romantic atmosphere. For example, it will suggest coordination that incorporates candles and flowers. Furthermore, based on the user's life events, the generation AI will suggest interior design that prioritizes safety for a household that has just had a baby. For example, it will suggest furniture with no corners and non-slip flooring. Furthermore, based on the user's life events, the generation AI will suggest interior design that will help a household adapt to their new environment for a new home. For example, it will suggest furniture and layout that will increase storage space. In this way, it is possible to suggest interior coordination according to the user's life events.

[0054] The interior concierge system can also suggest interior coordination that reflects the user's eco-consciousness. For example, the generation AI may suggest interior items that use renewable energy based on the user's eco-consciousness. For example, it may suggest lighting that uses solar panels or energy-efficient home appliances. The generation AI may also suggest furniture and decorations that use recycled materials based on the user's eco-consciousness. For example, it may suggest items that use recycled wood or recycled plastic. The generation AI may also suggest environmentally friendly interior coordination based on the user's eco-consciousness. For example, it may suggest furniture and decorations that use low-VOC paint. This makes it possible to suggest interior coordination that reflects the user's eco-consciousness.

[0055] The interior concierge system can also suggest interior coordination that takes into account the user's cultural background. For example, the generation AI will suggest traditional designs and decorations based on the user's cultural background. For example, it will suggest Japanese-style designs and decorations to a Japanese user. The generation AI will also suggest interior coordination that incorporates elements from other cultures based on the user's cultural background. For example, it will suggest Asian-style designs to an American user. The generation AI will also suggest interior coordination that matches cultural events and festivals based on the user's cultural background. For example, it will suggest decorations to match Christmas or Halloween. This makes it possible to suggest interior coordination that takes the user's cultural background into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The preference and budget understanding unit uses the generation AI to understand the user's preferences and budget. For example, the generation AI asks the user questions such as "What style do you like?" and "What is your budget?" and analyzes the user's needs based on the answers. The generation AI can also analyze the user's past purchase history and social media posts to estimate preferences and budget. For example, it can understand the user's interior style trends based on data on furniture and decorative items purchased in the past. Step 2: The coordination suggestion unit proposes the optimal interior coordination based on preferences and budget. For example, the generation AI generates a list of furniture and decorations for conditions such as "a simple Scandinavian design with a budget of less than 100,000 yen," and proposes a specific layout plan. The generation AI can make more accurate suggestions by referring to related background information and topic models and understanding the context. Step 3: The selection unit selects specific furniture and decorative items based on the interior coordination. For example, the generation AI suggests items suitable for the user, such as "This sofa is within your budget and matches your style." The generation AI also works with online shopping sites to help users make purchases easily.

[0058] (Example 2) The interior concierge system according to an embodiment of the present invention is a system that proposes optimal interior coordination based on the user's preferences and budget, and supports the selection and placement of furniture and decorations. As a result, the interior concierge system can solve the user's taste and budget problems and create a stylish and beautiful space.

[0059] An interior concierge system according to an embodiment includes a preference and budget understanding unit, a coordination suggestion unit, and a selection unit. The preference and budget understanding unit uses a generation AI to understand a user's preferences and budget. For example, the generation AI may ask the user questions such as, "What style do you like?" or "What is your budget?" and analyze the user's needs based on the answers. The generation AI may also analyze the user's past purchase history and social media posts to estimate preferences and budget. For example, the generation AI may understand the user's interior style trends based on data on furniture and decorative items purchased in the past. The coordination suggestion unit proposes optimal interior coordination based on preferences and budget. For example, the generation AI may generate a list of furniture and decorative items and propose a specific layout plan for a given condition, such as "a simple Scandinavian design with a budget of less than 100,000 yen." The generation AI can make more accurate suggestions by referring to related background information and topic models and understanding the context. The selection unit selects specific furniture and decorative items based on the interior coordination. For example, the generation AI may suggest suitable items to the user, such as, "This sofa is within budget and matches the style." The generative AI also works with online shopping sites to help users easily make purchases, suggesting optimal interior coordination based on the user's preferences and budget, and selecting specific furniture and decorations.

[0060] The preference and budget understanding unit can estimate preferences and budget by analyzing a user's past purchase history and social media posts. For example, the preference and budget understanding unit uses a generation AI to analyze a user's past online shopping history and estimate preferences and budget from the category and price range of purchased products. For example, the generation AI identifies the user's interior style trends based on data on furniture and decorative items purchased in the past. The generation AI also analyzes the user's social media posts and estimates preferences and budget from the post content and images. For example, the generation AI identifies the user's preferred style and budget based on interior-related posts and the number of "likes." The generation AI also analyzes the user's past reviews and ratings to estimate preferences and budget. For example, the generation AI identifies the user's satisfaction level and budget based on the review content and rating scores of purchased products. This allows for a more accurate understanding of a user's preferences and budget by analyzing a user's past purchase history and social media posts.

[0061] The preference and budget understanding unit can propose the optimal interior style by taking into account the user's family composition and lifestyle. For example, the preference and budget understanding unit uses a generation AI to consider the user's family composition and propose an interior style for a home with children or pets. For example, it provides a layout plan that emphasizes durable furniture and safety. The generation AI also analyzes the user's lifestyle and proposes an interior style that suits their work or hobbies. For example, it proposes coordination that includes a comfortable workspace for a user who often works from home. The generation AI also considers the user's daily rhythm and proposes an interior style that suits a morning or night-type lifestyle. For example, it provides a layout plan that incorporates natural light for a morning-type user. In this way, it is possible to propose a more suitable interior style by taking into account the user's family composition and lifestyle.

[0062] The preference and budget understanding unit uses the emotion estimation function to analyze the emotions the user has toward the interior and understand the preferences based on the emotions. For example, the preference and budget understanding unit uses the emotion estimation function to analyze the facial expression of the user when looking at an interior image and understand the preferences. For example, it detects smiling or surprised expressions and suggests a style that elicits positive emotions. The emotion estimation function also analyzes the tone of the voice when the user answers questions about the interior and understands the preferences. For example, it identifies the user's emotions based on an excited or calm tone. The emotion estimation function also analyzes biometric data (heart rate and galvanic skin response) when the user answers a questionnaire about the interior and understands the preferences. For example, it identifies the user's emotions based on stress level and relaxation level. In this way, by analyzing the user's emotions, it is possible to understand the preferences more accurately.

[0063] The preference and budget understanding unit can use the voice assistant to engage in dialogue and understand the user's preferences and budget. For example, the generation AI asks the user questions about interior design through the voice assistant to understand their preferences and budget. For example, it asks questions such as, "What colors do you like?" or "What is your budget?" The voice assistant then analyzes the user's answers in real time, and the generation AI estimates the user's preferences and budget based on that information. For example, it converts the user's answers into text data and analyzes them. The voice assistant also collects detailed information about the interior design through dialogue with the user, and the generation AI makes optimal suggestions based on that information. For example, it collects information about the user's lifestyle and family composition. As a result, the voice assistant can be used to understand the user's preferences and budget in an interactive format.

[0064] The preference and budget understanding unit can analyze images and videos selected by the user to understand the user's preferences and budget. For example, the generation AI analyzes interior images selected by the user to understand the user's preferences and budget. For example, it analyzes the colors and design elements in the images to identify the user's preferences. The generation AI also analyzes interior-related videos watched by the user to understand the user's preferences and budget. For example, it analyzes the style of furniture and decorations in the videos to identify the user's preferences. The generation AI also analyzes the metadata of interior images and videos saved by the user to understand the user's preferences and budget. For example, it identifies the user's preferences based on the tags and descriptions of the images and videos. In this way, the user's preferences and budget can be understood by analyzing the images and videos selected by the user.

[0065] The preference and budget understanding unit uses the emotion estimation function to analyze the user's emotions toward the interior in real time and dynamically adjust the preferences and budget. For example, the preference and budget understanding unit uses the emotion estimation function to analyze the user's facial expression when viewing an interior image in real time and dynamically adjust the preferences and budget. For example, the unit changes the content of suggestions based on an image that evokes a strong positive emotion. The emotion estimation function also analyzes the tone of the user's voice when answering questions about the interior in real time and dynamically adjusts the preferences and budget. For example, if an excited tone is detected, the unit suggests increasing the budget. The emotion estimation function also analyzes biometric data in real time when the user answers a questionnaire about the interior and dynamically adjusts the preferences and budget. For example, if the user's level of relaxation is high, the unit suggests a relaxing interior. This allows the user's emotions to be analyzed in real time and the preferences and budget to be dynamically adjusted.

[0066] The coordination suggestion unit can suggest interior coordination that takes into account the season and trends, based on the user's preferences and budget. For example, the generation AI in the coordination suggestion unit suggests interior coordination for each season based on the user's preferences and budget. For example, in spring, it suggests furniture and decorations in bright colors. The generation AI also analyzes the latest interior trends and suggests coordination that incorporates the trends based on the user's preferences and budget. For example, it makes suggestions using popular designs and materials. The generation AI also suggests interior coordination that takes into account the season and trends, based on the user's preferences and budget. For example, it suggests furniture and decorations with a cool design in summer. This makes it possible to suggest interior coordination that takes into account the season and trends.

[0067] The coordination suggestion unit can suggest eco-friendly interior items based on the user's preferences and budget. In the coordination suggestion unit, for example, the generation AI suggests furniture and decorations made with eco-friendly materials based on the user's preferences and budget. For example, it suggests items made with reclaimed wood or recycled materials. The generation AI also suggests energy-efficient interior items. For example, it suggests coordinations that include LED lighting and energy-saving home appliances. The generation AI also suggests environmentally friendly interior items based on the user's preferences and budget. For example, it suggests furniture and decorations made with low-VOC paint. This makes it possible to suggest eco-friendly interior items.

[0068] The coordination suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed interior and suggest an optimal coordination based on the emotions. For example, the coordination suggestion unit uses the emotion estimation function to analyze the facial expression of the user when viewing an image of the proposed interior and suggest an optimal coordination based on the emotions. For example, the proposal content is adjusted based on an image with a lot of smiling faces. The emotion estimation function can also be used to analyze the tone of the user's voice when answering questions about the proposed interior and suggest an optimal coordination based on the emotions. For example, if an excited tone is detected, that style is emphasized. The emotion estimation function can also be used to analyze biometric data when the user answers a questionnaire about the proposed interior and suggest an optimal coordination based on the emotions. For example, if the relaxation level is high, a relaxing interior is suggested. In this way, by analyzing the user's emotions, more appropriate interior coordination can be suggested.

[0069] The coordination suggestion unit can suggest interior styles from different cultures and regions based on the user's preferences and budget. For example, the generation AI of the coordination suggestion unit suggests interior styles from different cultures, such as Nordic or Japanese, based on the user's preferences and budget. For example, it suggests a simple Nordic design or a calm Japanese design. The generation AI also suggests interior styles specific to a region, such as Mediterranean or Asian, based on the user's preferences and budget. For example, it suggests bright Mediterranean colors or Asian styles using natural materials. The generation AI also suggests a fusion of interior styles from different cultures and regions based on the user's preferences and budget. For example, it suggests a modern design that combines Nordic and Japanese styles. This makes it possible to suggest interior styles from different cultures and regions.

[0070] The coordination suggestion unit can suggest interior coordination including DIY ideas based on the user's preferences and budget. In the coordination suggestion unit, for example, the generation AI suggests interior items that can be created by DIY based on the user's preferences and budget. For example, it suggests how to make a handmade shelf or decorative item. The generation AI also suggests interior coordination including DIY projects based on the user's preferences and budget. For example, it suggests ways to remake old furniture or ideas for painting walls. The generation AI also suggests interior coordination that incorporates DIY ideas based on the user's preferences and budget. For example, it makes suggestions including handmade cushion covers and art pieces. In this way, it is possible to suggest interior coordination including DIY ideas.

[0071] The coordination suggestion unit can use the emotion estimation function to analyze the user's emotions toward the proposed interior in real time and dynamically adjust the proposed content. For example, the coordination suggestion unit can use the emotion estimation function to analyze the user's facial expression when viewing an image of the proposed interior in real time and dynamically adjust the proposed content. For example, the proposed content can be changed based on an image that evokes a strong positive emotion. The emotion estimation function can also be used to analyze the voice tone in real time when the user answers questions about the proposed interior and dynamically adjust the proposed content. For example, if an excited tone is detected, that style can be emphasized. The emotion estimation function can also be used to analyze biometric data in real time when the user answers a questionnaire about the proposed interior and dynamically adjust the proposed content. For example, if the user's level of relaxation is high, a relaxing interior can be suggested. In this way, the user's emotions can be analyzed in real time and the proposed content can be dynamically adjusted.

[0072] The selection unit can select furniture and decorative items from recycle shops and second-hand markets based on the user's preferences and budget. For example, the generation AI searches a database of recycle shops based on the user's preferences and budget to suggest suitable furniture and decorative items. For example, it lists used furniture that can be purchased within the budget. The generation AI also analyzes online platforms for second-hand markets based on the user's preferences and budget to select suitable items. For example, it makes suggestions based on information from auction sites and second-hand goods sales sites. The generation AI also selects furniture and decorative items from recycle shops and second-hand markets based on the user's preferences and budget and provides a purchase link. For example, it provides the user with a link to the purchase page for the selected item. This allows the selection of furniture and decorative items from recycle shops and second-hand markets.

[0073] The selection unit can suggest customizable furniture and decorative items based on the user's preferences and budget. For example, the generation AI suggests customizable furniture based on the user's preferences and budget. For example, it suggests sofas and tables with selectable colors and materials. The generation AI also suggests customizable decorative items based on the user's preferences and budget. For example, it suggests artwork and cushion covers with selectable designs and sizes. The generation AI also suggests customizable furniture and decorative items based on the user's preferences and budget and provides online customization options. For example, it provides the user with a link to a customization page. This allows the selection of customizable furniture and decorative items.

[0074] The selection unit uses the emotion estimation function to analyze the emotions the user has toward the selected furniture and decorations, and can suggest optimal items based on the emotions. For example, the selection unit uses the emotion estimation function to analyze the facial expressions of the user when viewing images of the selected furniture and decorations, and suggests optimal items based on the emotions. For example, the selection unit adjusts the suggested items based on images with a lot of smiling faces. The emotion estimation function also analyzes the tone of the voice when the user answers questions about the selected furniture and decorations, and suggests optimal items based on the emotions. For example, if an excited tone is detected, the item is highlighted. The emotion estimation function also analyzes biometric data when the user answers a questionnaire about the selected furniture and decorations, and suggests optimal items based on the emotions. For example, if the user has a high level of relaxation, relaxing items are suggested. In this way, by analyzing the user's emotions, more suitable furniture and decorations can be suggested.

[0075] The selection unit can suggest works by local craftsmen and artists based on the user's preferences and budget. For example, the generation AI can suggest furniture handmade by local craftsmen based on the user's preferences and budget. For example, it can suggest custom-made tables and chairs. The generation AI can also suggest decorative items created by local artists based on the user's preferences and budget. For example, it can suggest unique artworks and handmade cushion covers. The generation AI can also suggest works by local craftsmen and artists based on the user's preferences and budget and provide purchase links. For example, it can provide the user with links to local galleries and online shops. This allows the selection unit to suggest works by local craftsmen and artists.

[0076] The selection unit can suggest furniture and decoration rental services based on the user's preferences and budget. For example, the generation AI of the selection unit suggests furniture rental services based on the user's preferences and budget. For example, it suggests sofas and tables that can be rented for a short period of time. The generation AI also suggests decoration rental services based on the user's preferences and budget. For example, it suggests artworks and cushion covers that can be changed with the seasons. The generation AI also suggests furniture and decoration rental services based on the user's preferences and budget and provides rental links. For example, it provides the user with links to rental service websites. This makes it possible to suggest furniture and decoration rental services.

[0077] The selection unit can use the emotion estimation function to analyze the emotions the user has toward the selected furniture and decorations in real time and dynamically adjust the selection contents. For example, the selection unit can use the emotion estimation function to analyze the user's facial expression in real time when viewing images of the selected furniture and decorations and dynamically adjust the selection contents. For example, the selection unit can change the suggested contents based on images that show a strong positive emotion. The emotion estimation function can also be used to analyze the tone of the user's voice in real time when answering questions about the selected furniture and decorations and dynamically adjust the selection contents. For example, if an excited tone is detected, the selected item can be highlighted. The emotion estimation function can also be used to analyze biometric data in real time when the user answers a questionnaire about the selected furniture and decorations and dynamically adjust the selection contents. For example, if the user's level of relaxation is high, relaxing items can be suggested. In this way, the selection unit can analyze the user's emotions in real time and dynamically adjust the selection contents.

[0078] The system creates a 3D model of the user's room and simulates an optimal furniture layout plan. For example, the system uses a generation AI to create a 3D model based on the dimensions and layout of the user's room and simulate an optimal furniture layout plan. For example, the furniture layout can be visually confirmed on the 3D model. The generation AI also creates a 3D model of the user's room and simulates and compares different layout plans. For example, it proposes multiple layout plans and allows the user to select. The generation AI also takes into account traffic flow and visual balance when creating a 3D model of the user's room and simulating furniture layout. For example, it proposes layout plans that emphasize ease of use and aesthetic beauty. This allows the system to create a 3D model of the user's room and simulate an optimal layout plan.

[0079] The system is able to propose optimal furniture layout plans by taking into account the user's movement lines and lifestyle patterns. For example, the generation AI analyzes the user's movement lines and proposes optimal furniture layout plans. For example, it arranges furniture so that there are no obstacles in areas that are frequently passed through. The generation AI also analyzes the user's lifestyle patterns and proposes optimal furniture layout plans. For example, if there is a lot of activity in the living room, it proposes comfortable sofa and table placements. The generation AI also considers the user's movement lines and lifestyle patterns and proposes optimal layout plans. For example, it proposes a layout that smooths the movement lines between the kitchen and dining room. This makes it possible to propose optimal layout plans by taking into account the user's movement lines and lifestyle patterns.

[0080] The system can use the emotion estimation function to analyze the emotions a user has toward a proposed placement plan and propose an optimal placement plan based on the emotions. For example, the system uses the emotion estimation function to analyze the facial expression of the user when looking at the proposed placement plan and propose an optimal placement plan based on the emotions. For example, the system adjusts the proposed content based on a placement plan that shows a lot of smiles. The system also uses the emotion estimation function to analyze the tone of the user's voice when answering questions about the proposed placement plan and proposes an optimal placement plan based on the emotions. For example, if an excited tone is detected, the placement is emphasized. The system also uses the emotion estimation function to analyze biometric data when the user answers a questionnaire about the proposed placement plan and proposes an optimal placement plan based on the emotions. For example, if the user's level of relaxation is high, a relaxing placement is proposed. In this way, by analyzing the user's emotions, a more suitable placement plan can be proposed.

[0081] The system can dynamically adjust the user's room layout plan according to the season or event. In the system, for example, the generation AI adjusts the user's room layout plan according to the season. For example, it proposes a cool layout in the summer and a warm layout in the winter. The generation AI also adjusts the user's room layout plan according to events. For example, it proposes a layout suitable for a party or family gathering. The generation AI also dynamically adjusts the user's room layout plan according to the season or event. For example, it proposes a layout that matches seasonal events such as Christmas or Halloween. This makes it possible to dynamically adjust the layout plan according to the season or event.

[0082] The system can propose layout plans for the user's room from the perspective of feng shui and fortune telling. For example, the generation AI of the system proposes layout plans for the user's room based on the principles of feng shui. For example, it proposes a layout that takes into account the flow of good energy in feng shui. The generation AI also proposes layout plans for the user's room based on the results of fortune telling. For example, it proposes a layout that will bring good luck in fortune telling. The generation AI also proposes layout plans for the user's room from the perspective of feng shui and fortune telling. For example, it proposes a layout that takes both feng shui and fortune telling into account. This makes it possible to propose layout plans from the perspective of feng shui and fortune telling.

[0083] The system can use the emotion estimation function to analyze the emotions a user has toward a proposed placement plan in real time and dynamically adjust the placement plan. For example, the system uses the emotion estimation function to analyze the user's facial expression in real time when viewing the proposed placement plan and dynamically adjust the placement plan. For example, the system changes the proposed content based on a placement plan with a strong positive emotion. The system can also use the emotion estimation function to analyze the tone of voice in real time when the user answers questions about the proposed placement plan and dynamically adjust the placement plan. For example, if an excited tone is detected, the placement is emphasized. The system can also use the emotion estimation function to analyze biometric data in real time when the user answers a questionnaire about the proposed placement plan and dynamically adjust the placement plan. For example, if the user's level of relaxation is high, the system can suggest a relaxing placement. In this way, the system can analyze the user's emotions in real time and dynamically adjust the placement plan.

[0084] The system can prioritize the selection of eco-friendly materials and items when coordinating a total interior based on the user's preferences and budget. For example, the generation AI may prioritize the selection of furniture and decorations made with eco-friendly materials based on the user's preferences and budget. For example, it may suggest items made with reclaimed wood or recycled materials. The generation AI may also prioritize the selection of energy-efficient interior items. For example, it may suggest coordination that includes LED lighting and energy-saving home appliances. The generation AI may also prioritize the selection of environmentally friendly interior items based on the user's preferences and budget. For example, it may suggest furniture and decorations made with low-VOC paint. This allows the system to prioritize the selection of eco-friendly materials and items.

[0085] The system can take into consideration integration with smart home devices when coordinating a total interior design based on the user's preferences and budget. For example, the generation AI of the system proposes interior coordination that is integrated with smart home devices based on the user's preferences and budget. For example, it proposes coordination that includes smart lighting and a smart thermostat. The generation AI also proposes interior coordination that incorporates smart home devices based on the user's preferences and budget. For example, it proposes coordination that includes a voice assistant and a smart lock. The generation AI also proposes interior coordination that takes into consideration integration with smart home devices based on the user's preferences and budget. For example, it proposes coordination that includes smart home appliances and a security system. This enables total coordination that takes into consideration integration with smart home devices.

[0086] The system uses the emotion estimation function to analyze the emotions a user has about a total outfit and can suggest the optimal outfit based on the emotions. For example, the system uses the emotion estimation function to analyze the facial expression of the user when looking at a total outfit suggestion and suggests the optimal outfit based on the emotions. For example, the system adjusts the outfit based on suggestions that show a lot of smiles. The system also uses the emotion estimation function to analyze the tone of the user's voice when answering questions about the total outfit and suggests the optimal outfit based on the emotions. For example, if an excited tone is detected, the style is emphasized. The system also uses the emotion estimation function to analyze biometric data when the user answers a questionnaire about the total outfit and suggests the optimal outfit based on the emotions. For example, if the relaxation level is high, the system suggests a relaxing outfit. In this way, by analyzing the user's emotions, the system can suggest a more suitable total outfit.

[0087] The system can blend styles from different cultures and regions when creating a total interior coordination based on the user's preferences and budget. For example, the generation AI can propose a total coordination that blends interior styles from different cultures, such as Scandinavian and Japanese, based on the user's preferences and budget. For example, it can combine a simple Scandinavian design with a calm Japanese design. The generation AI can also propose a total coordination that blends regional interior styles, such as Mediterranean and Asian, based on the user's preferences and budget. For example, it can combine the bright colors of the Mediterranean style with the natural materials of Asian style. The generation AI can also propose a total coordination that blends interior styles from different cultures and regions based on the user's preferences and budget. For example, it can propose a modern design that combines Scandinavian and Japanese styles. This allows for a total coordination that blends styles from different cultures and regions.

[0088] The system can make suggestions including DIY ideas when coordinating total interior decor based on the user's preferences and budget. For example, the generative AI can propose total coordination including interior items that can be created by DIY based on the user's preferences and budget. For example, it can suggest how to make a handmade shelf or decorative item. The generative AI can also propose total coordination including DIY projects based on the user's preferences and budget. For example, it can suggest ways to remake old furniture or ideas for painting walls. The generative AI can also propose total coordination incorporating DIY ideas based on the user's preferences and budget. For example, it can make suggestions including handmade cushion covers or artwork. This makes it possible to propose total coordination including DIY ideas.

[0089] The system uses the emotion estimation function to analyze the emotions a user has about a total outfit in real time and dynamically adjust the outfit content. For example, the system uses the emotion estimation function to analyze the user's facial expression in real time when viewing a total outfit proposal and dynamically adjust the outfit content. For example, the system changes the outfit based on the proposal content that has a strong positive emotion. The system also uses the emotion estimation function to analyze the voice tone in real time when the user answers questions about the total outfit and dynamically adjust the outfit content. For example, if an excited tone is detected, the style is emphasized. The system also uses the emotion estimation function to analyze biometric data in real time when the user answers a questionnaire about the total outfit and dynamically adjust the outfit content. For example, if the user's level of relaxation is high, the system suggests a relaxing outfit. In this way, the system can analyze the user's emotions in real time and dynamically adjust the outfit content.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The interior concierge system can also suggest interior coordination that takes the user's health condition into consideration. For example, for a user with allergies, it can suggest furniture and decorations made from hypoallergenic materials. The generative AI can also analyze the user's health data and suggest interior coordination that will provide a comfortable sleeping environment. For example, it can suggest an appropriate mattress or blackout curtains. The generative AI can also consider the user's exercise habits and suggest interior coordination that includes setting up a home gym. For example, it can suggest the placement of exercise equipment and the necessary space. This makes it possible to suggest interior coordination that takes the user's health condition into consideration.

[0092] The interior concierge system can also suggest interior coordination that reflects the user's hobbies and interests. For example, for a user whose hobby is reading, it can suggest a comfortable reading space. For example, it can suggest appropriate lighting and bookcase placement. The generation AI can also suggest decorations and furniture related to the user's hobbies based on the user's hobbies. For example, for a user who likes music, it can suggest interior coordination that takes acoustics into consideration. The generation AI can also analyze the user's interests and suggest interior coordination based on those interests. For example, for a user who likes traveling, it can suggest a space to display photos and souvenirs from their travels. In this way, it can suggest interior coordination that reflects the user's hobbies and interests.

[0093] The interior concierge system can also estimate the user's emotions and suggest interior coordination that has a relaxing effect based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation function can be used to suggest interior coordination using colors and materials that have a relaxing effect. For example, coordination incorporating blue and green hues can be suggested. If the user is feeling relaxed, the emotion estimation function can be used to suggest interior coordination that will help maintain that emotion. For example, soft lighting and a comfortable sofa can be suggested. If the user is excited, the emotion estimation function can be used to suggest interior coordination that will create a calming atmosphere. For example, coordination using simple designs and natural materials can be suggested. In this way, it is possible to suggest interior coordination that has a relaxing effect while taking the user's emotions into consideration.

[0094] The interior concierge system can also estimate the user's emotions and suggest interior coordination that will enhance concentration based on the user's emotions. For example, using the emotion estimation function, if the user needs to concentrate, it can suggest colors and layouts that will enhance concentration. For example, it can suggest a coordination that incorporates blue and white hues. Also, using the emotion estimation function, if the user is relaxed, it can suggest interior design that will enhance concentration while maintaining that emotion. For example, it can suggest appropriate desk and chair placement. Also, using the emotion estimation function, if the user is tired, it can suggest interior design that has a refreshing effect. For example, it can suggest a coordination that incorporates plants. In this way, it is possible to suggest interior coordination that will enhance concentration while taking the user's emotions into consideration.

[0095] The interior concierge system can also estimate the user's emotions and suggest interior coordination that stimulates creativity based on the user's emotions. For example, if the user wants to engage in creative activities, the emotion estimation function can be used to suggest colors and layouts that stimulate creativity. For example, coordination that incorporates bright colors and unique designs can be suggested. Also, if the user is relaxed, the emotion estimation function can be used to suggest interior design that will enhance creativity while maintaining that emotion. For example, artwork or inspirational decorative items can be suggested. Also, if the user is tired, the emotion estimation function can be used to suggest interior design that has a refreshing effect. For example, coordination that incorporates natural light can be suggested. In this way, it is possible to suggest interior coordination that stimulates creativity while taking the user's emotions into consideration.

[0096] The interior concierge system can also suggest interior coordination according to the user's life events. For example, for a newlywed household, it will suggest interior design that creates a romantic atmosphere. For example, it will suggest coordination that incorporates candles and flowers. Furthermore, based on the user's life events, the generation AI will suggest interior design that prioritizes safety for a household that has just had a baby. For example, it will suggest furniture with no corners and non-slip flooring. Furthermore, based on the user's life events, the generation AI will suggest interior design that will help a household adapt to their new environment for a new home. For example, it will suggest furniture and layout that will increase storage space. In this way, it is possible to suggest interior coordination according to the user's life events.

[0097] The interior concierge system can also suggest interior coordination that reflects the user's eco-consciousness. For example, the generation AI may suggest interior items that use renewable energy based on the user's eco-consciousness. For example, it may suggest lighting that uses solar panels or energy-efficient home appliances. The generation AI may also suggest furniture and decorations that use recycled materials based on the user's eco-consciousness. For example, it may suggest items that use recycled wood or recycled plastic. The generation AI may also suggest environmentally friendly interior coordination based on the user's eco-consciousness. For example, it may suggest furniture and decorations that use low-VOC paint. This makes it possible to suggest interior coordination that reflects the user's eco-consciousness.

[0098] The interior concierge system can also estimate the user's emotions and suggest interior coordination that will boost energy based on those emotions. For example, using the emotion estimation function, if the user is in need of energy, it can suggest colors and layouts that will boost energy. For example, it can suggest coordination that incorporates red or orange hues. Also, using the emotion estimation function, if the user is relaxed, it can suggest interior design that will boost energy while maintaining that emotion. For example, it can suggest vibrant artwork or designs with movement. Also, using the emotion estimation function, if the user is tired, it can suggest interior design that has a refreshing effect. For example, it can suggest coordination that incorporates natural materials. In this way, it is possible to suggest interior coordination that will boost energy while taking the user's emotions into consideration.

[0099] The interior concierge system can also suggest interior coordination that takes into account the user's cultural background. For example, the generation AI will suggest traditional designs and decorations based on the user's cultural background. For example, it will suggest Japanese-style designs and decorations to a Japanese user. The generation AI will also suggest interior coordination that incorporates elements from other cultures based on the user's cultural background. For example, it will suggest Asian-style designs to an American user. The generation AI will also suggest interior coordination that matches cultural events and festivals based on the user's cultural background. For example, it will suggest decorations to match Christmas or Halloween. This makes it possible to suggest interior coordination that takes the user's cultural background into consideration.

[0100] The interior concierge system can also estimate the user's emotions and suggest interior coordination that enhances happiness based on the user's emotions. For example, if the user is feeling happy, the emotion estimation function can be used to suggest interior design that will help maintain that emotion. For example, it can suggest coordination that incorporates bright colors and natural light. If the user is feeling stressed, the emotion estimation function can be used to suggest interior design that has a relaxing effect. For example, it can suggest soft materials and comfortable furniture. If the user is feeling depressed, the emotion estimation function can be used to suggest interior design that will lift their spirits. For example, it can suggest artwork with positive messages or brightly colored ornaments. In this way, it is possible to suggest interior coordination that enhances happiness while taking the user's emotions into consideration.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The preference and budget understanding unit uses the generation AI to understand the user's preferences and budget. For example, the generation AI asks the user questions such as "What style do you like?" and "What is your budget?" and analyzes the user's needs based on the answers. The generation AI can also analyze the user's past purchase history and social media posts to estimate preferences and budget. For example, it can understand the user's interior style trends based on data on furniture and decorative items purchased in the past. Step 2: The coordination suggestion unit proposes the optimal interior coordination based on preferences and budget. For example, the generation AI generates a list of furniture and decorations for conditions such as "a simple Scandinavian design with a budget of less than 100,000 yen," and proposes a specific layout plan. The generation AI can make more accurate suggestions by referring to related background information and topic models and understanding the context. Step 3: The selection unit selects specific furniture and decorative items based on the interior coordination. For example, the generation AI suggests items suitable for the user, such as "This sofa is within your budget and matches your style." The generation AI also works with online shopping sites to help users make purchases easily.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0135] The data processing system 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, 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 robot 414 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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 preference and budget understanding unit that understands a user's preference and budget using a generation AI; a coordination suggestion unit that suggests an optimal interior coordination based on the preferences and budget; and a selection unit that selects specific furniture and decorative items based on the interior coordination. A system characterized by:

2. The preference and budget understanding unit The user's past purchase history and social media posts are analyzed to estimate the user's preferences and budget.

2. The system of claim 1.

3. The preference and budget understanding unit Taking into consideration the user's family structure and lifestyle, we propose the most suitable interior style.

2. The system of claim 1.

4. The preference and budget understanding unit Analyzing the user's feelings about the interior and understanding the user's preferences based on the feelings.

2. The system of claim 1.

5. The preference and budget understanding unit Engaging in a dialogue using a voice assistant to understand the preferences and budget of the user 2. The system of claim 1.

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