System

Abstract

An object of a system according to an embodiment is to transmit design contents in a natural language and efficiently generate a design drawing based on the design contents.SOLUTION: A system includes a design content transmission part and a design drawing generation part. The design content transmission unit transmits the design content in a natural language. The design drawing generation unit generates a design drawing based on the design content transmitted by the design content transmission unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process of communicating design content in natural language and generating design drawings based on that content is inefficient.

[0005] The system according to the embodiment aims to communicate design content in natural language and to efficiently generate design drawings based on that content. [Means for solving the problem]

[0006] The system according to the embodiment includes a design content transmission unit and a design drawing generation unit. The design content transmission unit transmits design content in natural language. The design drawing generation unit generates a design drawing based on the design content transmitted by the design content transmission unit. [Effects of the Invention]

[0007] The system according to the embodiment can communicate design content in natural language and efficiently generate design drawings based on that content. [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 radio wave environment improvement system according to the embodiment of the present invention is a system in which design details are communicated in natural language and a generation AI generates design drawings. This makes it possible for the radio wave environment improvement system to streamline design work and quickly improve the radio wave environment.

[0029] The radio wave environment improvement system according to the embodiment includes a design content transmission unit and a design drawing generation unit. The design content transmission unit transmits design content in natural language. For example, a user can input specific instructions such as, "Install the radio in the center of the building and position the antenna so that radio waves reach each room." The design content transmission unit also provides prompts to the generation AI to transmit the design content. For example, the generation AI may send a prompt such as, "Please specify the location where the radio will be installed." The design drawing generation unit generates a design drawing based on the design content transmitted by the design content transmission unit. For example, the generation AI uses image generation AI technology to generate a drawing showing the placement of the radio and antenna on a building floor plan. The design drawing generation unit also takes into account the structure and materials of the building when generating the design drawing. For example, the generation AI determines the optimal placement of the radio considering the thickness and materials of the building's walls. This allows the radio wave environment improvement system to streamline design work and quickly improve the radio wave environment.

[0030] The design content transmission unit can use voice input to enable the user to give instructions more intuitively. For example, the design content transmission unit allows the user to input design content by voice, and the generation AI analyzes the voice to understand the design content. For example, the design content transmission unit issues a voice instruction such as "Install the radio in the center of the building." The design content transmission unit also uses voice input to enable the user to communicate design content more intuitively. For example, as the user issues instructions by voice, the generation AI generates a blueprint in real time. The design content transmission unit also uses voice input to enable the user to communicate design content without using their hands. For example, as the user issues instructions by voice, the generation AI analyzes the instructions and generates a blueprint. This allows the user to give instructions intuitively using voice input.

[0031] The design content transmission unit can refer to past design data and automatically propose similar design content. For example, when a user inputs design content, the design content transmission unit adds a function of referring to past design data and automatically proposing similar design content. For example, if a similar design has been done in the past, that design content is used as a reference. The design content transmission unit also adds a function of automatically proposing a similar design to the design content input by the user based on the past design data. For example, the past design data is analyzed and optimal design content is proposed. The design content transmission unit also adds a function of referring to past design data when a user inputs design content and automatically proposing similar design content. For example, the optimal radio device placement is proposed based on the past design data. In this way, by referring to past design data and automatically proposing similar design content, the efficiency of the design work is improved.

[0032] The design content transmission unit adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. The design content transmission unit, for example, adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, multiple users input design content simultaneously, and the generation AI analyzes it and generates a design drawing. The design content transmission unit also uses the collaboration function to enable multiple users to input design content simultaneously. For example, team members input design content according to their respective roles, and the generation AI integrates it to generate a design drawing. The design content transmission unit also adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, by linking with a project management tool, team members can share design content in real time. In this way, adding a collaboration function that allows multiple users to input design content simultaneously improves the efficiency of team design work.

[0033] The design content transmission unit can enable a user to issue instructions while visually checking the design content using a 3D model when transmitting the design content. For example, when a user inputs design content, the design content transmission unit enables the user to issue instructions while visually checking the design content using a 3D model. For example, instructions are issued while checking the placement of radios on the 3D model. The design content transmission unit also enables the user to issue instructions while visually checking the design content using a 3D model. For example, instructions are issued while checking the placement of antennas on the 3D model. The design content transmission unit also enables the user to issue instructions while visually checking the design content using a 3D model when inputting design content. For example, instructions are issued while checking the structure of a building on the 3D model. This allows instructions to be issued while visually checking the design content using the 3D model.

[0034] The blueprint generation unit can propose the optimal placement of radio devices by taking into consideration the materials and structure of the building. For example, when the generation AI generates a blueprint, the blueprint generation unit proposes the optimal placement of radio devices by taking into consideration the materials and structure of the building. For example, the placement of radio devices is determined by taking into consideration the material and thickness of the walls. The blueprint generation unit also analyzes the structure of the building, and the generation AI proposes the optimal placement of radio devices. For example, the placement of radio devices is determined by taking into consideration the number of floors of the building and the layout of rooms. The blueprint generation unit also proposes the optimal placement of radio devices by taking into consideration the materials and structure of the building when the generation AI generates a blueprint. For example, the placement of radio devices is determined by taking into consideration the shape and size of the building. In this way, the optimal placement of radio devices is proposed by taking into consideration the materials and structure of the building.

[0035] The blueprint generation unit can perform a simulation on the generated blueprint to visualize the propagation status of radio waves. The blueprint generation unit, for example, performs a simulation on the generated blueprint to add a function to visualize the propagation status of radio waves. For example, the strength and range of radio waves are visually displayed. The blueprint generation unit also adds a function to visualize the radio wave propagation status of the generated blueprint using a simulation. For example, radio wave interference and blockage are visually displayed. The blueprint generation unit also adds a function to perform a simulation on the generated blueprint to visualize the propagation status of radio waves. For example, the reach and strength of radio waves are displayed in different colors. In this way, the blueprint generation unit can perform a simulation on the generated blueprint to visualize the propagation status of radio waves.

[0036] The design drawing generation unit can present a plurality of different design options and allow the user to select from them. For example, when the generation AI generates a design drawing, the design drawing generation unit presents a plurality of different design options and allows the user to select from them. For example, the design drawing generation unit presents a plurality of patterns for radio placement and antenna placement. The design drawing generation unit also generates different design options and allows the user to select from them. For example, the design drawing generation unit presents a plurality of patterns for radio placement and allows the user to select from the optimal placement. The design drawing generation unit also presents a plurality of different design options when the generation AI generates a design drawing and allows the user to select from them. For example, the design drawing generation unit presents a plurality of patterns for antenna placement and allows the user to select from the optimal placement. In this way, a plurality of different design options can be presented and allowed the user to select from them.

[0037] The blueprint generation unit can use augmented reality (AR) technology to enable the generated blueprint to be viewed within an actual building. The blueprint generation unit, for example, uses augmented reality (AR) technology to enable the generated blueprint to be viewed within an actual building. For example, a smartphone or tablet is used to display the blueprint superimposed within the building. The blueprint generation unit also uses augmented reality (AR) technology to enable the generated blueprint to be viewed within the actual building. For example, AR glasses are used to display the blueprint superimposed within the building. The blueprint generation unit also uses augmented reality (AR) technology to enable the generated blueprint to be viewed within the actual building. For example, an AR app is used to display the blueprint superimposed within the building. This allows the generated blueprint to be viewed within the actual building using augmented reality (AR) technology.

[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] When a user inputs design content, the design content transmission unit can refer to past design data and automatically suggest similar design content. For example, if a similar design has been done in the past, the design content is used as a reference. The design content transmission unit can also automatically suggest a design similar to the design content input by the user based on the past design data. For example, the past design data can be analyzed to suggest optimal design content. The design content transmission unit can also refer to past design data when a user inputs design content and automatically suggest similar design content. For example, the optimal radio device placement can be suggested based on the past design data. In this way, by referring to past design data and automatically suggesting similar design content, the efficiency of the design work is improved.

[0040] The design content transmission unit can use voice input to enable the user to give instructions more intuitively. For example, the user can input design content by voice, and the generation AI can analyze the voice and understand the design content. For example, a voice instruction such as "Install the radio in the center of the building" can be given. The design content transmission unit can also use voice input to enable the user to communicate design content more intuitively. For example, the generation AI can generate a blueprint in real time as the user gives instructions by voice. The design content transmission unit can also use voice input to enable the user to communicate design content without using their hands. For example, the user can give instructions by voice, and the generation AI can analyze the instructions and generate a blueprint. This allows the user to give instructions intuitively using voice input.

[0041] The design content transmission unit can add a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, multiple users can input design content simultaneously, and the generation AI can analyze it and generate a design drawing. The design content transmission unit can also use the collaboration function to allow multiple users to input design content simultaneously. For example, team members can input design content according to their respective roles, and the generation AI can integrate it to generate a design drawing. The design content transmission unit can also add a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, it can link with a project management tool to enable team members to share design content in real time. This adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work.

[0042] The design content transmission unit can enable the user to issue instructions while visually checking the design content using a 3D model when transmitting the design content. For example, when a user inputs design content, the user can issue instructions while visually checking the design content using a 3D model. For example, instructions can be issued while checking the placement of radios on the 3D model. The design content transmission unit can also enable the user to issue instructions while visually checking the design content using a 3D model. For example, instructions can be issued while checking the placement of antennas on the 3D model. The design content transmission unit can also enable the user to issue instructions while visually checking the design content using a 3D model when inputting design content. For example, instructions can be issued while checking the structure of a building on the 3D model. This allows instructions to be issued while visually checking the design content using a 3D model.

[0043] The blueprint generation unit can consider the materials and structure of the building and propose the optimal placement of radios. For example, when the generation AI generates a blueprint, it can consider the materials and structure of the building and propose the optimal placement of radios. For example, it can determine the placement of radios by considering the material and thickness of the walls. The blueprint generation unit can also analyze the structure of the building, and the generation AI can propose the optimal placement of radios. For example, it can determine the placement of radios by considering the number of floors of the building and the layout of rooms. The blueprint generation unit can also consider the materials and structure of the building when the generation AI generates a blueprint and propose the optimal placement of radios. For example, it can determine the placement of radios by considering the shape and size of the building. This makes it possible to propose the optimal placement of radios by considering the materials and structure of the building.

[0044] The blueprint generation unit can perform a simulation on the generated blueprint to visualize the radio wave propagation status. For example, a function can be added to perform a simulation on the generated blueprint to visualize the radio wave propagation status. For example, the strength and range of radio waves can be visually displayed. The blueprint generation unit can also add a function to visualize the radio wave propagation status of the generated blueprint using a simulation. For example, radio wave interference and obstruction can be visually displayed. The blueprint generation unit can also add a function to perform a simulation on the generated blueprint to visualize the radio wave propagation status. For example, the reach and strength of radio waves can be displayed in different colors. In this way, a simulation can be performed on the generated blueprint to visualize the radio wave propagation status.

[0045] The design drawing generation unit can present a plurality of different design options and allow the user to select from them. For example, when the generation AI generates a design drawing, it can present a plurality of different design options and allow the user to select from them. For example, it can present a plurality of patterns for radio placement and antenna placement. The design drawing generation unit can also generate a plurality of different design options and allow the user to select from them. For example, it can present a plurality of patterns for radio placement and allow the user to select the optimal placement. The design drawing generation unit can also present a plurality of different design options when the generation AI generates a design drawing and allow the user to select from them. For example, it can present a plurality of patterns for antenna placement and allow the user to select the optimal placement. In this way, it is possible to present a plurality of different design options and allow the user to select from them.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The design content transmission unit transmits the design content in natural language. For example, a user can input specific instructions such as, "Install the radio in the center of the building and position the antenna so that radio waves reach each room." The design content transmission unit also provides prompts to the generation AI to transmit the design content. For example, it sends a prompt such as, "Please specify the location where the radio will be installed" to the generation AI. Step 2: The blueprint generation unit generates a blueprint based on the design content transmitted by the design content transmission unit. For example, the generation AI uses image generation AI technology to generate a diagram showing the placement of radios and antennas on a building floor plan. In addition, the blueprint generation unit takes into account the structure and materials of the building when generating the blueprint. For example, it determines the optimal placement of radios by taking into account the thickness and materials of the building's walls.

[0048] (Example 2) The radio wave environment improvement system according to the embodiment of the present invention is a system in which design details are communicated in natural language and a generation AI generates design drawings. This makes it possible for the radio wave environment improvement system to streamline design work and quickly improve the radio wave environment.

[0049] The radio wave environment improvement system according to the embodiment includes a design content transmission unit and a design drawing generation unit. The design content transmission unit transmits design content in natural language. For example, a user can input specific instructions such as, "Install the radio in the center of the building and position the antenna so that radio waves reach each room." The design content transmission unit also provides prompts to the generation AI to transmit the design content. For example, the generation AI may send a prompt such as, "Please specify the location where the radio will be installed." The design drawing generation unit generates a design drawing based on the design content transmitted by the design content transmission unit. For example, the generation AI uses image generation AI technology to generate a drawing showing the placement of the radio and antenna on a building floor plan. The design drawing generation unit also takes into account the structure and materials of the building when generating the design drawing. For example, the generation AI determines the optimal placement of the radio considering the thickness and materials of the building's walls. This allows the radio wave environment improvement system to streamline design work and quickly improve the radio wave environment.

[0050] The design content transmission unit can use the emotion estimation function to analyze the user's emotions when communicating the design content and provide feedback to elicit positive emotions. For example, when the user inputs the design content, the design content transmission unit uses the emotion estimation function to analyze the user's emotions in real time and provide feedback to elicit positive emotions. For example, if the user is feeling stressed, the design content transmission unit displays advice to help the user relax. The design content transmission unit also uses the emotion estimation function to analyze the user's emotional state when inputting the design content and provides an interface to elicit positive emotions. For example, the design content transmission unit employs a design and color usage that makes the user feel joy. The design content transmission unit also uses the emotion estimation function to analyze the user's emotions when inputting the design content and provides feedback to elicit positive emotions. For example, the design content transmission unit displays a message that makes the user feel a sense of accomplishment. In this way, the efficiency of the design work is improved by analyzing the user's emotions and eliciting positive emotions.

[0051] The design content transmission unit can use voice input to enable the user to give instructions more intuitively. For example, the design content transmission unit allows the user to input design content by voice, and the generation AI analyzes the voice to understand the design content. For example, the design content transmission unit issues a voice instruction such as "Install the radio in the center of the building." The design content transmission unit also uses voice input to enable the user to communicate design content more intuitively. For example, as the user issues instructions by voice, the generation AI generates a blueprint in real time. The design content transmission unit also uses voice input to enable the user to communicate design content without using their hands. For example, as the user issues instructions by voice, the generation AI analyzes the instructions and generates a blueprint. This allows the user to give instructions intuitively using voice input.

[0052] The design content transmission unit can refer to past design data and automatically propose similar design content. For example, when a user inputs design content, the design content transmission unit adds a function of referring to past design data and automatically proposing similar design content. For example, if a similar design has been done in the past, that design content is used as a reference. The design content transmission unit also adds a function of automatically proposing a similar design to the design content input by the user based on the past design data. For example, the past design data is analyzed and optimal design content is proposed. The design content transmission unit also adds a function of referring to past design data when a user inputs design content and automatically proposing similar design content. For example, the optimal radio device placement is proposed based on the past design data. In this way, by referring to past design data and automatically proposing similar design content, the efficiency of the design work is improved.

[0053] The design content transmission unit adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. The design content transmission unit, for example, adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, multiple users input design content simultaneously, and the generation AI analyzes it and generates a design drawing. The design content transmission unit also uses the collaboration function to enable multiple users to input design content simultaneously. For example, team members input design content according to their respective roles, and the generation AI integrates it to generate a design drawing. The design content transmission unit also adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, by linking with a project management tool, team members can share design content in real time. In this way, adding a collaboration function that allows multiple users to input design content simultaneously improves the efficiency of team design work.

[0054] The design content transmission unit can enable a user to issue instructions while visually checking the design content using a 3D model when transmitting the design content. For example, when a user inputs design content, the design content transmission unit enables the user to issue instructions while visually checking the design content using a 3D model. For example, instructions are issued while checking the placement of radios on the 3D model. The design content transmission unit also enables the user to issue instructions while visually checking the design content using a 3D model. For example, instructions are issued while checking the placement of antennas on the 3D model. The design content transmission unit also enables the user to issue instructions while visually checking the design content using a 3D model when inputting design content. For example, instructions are issued while checking the structure of a building on the 3D model. This allows instructions to be issued while visually checking the design content using the 3D model.

[0055] The design content transmission unit can use the emotion estimation function to analyze the stress level of a user when communicating design content and provide an interface for reducing stress. For example, when the user inputs design content, the design content transmission unit uses the emotion estimation function to analyze the stress level and provide an interface for reducing stress. For example, by playing music for relaxation. The design content transmission unit also uses the emotion estimation function to analyze the stress level of a user when inputting design content and provides an interface for reducing stress. For example, by displaying advice for relaxing if the user feels stressed. The design content transmission unit also uses the emotion estimation function to analyze the stress level of a user when inputting design content and provides an interface for reducing stress. For example, by displaying a visual for relaxing if the user feels stressed. In this way, the design content transmission unit analyzes the stress level of a user using the emotion estimation function and provides an interface for reducing stress.

[0056] The blueprint generation unit can analyze the user's emotional response to the generated blueprint using the emotion estimation function and make suggested modifications to elicit a positive response. The blueprint generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the generated blueprint and makes suggested modifications to elicit a positive response. For example, it makes suggestions to improve parts that the user feels dissatisfied with. The blueprint generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated blueprint and makes suggested modifications to elicit a positive response. For example, it proposes design changes that will satisfy the user. The blueprint generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated blueprint and makes suggested modifications to elicit a positive response. For example, it proposes changes to the layout that will make the user happy. In this way, the user's emotional response is analyzed and suggested modifications to elicit a positive response are made.

[0057] The blueprint generation unit can propose the optimal placement of radio devices by taking into consideration the materials and structure of the building. For example, when the generation AI generates a blueprint, the blueprint generation unit proposes the optimal placement of radio devices by taking into consideration the materials and structure of the building. For example, the placement of radio devices is determined by taking into consideration the material and thickness of the walls. The blueprint generation unit also analyzes the structure of the building, and the generation AI proposes the optimal placement of radio devices. For example, the placement of radio devices is determined by taking into consideration the number of floors of the building and the layout of rooms. The blueprint generation unit also proposes the optimal placement of radio devices by taking into consideration the materials and structure of the building when the generation AI generates a blueprint. For example, the placement of radio devices is determined by taking into consideration the shape and size of the building. In this way, the optimal placement of radio devices is proposed by taking into consideration the materials and structure of the building.

[0058] The blueprint generation unit can perform a simulation on the generated blueprint to visualize the propagation status of radio waves. The blueprint generation unit, for example, performs a simulation on the generated blueprint to add a function to visualize the propagation status of radio waves. For example, the strength and range of radio waves are visually displayed. The blueprint generation unit also adds a function to visualize the radio wave propagation status of the generated blueprint using a simulation. For example, radio wave interference and blockage are visually displayed. The blueprint generation unit also adds a function to perform a simulation on the generated blueprint to visualize the propagation status of radio waves. For example, the reach and strength of radio waves are displayed in different colors. In this way, the blueprint generation unit can perform a simulation on the generated blueprint to visualize the propagation status of radio waves.

[0059] The design drawing generation unit can present a plurality of different design options and allow the user to select from them. For example, when the generation AI generates a design drawing, the design drawing generation unit presents a plurality of different design options and allows the user to select from them. For example, the design drawing generation unit presents a plurality of patterns for radio placement and antenna placement. The design drawing generation unit also generates different design options and allows the user to select from them. For example, the design drawing generation unit presents a plurality of patterns for radio placement and allows the user to select from the optimal placement. The design drawing generation unit also presents a plurality of different design options when the generation AI generates a design drawing and allows the user to select from them. For example, the design drawing generation unit presents a plurality of patterns for antenna placement and allows the user to select from the optimal placement. In this way, a plurality of different design options can be presented and allowed the user to select from them.

[0060] The blueprint generation unit can use augmented reality (AR) technology to enable the generated blueprint to be viewed within an actual building. The blueprint generation unit, for example, uses augmented reality (AR) technology to enable the generated blueprint to be viewed within an actual building. For example, a smartphone or tablet is used to display the blueprint superimposed within the building. The blueprint generation unit also uses augmented reality (AR) technology to enable the generated blueprint to be viewed within the actual building. For example, AR glasses are used to display the blueprint superimposed within the building. The blueprint generation unit also uses augmented reality (AR) technology to enable the generated blueprint to be viewed within the actual building. For example, an AR app is used to display the blueprint superimposed within the building. This allows the generated blueprint to be viewed within the actual building using augmented reality (AR) technology.

[0061] The blueprint generation unit can monitor the user's emotional response to the generated blueprint in real time using the emotion estimation function and continuously propose an optimal design. The blueprint generation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the generated blueprint in real time and continuously propose an optimal design. For example, the design content is adjusted based on the user's emotional response. The blueprint generation unit also monitors the user's emotional response to the generated blueprint in real time and continuously proposes an optimal design. For example, it proposes design changes that will satisfy the user. The blueprint generation unit also uses the emotion estimation function to monitor the user's emotional response to the generated blueprint in real time and continuously propose an optimal design. For example, it regenerates the blueprint based on the user's emotion data. This makes it possible to monitor the user's emotional response in real time and continuously propose an optimal design.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] When a user inputs design content, the design content transmission unit can refer to past design data and automatically suggest similar design content. For example, if a similar design has been done in the past, the design content is used as a reference. The design content transmission unit can also automatically suggest a design similar to the design content input by the user based on the past design data. For example, the past design data can be analyzed to suggest optimal design content. The design content transmission unit can also refer to past design data when a user inputs design content and automatically suggest similar design content. For example, the optimal radio device placement can be suggested based on the past design data. In this way, by referring to past design data and automatically suggesting similar design content, the efficiency of the design work is improved.

[0064] The design content transmission unit can use the emotion estimation function to analyze the user's emotions when transmitting design content and provide feedback to elicit positive emotions. For example, when the user inputs design content, the emotion estimation function can be used to analyze emotions in real time and provide feedback to elicit positive emotions. For example, if the user is feeling stressed, advice to relax can be displayed. The design content transmission unit can also use the emotion estimation function to analyze the user's emotional state when inputting design content and provide an interface to elicit positive emotions. For example, a design and color usage that makes the user feel joy can be adopted. The design content transmission unit can also use the emotion estimation function to analyze emotions when the user inputs design content and provide feedback to elicit positive emotions. For example, a message that makes the user feel a sense of accomplishment can be displayed. In this way, the user's emotions are analyzed and positive emotions are elicited, thereby improving the efficiency of design work.

[0065] The design content transmission unit can use voice input to enable the user to give instructions more intuitively. For example, the user can input design content by voice, and the generation AI can analyze the voice and understand the design content. For example, a voice instruction such as "Install the radio in the center of the building" can be given. The design content transmission unit can also use voice input to enable the user to communicate design content more intuitively. For example, the generation AI can generate a blueprint in real time as the user gives instructions by voice. The design content transmission unit can also use voice input to enable the user to communicate design content without using their hands. For example, the user can give instructions by voice, and the generation AI can analyze the instructions and generate a blueprint. This allows the user to give instructions intuitively using voice input.

[0066] The design content transmission unit can add a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, multiple users can input design content simultaneously, and the generation AI can analyze it and generate a design drawing. The design content transmission unit can also use the collaboration function to allow multiple users to input design content simultaneously. For example, team members can input design content according to their respective roles, and the generation AI can integrate it to generate a design drawing. The design content transmission unit can also add a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work. For example, it can link with a project management tool to enable team members to share design content in real time. This adds a collaboration function that allows multiple users to input design content simultaneously, thereby improving the efficiency of team design work.

[0067] The design content transmission unit can enable the user to issue instructions while visually checking the design content using a 3D model when transmitting the design content. For example, when a user inputs design content, the user can issue instructions while visually checking the design content using a 3D model. For example, instructions can be issued while checking the placement of radios on the 3D model. The design content transmission unit can also enable the user to issue instructions while visually checking the design content using a 3D model. For example, instructions can be issued while checking the placement of antennas on the 3D model. The design content transmission unit can also enable the user to issue instructions while visually checking the design content using a 3D model when inputting design content. For example, instructions can be issued while checking the structure of a building on the 3D model. This allows instructions to be issued while visually checking the design content using a 3D model.

[0068] The design content transmission unit can use the emotion estimation function to analyze the stress level of a user when transmitting design content and provide an interface for reducing stress. For example, when a user inputs design content, the emotion estimation function can be used to analyze the stress level and provide an interface for reducing stress. For example, music for relaxation can be played. The design content transmission unit can also use the emotion estimation function to analyze the stress level of a user when inputting design content and provide an interface for reducing stress. For example, advice on how to relax if the user feels stressed can be displayed. The design content transmission unit can also use the emotion estimation function to analyze the stress level of a user when inputting design content and provide an interface for reducing stress. For example, a visual for helping the user relax if the user feels stressed can be displayed. In this way, the design content transmission unit can analyze the stress level of a user when inputting design content and provide an interface for reducing stress.

[0069] The blueprint generation unit can analyze the user's emotional response to the generated blueprint using the emotion estimation function and make suggested modifications to elicit a positive response. For example, the emotion estimation function can be used to analyze the user's emotional response to the generated blueprint and make suggested modifications to elicit a positive response. For example, a suggestion can be made to improve a part that the user feels dissatisfied with. The blueprint generation unit can also use the emotion estimation function to analyze the user's emotional response to the generated blueprint and make suggested modifications to elicit a positive response. For example, a design change that will satisfy the user can be suggested. The blueprint generation unit can also use the emotion estimation function to analyze the user's emotional response to the generated blueprint and make suggested modifications to elicit a positive response. For example, a layout change that will make the user happy can be suggested. In this way, the user's emotional response can be analyzed and suggested modifications to elicit a positive response can be made.

[0070] The blueprint generation unit can consider the materials and structure of the building and propose the optimal placement of radios. For example, when the generation AI generates a blueprint, it can consider the materials and structure of the building and propose the optimal placement of radios. For example, it can determine the placement of radios by considering the material and thickness of the walls. The blueprint generation unit can also analyze the structure of the building, and the generation AI can propose the optimal placement of radios. For example, it can determine the placement of radios by considering the number of floors of the building and the layout of rooms. The blueprint generation unit can also consider the materials and structure of the building when the generation AI generates a blueprint and propose the optimal placement of radios. For example, it can determine the placement of radios by considering the shape and size of the building. This makes it possible to propose the optimal placement of radios by considering the materials and structure of the building.

[0071] The blueprint generation unit can perform a simulation on the generated blueprint to visualize the radio wave propagation status. For example, a function can be added to perform a simulation on the generated blueprint to visualize the radio wave propagation status. For example, the strength and range of radio waves can be visually displayed. The blueprint generation unit can also add a function to visualize the radio wave propagation status of the generated blueprint using a simulation. For example, radio wave interference and obstruction can be visually displayed. The blueprint generation unit can also add a function to perform a simulation on the generated blueprint to visualize the radio wave propagation status. For example, the reach and strength of radio waves can be displayed in different colors. In this way, a simulation can be performed on the generated blueprint to visualize the radio wave propagation status.

[0072] The design drawing generation unit can present a plurality of different design options and allow the user to select from them. For example, when the generation AI generates a design drawing, it can present a plurality of different design options and allow the user to select from them. For example, it can present a plurality of patterns for radio placement and antenna placement. The design drawing generation unit can also generate a plurality of different design options and allow the user to select from them. For example, it can present a plurality of patterns for radio placement and allow the user to select the optimal placement. The design drawing generation unit can also present a plurality of different design options when the generation AI generates a design drawing and allow the user to select from them. For example, it can present a plurality of patterns for antenna placement and allow the user to select the optimal placement. In this way, it is possible to present a plurality of different design options and allow the user to select from them.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The design content transmission unit transmits the design content in natural language. For example, a user can input specific instructions such as, "Install the radio in the center of the building and position the antenna so that radio waves reach each room." The design content transmission unit also provides prompts to the generation AI to transmit the design content. For example, it sends a prompt such as, "Please specify the location where the radio will be installed" to the generation AI. Step 2: The blueprint generation unit generates a blueprint based on the design content transmitted by the design content transmission unit. For example, the generation AI uses image generation AI technology to generate a diagram showing the placement of radios and antennas on a building floor plan. In addition, the blueprint generation unit takes into account the structure and materials of the building when generating the blueprint. For example, it determines the optimal placement of radios by taking into account the thickness and materials of the building's walls.

[0075] 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.

[0076] 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 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, a 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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]

[0142] 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 design content transmission unit that transmits design content in natural language; a design drawing generation unit that generates a design drawing based on the design content transmitted by the design content transmission unit; A system characterized by:

2. The design content transmission unit Analyze users' emotions when communicating design content and provide feedback to elicit positive emotions 2. The system of claim 1.

3. The design content transmission unit By using voice input, users can give instructions more intuitively.

2. The system of claim 1.

4. The design content transmission unit Referencing past design data and automatically proposing similar design content 2. The system of claim 1.

5. The design content transmission unit Add a collaboration function that allows multiple users to input design content simultaneously, streamlining team design work.

2. The system of claim 1.

6. The design content transmission unit When communicating design details, 3D models can be used to give instructions while visually checking them.

2. The system of claim 1.

7. The design content transmission unit Analyze the user's stress level when communicating design content and provide an interface to reduce stress 2. The system of claim 1.

8. The design drawing generation unit Analyze users' emotional reactions to the generated blueprints and suggest modifications to elicit positive reactions.

2. The system of claim 1.

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