System

Abstract

An object of a system according to an embodiment is to efficiently digitize a handwritten floor plan.SOLUTION: A system includes a handwritten floor plan acquisition part, an analysis part, and a generation part. The handwritten floor plan acquisition unit acquires a handwritten floor plan. The analysis part analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition part. The generation part generates a digital floor plan on the basis of the information analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of requiring a lot of man-hours to digitize handwritten floor plans.

[0005] The system according to the embodiment aims to efficiently digitize handwritten floor plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a handwritten floor plan acquisition unit, an analysis unit, and a generation unit. The handwritten floor plan acquisition unit acquires a handwritten floor plan. The analysis unit analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition unit. The generation unit generates a digital floor plan based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently digitize handwritten floor plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The floor plan creation system according to an embodiment of the present invention is a system that automatically digitizes handwritten floor plans and efficiently creates floor plans. As a result, the floor plan creation system can significantly reduce the man-hours required to create floor plans for new apartments, new detached houses, extensions, renovations, etc.

[0029] A floor plan creation system according to an embodiment includes a handwritten floor plan acquisition unit, an analysis unit, and a generation unit. The handwritten floor plan acquisition unit acquires a handwritten floor plan. For example, the handwritten floor plan can be scanned and saved as image data. Alternatively, the handwritten floor plan can be photographed with a smartphone camera and acquired as image data. Furthermore, the handwritten floor plan can be drawn with a digital pen, which then converts the drawing into digital data in real time. The analysis unit analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition unit. For example, the generation AI can analyze the handwritten floor plan using image recognition technology to recognize the location and dimensions of each room and facility. The generation AI can also analyze specific symbols and marks included in the handwritten floor plan using pattern recognition technology. Furthermore, the generation AI can analyze the handwriting and drawing style of the handwritten floor plan to more accurately reflect the designer's intentions. The generation unit generates a digital floor plan based on the information analyzed by the analysis unit. For example, the generation AI can automatically generate a digital floor plan based on the analyzed information using an illustrator tool. The generation AI can also automatically add color coding and texture to digital floor plans to make them easier to understand visually. Furthermore, the generation AI can automatically generate digital floor plans as 3D models, providing three-dimensional visualization. As a result, the floor plan creation system according to the embodiment can automatically digitize handwritten floor plans and efficiently create floor plans. For example, architectural firms can quickly create blueprints for new construction or renovation projects, significantly reducing labor costs. Publishers can efficiently create floor plans for housing-related books and magazines. For example, architectural firms can handdraw blueprints for new apartment buildings and input them into the generation AI, which quickly generates digital blueprints. Publishers can efficiently create floor plans for housing-related books and magazines. For example, architectural firms can handdraw blueprints for new apartment buildings and input them into the generation AI, which quickly generates digital blueprints.

[0030] The analysis unit analyzes the handwriting and drawing style of handwritten floor plans to more accurately reflect the designer's intentions. For example, the analysis unit analyzes the handwriting of handwritten floor plans to learn the designer's drawing style. For example, it recognizes the line thickness and angle that a particular designer often uses and reflects that when digitizing. The analysis unit also accurately reproduces the fine details intended by the designer by analyzing the drawing style. For example, it faithfully reproduces subtle handwritten curves and decorations in the digital drawing. To reflect the designer's intentions, the analysis unit also analyzes specific symbols and marks contained in handwritten floor plans and generates digital drawings based on them. For example, if a specific symbol indicates a door or window, its position is accurately digitized. This makes it possible to generate digital floor plans that accurately reflect the designer's intentions.

[0031] The analysis unit can automatically convert annotations and notes included in handwritten floor plans into text and integrate them into digital floor plans. For example, the analysis unit automatically converts annotations and notes included in handwritten floor plans into text using OCR technology. For example, it extracts room names and dimensions written by the designer by hand as text data. The analysis unit then integrates the converted text annotations and notes into the digital floor plan and displays them visually in an easy-to-understand manner. For example, it places room names and dimensions on the digital drawing. The analysis unit also analyzes the annotations and notes included in the handwritten floor plan and links related information to the digital floor plan. For example, it reflects information about materials and equipment written in the annotations in the digital drawing. This allows handwritten annotations and notes to be integrated into the digital floor plan.

[0032] The generation unit can digitize not only handwritten floor plans, but also handwritten furniture layout plans and interior design drawings. For example, the generation unit scans a handwritten furniture layout plan and digitizes it using generation AI. For example, it recognizes the position and size of furniture and reflects it in the digital drawing. The generation unit also analyzes and digitizes handwritten interior design drawings. For example, it recognizes wallpaper and flooring designs and applies them to the digital drawing. The generation unit also integrates handwritten floor plans and furniture layout plans to generate digital floor plans. For example, it integrates and digitizes the room layout and furniture arrangement. This makes it possible to digitize handwritten furniture layout plans and interior design drawings.

[0033] When digitizing a handwritten floor plan, the generation unit can automatically generate a 3D model as well, providing a three-dimensional visualization. For example, the generation unit analyzes the handwritten floor plan and automatically generates a 3D model. For example, the generation unit estimates the height and depth of the room and creates a three-dimensional floor plan. The generation unit also visualizes the generated 3D model, allowing the user to freely rotate and zoom it. For example, the generation unit displays the floor plan in three dimensions using a 3D viewer. The generation unit also generates a 3D model based on the handwritten floor plan and overlays it on the actual space using VR or AR technology. For example, the digital floor plan is displayed on the actual space using a smartphone or tablet. This allows the handwritten floor plan to be automatically generated as a 3D model, providing a three-dimensional visualization.

[0034] The generation unit automatically adds color coding and texture to the digital floor plan based on the information of the handwritten floor plan analyzed by the generation AI, making it visually easier to understand. For example, the generation unit automatically assigns different colors to each room based on the information analyzed by the generation AI. For example, the living room may be color-coded blue and the kitchen green. The generation unit also automatically adds textures of flooring and wall materials to the digital floor plan. For example, wood-grain floors and tile walls may be reflected in the digital drawing. The generation unit also visually emphasizes rooms according to their purpose based on the information analyzed by the generation AI. For example, important rooms and facilities may be displayed prominently. This allows the generation unit to add color coding and texture to the digital floor plan, making it visually easier to understand.

[0035] The generation unit can add an automatic check function based on the Building Standards Act and local regulations to the digital floor plan to confirm legal compliance. For example, the generation unit automatically checks whether the digital floor plan complies with the Building Standards Act based on information analyzed by the generation AI. For example, it checks whether the height and area of ​​rooms meet regulations. The generation unit also adds an automatic check function based on local regulations to confirm whether the digital floor plan complies with local building regulations. For example, it takes into account regulations regarding fire prevention areas and landscape preservation areas. The generation unit also automatically generates a checklist for the digital floor plan to confirm legal compliance. For example, it checks whether necessary equipment and evacuation routes are properly located. This makes it possible to automatically confirm the legal compliance of the digital floor plan.

[0036] When generating a digital floor plan, the generation unit can automatically make design proposals that take energy efficiency and environmental impact into consideration. For example, the generation unit automatically makes design proposals that take energy efficiency into consideration based on information analyzed by the generation AI. For example, it may optimize the placement of windows and the selection of insulation materials. The generation unit also automatically makes proposals to reduce environmental impact when generating a digital floor plan. For example, it may propose the use of renewable energy or the introduction of energy-saving equipment. The generation unit also reflects design proposals that take energy efficiency and environmental impact into the digital floor plan. For example, it indicates the installation location of solar panels or the placement of a rainwater utilization system. This allows for automatic design proposals that take energy efficiency and environmental impact into consideration.

[0037] When generating a digital floor plan, the generation unit can present multiple different design options to allow the user to select from. For example, the generation unit automatically generates multiple design options based on information analyzed by the generation AI. For example, it presents floor plans with different layouts and color schemes. The generation unit also provides an interactive interface so that the user can select from multiple design options. For example, it adds a function to switch designs by clicking or tapping. The generation unit also visually displays the features and advantages of each option to make it easier to compare the design options. For example, it shows differences in energy efficiency and cost in a graph. This makes it possible to present multiple different design options to allow the user to select from.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The floor plan creation system can further include a voice input unit. The voice input unit converts the floor plan information verbally provided by the user into text data using voice recognition technology and sends it to the analysis unit. For example, if the user specifies "the living room is 20 square meters," that information is automatically reflected in the digital floor plan. In addition, when the voice input unit receives a question about the floor plan from the user, the generation unit can automatically generate a response and provide feedback via voice. This allows the user to create floor plans using only their voice, without having to write or type on a keyboard.

[0040] The floor plan creation system can further include an environmental data acquisition unit. The environmental data acquisition unit acquires environmental data about the planned construction site and sends it to the analysis unit. For example, data such as sunshine hours, wind direction, and ambient noise levels is collected, and the analysis unit uses this data to propose the optimal floor plan. The environmental data acquisition unit can also acquire data such as seasonal temperature changes and precipitation amounts, supporting energy-efficient designs. This allows for the creation of environmentally friendly floor plans.

[0041] The floor plan creation system may further include a furniture layout suggestion unit. The furniture layout suggestion unit automatically suggests the layout of furniture specified by the user. For example, if the user instructs, "I want to place a sofa in the living room," the unit suggests the optimal location for the furniture. The furniture layout suggestion unit may also present multiple layout options based on the user's lifestyle and preferences. For example, it may suggest layouts based on family composition and hobbies. This allows the user to efficiently decide on the layout of furniture.

[0042] The floor plan creation system can further include a material selection unit. The material selection unit suggests the most suitable material based on the material information specified by the user. For example, if the user indicates, "I want to use wood for the flooring," the unit will provide information such as the most suitable type of wood, its price, and durability. The material selection unit can also select materials that take environmental impact into consideration. For example, it will suggest renewable materials and low-VOC (volatile organic compound) materials. This allows the user to select environmentally friendly materials.

[0043] The floor plan creation system can further include a cost calculation unit. The cost calculation unit automatically calculates the total construction cost based on the floor plan, materials, and equipment information specified by the user. For example, if the user instructs, "I want to know the construction cost for this floor plan," the cost calculation unit will provide a detailed estimate. The cost calculation unit can also compare the costs of different design options. For example, it can present the cost difference when different materials and equipment are used. This allows the user to select the optimal design according to their budget.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The handwritten floor plan acquisition unit acquires a handwritten floor plan. For example, the handwritten floor plan can be scanned and saved as image data. Alternatively, the handwritten floor plan can be photographed with a smartphone camera and acquired as image data. Furthermore, the handwritten floor plan can be drawn with a digital pen, which can then convert the drawing into digital data in real time. Step 2: The analysis unit analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition unit. For example, the generation AI can use image recognition technology to analyze the handwritten floor plan and recognize the location and dimensions of each room and facility. The generation AI can also use pattern recognition technology to analyze specific symbols and marks contained in the handwritten floor plan. Furthermore, the generation AI can analyze the handwriting and drawing style of the handwritten floor plan to more accurately reflect the designer's intentions. Step 3: The generation unit generates a digital floor plan based on the information analyzed by the analysis unit. For example, the generation AI uses the illustrator tool to automatically generate a digital floor plan based on the analyzed information. The generation AI can also automatically add color coding and texture to the digital floor plan to make it visually easier to understand. Furthermore, the generation AI can also automatically generate a 3D model of the digital floor plan to provide a three-dimensional visualization.

[0046] (Example 2) The floor plan creation system according to an embodiment of the present invention is a system that automatically digitizes handwritten floor plans and efficiently creates floor plans. As a result, the floor plan creation system can significantly reduce the man-hours required to create floor plans for new apartments, new detached houses, extensions, renovations, etc.

[0047] A floor plan creation system according to an embodiment includes a handwritten floor plan acquisition unit, an analysis unit, and a generation unit. The handwritten floor plan acquisition unit acquires a handwritten floor plan. For example, the handwritten floor plan can be scanned and saved as image data. Alternatively, the handwritten floor plan can be photographed with a smartphone camera and acquired as image data. Furthermore, the handwritten floor plan can be drawn with a digital pen, which then converts the drawing into digital data in real time. The analysis unit analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition unit. For example, the generation AI can analyze the handwritten floor plan using image recognition technology to recognize the location and dimensions of each room and facility. The generation AI can also analyze specific symbols and marks included in the handwritten floor plan using pattern recognition technology. Furthermore, the generation AI can analyze the handwriting and drawing style of the handwritten floor plan to more accurately reflect the designer's intentions. The generation unit generates a digital floor plan based on the information analyzed by the analysis unit. For example, the generation AI can automatically generate a digital floor plan based on the analyzed information using an illustrator tool. The generation AI can also automatically add color coding and texture to digital floor plans to make them easier to understand visually. Furthermore, the generation AI can automatically generate digital floor plans as 3D models, providing three-dimensional visualization. As a result, the floor plan creation system according to the embodiment can automatically digitize handwritten floor plans and efficiently create floor plans. For example, architectural firms can quickly create blueprints for new construction or renovation projects, significantly reducing labor costs. Publishers can efficiently create floor plans for housing-related books and magazines. For example, architectural firms can handdraw blueprints for new apartment buildings and input them into the generation AI, which quickly generates digital blueprints. Publishers can efficiently create floor plans for housing-related books and magazines. For example, architectural firms can handdraw blueprints for new apartment buildings and input them into the generation AI, which quickly generates digital blueprints.

[0048] The analysis unit analyzes the handwriting and drawing style of handwritten floor plans to more accurately reflect the designer's intentions. For example, the analysis unit analyzes the handwriting of handwritten floor plans to learn the designer's drawing style. For example, it recognizes the line thickness and angle that a particular designer often uses and reflects that when digitizing. The analysis unit also accurately reproduces the fine details intended by the designer by analyzing the drawing style. For example, it faithfully reproduces subtle handwritten curves and decorations in the digital drawing. To reflect the designer's intentions, the analysis unit also analyzes specific symbols and marks contained in handwritten floor plans and generates digital drawings based on them. For example, if a specific symbol indicates a door or window, its position is accurately digitized. This makes it possible to generate digital floor plans that accurately reflect the designer's intentions.

[0049] The analysis unit can automatically convert annotations and notes included in handwritten floor plans into text and integrate them into digital floor plans. For example, the analysis unit automatically converts annotations and notes included in handwritten floor plans into text using OCR technology. For example, it extracts room names and dimensions written by the designer by hand as text data. The analysis unit then integrates the converted text annotations and notes into the digital floor plan and displays them visually in an easy-to-understand manner. For example, it places room names and dimensions on the digital drawing. The analysis unit also analyzes the annotations and notes included in the handwritten floor plan and links related information to the digital floor plan. For example, it reflects information about materials and equipment written in the annotations in the digital drawing. This allows handwritten annotations and notes to be integrated into the digital floor plan.

[0050] The analysis unit can analyze handwritten floor plans, estimate the designer's emotions, and prioritize digitization of floor plans that reflect specific emotions. The analysis unit, for example, analyzes handwritten floor plans and estimates the designer's emotions. For example, emotions are estimated based on pen pressure and drawing speed, and floor plans that reflect positive emotions are prioritized for digitization. The analysis unit also uses the emotion estimation function to identify parts drawn by the designer with specific emotions and highlights and digitizes those parts. For example, parts drawn by the designer with particular effort are reproduced in detail. The analysis unit also analyzes the designer's emotions, and when digitizing floor plans that reflect emotions, applies colors and styles according to the emotions. For example, parts that reflect positive emotions are displayed in bright colors. This allows floor plans that reflect the designer's emotions to be prioritized for digitization.

[0051] The generation unit can digitize not only handwritten floor plans, but also handwritten furniture layout plans and interior design drawings. For example, the generation unit scans a handwritten furniture layout plan and digitizes it using generation AI. For example, it recognizes the position and size of furniture and reflects it in the digital drawing. The generation unit also analyzes and digitizes handwritten interior design drawings. For example, it recognizes wallpaper and flooring designs and applies them to the digital drawing. The generation unit also integrates handwritten floor plans and furniture layout plans to generate digital floor plans. For example, it integrates and digitizes the room layout and furniture arrangement. This makes it possible to digitize handwritten furniture layout plans and interior design drawings.

[0052] When digitizing a handwritten floor plan, the generation unit can automatically generate a 3D model as well, providing a three-dimensional visualization. For example, the generation unit analyzes the handwritten floor plan and automatically generates a 3D model. For example, the generation unit estimates the height and depth of the room and creates a three-dimensional floor plan. The generation unit also visualizes the generated 3D model, allowing the user to freely rotate and zoom it. For example, the generation unit displays the floor plan in three dimensions using a 3D viewer. The generation unit also generates a 3D model based on the handwritten floor plan and overlays it on the actual space using VR or AR technology. For example, the digital floor plan is displayed on the actual space using a smartphone or tablet. This allows the handwritten floor plan to be automatically generated as a 3D model, providing a three-dimensional visualization.

[0053] The analysis unit analyzes the handwritten floor plan, analyzes the user's emotions in real time when drawing the handwritten floor plan, and can provide feedback that elicits positive emotions. For example, the analysis unit uses an emotion estimation function to analyze the user's emotions in real time when drawing the handwritten floor plan. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also provides feedback that encourages the user to have positive emotions based on the emotion estimation data. For example, it presents encouraging messages or success stories. The analysis unit also monitors the user's emotions in real time, and if negative emotions are detected, it provides advice to elicit positive emotions. For example, it plays relaxing music. In this way, the user's emotions can be analyzed in real time and feedback that elicits positive emotions can be provided.

[0054] The generation unit automatically adds color coding and texture to the digital floor plan based on the information of the handwritten floor plan analyzed by the generation AI, making it visually easier to understand. For example, the generation unit automatically assigns different colors to each room based on the information analyzed by the generation AI. For example, the living room may be color-coded blue and the kitchen green. The generation unit also automatically adds textures of flooring and wall materials to the digital floor plan. For example, wood-grain floors and tile walls may be reflected in the digital drawing. The generation unit also visually emphasizes rooms according to their purpose based on the information analyzed by the generation AI. For example, important rooms and facilities may be displayed prominently. This allows the generation unit to add color coding and texture to the digital floor plan, making it visually easier to understand.

[0055] The generation unit can add an automatic check function based on the Building Standards Act and local regulations to the digital floor plan to confirm legal compliance. For example, the generation unit automatically checks whether the digital floor plan complies with the Building Standards Act based on information analyzed by the generation AI. For example, it checks whether the height and area of ​​rooms meet regulations. The generation unit also adds an automatic check function based on local regulations to confirm whether the digital floor plan complies with local building regulations. For example, it takes into account regulations regarding fire prevention areas and landscape preservation areas. The generation unit also automatically generates a checklist for the digital floor plan to confirm legal compliance. For example, it checks whether necessary equipment and evacuation routes are properly located. This makes it possible to automatically confirm the legal compliance of the digital floor plan.

[0056] The generation unit can use the emotion estimation function to estimate the user's most preferred design style and generate a digital floor plan based on that style. The generation unit, for example, uses the emotion estimation function to estimate the user's most preferred design style. For example, the preferred style is identified based on the user's past selections and reactions. The generation unit then automatically generates a digital floor plan based on the estimated design style. For example, a design according to a style such as modern, classic, or minimalist is applied. The generation unit also analyzes the user's emotional reaction in real time and adjusts the design style based on the results. For example, a style to which the user has a positive reaction is preferentially adopted. This makes it possible to generate a digital floor plan based on the user's most preferred design style.

[0057] When generating a digital floor plan, the generation unit can automatically make design proposals that take energy efficiency and environmental impact into consideration. For example, the generation unit automatically makes design proposals that take energy efficiency into consideration based on information analyzed by the generation AI. For example, it may optimize the placement of windows and the selection of insulation materials. The generation unit also automatically makes proposals to reduce environmental impact when generating a digital floor plan. For example, it may propose the use of renewable energy or the introduction of energy-saving equipment. The generation unit also reflects design proposals that take energy efficiency and environmental impact into the digital floor plan. For example, it indicates the installation location of solar panels or the placement of a rainwater utilization system. This allows for automatic design proposals that take energy efficiency and environmental impact into consideration.

[0058] When generating a digital floor plan, the generation unit can present multiple different design options to allow the user to select from. For example, the generation unit automatically generates multiple design options based on information analyzed by the generation AI. For example, it presents floor plans with different layouts and color schemes. The generation unit also provides an interactive interface so that the user can select from multiple design options. For example, it adds a function to switch designs by clicking or tapping. The generation unit also visually displays the features and advantages of each option to make it easier to compare the design options. For example, it shows differences in energy efficiency and cost in a graph. This makes it possible to present multiple different design options to allow the user to select from.

[0059] The generation unit uses the emotion estimation function to monitor the emotional responses of users when they review digital floor plans in real time and can propose optimal designs. The generation unit, for example, uses the emotion estimation function to monitor the emotional responses of users when they review digital floor plans in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit also builds a system that proposes optimal designs based on the user's emotional response data. For example, it prioritizes presenting designs that have a high number of positive emotional responses. The generation unit also collects emotion estimation data in real time and uses it to improve the design. For example, it dynamically adjusts the design according to changes in the user's emotions. This makes it possible to monitor the user's emotional responses in real time and propose optimal designs.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The floor plan creation system can further include a voice input unit. The voice input unit converts the floor plan information verbally provided by the user into text data using voice recognition technology and sends it to the analysis unit. For example, if the user specifies "the living room is 20 square meters," that information is automatically reflected in the digital floor plan. In addition, when the voice input unit receives a question about the floor plan from the user, the generation unit can automatically generate a response and provide feedback via voice. This allows the user to create floor plans using only their voice, without having to write or type on a keyboard.

[0062] The floor plan creation system can further include an environmental data acquisition unit. The environmental data acquisition unit acquires environmental data about the planned construction site and sends it to the analysis unit. For example, data such as sunshine hours, wind direction, and ambient noise levels is collected, and the analysis unit uses this data to propose the optimal floor plan. The environmental data acquisition unit can also acquire data such as seasonal temperature changes and precipitation amounts, supporting energy-efficient designs. This allows for the creation of environmentally friendly floor plans.

[0063] The floor plan creation system may further include a furniture layout suggestion unit. The furniture layout suggestion unit automatically suggests the layout of furniture specified by the user. For example, if the user instructs, "I want to place a sofa in the living room," the unit suggests the optimal location for the furniture. The furniture layout suggestion unit may also present multiple layout options based on the user's lifestyle and preferences. For example, it may suggest layouts based on family composition and hobbies. This allows the user to efficiently decide on the layout of furniture.

[0064] The floor plan creation system can further include a material selection unit. The material selection unit suggests the most suitable material based on the material information specified by the user. For example, if the user indicates, "I want to use wood for the flooring," the unit will provide information such as the most suitable type of wood, its price, and durability. The material selection unit can also select materials that take environmental impact into consideration. For example, it will suggest renewable materials and low-VOC (volatile organic compound) materials. This allows the user to select environmentally friendly materials.

[0065] The floor plan creation system can further include a cost calculation unit. The cost calculation unit automatically calculates the total construction cost based on the floor plan, materials, and equipment information specified by the user. For example, if the user instructs, "I want to know the construction cost for this floor plan," the cost calculation unit will provide a detailed estimate. The cost calculation unit can also compare the costs of different design options. For example, it can present the cost difference when different materials and equipment are used. This allows the user to select the optimal design according to their budget.

[0066] The analysis unit can analyze handwritten floor plans, estimate the user's emotions, and suggest floor plans that will help the user relax based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can suggest floor plans that have a relaxing effect. Specifically, it can suggest rooms that allow plenty of natural light or are located in quiet areas. The analysis unit can also adjust colors and interior styles according to the user's emotions. For example, it can suggest colors that have a relaxing effect or calming designs. This makes it possible to create floor plans that take the user's emotions into consideration.

[0067] The analysis unit can analyze handwritten floor plans, estimate the user's emotions, and, based on the estimated emotions, suggest floor plans that will most satisfy the user. For example, if the user is feeling happy, the analysis unit will suggest floor plans that emphasize that emotion. Specifically, it will suggest spacious living rooms and designs that create an open feeling. The analysis unit can also make suggestions to improve the functionality and convenience of the floor plan based on the user's emotions. For example, it will suggest layouts that optimize household chore flow and designs that provide ample storage space. This makes it possible to create floor plans that will satisfy the user based on their emotions.

[0068] The analysis unit can analyze handwritten floor plans, estimate the user's emotions, and, based on the estimated emotions, suggest floor plans that give the user a sense of security. For example, if the user feels anxious, the analysis unit will suggest floor plans that give the user a sense of security. Specifically, it will suggest layouts that are highly secure and designs that ensure privacy. The analysis unit can also make suggestions to improve the safety and comfort of the floor plan based on the user's emotions. For example, it will suggest barrier-free designs and ensuring evacuation routes. This makes it possible to create floor plans that give the user a sense of security and take the user's emotions into consideration.

[0069] The analysis unit can analyze handwritten floor plans, estimate the user's emotions, and, based on the estimated emotions, suggest floor plans that allow the user to unleash their creativity. For example, if the user is feeling excited or curious, the analysis unit can suggest floor plans that stimulate those emotions. Specifically, it can suggest open spaces or multipurpose rooms. The analysis unit can also make suggestions to improve the flexibility and diversity of floor plans based on the user's emotions. For example, it can suggest movable partitions or layouts that can adapt to changes. This makes it possible to create floor plans that bring out the user's creativity based on their emotions.

[0070] The analysis unit can analyze handwritten floor plans, estimate the user's emotions, and suggest floor plans that will help the user relax based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can suggest floor plans that have a relaxing effect. Specifically, it can suggest rooms that allow plenty of natural light or are located in quiet areas. The analysis unit can also adjust colors and interior styles according to the user's emotions. For example, it can suggest colors that have a relaxing effect or calming designs. This makes it possible to create floor plans that take the user's emotions into consideration.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The handwritten floor plan acquisition unit acquires a handwritten floor plan. For example, the handwritten floor plan can be scanned and saved as image data. Alternatively, the handwritten floor plan can be photographed with a smartphone camera and acquired as image data. Furthermore, the handwritten floor plan can be drawn with a digital pen, which can then convert the drawing into digital data in real time. Step 2: The analysis unit analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition unit. For example, the generation AI can use image recognition technology to analyze the handwritten floor plan and recognize the location and dimensions of each room and facility. The generation AI can also use pattern recognition technology to analyze specific symbols and marks contained in the handwritten floor plan. Furthermore, the generation AI can analyze the handwriting and drawing style of the handwritten floor plan to more accurately reflect the designer's intentions. Step 3: The generation unit generates a digital floor plan based on the information analyzed by the analysis unit. For example, the generation AI uses the illustrator tool to automatically generate a digital floor plan based on the analyzed information. The generation AI can also automatically add color coding and texture to the digital floor plan to make it visually easier to understand. Furthermore, the generation AI can also automatically generate a 3D model of the digital floor plan to provide a three-dimensional visualization.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

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

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a handwritten floor plan acquisition unit that acquires a handwritten floor plan; an analysis unit that analyzes the handwritten floor plan acquired by the handwritten floor plan acquisition unit; a generation unit that generates a digital floor plan based on the information analyzed by the analysis unit. A system characterized by:

2. The analysis unit Analyze the handwriting and drawing style of the handwritten floor plan to more accurately reflect the designer's intentions 2. The system of claim 1.

3. The analysis unit The annotations and notes contained in the handwritten floor plan are automatically converted into text and integrated into the digital floor plan.

2. The system of claim 1.

4. The analysis unit The handwritten floor plans are analyzed, the designer's emotions are estimated, and floor plans that reflect specific emotions are preferentially digitized.

2. The system of claim 1.

5. The generation unit Digitize not only the handwritten floor plans but also the handwritten furniture layout and interior design drawings.

2. The system of claim 1.

Images