system

Abstract

The system according to the embodiment aims to analyze a photo of a room and propose an optimal layout and furniture. [Solution] A system according to an embodiment includes an analysis unit, a proposal unit, and a presentation unit. The analysis unit analyzes a photo of a room. The proposal unit proposes a room layout based on the information analyzed by the analysis unit. The presentation unit presents furniture or storage that matches the layout proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to obtain specific suggestions for optimizing room layouts.

[0005] The system according to the embodiment aims to analyze a photo of a room and propose an optimal layout and furniture. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and a presentation unit. The analysis unit analyzes a photo of a room. The proposal unit proposes a room layout based on the information analyzed by the analysis unit. The presentation unit presents furniture or storage that matches the layout proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze photos of a room and suggest optimal layouts and furniture. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A layout proposal system according to an embodiment of the present invention is a system in which a user takes a photo of a room and an AI analyzes the photo to propose a room layout. In this layout proposal system, a user takes a photo of a room and uploads the photo to the system. The AI ​​then analyzes the photo to determine the size and shape of the room, the layout of existing furniture, and other factors. The AI ​​then proposes an optimal layout and presents it to the user. Furthermore, the AI ​​presents furniture and storage that fit the proposed layout, allowing the user to select. For example, when a user takes a photo of a room and uploads it to the system, the AI ​​analyzes the size and shape of the room and proposes an optimal furniture layout. The AI ​​also lists furniture and storage that fit the proposed layout, allowing the user to select. This allows users to easily find the optimal layout and furniture for their room. The layout proposal system thus allows users to easily find the optimal layout and furniture for their room.

[0029] A layout proposal system according to an embodiment includes an analysis unit, a proposal unit, and a presentation unit. The analysis unit analyzes a photo of a room. The analysis unit analyzes, for example, the size and shape of the room and the layout of existing furniture. The analysis unit measures, for example, the size of the room in square meters and identifies the shape of the room. The analysis unit also identifies the layout of existing furniture and determines the position and size of each piece of furniture. The proposal unit proposes a room layout based on the information analyzed by the analysis unit. The proposal unit calculates an optimal layout based, for example, on the size and shape of the room and the layout of the existing furniture. The proposal unit calculates, for example, the optimal placement of sofas and tables and presents the result to the user. The presentation unit presents furniture and storage that matches the layout proposed by the proposal unit. The presentation unit, for example, lists optimal furniture and storage based on the proposed layout. The presentation unit, for example, lists optimal models of sofas and tables and presents the list to the user. This allows the layout proposal system according to an embodiment to easily find the optimal layout and furniture for their room.

[0030] The analysis unit can analyze the size or shape of a room and the layout of existing furniture. For example, the analysis unit measures the size of the room in square meters and identifies the shape of the room. For example, the analysis unit measures the dimensions of the room and calculates parameters for identifying the shape of the room. The analysis unit also identifies the layout of existing furniture and determines the position and size of each piece of furniture. For example, the analysis unit acquires position information of furniture and analyzes the layout pattern. This allows for more accurate layout proposals by analyzing the size and shape of the room and the layout of existing furniture.

[0031] The suggestion unit can suggest a layout based on the size or shape of the room and the arrangement of existing furniture. The suggestion unit calculates an optimal layout based on, for example, the size or shape of the room and the arrangement of existing furniture. The suggestion unit calculates the optimal arrangement of, for example, sofas and tables, and presents the result to the user. The suggestion unit uses an algorithm for calculating the optimal layout based on, for example, the size or shape of the room and the arrangement of existing furniture. For example, the suggestion unit uses an algorithm that inputs the size or shape of the room and the arrangement of existing furniture and outputs an optimal layout. In this way, the optimal layout can be suggested based on the size or shape of the room and the arrangement of existing furniture, thereby providing the optimal layout for the user.

[0032] The presentation unit can list furniture or storage that matches the proposed layout and present it to the user. The presentation unit, for example, lists optimal furniture and storage based on the proposed layout. The presentation unit, for example, lists optimal models of sofas and tables and presents the list to the user. The presentation unit, for example, uses an algorithm for listing optimal furniture and storage based on the proposed layout. For example, the presentation unit uses an algorithm that takes the proposed layout as input and outputs optimal furniture and storage. In this way, furniture and storage that match the proposed layout are listed and presented to the user, making it easier for the user to make a selection.

[0033] The analysis unit can recognize objects in a photograph and identify their respective positions or sizes. For example, the analysis unit recognizes objects in a photograph and identifies their respective positions and sizes. For example, the analysis unit recognizes objects in a photograph using an image recognition algorithm. For example, the analysis unit recognizes objects in a photograph using a machine learning model. For example, the analysis unit acquires position information of the object and identifies the position using a position identification algorithm. For example, the analysis unit acquires dimensional information of the object and identifies the size using a size identification algorithm. This enables more accurate analysis by recognizing objects in a photograph and identifying their respective positions and sizes.

[0034] The suggestion unit can calculate the placement of sofas or tables. The suggestion unit, for example, calculates the optimal placement of sofas and tables. The suggestion unit, for example, uses a placement algorithm to calculate the optimal placement of sofas and tables. The suggestion unit, for example, calculates the optimal placement of sofas and tables based on evaluation criteria. The suggestion unit, for example, uses an algorithm for calculating the placement of sofas and tables. For example, the suggestion unit uses an algorithm that receives the placement of sofas and tables as input and outputs the optimal placement. In this way, by calculating the optimal placement of sofas and tables, it is possible to provide an optimal layout for the user.

[0035] The presentation unit can list sofa or table models. The presentation unit, for example, lists optimal sofa or table models. The presentation unit, for example, lists optimal sofa or table models based on the type of furniture or evaluation criteria. The presentation unit, for example, uses an algorithm for listing sofa or table models. For example, the presentation unit uses an algorithm that inputs a sofa or table model and outputs an optimal model. In this way, optimal sofa or table models are listed, making it easier for the user to make a selection.

[0036] The analysis unit can take photos of a room from multiple angles and generate and analyze a 3D model. For example, the analysis unit takes photos of a room from multiple angles and generates and analyzes a 3D model. For example, the analysis unit takes photos from the four corners of the room and generates and analyzes a 3D model. For example, the analysis unit takes photos from the center of the room with a 360-degree camera and generates and analyzes a 3D model. For example, the analysis unit takes photos from the ceiling of the room and generates and analyzes a 3D model. For example, the analysis unit generates a 3D model from the photo of the room using 3D modeling technology. For example, the analysis unit generates a 3D model from the photo of the room using 3D modeling software. This enables more accurate analysis by taking photos of a room from multiple angles and generating and analyzing a 3D model.

[0037] The analysis unit can analyze the lighting conditions in a room and use the results as data for proposing an optimal furniture layout. The analysis unit, for example, analyzes the lighting conditions in a room and uses the results as data for proposing an optimal furniture layout. The analysis unit, for example, analyzes the lighting conditions in a room during the day and at night and proposes an optimal furniture layout. The analysis unit, for example, analyzes how natural light enters a room and proposes an optimal furniture layout. The analysis unit, for example, analyzes the placement of artificial lighting in a room and proposes an optimal furniture layout. The analysis unit, for example, measures illuminance and analyzes the lighting conditions in the room. The analysis unit, for example, acquires positional information of light sources and analyzes the lighting conditions in the room. By analyzing the lighting conditions in the room, the results can be used as data for proposing an optimal furniture layout.

[0038] The analysis unit can analyze color information included in a photo of a room and propose a layout that takes color harmony into consideration. The analysis unit, for example, analyzes color information included in a photo of a room and proposes a layout that takes color harmony into consideration. The analysis unit, for example, analyzes color information of the walls and floors of the room and proposes a furniture arrangement that takes color harmony into consideration. The analysis unit, for example, analyzes color information of existing furniture in the room and proposes new furniture that takes color harmony into consideration. The analysis unit, for example, analyzes the color temperature of the lighting in the room and proposes a layout that takes color harmony into consideration. The analysis unit, for example, analyzes color information using methods for measuring hue, saturation, and brightness. The analysis unit, for example, evaluates color harmony using color theory or a harmony algorithm. As a result, by analyzing color information included in a photo of a room, it is possible to propose a layout that takes color harmony into consideration.

[0039] The analysis unit can analyze the positions of windows or doors included in a photo of a room and propose a layout that takes into account how natural light is let in. For example, the analysis unit analyzes the positions of windows and doors included in a photo of a room and proposes a layout that takes into account how natural light is let in. For example, the analysis unit analyzes the positions of windows in a room and proposes a furniture layout that maximizes the use of natural light. For example, the analysis unit analyzes the positions of doors in a room and proposes a furniture layout that takes into account traffic lines. For example, the analysis unit analyzes the positions of windows and doors in a room and proposes a layout that takes ventilation into account. For example, the analysis unit identifies the positions of windows using window dimensions and a position identification algorithm. For example, the analysis unit identifies the position of doors using door dimensions and a position identification algorithm. For example, the analysis unit evaluates how natural light is let in using a method for measuring the angle of incidence and light intensity of light. In this way, by analyzing the positions of windows and doors included in a photo of a room, it is possible to propose a layout that takes into account how natural light is let in.

[0040] The suggestion unit can propose a layout customized based on the user's lifestyle or hobbies when making a proposal. For example, the suggestion unit proposes a layout customized based on the user's lifestyle and hobbies when making a proposal. For example, if the user likes reading, the suggestion unit proposes a layout that includes a reading space. For example, if the user likes cooking, the suggestion unit proposes an enhanced kitchen layout. For example, if the user has a pet, the suggestion unit proposes a layout that takes pet space into consideration. For example, the suggestion unit customizes the layout based on specific definitions and evaluation criteria of lifestyles and hobbies. For example, the suggestion unit proposes a layout taking into consideration the user's lifestyle habits and daily behavior patterns. In this way, by proposing a layout customized based on the user's lifestyle and hobbies, it is possible to make suggestions that are more suitable for the user.

[0041] The suggestion unit can apply different layout algorithms depending on the purpose of use of the room when making a proposal. For example, the suggestion unit applies different layout algorithms depending on the purpose of use of the room when making a proposal. For example, in the case of a living room, the suggestion unit proposes a layout that makes it easy for family members to gather. For example, in the case of an office, the suggestion unit proposes a layout that improves work efficiency. For example, in the case of a bedroom, the suggestion unit proposes a layout that allows people to relax. For example, the suggestion unit applies a layout algorithm based on a specific definition of the purpose of use and evaluation criteria. For example, the suggestion unit proposes a layout using an optimization algorithm or a heuristic algorithm. In this way, by applying different layout algorithms depending on the purpose of use of the room, more appropriate layout proposals can be made.

[0042] The suggestion unit can suggest a layout by referring to the user's past layout history when making a proposal. For example, the suggestion unit suggests an optimal layout by referring to the user's past layout history when making a proposal. For example, the suggestion unit suggests an optimal layout based on layouts previously selected by the user. For example, the suggestion unit makes a proposal by analyzing preference trends from the user's past layout history. For example, the suggestion unit suggests a layout that reflects improvements by referring to the user's past layout history. For example, the suggestion unit suggests a layout based on a specific method for collecting layout history and evaluation criteria. For example, the suggestion unit analyzes past layout data and history data to suggest an optimal layout. This makes it possible to suggest a more appropriate layout by referring to the user's past layout history.

[0043] When proposing a layout, the suggestion unit can propose a layout taking into consideration the user's family composition or whether or not the user has pets. For example, when proposing a layout, the suggestion unit proposes a layout taking into consideration the user's family composition and whether or not the user has pets. For example, if the user has children, the suggestion unit proposes a layout that takes safety into consideration. For example, if the user has pets, the suggestion unit proposes a layout that takes pet space into consideration. For example, if the user lives alone, the suggestion unit proposes a simple and functional layout. For example, the suggestion unit proposes a layout based on specific definitions and evaluation criteria of the family composition and whether or not the user has pets. For example, the suggestion unit proposes a layout taking into consideration the number of family members, age composition, and type and number of pets. This makes it possible to propose a more appropriate layout by taking into consideration the user's family composition and whether or not the user has pets.

[0044] The presentation unit can list furniture or storage based on the user's budget at the time of presentation. The presentation unit, for example, lists optimal furniture or storage based on the user's budget at the time of presentation. The presentation unit, for example, lists furniture or storage that can be purchased within the user's budget. The presentation unit, for example, suggests furniture or storage with high cost performance according to the user's budget. The presentation unit, for example, provides options in multiple price ranges based on the user's budget. The presentation unit, for example, lists furniture or storage based on a specific definition of the budget and evaluation criteria. The presentation unit suggests furniture or storage taking into consideration, for example, the budget range and allocation method. This makes it possible to make suggestions that meet the user's needs by listing optimal furniture or storage based on the user's budget.

[0045] The presentation unit can suggest furniture or storage customized based on the user's preferences at the time of presentation. For example, the presentation unit suggests furniture or storage customized based on the user's preferences at the time of presentation. The presentation unit suggests furniture or storage that matches the preferences, for example, based on the user's past selection history. The presentation unit suggests customized furniture or storage based on the user's preferred style or color, for example. The presentation unit suggests optimal furniture or storage based on the user's preferred brand or design, for example. The presentation unit suggests furniture or storage based on, for example, a specific definition of preferences or evaluation criteria. The presentation unit suggests furniture or storage taking into consideration, for example, color preferences or design preferences. This makes it possible to suggest furniture or storage customized based on the user's preferences, thereby making it possible to make suggestions that are more suitable for the user.

[0046] The presentation unit can list furniture or storage units by referring to the user's past purchase history when presenting the items. The presentation unit, for example, lists optimal furniture or storage units by referring to the user's past purchase history when presenting the items. The presentation unit, for example, lists furniture or storage units that match the user's preferences based on the user's past purchase history. The presentation unit, for example, suggests furniture or storage units of the same brand or design based on the user's past purchase history. The presentation unit, for example, lists furniture or storage units that reflect improvements based on the user's past purchase history. The presentation unit, for example, lists furniture or storage units based on a specific method for collecting purchase history and evaluation criteria. The presentation unit, for example, analyzes past purchase data and history data to suggest optimal furniture or storage units. This makes it possible to suggest more appropriate furniture or storage units by referring to the user's past purchase history.

[0047] The presentation unit can list furniture or storage units that are available for immediate purchase by referring to inventory information of furniture stores in the user's area at the time of presentation. The presentation unit, for example, lists furniture or storage units that are available for immediate purchase by referring to inventory information of furniture stores in the user's area at the time of presentation. The presentation unit, for example, lists furniture or storage units that are available for immediate purchase based on inventory information of furniture stores in the user's area. The presentation unit, for example, refers to inventory information of furniture stores in the user's area and suggests furniture or storage units that can be delivered in the shortest time. The presentation unit, for example, lists furniture or storage units that include specific sale or discount information based on inventory information of furniture stores in the user's area. The presentation unit, for example, lists furniture or storage units based on a specific method for collecting inventory information and evaluation criteria. The presentation unit suggests furniture or storage units by taking into account, for example, an inventory database and an inventory update frequency. This makes it possible to suggest furniture or storage units that are available for immediate purchase by referring to inventory information of furniture stores in the user's area.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can also analyze the acoustic characteristics contained in a photo of the room and propose a layout that takes the acoustic environment into consideration. For example, the analysis unit analyzes the materials of the room's walls and floors and evaluates sound reverberation and absorption characteristics. The analysis unit simulates how sound propagates based on, for example, the shape of the room and the layout of furniture. The analysis unit proposes, for example, optimal speaker placement and soundproofing measures that take the acoustic characteristics into consideration. This makes it possible to propose a layout that optimizes the acoustic environment.

[0050] The suggestion unit can also suggest a layout taking into consideration the user's health condition. For example, if the user has lower back pain, the suggestion unit suggests a furniture arrangement that puts less strain on the lower back. For example, if the user has allergies, the suggestion unit suggests furniture arrangements and materials that reduce allergens. For example, if the user has a visual impairment, the suggestion unit suggests a visually easy-to-understand layout. This makes it possible to suggest a layout that takes into consideration the user's health condition.

[0051] The presentation unit can also present a list of furniture and home appliances that take energy efficiency into consideration based on the proposed layout. For example, the presentation unit lists lighting fixtures and home appliances with high energy efficiency. The presentation unit suggests, for example, window placement and curtain selection that maximizes sunlight utilization. The presentation unit lists, for example, furniture and materials with high insulation performance and makes suggestions for reducing energy consumption. This makes it possible to select furniture and home appliances that take energy efficiency into consideration.

[0052] The analysis unit can also analyze the types and arrangement of plants included in a photo of a room and propose a layout to optimize the plant's growing environment. For example, the analysis unit analyzes the amount of light entering the room and the humidity level and proposes the optimal plant placement. For example, the analysis unit suggests the frequency and amount of watering depending on the type of plant. For example, the analysis unit suggests furniture placement to maintain the temperature and humidity suitable for plant growth. This makes it possible to propose a layout to optimize the plant's growing environment.

[0053] The analysis unit can also analyze temperature information contained in photos of rooms and propose optimal layouts for each season. For example, the analysis unit proposes furniture arrangements that create a feeling of coolness in the summer. For example, the analysis unit proposes furniture arrangements that create a feeling of warmth in the winter. For example, the analysis unit proposes layouts that take into account the hours of sunlight and wind flow for each season. This makes it possible to propose comfortable layouts according to the season.

[0054] The analysis unit can also analyze material information contained in photos of rooms and make layout suggestions that take allergy prevention measures into consideration. For example, the analysis unit analyzes the materials of the room's carpets and curtains and makes suggestions for reducing allergens. For example, the analysis unit analyzes the materials of furniture and suggests materials that are less likely to cause allergies. For example, the analysis unit suggests the optimal placement of air purifiers and supports the removal of allergens. This makes it possible to make layout suggestions that take allergy prevention measures into consideration.

[0055] The analysis unit can also analyze traffic flow information contained in photos of the room and propose a layout that takes into account efficient traffic flow. For example, the analysis unit simulates people's movements within the room and proposes optimal furniture placement. The analysis unit makes proposals to optimize traffic flow in specific areas, such as the kitchen or living room. The analysis unit, for example, proposes furniture placement and storage that take traffic flow into account, making daily movements smoother. This makes it possible to propose a layout that takes into account efficient traffic flow.

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

[0057] Step 1: The analysis unit analyzes the photo of the room. Specifically, it analyzes the size and shape of the room, as well as the layout of existing furniture, measures the size of the room in square meters, and identifies the shape of the room. It also identifies the layout of existing furniture and determines the position and size of each piece of furniture. Step 2: The proposal unit proposes a room layout based on the information analyzed by the analysis unit. Specifically, it calculates the optimal layout based on the size and shape of the room and the layout of existing furniture, calculates the optimal placement of sofas and tables, and presents the results to the user. Step 3: The presentation unit presents furniture and storage that fit the layout proposed by the proposal unit. Specifically, the presentation unit lists optimal furniture and storage based on the proposed layout, lists optimal models of sofas and tables, and presents the list to the user.

[0058] (Example 2) A layout proposal system according to an embodiment of the present invention is a system in which a user takes a photo of a room and an AI analyzes the photo to propose a room layout. In this layout proposal system, a user takes a photo of a room and uploads the photo to the system. The AI ​​then analyzes the photo to determine the size and shape of the room, the layout of existing furniture, and other factors. The AI ​​then proposes an optimal layout and presents it to the user. Furthermore, the AI ​​presents furniture and storage that fit the proposed layout, allowing the user to select. For example, when a user takes a photo of a room and uploads it to the system, the AI ​​analyzes the size and shape of the room and proposes an optimal furniture layout. The AI ​​also lists furniture and storage that fit the proposed layout, allowing the user to select. This allows users to easily find the optimal layout and furniture for their room. The layout proposal system thus allows users to easily find the optimal layout and furniture for their room.

[0059] A layout proposal system according to an embodiment includes an analysis unit, a proposal unit, and a presentation unit. The analysis unit analyzes a photo of a room. The analysis unit analyzes, for example, the size and shape of the room and the layout of existing furniture. The analysis unit measures, for example, the size of the room in square meters and identifies the shape of the room. The analysis unit also identifies the layout of existing furniture and determines the position and size of each piece of furniture. The proposal unit proposes a room layout based on the information analyzed by the analysis unit. The proposal unit calculates an optimal layout based, for example, on the size and shape of the room and the layout of the existing furniture. The proposal unit calculates, for example, the optimal placement of sofas and tables and presents the result to the user. The presentation unit presents furniture and storage that matches the layout proposed by the proposal unit. The presentation unit, for example, lists optimal furniture and storage based on the proposed layout. The presentation unit, for example, lists optimal models of sofas and tables and presents the list to the user. This allows the layout proposal system according to an embodiment to easily find the optimal layout and furniture for their room.

[0060] The analysis unit can analyze the size or shape of a room and the layout of existing furniture. For example, the analysis unit measures the size of the room in square meters and identifies the shape of the room. For example, the analysis unit measures the dimensions of the room and calculates parameters for identifying the shape of the room. The analysis unit also identifies the layout of existing furniture and determines the position and size of each piece of furniture. For example, the analysis unit acquires position information of furniture and analyzes the layout pattern. This allows for more accurate layout proposals by analyzing the size and shape of the room and the layout of existing furniture.

[0061] The suggestion unit can suggest a layout based on the size or shape of the room and the arrangement of existing furniture. The suggestion unit calculates an optimal layout based on, for example, the size or shape of the room and the arrangement of existing furniture. The suggestion unit calculates the optimal arrangement of, for example, sofas and tables, and presents the result to the user. The suggestion unit uses an algorithm for calculating the optimal layout based on, for example, the size or shape of the room and the arrangement of existing furniture. For example, the suggestion unit uses an algorithm that inputs the size or shape of the room and the arrangement of existing furniture and outputs an optimal layout. In this way, the optimal layout can be suggested based on the size or shape of the room and the arrangement of existing furniture, thereby providing the optimal layout for the user.

[0062] The presentation unit can list furniture or storage that matches the proposed layout and present it to the user. The presentation unit, for example, lists optimal furniture and storage based on the proposed layout. The presentation unit, for example, lists optimal models of sofas and tables and presents the list to the user. The presentation unit, for example, uses an algorithm for listing optimal furniture and storage based on the proposed layout. For example, the presentation unit uses an algorithm that takes the proposed layout as input and outputs optimal furniture and storage. In this way, furniture and storage that match the proposed layout are listed and presented to the user, making it easier for the user to make a selection.

[0063] The analysis unit can recognize objects in a photograph and identify their respective positions or sizes. For example, the analysis unit recognizes objects in a photograph and identifies their respective positions and sizes. For example, the analysis unit recognizes objects in a photograph using an image recognition algorithm. For example, the analysis unit recognizes objects in a photograph using a machine learning model. For example, the analysis unit acquires position information of the object and identifies the position using a position identification algorithm. For example, the analysis unit acquires dimensional information of the object and identifies the size using a size identification algorithm. This enables more accurate analysis by recognizing objects in a photograph and identifying their respective positions and sizes.

[0064] The suggestion unit can calculate the placement of sofas or tables. The suggestion unit, for example, calculates the optimal placement of sofas and tables. The suggestion unit, for example, uses a placement algorithm to calculate the optimal placement of sofas and tables. The suggestion unit, for example, calculates the optimal placement of sofas and tables based on evaluation criteria. The suggestion unit, for example, uses an algorithm for calculating the placement of sofas and tables. For example, the suggestion unit uses an algorithm that receives the placement of sofas and tables as input and outputs the optimal placement. In this way, by calculating the optimal placement of sofas and tables, it is possible to provide an optimal layout for the user.

[0065] The presentation unit can list sofa or table models. The presentation unit, for example, lists optimal sofa or table models. The presentation unit, for example, lists optimal sofa or table models based on the type of furniture or evaluation criteria. The presentation unit, for example, uses an algorithm for listing sofa or table models. For example, the presentation unit uses an algorithm that inputs a sofa or table model and outputs an optimal model. In this way, optimal sofa or table models are listed, making it easier for the user to make a selection.

[0066] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the accuracy of the analysis based on the estimated user's emotion. The analysis unit, for example, uses an emotion recognition algorithm to estimate the user's emotion. The analysis unit, for example, collects emotion data and estimates the emotion using the emotion recognition algorithm. The analysis unit, for example, uses an algorithm for adjusting the accuracy of the analysis based on the emotion data. For example, if the user is feeling stressed, the analysis unit increases the accuracy of the analysis to provide results quickly. For example, if the user is relaxed, the analysis unit performs a detailed analysis and provides more layout options. For example, if the user is in a hurry, the analysis unit adjusts the accuracy of the analysis to provide results quickly. In this way, by adjusting the accuracy of the analysis based on the user's emotion, more appropriate analysis results can be provided.

[0067] The analysis unit can take photos of a room from multiple angles and generate and analyze a 3D model. For example, the analysis unit takes photos of a room from multiple angles and generates and analyzes a 3D model. For example, the analysis unit takes photos from the four corners of the room and generates and analyzes a 3D model. For example, the analysis unit takes photos from the center of the room with a 360-degree camera and generates and analyzes a 3D model. For example, the analysis unit takes photos from the ceiling of the room and generates and analyzes a 3D model. For example, the analysis unit generates a 3D model from the photo of the room using 3D modeling technology. For example, the analysis unit generates a 3D model from the photo of the room using 3D modeling software. This enables more accurate analysis by taking photos of a room from multiple angles and generating and analyzing a 3D model.

[0068] The analysis unit can analyze the lighting conditions in a room and use the results as data for proposing an optimal furniture layout. The analysis unit, for example, analyzes the lighting conditions in a room and uses the results as data for proposing an optimal furniture layout. The analysis unit, for example, analyzes the lighting conditions in a room during the day and at night and proposes an optimal furniture layout. The analysis unit, for example, analyzes how natural light enters a room and proposes an optimal furniture layout. The analysis unit, for example, analyzes the placement of artificial lighting in a room and proposes an optimal furniture layout. The analysis unit, for example, measures illuminance and analyzes the lighting conditions in the room. The analysis unit, for example, acquires positional information of light sources and analyzes the lighting conditions in the room. By analyzing the lighting conditions in the room, the results can be used as data for proposing an optimal furniture layout.

[0069] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using an emotion recognition algorithm. The analysis unit, for example, collects emotion data and estimates the emotion using an emotion recognition algorithm. The analysis unit, for example, uses an algorithm to adjust the display method of the analysis results based on the emotion data. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotion.

[0070] The analysis unit can analyze color information included in a photo of a room and propose a layout that takes color harmony into consideration. The analysis unit, for example, analyzes color information included in a photo of a room and proposes a layout that takes color harmony into consideration. The analysis unit, for example, analyzes color information of the walls and floors of the room and proposes a furniture arrangement that takes color harmony into consideration. The analysis unit, for example, analyzes color information of existing furniture in the room and proposes new furniture that takes color harmony into consideration. The analysis unit, for example, analyzes the color temperature of the lighting in the room and proposes a layout that takes color harmony into consideration. The analysis unit, for example, analyzes color information using methods for measuring hue, saturation, and brightness. The analysis unit, for example, evaluates color harmony using color theory or a harmony algorithm. As a result, by analyzing color information included in a photo of a room, it is possible to propose a layout that takes color harmony into consideration.

[0071] The analysis unit can analyze the positions of windows or doors included in a photo of a room and propose a layout that takes into account how natural light is let in. For example, the analysis unit analyzes the positions of windows and doors included in a photo of a room and proposes a layout that takes into account how natural light is let in. For example, the analysis unit analyzes the positions of windows in a room and proposes a furniture layout that maximizes the use of natural light. For example, the analysis unit analyzes the positions of doors in a room and proposes a furniture layout that takes into account traffic lines. For example, the analysis unit analyzes the positions of windows and doors in a room and proposes a layout that takes ventilation into account. For example, the analysis unit identifies the positions of windows using window dimensions and a position identification algorithm. For example, the analysis unit identifies the position of doors using door dimensions and a position identification algorithm. For example, the analysis unit evaluates how natural light is let in using a method for measuring the angle of incidence and light intensity of light. In this way, by analyzing the positions of windows and doors included in a photo of a room, it is possible to propose a layout that takes into account how natural light is let in.

[0072] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion using an emotion recognition algorithm. For example, the suggestion unit collects emotion data and estimates the emotion using the emotion recognition algorithm. For example, the suggestion unit uses an algorithm to adjust the way the suggestion is expressed based on the emotion data. For example, if the user is relaxed, the suggestion unit makes a suggestion including a detailed explanation. For example, if the user is in a hurry, the suggestion unit makes a concise and to-the-point suggestion. For example, if the user is excited, the suggestion unit makes a suggestion with a visually stimulating effect. This enables more appropriate suggestions to be made by adjusting the way the suggestion is expressed based on the user's emotion.

[0073] The suggestion unit can propose a layout customized based on the user's lifestyle or hobbies when making a proposal. For example, the suggestion unit proposes a layout customized based on the user's lifestyle and hobbies when making a proposal. For example, if the user likes reading, the suggestion unit proposes a layout that includes a reading space. For example, if the user likes cooking, the suggestion unit proposes an enhanced kitchen layout. For example, if the user has a pet, the suggestion unit proposes a layout that takes pet space into consideration. For example, the suggestion unit customizes the layout based on specific definitions and evaluation criteria of lifestyles and hobbies. For example, the suggestion unit proposes a layout taking into consideration the user's lifestyle habits and daily behavior patterns. In this way, by proposing a layout customized based on the user's lifestyle and hobbies, it is possible to make suggestions that are more suitable for the user.

[0074] The suggestion unit can apply different layout algorithms depending on the purpose of use of the room when making a proposal. For example, the suggestion unit applies different layout algorithms depending on the purpose of use of the room when making a proposal. For example, in the case of a living room, the suggestion unit proposes a layout that makes it easy for family members to gather. For example, in the case of an office, the suggestion unit proposes a layout that improves work efficiency. For example, in the case of a bedroom, the suggestion unit proposes a layout that allows people to relax. For example, the suggestion unit applies a layout algorithm based on a specific definition of the purpose of use and evaluation criteria. For example, the suggestion unit proposes a layout using an optimization algorithm or a heuristic algorithm. In this way, by applying different layout algorithms depending on the purpose of use of the room, more appropriate layout proposals can be made.

[0075] The suggestion unit can estimate a user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, the suggestion unit estimates a user's emotion and determines the priority of suggestions based on the estimated user's emotion. For example, the suggestion unit estimates a user's emotion using an emotion recognition algorithm. For example, the suggestion unit collects emotion data and estimates the emotion using an emotion recognition algorithm. For example, the suggestion unit uses an algorithm for determining the priority of suggestions based on the emotion data. For example, if the user is feeling stressed, the suggestion unit preferentially suggests a relaxing layout. For example, if the user is relaxed, the suggestion unit preferentially suggests a detailed layout option. For example, if the user is in a hurry, the suggestion unit preferentially suggests a concise and to-the-point layout. This enables more appropriate suggestions by determining the priority of suggestions based on the user's emotion.

[0076] The suggestion unit can suggest a layout by referring to the user's past layout history when making a proposal. For example, the suggestion unit suggests an optimal layout by referring to the user's past layout history when making a proposal. For example, the suggestion unit suggests an optimal layout based on layouts previously selected by the user. For example, the suggestion unit makes a proposal by analyzing preference trends from the user's past layout history. For example, the suggestion unit suggests a layout that reflects improvements by referring to the user's past layout history. For example, the suggestion unit suggests a layout based on a specific method for collecting layout history and evaluation criteria. For example, the suggestion unit analyzes past layout data and history data to suggest an optimal layout. This makes it possible to suggest a more appropriate layout by referring to the user's past layout history.

[0077] When proposing a layout, the suggestion unit can propose a layout taking into consideration the user's family composition or whether or not the user has pets. For example, when proposing a layout, the suggestion unit proposes a layout taking into consideration the user's family composition and whether or not the user has pets. For example, if the user has children, the suggestion unit proposes a layout that takes safety into consideration. For example, if the user has pets, the suggestion unit proposes a layout that takes pet space into consideration. For example, if the user lives alone, the suggestion unit proposes a simple and functional layout. For example, the suggestion unit proposes a layout based on specific definitions and evaluation criteria of the family composition and whether or not the user has pets. For example, the suggestion unit proposes a layout taking into consideration the number of family members, age composition, and type and number of pets. This makes it possible to propose a more appropriate layout by taking into consideration the user's family composition and whether or not the user has pets.

[0078] The presentation unit can estimate the user's emotions and adjust the list of furniture or storage items to be presented based on the estimated user's emotions. The presentation unit, for example, estimates the user's emotions and adjusts the list of furniture or storage items to be presented based on the estimated user's emotions. The presentation unit, for example, estimates the user's emotions using an emotion recognition algorithm. The presentation unit, for example, collects emotion data and estimates the emotion using the emotion recognition algorithm. The presentation unit, for example, uses an algorithm for adjusting the list to be presented based on the emotion data. For example, the presentation unit provides a detailed list of furniture or storage items when the user is relaxed. For example, the presentation unit provides a concise and to-the-point list when the user is in a hurry. For example, the presentation unit provides a list with a visually stimulating effect when the user is excited. This enables more appropriate suggestions to be made by adjusting the list of furniture or storage items to be presented based on the user's emotions.

[0079] The presentation unit can list furniture or storage based on the user's budget at the time of presentation. The presentation unit, for example, lists optimal furniture or storage based on the user's budget at the time of presentation. The presentation unit, for example, lists furniture or storage that can be purchased within the user's budget. The presentation unit, for example, suggests furniture or storage with high cost performance according to the user's budget. The presentation unit, for example, provides options in multiple price ranges based on the user's budget. The presentation unit, for example, lists furniture or storage based on a specific definition of the budget and evaluation criteria. The presentation unit suggests furniture or storage taking into consideration, for example, the budget range and allocation method. This makes it possible to make suggestions that meet the user's needs by listing optimal furniture or storage based on the user's budget.

[0080] The presentation unit can suggest furniture or storage customized based on the user's preferences at the time of presentation. For example, the presentation unit suggests furniture or storage customized based on the user's preferences at the time of presentation. The presentation unit suggests furniture or storage that matches the preferences, for example, based on the user's past selection history. The presentation unit suggests customized furniture or storage based on the user's preferred style or color, for example. The presentation unit suggests optimal furniture or storage based on the user's preferred brand or design, for example. The presentation unit suggests furniture or storage based on, for example, a specific definition of preferences or evaluation criteria. The presentation unit suggests furniture or storage taking into consideration, for example, color preferences or design preferences. This makes it possible to suggest furniture or storage customized based on the user's preferences, thereby making it possible to make suggestions that are more suitable for the user.

[0081] The presentation unit can estimate the user's emotion and adjust the presentation order based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion and adjusts the presentation order based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion using an emotion recognition algorithm. The presentation unit, for example, collects emotion data and estimates the emotion using the emotion recognition algorithm. The presentation unit, for example, uses an algorithm for adjusting the presentation order based on the emotion data. For example, when the user is relaxed, the presentation unit presents information in an order that includes detailed information. For example, when the user is in a hurry, the presentation unit presents information in an order that emphasizes the main points. For example, when the user is excited, the presentation unit presents information in a visually stimulating order. This enables more appropriate suggestions to be made by adjusting the presentation order based on the user's emotion.

[0082] The presentation unit can list furniture or storage units by referring to the user's past purchase history when presenting the items. The presentation unit, for example, lists optimal furniture or storage units by referring to the user's past purchase history when presenting the items. The presentation unit, for example, lists furniture or storage units that match the user's preferences based on the user's past purchase history. The presentation unit, for example, suggests furniture or storage units of the same brand or design based on the user's past purchase history. The presentation unit, for example, lists furniture or storage units that reflect improvements based on the user's past purchase history. The presentation unit, for example, lists furniture or storage units based on a specific method for collecting purchase history and evaluation criteria. The presentation unit, for example, analyzes past purchase data and history data to suggest optimal furniture or storage units. This makes it possible to suggest more appropriate furniture or storage units by referring to the user's past purchase history.

[0083] The presentation unit can list furniture or storage units that are available for immediate purchase by referring to inventory information of furniture stores in the user's area at the time of presentation. The presentation unit, for example, lists furniture or storage units that are available for immediate purchase by referring to inventory information of furniture stores in the user's area at the time of presentation. The presentation unit, for example, lists furniture or storage units that are available for immediate purchase based on inventory information of furniture stores in the user's area. The presentation unit, for example, refers to inventory information of furniture stores in the user's area and suggests furniture or storage units that can be delivered in the shortest time. The presentation unit, for example, lists furniture or storage units that include specific sale or discount information based on inventory information of furniture stores in the user's area. The presentation unit, for example, lists furniture or storage units based on a specific method for collecting inventory information and evaluation criteria. The presentation unit suggests furniture or storage units by taking into account, for example, an inventory database and an inventory update frequency. This makes it possible to suggest furniture or storage units that are available for immediate purchase by referring to inventory information of furniture stores in the user's area. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, proposal unit, and presentation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit takes a photo of a room using the camera 42 of the smart device 14 and analyzes the photo using the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates an optimal layout based on the analyzed information. The presentation unit presents the proposed layout and a list of furniture to the user using, for example, the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit takes a photo of a room using the camera 42 of the smart glasses 214 and analyzes the photo by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates an optimal layout based on the analyzed information. The presentation unit presents the proposed layout and a list of furniture to the user, for example, using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, proposal unit, and presentation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit takes a photo of the room using the camera 42 of the headset type terminal 314 and analyzes the photo using the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal layout based on the analyzed information. The presentation unit presents the proposed layout and a list of furniture to the user using, for example, the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit takes a photo of the room using the camera 42 of the robot 414 and analyzes the photo by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal layout based on the analyzed information. The presentation unit presents the proposed layout and a list of furniture to the user using, for example, a display of the robot 414.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The analysis unit can also analyze the acoustic characteristics contained in a photo of the room and propose a layout that takes the acoustic environment into consideration. For example, the analysis unit analyzes the materials of the room's walls and floors and evaluates sound reverberation and absorption characteristics. The analysis unit simulates how sound propagates based on, for example, the shape of the room and the layout of furniture. The analysis unit proposes, for example, optimal speaker placement and soundproofing measures that take the acoustic characteristics into consideration. This makes it possible to propose a layout that optimizes the acoustic environment.

[0086] The suggestion unit can also suggest a layout taking into consideration the user's health condition. For example, if the user has lower back pain, the suggestion unit suggests a furniture arrangement that puts less strain on the lower back. For example, if the user has allergies, the suggestion unit suggests furniture arrangements and materials that reduce allergens. For example, if the user has a visual impairment, the suggestion unit suggests a visually easy-to-understand layout. This makes it possible to suggest a layout that takes into consideration the user's health condition.

[0087] The presentation unit can also present a list of furniture and home appliances that take energy efficiency into consideration based on the proposed layout. For example, the presentation unit lists lighting fixtures and home appliances with high energy efficiency. The presentation unit suggests, for example, window placement and curtain selection that maximizes sunlight utilization. The presentation unit lists, for example, furniture and materials with high insulation performance and makes suggestions for reducing energy consumption. This makes it possible to select furniture and home appliances that take energy efficiency into consideration.

[0088] The analysis unit can also analyze the types and arrangement of plants included in a photo of a room and propose a layout to optimize the plant's growing environment. For example, the analysis unit analyzes the amount of light entering the room and the humidity level and proposes the optimal plant placement. For example, the analysis unit suggests the frequency and amount of watering depending on the type of plant. For example, the analysis unit suggests furniture placement to maintain the temperature and humidity suitable for plant growth. This makes it possible to propose a layout to optimize the plant's growing environment.

[0089] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit selects the timing to make detailed layout suggestions. For example, if the user is busy, the suggestion unit quickly makes concise and to-the-point suggestions. For example, if the user is excited, the suggestion unit selects the timing to make visually stimulating suggestions. In this way, more appropriate suggestions can be made by adjusting the timing of suggestions based on the user's emotions.

[0090] The analysis unit can also analyze temperature information contained in photos of rooms and propose optimal layouts for each season. For example, the analysis unit proposes furniture arrangements that create a feeling of coolness in the summer. For example, the analysis unit proposes furniture arrangements that create a feeling of warmth in the winter. For example, the analysis unit proposes layouts that take into account the hours of sunlight and wind flow for each season. This makes it possible to propose comfortable layouts according to the season.

[0091] The suggestion unit can also estimate the user's emotions and personalize the content of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit suggests a layout that helps the user relax. For example, if the user is excited, the suggestion unit suggests a layout that supports active activities. For example, if the user is sad, the suggestion unit suggests a layout that brightens the user's mood. This allows for more appropriate suggestions to be made by personalizing the content of suggestions based on the user's emotions.

[0092] The analysis unit can also analyze material information contained in photos of rooms and make layout suggestions that take allergy prevention measures into consideration. For example, the analysis unit analyzes the materials of the room's carpets and curtains and makes suggestions for reducing allergens. For example, the analysis unit analyzes the materials of furniture and suggests materials that are less likely to cause allergies. For example, the analysis unit suggests the optimal placement of air purifiers and supports the removal of allergens. This makes it possible to make layout suggestions that take allergy prevention measures into consideration.

[0093] The suggestion unit can also estimate the user's emotions and adjust the visual style of the suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit suggests calm colors and designs. For example, if the user is excited, the suggestion unit suggests bright colors and dynamic designs. For example, if the user is sad, the suggestion unit suggests bright and uplifting colors and designs. This allows for more appropriate suggestions by adjusting the visual style of the suggestions based on the user's emotions.

[0094] The analysis unit can also analyze traffic flow information contained in photos of the room and propose a layout that takes into account efficient traffic flow. For example, the analysis unit simulates people's movements within the room and proposes optimal furniture placement. The analysis unit makes proposals to optimize traffic flow in specific areas, such as the kitchen or living room. The analysis unit, for example, proposes furniture placement and storage that take traffic flow into account, making daily movements smoother. This makes it possible to propose a layout that takes into account efficient traffic flow.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The analysis unit analyzes the photo of the room. Specifically, it analyzes the size and shape of the room, as well as the layout of existing furniture, measures the size of the room in square meters, and identifies the shape of the room. It also identifies the layout of existing furniture and determines the position and size of each piece of furniture. Step 2: The proposal unit proposes a room layout based on the information analyzed by the analysis unit. Specifically, it calculates the optimal layout based on the size and shape of the room and the layout of existing furniture, calculates the optimal placement of sofas and tables, and presents the results to the user. Step 3: The presentation unit presents furniture and storage that fit the layout proposed by the proposal unit. Specifically, the presentation unit lists optimal furniture and storage based on the proposed layout, lists optimal models of sofas and tables, and presents the list to the user.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 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 photos of the room; a proposal unit that proposes a room layout based on the information analyzed by the analysis unit; a presentation unit that presents furniture or storage that matches the layout proposed by the proposal unit. A system characterized by:

2. The analysis unit Analyze the size or shape of the room and the layout of existing furniture The system of claim 1 .

3. The proposal unit Suggest layouts based on room size or shape and existing furniture arrangement The system of claim 1 .

4. The presentation unit List and present to the user furniture or storage that fits the proposed layout The system of claim 1 .

5. The analysis unit Recognize objects in a photo and determine their location or size The system of claim 1 .

6. The proposal unit Calculate sofa or table placement The system of claim 1 .

7. The presentation unit List the sofa or table model The system of claim 1 .

8. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The system of claim 1 .

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