System

Abstract

An object of a system according to an embodiment is to analyze information on a room of a user and specify an optimum storage plan and an unnecessary article.SOLUTION: A system according to an embodiment includes an analysis unit, a proposal unit, a visualization unit, a recognition unit, and a specification unit. The analysis unit analyzes information on the room of the user. The proposal unit creates a storage plan based on the information analyzed by the analysis unit and proposes a storage idea. The visualization unit visualizes the storage idea proposed by the proposal unit by a photograph or a graph. The recognition unit recognizes belongings of a user in a room. The specification unit specifies an unnecessary article on the basis of the belongings recognized by the recognition unit and proposes a disposal method thereof.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently plan storage in a user's room or identify unnecessary items, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze information about a user's room, create an optimal storage plan, and identify unnecessary items. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, a visualization unit, a recognition unit, and an identification unit. The analysis unit analyzes information about the user's room. The proposal unit creates a storage plan based on the information analyzed by the analysis unit and proposes storage ideas. The visualization unit visualizes the storage ideas proposed by the proposal unit using photos and graphs. The recognition unit recognizes belongings in the user's room. The identification unit identifies unnecessary items based on the belongings recognized by the recognition unit and proposes methods for disposing of them. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about a user's room, create an optimal storage plan, and identify unnecessary items. [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 storage proposal system according to an embodiment of the present invention is a system that proposes optimal storage space layouts and ideas by taking into consideration the size and needs of a user's room. This allows the storage proposal system to analyze information about the user's room, create a storage plan, and propose storage ideas. It can also visualize the storage situation with photos and graphs and suggest areas for improvement. Furthermore, it can recognize the user's belongings, suggest necessary storage items, and identify unnecessary items and suggest ways to dispose of them.

[0029] A storage suggestion system according to an embodiment includes an analysis unit, a proposal unit, a visualization unit, a recognition unit, and an identification unit. The analysis unit analyzes information about a user's room. For example, when a user inputs a photo and dimensions of the room into the app, the analysis unit uses the information to determine the size and shape of the room and proposes optimal storage space layouts. The proposal unit creates a storage plan based on the information analyzed by the analysis unit and proposes storage ideas. For example, it makes specific suggestions such as, "It would be convenient to install a drawer-type storage box in this closet." The visualization unit visualizes the storage ideas proposed by the proposal unit using photos and graphs. For example, it visually indicates storage efficiency by displaying, for example, "There is still free space on this shelf." The recognition unit recognizes the user's belongings. For example, AI analyzes the type and quantity of belongings and proposes optimal storage items. The identification unit identifies unnecessary items based on the belongings recognized by the recognition unit and proposes methods for disposing of them. For example, it makes specific suggestions such as, "It would be a good idea to recycle these old magazines." As a result, the storage proposal system according to the embodiment analyzes information about the user's room, proposes and visualizes the optimal storage plan, recognizes belongings, identifies unnecessary items, and proposes methods for disposing of them, thereby achieving efficient storage.

[0030] The analysis unit can analyze the user's lifestyle patterns and suggest optimal storage layouts. For example, the analysis unit uses the generation AI to suggest optimal storage layouts based on lifestyle pattern data entered by the user into the app. For example, frequently used items may be placed near the entrance to shorten morning preparation time. The analysis unit also analyzes the user's lifestyle patterns and suggests storage layouts that optimize movement lines. For example, cooking utensils and tableware may be efficiently placed to smooth movement from the kitchen to the living room. The analysis unit also uses the generation AI to analyze the user's lifestyle patterns and prioritize the placement of items that are used frequently during specific times of the day. For example, remote controls and blankets that are used at night may be placed within easy reach in the living room. In this way, optimal storage layouts based on the user's lifestyle patterns are suggested, improving the efficiency of everyday life.

[0031] The analysis unit can perform an analysis that takes into account not only the size and shape of a room, but also furniture layout and traffic flow. For example, the analysis unit uses the generation AI to propose optimal storage layouts based on furniture layout data entered into the app by the user. For example, creating storage space around large furniture ensures traffic flow. The analysis unit also analyzes the shape of the room and furniture layout to propose storage layouts that optimize traffic flow. For example, it considers the placement of sofas and tables and arranges storage space to keep aisles wide. The analysis unit also uses the generation AI to analyze furniture layout and traffic flow to propose efficient storage layouts. For example, it places frequently used items along traffic flow lines to make them easier to access. This allows the system to propose more efficient storage layouts by performing an analysis that takes into account the size and shape of the room, furniture layout, and traffic flow.

[0032] The analysis unit can propose storage layouts that take into account the movements of pets and children. The analysis unit, for example, analyzes the movements of pets and proposes storage layouts that allow pets to stay safe. For example, dangerous items are stored at a height that pets cannot reach. The analysis unit also analyzes the movements of children and proposes storage layouts that are safe and easy to use. For example, storage spaces are provided at low positions so that children can easily tidy up by themselves. The analysis unit also considers the movements of pets and children and proposes storage layouts that ensure movement lines. For example, storage spaces are arranged while ensuring play areas for pets and play spaces for children. In this way, by proposing storage layouts that take into account the movements of pets and children, a safe and easy-to-use storage environment is provided.

[0033] The analysis unit can share the analysis results of the room's size and needs with other users and obtain community-based feedback. The analysis unit, for example, shares the analysis results of the room's size and needs with the community and obtains feedback from other users. For example, it can refer to advice from users who have rooms of the same size. The analysis unit also shares the analysis results and collects community-based feedback. For example, it can refer to storage ideas that other users have actually tried. The analysis unit also improves the analysis results based on community feedback. For example, it can incorporate the opinions of other users to propose more practical storage layouts. In this way, by sharing the analysis results with other users and obtaining community-based feedback, it is possible to propose more practical storage layouts.

[0034] The suggestion unit can propose storage plans for each season. In the suggestion unit, for example, the generation AI proposes storage plans for each season. For example, winter clothes are stored at the back of the closet in summer, and summer items are placed at the front. The suggestion unit also proposes storage plans for each season and optimizes the placement of items according to the season. For example, in summer, electric fans and sunglasses are placed in places where they are easy to access. The suggestion unit also proposes storage plans for each season using the generation AI, and efficiently stores items needed for each season. For example, in spring, a picnic set for cherry blossom viewing is placed at the front. In this way, by proposing storage plans for each season, efficient storage according to the season is possible.

[0035] The suggestion unit can propose a storage plan that reflects the user's hobbies and lifestyle. For example, the suggestion unit proposes a storage plan that reflects the user's hobbies and lifestyle. For example, for a user whose hobby is reading, the suggestion unit optimizes the layout of bookshelves. Furthermore, the suggestion unit uses a generation AI to analyze the user's lifestyle and propose a storage plan based on that analysis. For example, for a user who enjoys the outdoors, the suggestion unit ensures storage space for camping equipment. Furthermore, the suggestion unit proposes an individualized storage plan that takes into account the user's hobbies and lifestyle. For example, for a user whose hobby is cooking, the suggestion unit efficiently arranges storage space in the kitchen. In this way, individualized storage becomes possible by proposing a storage plan based on the user's hobbies and lifestyle.

[0036] The proposal unit can propose a storage plan customized to meet the needs of all family members. For example, the proposal unit considers the needs of all family members and the generation AI proposes a customized storage plan. For example, it reserves space to store children's toys. The proposal unit also analyzes the needs of all family members and proposes a storage plan based on that. For example, it optimizes storage in shared spaces so that it is easy for all family members to use. The proposal unit also proposes a storage plan that reflects the needs of all family members. For example, it provides storage space that suits each family member's hobbies and lifestyles. In this way, by proposing a storage plan based on the needs of all family members, a storage environment that is easy for all family members to use is provided.

[0037] The proposal unit can propose storage plans that can be applied when moving or renovating. For example, the proposal unit proposes storage plans that can be applied when moving or renovating. For example, it proposes storage arrangements that match the layout of a new home. The proposal unit also uses the generation AI to analyze needs when moving or renovating and proposes storage plans based on that analysis. For example, it provides storage space that matches the layout of a new home. The proposal unit also proposes storage plans that can be applied when moving or renovating. For example, it optimizes furniture arrangement and storage space to suit a new home. In this way, by proposing storage plans that can be applied when moving or renovating, efficient storage can be achieved in a new home as well.

[0038] The visualization unit can visualize the storage situation using a 3D model. In the visualization unit, for example, the generation AI visualizes the storage situation using a 3D model and provides it to the user. For example, the storage layout of the entire room is displayed in 3D. The visualization unit also visualizes the storage situation using a 3D model to allow the user to intuitively understand it. For example, the available storage space is displayed in 3D. In addition, the visualization unit visualizes the storage situation using a 3D model using the generation AI, making it easier for the user to grasp areas for improvement. For example, wasted storage space is shown in 3D. In this way, by visualizing the storage situation using a 3D model, the user can intuitively understand the efficiency of storage.

[0039] The visualization unit can display changes in the storage situation in time lapse to visually show the effects of improvement. For example, the visualization unit displays changes in the storage situation in time lapse to visually show the effects of improvement. For example, it compares the state before and after storage. The visualization unit also has the generation AI display changes in the storage situation in time lapse and provide this to the user. For example, it shows the progress of storage work in time lapse. The visualization unit also displays changes in the storage situation in time lapse to allow the user to see the effects of the improvement. For example, it shows the efficiency of storage space in time lapse. In this way, by displaying changes in the storage situation in time lapse, the user can easily see the effects of the improvement.

[0040] The visualization unit can compare the storage situation with other users and learn best practices. For example, the generation AI in the visualization unit compares the storage situation with other users and suggests best practices. For example, it takes into account storage ideas from users who have rooms of the same size. The visualization unit can also compare with other users' storage situations and learn best practices. For example, it can incorporate ideas from users with high storage efficiency. The visualization unit can also analyze other users' storage situations and suggest best practices. For example, it can provide ideas for reducing wasted storage space. In this way, by comparing the storage situation with other users and learning best practices, it is possible to adopt more efficient storage methods.

[0041] The visualization unit can share the visualization data of the storage situation with family and friends and make improvements together. For example, the visualization unit shares the visualization data of the storage situation with family and friends and make improvements together. For example, the whole family can make a storage plan together. The visualization unit also shares the visualization data of the storage situation with the generation AI and makes improvements together. For example, the family can take advice from friends into consideration. The visualization unit also shares the visualization data of the storage situation and makes improvements together with family and friends. For example, the family can consider the layout of storage space together. In this way, more effective storage improvements can be made by sharing the visualization data of the storage situation with family and friends and making improvements together.

[0042] The recognition unit can analyze how often belongings are used and suggest the optimal storage location. For example, the generation AI in the recognition unit analyzes how often belongings are used and suggests the optimal storage location. For example, it places frequently used items in an easily reachable location. The recognition unit can also analyze how often belongings are used and suggest efficient storage locations. For example, it can appropriately place items whose use frequency changes with the seasons. The recognition unit can also analyze how often belongings are used and suggest the optimal storage location. For example, it can store less frequently used items in the back. This improves the efficiency of everyday life by suggesting the optimal storage location based on how often belongings are used.

[0043] The recognition unit can customize the recognition results of belongings to suit the user's preferences. For example, the recognition unit customizes the recognition results of belongings to suit the user's preferences. For example, it may suggest storage items with designs that the user likes. The recognition unit also analyzes the recognition results of belongings using a generation AI and suggests storage items that suit the user's preferences. For example, it may provide a storage method that suits the user's lifestyle. The recognition unit also customizes the recognition results of belongings and suggests storage items that suit the user's preferences. For example, it may suggest storage boxes that are easy for the user to use. In this way, personalized storage can be achieved by customizing the recognition results of belongings to suit the user's preferences.

[0044] The recognition unit can share the results of the recognition of belongings with other users and exchange storage ideas. For example, the recognition unit shares the results of the recognition of belongings with other users and exchanges storage ideas. For example, sharing storage methods with users who have the same hobbies. The recognition unit also analyzes the results of the recognition of belongings using the generation AI and shares them with other users. For example, exchanging storage ideas in a community. The recognition unit also shares the results of the recognition of belongings and exchanges storage ideas with other users. For example, using the layout of storage space as a reference. In this way, by sharing the results of the recognition of belongings with other users and exchanging storage ideas, a wider variety of storage methods can be adopted.

[0045] The recognition unit can make the results of the recognition of belongings available when moving or renovating. For example, the recognition unit can make the results of the recognition of belongings available when moving or renovating. For example, by reflecting them in a storage plan for a new home. The recognition unit also analyzes the results of the recognition of belongings using the generation AI and uses them when moving or renovating. For example, by proposing a storage layout that matches the floor plan of the new home. The recognition unit also uses the results of the recognition of belongings to propose a storage plan that can be applied when moving or renovating. For example, by optimizing furniture layout and storage space to suit the new home. In this way, the results of the recognition of belongings can be used when moving or renovating, enabling efficient storage in the new home.

[0046] The identification unit can suggest recycling methods for unwanted items. For example, the generation AI identifies unwanted items and suggests recycling methods. For example, taking old clothes to a recycling shop. The identification unit also suggests recycling methods for unwanted items and provides environmentally friendly disposal methods. For example, taking electronic devices to a recycling center. The identification unit also analyzes unwanted items using the generation AI and suggests recycling methods. For example, taking plastic products for recycling. In this way, suggesting recycling methods for unwanted items enables environmentally friendly disposal.

[0047] The identification unit can customize the disposal method for unwanted items to match local recycling rules. For example, the identification unit customizes the disposal method for unwanted items to match local recycling rules. For example, it makes suggestions taking into account the recycling rules for each region. The identification unit also uses the generation AI to analyze local recycling rules and propose disposal methods based on those rules. For example, it provides information on local recycling centers. The identification unit also customizes the disposal method for unwanted items to match local recycling rules. For example, it proposes a sorting method that complies with local recycling rules. In this way, customizing the disposal method for unwanted items to match local recycling rules makes it possible to dispose of items in a way that is appropriate for the region.

[0048] The identification unit can propose a platform for exchanging unwanted items with other users. For example, the generation AI identifies unwanted items and proposes a platform for exchanging them with other users. For example, exchanging unwanted books with other users. The identification unit also proposes a platform for exchanging unwanted items with other users to encourage the reuse of items. For example, exchanging furniture or home appliances. The identification unit also analyzes unwanted items with the generation AI and proposes a platform for exchanging them with other users. For example, exchanging clothing or accessories. In this way, by proposing a platform for exchanging unwanted items with other users, the reuse of items is promoted.

[0049] The identification unit can specialize in environmentally friendly methods of disposing of unwanted items. For example, the identification unit may specialize in environmentally friendly methods of disposing of unwanted items. For example, it may encourage recycling or reuse. The identification unit may also use the generative AI to identify unwanted items and suggest environmentally friendly methods of disposal. For example, it may introduce recycling centers or donation destinations. The identification unit may also specialize in environmentally friendly methods of disposing of unwanted items. For example, it may suggest recycling plastic products or proper disposal of electronic devices. This contributes to environmental protection by specializing in environmentally friendly methods of disposing of unwanted items.

[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 analysis unit can analyze the user's lifestyle patterns and suggest optimal storage layouts. For example, the generation AI suggests optimal storage layouts based on lifestyle pattern data entered by the user into the app. For example, frequently used items could be placed near the entrance to shorten morning preparation time. The analysis unit also analyzes the user's lifestyle patterns and suggests storage layouts that optimize movement lines. For example, cooking utensils and tableware could be efficiently placed to smooth movement from the kitchen to the living room. The analysis unit also uses the generation AI to analyze the user's lifestyle patterns and prioritize the placement of items that are used frequently during specific times of the day. For example, remote controls and blankets that are used at night could be placed within easy reach in the living room. This suggests optimal storage layouts based on the user's lifestyle patterns, improving the efficiency of everyday life.

[0052] The suggestion unit can propose storage plans for each season. For example, the generation AI proposes storage plans for each season. For example, winter clothes are stored at the back of the closet in summer, and summer items are placed at the front. The suggestion unit also proposes storage plans for each season and optimizes the placement of items according to the season. For example, in summer, electric fans and sunglasses are placed in places where they are easy to access. The suggestion unit also proposes storage plans for each season, and efficiently stores items needed for each season. For example, in spring, a picnic set for cherry blossom viewing is placed at the front. In this way, by proposing storage plans for each season, efficient storage according to the season is possible.

[0053] The visualization unit can visualize the storage situation using a 3D model. For example, the generation AI visualizes the storage situation using a 3D model and provides it to the user. For example, the storage layout of the entire room is displayed in 3D. The visualization unit also visualizes the storage situation using a 3D model, allowing the user to intuitively understand it. For example, it displays the available storage space in 3D. The visualization unit also visualizes the storage situation using a 3D model, making it easier for the user to grasp areas for improvement. For example, it shows wasted storage space in 3D. In this way, by visualizing the storage situation using a 3D model, the user can intuitively understand storage efficiency.

[0054] The recognition unit can analyze how often belongings are used and suggest the optimal storage location. For example, the generation AI can analyze how often belongings are used and suggest the optimal storage location. For example, it can place frequently used items in an easily reachable location. The recognition unit can also analyze how often belongings are used and suggest efficient storage locations. For example, it can appropriately place items whose frequency of use changes with the seasons. The recognition unit can also analyze how often belongings are used and suggest the optimal storage location. For example, it can store less frequently used items in the back. This improves the efficiency of everyday life by suggesting the optimal storage location based on how often belongings are used.

[0055] The identification unit can suggest ways to recycle unwanted items. For example, the generation AI can identify unwanted items and suggest ways to recycle them. For example, taking old clothes to a recycling shop. The identification unit can also suggest ways to recycle unwanted items and provide environmentally friendly disposal methods. For example, taking electronic devices to a recycling center. The identification unit can also analyze unwanted items using the generation AI and suggest ways to recycle them. For example, taking plastic products for recycling. This allows for environmentally friendly disposal by suggesting ways to recycle unwanted items.

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

[0057] Step 1: The analysis unit analyzes the user's room information. For example, when the user inputs a photo and dimensions of the room into the app, the analysis unit uses that information to determine the size and shape of the room and suggests optimal storage space layouts. Step 2: The proposal department creates a storage plan based on the information analyzed by the analysis department and proposes storage ideas. For example, it makes a specific suggestion such as, "It would be convenient to install a drawer-type storage box in this closet." Step 3: The visualization department visualizes the storage ideas proposed by the proposal department using photos and graphs. For example, it visually shows the storage efficiency by showing that there is still free space on this shelf. Step 4: The recognition unit recognizes the belongings in the user's room. For example, AI analyzes the type and quantity of belongings and suggests optimal storage items. Step 5: The identification unit identifies unwanted items based on the belongings recognized by the recognition unit and suggests how to dispose of them. For example, it makes a specific suggestion such as, "It would be a good idea to recycle this old magazine."

[0058] (Example 2) The storage proposal system according to an embodiment of the present invention is a system that proposes optimal storage space layouts and ideas by taking into consideration the size and needs of a user's room. This allows the storage proposal system to analyze information about the user's room, create a storage plan, and propose storage ideas. It can also visualize the storage situation with photos and graphs and suggest areas for improvement. Furthermore, it can recognize the user's belongings, suggest necessary storage items, and identify unnecessary items and suggest ways to dispose of them.

[0059] A storage suggestion system according to an embodiment includes an analysis unit, a proposal unit, a visualization unit, a recognition unit, and an identification unit. The analysis unit analyzes information about a user's room. For example, when a user inputs a photo and dimensions of the room into the app, the analysis unit uses the information to determine the size and shape of the room and proposes optimal storage space layouts. The proposal unit creates a storage plan based on the information analyzed by the analysis unit and proposes storage ideas. For example, it makes specific suggestions such as, "It would be convenient to install a drawer-type storage box in this closet." The visualization unit visualizes the storage ideas proposed by the proposal unit using photos and graphs. For example, it visually indicates storage efficiency by displaying, for example, "There is still free space on this shelf." The recognition unit recognizes the user's belongings. For example, AI analyzes the type and quantity of belongings and proposes optimal storage items. The identification unit identifies unnecessary items based on the belongings recognized by the recognition unit and proposes methods for disposing of them. For example, it makes specific suggestions such as, "It would be a good idea to recycle these old magazines." As a result, the storage proposal system according to the embodiment analyzes information about the user's room, proposes and visualizes the optimal storage plan, recognizes belongings, identifies unnecessary items, and proposes methods for disposing of them, thereby achieving efficient storage.

[0060] The analysis unit can analyze the user's lifestyle patterns and suggest optimal storage layouts. For example, the analysis unit uses the generation AI to suggest optimal storage layouts based on lifestyle pattern data entered by the user into the app. For example, frequently used items may be placed near the entrance to shorten morning preparation time. The analysis unit also analyzes the user's lifestyle patterns and suggests storage layouts that optimize movement lines. For example, cooking utensils and tableware may be efficiently placed to smooth movement from the kitchen to the living room. The analysis unit also uses the generation AI to analyze the user's lifestyle patterns and prioritize the placement of items that are used frequently during specific times of the day. For example, remote controls and blankets that are used at night may be placed within easy reach in the living room. In this way, optimal storage layouts based on the user's lifestyle patterns are suggested, improving the efficiency of everyday life.

[0061] The analysis unit can perform an analysis that takes into account not only the size and shape of a room, but also furniture layout and traffic flow. For example, the analysis unit uses the generation AI to propose optimal storage layouts based on furniture layout data entered into the app by the user. For example, creating storage space around large furniture ensures traffic flow. The analysis unit also analyzes the shape of the room and furniture layout to propose storage layouts that optimize traffic flow. For example, it considers the placement of sofas and tables and arranges storage space to keep aisles wide. The analysis unit also uses the generation AI to analyze furniture layout and traffic flow to propose efficient storage layouts. For example, it places frequently used items along traffic flow lines to make them easier to access. This allows the system to propose more efficient storage layouts by performing an analysis that takes into account the size and shape of the room, furniture layout, and traffic flow.

[0062] The analysis unit can use the emotion estimation function to suggest a spatial layout that the user finds comfortable. For example, the analysis unit uses the emotion estimation function to suggest a spatial layout that the user finds comfortable. For example, the furniture arrangement in the living room is adjusted so that the user can relax. The analysis unit also analyzes the user's emotion data to suggest a comfortable spatial layout. For example, storage space may be arranged to make it easy to keep things tidy in order to reduce stress. The analysis unit also uses the emotion estimation function to suggest a spatial layout that the user finds comfortable. For example, the storage arrangement around a window may be adjusted to make it easier to let in natural light. In this way, satisfaction with the living environment is improved by suggesting a comfortable spatial layout based on the user's emotions.

[0063] The analysis unit can propose storage layouts that take into account the movements of pets and children. The analysis unit, for example, analyzes the movements of pets and proposes storage layouts that allow pets to stay safe. For example, dangerous items are stored at a height that pets cannot reach. The analysis unit also analyzes the movements of children and proposes storage layouts that are safe and easy to use. For example, storage spaces are provided at low positions so that children can easily tidy up by themselves. The analysis unit also considers the movements of pets and children and proposes storage layouts that ensure movement lines. For example, storage spaces are arranged while ensuring play areas for pets and play spaces for children. In this way, by proposing storage layouts that take into account the movements of pets and children, a safe and easy-to-use storage environment is provided.

[0064] The analysis unit can share the analysis results of the room's size and needs with other users and obtain community-based feedback. The analysis unit, for example, shares the analysis results of the room's size and needs with the community and obtains feedback from other users. For example, it can refer to advice from users who have rooms of the same size. The analysis unit also shares the analysis results and collects community-based feedback. For example, it can refer to storage ideas that other users have actually tried. The analysis unit also improves the analysis results based on community feedback. For example, it can incorporate the opinions of other users to propose more practical storage layouts. In this way, by sharing the analysis results with other users and obtaining community-based feedback, it is possible to propose more practical storage layouts.

[0065] The analysis unit can use the emotion estimation function to suggest a storage layout that is less stressful for the user. The analysis unit, for example, uses the emotion estimation function to suggest a storage layout that is less stressful for the user. For example, frequently used items are placed in locations that make them easy to access. The analysis unit also analyzes the user's emotion data to suggest a storage layout that reduces stress. For example, it suggests a visually neat layout. The analysis unit also uses the emotion estimation function to suggest a storage layout that is less stressful for the user. For example, it efficiently arranges storage space to smooth traffic flow. In this way, by suggesting a storage layout that is less stressful for the user based on the user's emotions, the comfort of the living environment is improved.

[0066] The suggestion unit can propose storage plans for each season. In the suggestion unit, for example, the generation AI proposes storage plans for each season. For example, winter clothes are stored at the back of the closet in summer, and summer items are placed at the front. The suggestion unit also proposes storage plans for each season and optimizes the placement of items according to the season. For example, in summer, electric fans and sunglasses are placed in places where they are easy to access. The suggestion unit also proposes storage plans for each season using the generation AI, and efficiently stores items needed for each season. For example, in spring, a picnic set for cherry blossom viewing is placed at the front. In this way, by proposing storage plans for each season, efficient storage according to the season is possible.

[0067] The suggestion unit can propose a storage plan that reflects the user's hobbies and lifestyle. For example, the suggestion unit proposes a storage plan that reflects the user's hobbies and lifestyle. For example, for a user whose hobby is reading, the suggestion unit optimizes the layout of bookshelves. Furthermore, the suggestion unit uses a generation AI to analyze the user's lifestyle and propose a storage plan based on that analysis. For example, for a user who enjoys the outdoors, the suggestion unit ensures storage space for camping equipment. Furthermore, the suggestion unit proposes an individualized storage plan that takes into account the user's hobbies and lifestyle. For example, for a user whose hobby is cooking, the suggestion unit efficiently arranges storage space in the kitchen. In this way, individualized storage becomes possible by proposing a storage plan based on the user's hobbies and lifestyle.

[0068] The suggestion unit can use the emotion estimation function to suggest ideas that allow the user to enjoy storing things. For example, the suggestion unit uses the emotion estimation function to suggest ideas that allow the user to enjoy storing things. For example, colorful storage boxes are used. The suggestion unit also analyzes the user's emotion data to suggest ideas that allow the user to enjoy storing things. For example, it provides ideas for decorating storage spaces. The suggestion unit also uses the emotion estimation function to suggest ideas that allow the user to enjoy storing things. For example, it makes the storage work enjoyable like a game. In this way, by suggesting ideas that allow the user to enjoy storing things, motivation for the storage work is improved.

[0069] The proposal unit can propose a storage plan customized to meet the needs of all family members. For example, the proposal unit considers the needs of all family members and the generation AI proposes a customized storage plan. For example, it reserves space to store children's toys. The proposal unit also analyzes the needs of all family members and proposes a storage plan based on that. For example, it optimizes storage in shared spaces so that it is easy for all family members to use. The proposal unit also proposes a storage plan that reflects the needs of all family members. For example, it provides storage space that suits each family member's hobbies and lifestyles. In this way, by proposing a storage plan based on the needs of all family members, a storage environment that is easy for all family members to use is provided.

[0070] The proposal unit can propose storage plans that can be applied when moving or renovating. For example, the proposal unit proposes storage plans that can be applied when moving or renovating. For example, it proposes storage arrangements that match the layout of a new home. The proposal unit also uses the generation AI to analyze needs when moving or renovating and proposes storage plans based on that analysis. For example, it provides storage space that matches the layout of a new home. The proposal unit also proposes storage plans that can be applied when moving or renovating. For example, it optimizes furniture arrangement and storage space to suit a new home. In this way, by proposing storage plans that can be applied when moving or renovating, efficient storage can be achieved in a new home as well.

[0071] The suggestion unit can use the emotion estimation function to suggest a storage plan that will give the user a sense of satisfaction. The suggestion unit, for example, uses the emotion estimation function to suggest a storage plan that will give the user a sense of satisfaction. For example, it suggests storage items with a design that the user likes. The suggestion unit also analyzes the user's emotion data to suggest a storage plan that will give the user a sense of satisfaction. For example, it suggests a storage layout that will create a space where the user can relax. The suggestion unit also uses the emotion estimation function to suggest a storage plan that will give the user a sense of satisfaction. For example, it suggests customized storage that matches the user's preferences. In this way, suggesting a storage plan that will give the user a sense of satisfaction improves satisfaction with the living environment.

[0072] The visualization unit can visualize the storage situation using a 3D model. In the visualization unit, for example, the generation AI visualizes the storage situation using a 3D model and provides it to the user. For example, the storage layout of the entire room is displayed in 3D. The visualization unit also visualizes the storage situation using a 3D model to allow the user to intuitively understand it. For example, the available storage space is displayed in 3D. In addition, the visualization unit visualizes the storage situation using a 3D model using the generation AI, making it easier for the user to grasp areas for improvement. For example, wasted storage space is shown in 3D. In this way, by visualizing the storage situation using a 3D model, the user can intuitively understand the efficiency of storage.

[0073] The visualization unit can display changes in the storage situation in time lapse to visually show the effects of improvement. For example, the visualization unit displays changes in the storage situation in time lapse to visually show the effects of improvement. For example, it compares the state before and after storage. The visualization unit also has the generation AI display changes in the storage situation in time lapse and provide this to the user. For example, it shows the progress of storage work in time lapse. The visualization unit also displays changes in the storage situation in time lapse to allow the user to see the effects of the improvement. For example, it shows the efficiency of storage space in time lapse. In this way, by displaying changes in the storage situation in time lapse, the user can easily see the effects of the improvement.

[0074] The visualization unit can use the emotion estimation function to suggest points that the user will feel positive about improving. For example, the visualization unit uses the emotion estimation function to suggest points that the user will feel positive about improving. For example, it suggests storage tasks that will give the user a sense of accomplishment. The visualization unit also analyzes the user's emotion data and suggests improvement points that will elicit positive emotions. For example, it suggests storage arrangements that will give the user a sense of satisfaction. The visualization unit also uses the emotion estimation function to suggest points that the user will feel positive about improving. For example, it provides ideas that will allow the user to store things while having fun. In this way, suggesting points that the user will feel positive about improving increases motivation for storage improvements.

[0075] The visualization unit can compare the storage situation with other users and learn best practices. For example, the generation AI in the visualization unit compares the storage situation with other users and suggests best practices. For example, it takes into account storage ideas from users who have rooms of the same size. The visualization unit can also compare with other users' storage situations and learn best practices. For example, it can incorporate ideas from users with high storage efficiency. The visualization unit can also analyze other users' storage situations and suggest best practices. For example, it can provide ideas for reducing wasted storage space. In this way, by comparing the storage situation with other users and learning best practices, it is possible to adopt more efficient storage methods.

[0076] The visualization unit can share the visualization data of the storage situation with family and friends and make improvements together. For example, the visualization unit shares the visualization data of the storage situation with family and friends and make improvements together. For example, the whole family can make a storage plan together. The visualization unit also shares the visualization data of the storage situation with the generation AI and makes improvements together. For example, the family can take advice from friends into consideration. The visualization unit also shares the visualization data of the storage situation and makes improvements together with family and friends. For example, the family can consider the layout of storage space together. In this way, more effective storage improvements can be made by sharing the visualization data of the storage situation with family and friends and making improvements together.

[0077] The visualization unit can use the emotion estimation function to suggest improvement points that will allow the user to maintain their motivation. For example, the visualization unit uses the emotion estimation function to suggest improvement points that will allow the user to maintain their motivation. For example, it suggests storage tasks that will give the user a sense of accomplishment. The visualization unit also analyzes the user's emotion data to suggest improvement points that will allow the user to maintain their motivation. For example, it provides ideas that will allow the user to store things while having fun. The visualization unit also uses the emotion estimation function to suggest improvement points that will allow the user to maintain their motivation. For example, it makes the storage task enjoyable like a game. In this way, suggesting improvement points that will allow the user to maintain their motivation increases the continuity of storage improvements.

[0078] The recognition unit can analyze how often belongings are used and suggest the optimal storage location. For example, the generation AI in the recognition unit analyzes how often belongings are used and suggests the optimal storage location. For example, it places frequently used items in an easily reachable location. The recognition unit can also analyze how often belongings are used and suggest efficient storage locations. For example, it can appropriately place items whose use frequency changes with the seasons. The recognition unit can also analyze how often belongings are used and suggest the optimal storage location. For example, it can store less frequently used items in the back. This improves the efficiency of everyday life by suggesting the optimal storage location based on how often belongings are used.

[0079] The recognition unit can customize the recognition results of belongings to suit the user's preferences. For example, the recognition unit customizes the recognition results of belongings to suit the user's preferences. For example, it may suggest storage items with designs that the user likes. The recognition unit also analyzes the recognition results of belongings using a generation AI and suggests storage items that suit the user's preferences. For example, it may provide a storage method that suits the user's lifestyle. The recognition unit also customizes the recognition results of belongings and suggests storage items that suit the user's preferences. For example, it may suggest storage boxes that are easy for the user to use. In this way, personalized storage can be achieved by customizing the recognition results of belongings to suit the user's preferences.

[0080] The recognition unit can use the emotion estimation function to suggest a storage method for belongings that the user is attached to. For example, the recognition unit uses the emotion estimation function to suggest a storage method for belongings that the user is attached to. For example, placing memorable items in a place where they are easy to see. The recognition unit also analyzes the user's emotion data and suggests a storage method for belongings that the user is attached to. For example, storing items that the user cherishes in a special place. The recognition unit also uses the emotion estimation function to suggest a storage method for belongings that the user is attached to. For example, reserving space to display memorable photos. In this way, suggesting a storage method for belongings that the user is attached to makes it easier to manage belongings.

[0081] The recognition unit can share the results of the recognition of belongings with other users and exchange storage ideas. For example, the recognition unit shares the results of the recognition of belongings with other users and exchanges storage ideas. For example, sharing storage methods with users who have the same hobbies. The recognition unit also analyzes the results of the recognition of belongings using the generation AI and shares them with other users. For example, exchanging storage ideas in a community. The recognition unit also shares the results of the recognition of belongings and exchanges storage ideas with other users. For example, using the layout of storage space as a reference. In this way, by sharing the results of the recognition of belongings with other users and exchanging storage ideas, a wider variety of storage methods can be adopted.

[0082] The recognition unit can make the results of the recognition of belongings available when moving or renovating. For example, the recognition unit can make the results of the recognition of belongings available when moving or renovating. For example, by reflecting them in a storage plan for a new home. The recognition unit also analyzes the results of the recognition of belongings using the generation AI and uses them when moving or renovating. For example, by proposing a storage layout that matches the floor plan of the new home. The recognition unit also uses the results of the recognition of belongings to propose a storage plan that can be applied when moving or renovating. For example, by optimizing furniture layout and storage space to suit the new home. In this way, the results of the recognition of belongings can be used when moving or renovating, enabling efficient storage in the new home.

[0083] The recognition unit can use the emotion estimation function to suggest storage items that will give the user a sense of satisfaction. The recognition unit, for example, uses the emotion estimation function to suggest storage items that will give the user a sense of satisfaction. For example, it may suggest storage boxes with a design that the user likes. The recognition unit also analyzes the user's emotion data to suggest storage items that will give the user a sense of satisfaction. For example, it may suggest storage items that will help the user create a space where they can relax. The recognition unit also uses the emotion estimation function to suggest storage items that will give the user a sense of satisfaction. For example, it may suggest storage that is customized to suit the user's preferences. In this way, suggesting storage items that will give the user a sense of satisfaction improves satisfaction with the storage environment.

[0084] The identification unit can suggest recycling methods for unwanted items. For example, the generation AI identifies unwanted items and suggests recycling methods. For example, taking old clothes to a recycling shop. The identification unit also suggests recycling methods for unwanted items and provides environmentally friendly disposal methods. For example, taking electronic devices to a recycling center. The identification unit also analyzes unwanted items using the generation AI and suggests recycling methods. For example, taking plastic products for recycling. In this way, suggesting recycling methods for unwanted items enables environmentally friendly disposal.

[0085] The identification unit can customize the disposal method for unwanted items to match local recycling rules. For example, the identification unit customizes the disposal method for unwanted items to match local recycling rules. For example, it makes suggestions taking into account the recycling rules for each region. The identification unit also uses the generation AI to analyze local recycling rules and propose disposal methods based on those rules. For example, it provides information on local recycling centers. The identification unit also customizes the disposal method for unwanted items to match local recycling rules. For example, it proposes a sorting method that complies with local recycling rules. In this way, customizing the disposal method for unwanted items to match local recycling rules makes it possible to dispose of items in a way that is appropriate for the region.

[0086] The identification unit can use the emotion estimation function to suggest a method that allows the user to dispose of items without feeling guilty. For example, the identification unit uses the emotion estimation function to suggest a method that allows the user to dispose of items without feeling guilty. For example, donating unwanted items. The identification unit also analyzes the user's emotion data and suggests a disposal method that reduces guilt. For example, encouraging recycling and reuse. The identification unit also uses the emotion estimation function to suggest a method that allows the user to dispose of items without feeling guilty. For example, taking the unwanted items to a recycle shop. This makes it easier to dispose of unwanted items by suggesting a method that allows the user to dispose of items without feeling guilty.

[0087] The identification unit can propose a platform for exchanging unwanted items with other users. For example, the generation AI identifies unwanted items and proposes a platform for exchanging them with other users. For example, exchanging unwanted books with other users. The identification unit also proposes a platform for exchanging unwanted items with other users to encourage the reuse of items. For example, exchanging furniture or home appliances. The identification unit also analyzes unwanted items with the generation AI and proposes a platform for exchanging them with other users. For example, exchanging clothing or accessories. In this way, by proposing a platform for exchanging unwanted items with other users, the reuse of items is promoted.

[0088] The identification unit can specialize in environmentally friendly methods of disposing of unwanted items. For example, the identification unit may specialize in environmentally friendly methods of disposing of unwanted items. For example, it may encourage recycling or reuse. The identification unit may also use the generative AI to identify unwanted items and suggest environmentally friendly methods of disposal. For example, it may introduce recycling centers or donation destinations. The identification unit may also specialize in environmentally friendly methods of disposing of unwanted items. For example, it may suggest recycling plastic products or proper disposal of electronic devices. This contributes to environmental protection by specializing in environmentally friendly methods of disposing of unwanted items.

[0089] The identification unit can use the emotion estimation function to suggest ways for the user to feel positive about disposal. For example, the identification unit uses the emotion estimation function to suggest ways for the user to feel positive about disposal. For example, donating unwanted items makes the user feel like they are contributing to society. The identification unit also analyzes the user's emotion data and suggests disposal methods that elicit positive emotions. For example, encouraging recycling and reuse contributes to environmental protection. The identification unit also uses the emotion estimation function to suggest ways for the user to feel positive about disposal. For example, finding new value in unwanted items by bringing them to a recycle shop. In this way, suggesting ways for the user to feel positive about disposal makes it easier to dispose of unwanted items.

[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 analysis unit can analyze the user's lifestyle patterns and suggest optimal storage layouts. For example, the generation AI suggests optimal storage layouts based on lifestyle pattern data entered by the user into the app. For example, frequently used items could be placed near the entrance to shorten morning preparation time. The analysis unit also analyzes the user's lifestyle patterns and suggests storage layouts that optimize movement lines. For example, cooking utensils and tableware could be efficiently placed to smooth movement from the kitchen to the living room. The analysis unit also uses the generation AI to analyze the user's lifestyle patterns and prioritize the placement of items that are used frequently during specific times of the day. For example, remote controls and blankets that are used at night could be placed within easy reach in the living room. This suggests optimal storage layouts based on the user's lifestyle patterns, improving the efficiency of everyday life.

[0092] The analysis unit can use the emotion estimation function to suggest a spatial layout that the user finds comfortable. For example, the emotion estimation function is used to suggest a spatial layout that the user finds comfortable. For example, the furniture arrangement in the living room is adjusted so that the user can relax. The analysis unit also analyzes the user's emotion data to suggest a comfortable spatial layout. For example, storage space is arranged to make it easy to keep tidy in order to reduce stress. The analysis unit also uses the emotion estimation function to suggest a spatial layout that the user finds comfortable. For example, storage arrangement around windows is adjusted to make it easier to let in natural light. In this way, satisfaction with the living environment is improved by suggesting a comfortable spatial layout based on the user's emotions.

[0093] The suggestion unit can propose storage plans for each season. For example, the generation AI proposes storage plans for each season. For example, winter clothes are stored at the back of the closet in summer, and summer items are placed at the front. The suggestion unit also proposes storage plans for each season and optimizes the placement of items according to the season. For example, in summer, electric fans and sunglasses are placed in places where they are easy to access. The suggestion unit also proposes storage plans for each season, and efficiently stores items needed for each season. For example, in spring, a picnic set for cherry blossom viewing is placed at the front. In this way, by proposing storage plans for each season, efficient storage according to the season is possible.

[0094] The suggestion unit can use the emotion estimation function to suggest ideas that allow the user to enjoy storing things. For example, the emotion estimation function is used to suggest ideas that allow the user to enjoy storing things. For example, colorful storage boxes are used. The suggestion unit also analyzes the user's emotion data to suggest ideas that allow the user to enjoy storing things. For example, ideas for decorating storage spaces are provided. The suggestion unit also uses the emotion estimation function to suggest ideas that allow the user to enjoy storing things. For example, the suggestion unit makes the storage work enjoyable like a game. In this way, by suggesting ideas that allow the user to enjoy storing things, motivation for the storage work is improved.

[0095] The visualization unit can visualize the storage situation using a 3D model. For example, the generation AI visualizes the storage situation using a 3D model and provides it to the user. For example, the storage layout of the entire room is displayed in 3D. The visualization unit also visualizes the storage situation using a 3D model, allowing the user to intuitively understand it. For example, it displays the available storage space in 3D. The visualization unit also visualizes the storage situation using a 3D model, making it easier for the user to grasp areas for improvement. For example, it shows wasted storage space in 3D. In this way, by visualizing the storage situation using a 3D model, the user can intuitively understand storage efficiency.

[0096] The visualization unit can use the emotion estimation function to suggest points that the user will feel positive about improving. For example, the emotion estimation function can be used to suggest points that the user will feel positive about improving. For example, it can suggest storage tasks that will give the user a sense of accomplishment. The visualization unit can also analyze the user's emotion data and suggest improvement points that will elicit positive emotions. For example, it can suggest storage layouts that will give the user a sense of satisfaction. The visualization unit can also use the emotion estimation function to suggest points that the user will feel positive about improving. For example, it can provide ideas that will allow the user to store things while having fun. In this way, suggesting points that the user will feel positive about improving increases motivation for storage improvements.

[0097] The recognition unit can analyze how often belongings are used and suggest the optimal storage location. For example, the generation AI can analyze how often belongings are used and suggest the optimal storage location. For example, it can place frequently used items in an easily reachable location. The recognition unit can also analyze how often belongings are used and suggest efficient storage locations. For example, it can appropriately place items whose frequency of use changes with the seasons. The recognition unit can also analyze how often belongings are used and suggest the optimal storage location. For example, it can store less frequently used items in the back. This improves the efficiency of everyday life by suggesting the optimal storage location based on how often belongings are used.

[0098] The recognition unit can use the emotion estimation function to suggest a storage method for belongings that the user is attached to. For example, the emotion estimation function is used to suggest a storage method for belongings that the user is attached to. For example, by placing memorable items in a place where they are easy to see. The recognition unit also analyzes the user's emotion data and suggests a storage method for belongings that the user is attached to. For example, by storing items that the user cherishes in a special place. The recognition unit also uses the emotion estimation function to suggest a storage method for belongings that the user is attached to. For example, by reserving space to display memorable photos. In this way, suggesting a storage method for belongings that the user is attached to makes it easier to manage belongings.

[0099] The identification unit can suggest ways to recycle unwanted items. For example, the generation AI can identify unwanted items and suggest ways to recycle them. For example, taking old clothes to a recycling shop. The identification unit can also suggest ways to recycle unwanted items and provide environmentally friendly disposal methods. For example, taking electronic devices to a recycling center. The identification unit can also analyze unwanted items using the generation AI and suggest ways to recycle them. For example, taking plastic products for recycling. This allows for environmentally friendly disposal by suggesting ways to recycle unwanted items.

[0100] The identification unit can use the emotion estimation function to suggest ways that allow the user to dispose of items without feeling guilty. For example, the emotion estimation function can be used to suggest ways that allow the user to dispose of items without feeling guilty. For example, donating unwanted items. The identification unit can also analyze the user's emotion data and suggest disposal methods that reduce guilt. For example, encouraging recycling and reuse. The identification unit can also use the emotion estimation function to suggest ways that allow the user to dispose of items without feeling guilty. For example, taking unwanted items to a recycling shop. This makes it easier to dispose of unwanted items by suggesting ways that allow the user to dispose of items without feeling guilty.

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

[0102] Step 1: The analysis unit analyzes the user's room information. For example, when the user inputs a photo and dimensions of the room into the app, the analysis unit uses that information to determine the size and shape of the room and suggests optimal storage space layouts. Step 2: The proposal department creates a storage plan based on the information analyzed by the analysis department and proposes storage ideas. For example, it makes a specific suggestion such as, "It would be convenient to install a drawer-type storage box in this closet." Step 3: The visualization department visualizes the storage ideas proposed by the proposal department using photos and graphs. For example, it visually shows the storage efficiency by showing that there is still free space on this shelf. Step 4: The recognition unit recognizes the belongings in the user's room. For example, AI analyzes the type and quantity of belongings and suggests optimal storage items. Step 5: The identification unit identifies unwanted items based on the belongings recognized by the recognition unit and suggests how to dispose of them. For example, it makes a specific suggestion such as, "It would be a good idea to recycle this old magazine."

[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. an analysis unit that analyzes information about the user's room; a proposal unit that creates a storage plan based on the information analyzed by the analysis unit and proposes storage ideas; a visualization unit that visualizes the storage ideas proposed by the proposal unit using photographs and graphs; a recognition unit that recognizes belongings in the user's room; an identification unit that identifies unnecessary items based on the belongings recognized by the recognition unit and suggests a method for disposing of the items. A system characterized by:

2. The analysis unit Analyzes users' lifestyle patterns and suggests optimal storage layouts 2. The system of claim 1.

3. The analysis unit The analysis takes into account not only the size and shape of the room, but also the furniture arrangement and traffic flow.

2. The system of claim 1.

4. The analysis unit Propose a spatial layout that users find comfortable 2. The system of claim 1.

5. The analysis unit Propose storage arrangements that take into consideration the movements of pets and children 2. The system of claim 1.

6. The analysis unit Share the results of your room size and needs analysis with other users to get community-based feedback 2. The system of claim 1.

7. The analysis unit Propose storage layouts that users will find less stressful 2. The system of claim 1.

8. The proposal unit Propose seasonal storage plans 2. The system of claim 1.

Images