System

Abstract

An object of a system according to an embodiment is to accurately calculate the areas of an outer wall and a roof from a drawing and give an appropriate instruction to a painting robot.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires drawing data in a digital format. The analysis part analyzes the drawing data acquired by the acquisition part and calculates the areas of the outer wall and the roof. The generation unit generates digital data for instructing a coating method for the coating robot based on the area calculated by the analysis unit. The control unit performs painting based on the digital data generated by the generation 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] With conventional technology, it was difficult to accurately calculate the area of ​​the exterior walls and roof from drawings and provide appropriate instructions to the painting robot.

[0005] The system according to the embodiment aims to accurately calculate the area of ​​the exterior walls and roof from drawings and give appropriate instructions to the painting robot. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires digital drawing data. The analysis unit analyzes the drawing data acquired by the acquisition unit and calculates the areas of the exterior walls and roof. The generation unit generates digital data that instructs a painting robot on a painting method based on the areas calculated by the analysis unit. The control unit performs painting based on the digital data generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately calculate the area of ​​the exterior walls and roof from the drawings and give appropriate instructions to the painting robot. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention calculates the area of ​​exterior walls and roofs from drawings and digitizes painting instructions for painting robots. This system acquires digital drawing data, analyzes it using AI to calculate the area of ​​the exterior walls and roofs, generates digital data instructing the painting robot on how to paint, and the painting robot follows the instructions. For example, when acquiring digital drawing data, the drawings are input in digital format, such as scanned image data or CAD data. Next, AI analyzes the input drawing data, identifies the exterior wall and roof sections, and calculates their respective areas. Based on the calculated areas, digital data instructing the painting robot on how to paint is generated, and this digital data is transmitted to the painting robot. The painting robot then paints based on the received digital data. This allows the area of ​​the exterior walls and roofs to be accurately calculated from the drawings and the painting instructions to be digitized for painting robots. This system improves the efficiency and accuracy of painting work. For example, when painting large buildings, accurate painting by painting robots can be expected to reduce work time and improve quality.

[0029] A painting system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires digital drawing data. Examples of digital drawing data include, but are not limited to, JPEG, PNG, and CAD data. The acquisition unit can acquire, for example, scanned image data or CAD data. The analysis unit uses AI to analyze the drawing data acquired by the acquisition unit, identify the exterior wall and roof portions, and calculate their respective areas. For example, the analysis unit can use deep learning to recognize the outline of the exterior wall on the drawing and calculate its area. The analysis unit can also use a machine learning algorithm to recognize the outline of the roof and calculate its area. The generation unit generates digital data based on the calculated areas that instructs the painting robot which parts to paint and in what order. For example, the generation unit generates digital data that instructs the painting order and the painting area. The control unit causes the painting robot to paint based on the digital data generated by the generation unit. For example, the control unit instructs the painting robot on the painting order and method. This allows the system to perform a comprehensive process from acquiring drawing data to controlling the painting robot.

[0030] The acquisition unit can acquire digital drawing data such as scanned image data or CAD data. The scanned image data may include, but is not limited to, various resolutions and file formats. For example, the acquisition unit can scan a drawing using a scanner and save it as image data. Furthermore, when acquiring CAD data, the acquisition unit can use file formats such as DXF and DWG. This allows acquisition of drawing data in a variety of formats.

[0031] The analysis unit can use AI to identify the exterior wall and roof portions on the drawing and calculate their respective areas. For example, the analysis unit can use deep learning to recognize the outline of the exterior wall on the drawing and calculate its area. For example, the analysis unit can use an image analysis algorithm to identify the outline of the exterior wall and count the number of pixels to calculate the area. The analysis unit can also use a machine learning algorithm to recognize the outline of the roof and calculate its area. For example, the analysis unit can analyze vector data to calculate the area of ​​the roof. This makes it possible to accurately calculate the areas of the exterior walls and roof using AI.

[0032] The generation unit can generate digital data instructing the painting robot which parts to paint and in what order based on the calculated area. The generation unit generates digital data instructing, for example, the painting order or the painting range. For example, the generation unit can generate digital data instructing the painting robot which parts to paint and in what order based on the area of ​​an exterior wall. The generation unit can also generate digital data instructing the painting robot which parts to paint and in what order based on the area of ​​a roof. This allows the painting robot's operation to be efficiently instructed.

[0033] The control unit allows the painting robot to paint based on the received digital data. The control unit, for example, instructs the painting robot on the painting sequence and method. For example, when painting a specific part of an exterior wall, the control unit can instruct the painting robot to execute the painting sequence and method according to the digital data. Furthermore, when painting a specific part of a roof, the control unit can also execute the painting sequence and method according to the digital data. This allows the painting robot to paint as instructed.

[0034] When acquiring drawing data, the acquisition unit can analyze the user's past acquisition history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of drawing data formats that the user has used in the past. For example, the acquisition unit can suggest the most efficient acquisition method based on the user's past acquisition history. The acquisition unit can also analyze the quality of drawing data acquired by the user in the past and select the optimal acquisition method. For example, the acquisition unit can analyze past acquisition data and acquisition frequency to select the optimal acquisition method. This makes it possible to select the optimal acquisition method based on the user's past acquisition history.

[0035] When acquiring the drawing data, the acquisition unit can filter the drawing data based on the type and scale of the building. For example, in the case of a large building, the acquisition unit preferentially acquires detailed drawing data. For example, the acquisition unit can filter and acquire appropriate drawing data based on the type and scale of the building. In addition, in the case of a small building, the acquisition unit can also acquire simplified drawing data. For example, the acquisition unit can filter and acquire appropriate drawing data depending on the type of specific building, such as a residential building or a commercial facility. This makes it possible to acquire appropriate drawing data depending on the type and scale of the building.

[0036] When acquiring drawing data, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user uses voice input, the acquisition unit can acquire the drawing data using voice recognition technology. For example, if the user uses text input, the acquisition unit can acquire the drawing data using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can also acquire the drawing data using image recognition technology. For example, the acquisition unit can select the optimal acquisition means depending on the user's input method. This makes it possible to select the optimal acquisition means for drawing data depending on the user's input method.

[0037] When acquiring drawing data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the acquisition unit can prioritize acquiring drawing data related to that area. For example, if the user is moving, the acquisition unit can prioritize acquiring drawing data closest to the user's current location. Furthermore, if the user is involved in a specific construction project, the acquisition unit can prioritize acquiring drawing data related to that project. For example, the acquisition unit can acquire the user's geographical location information using GPS data or a location information service and select highly relevant data based on that information. This makes it possible to acquire highly relevant drawing data based on the user's geographical location information.

[0038] When acquiring the drawing data, the acquisition unit can analyze the user's social media activities and acquire related data. For example, the acquisition unit can acquire drawing data related to a construction project shared by the user on social media. For example, the acquisition unit can analyze the content of the user's social media posts and acquire related drawing data. The acquisition unit can also acquire related drawing data by referring to the activities of the user's friends on social media. For example, the acquisition unit can select related data based on the social media activities. This makes it possible to acquire related drawing data based on the user's social media activities.

[0039] When acquiring drawing data, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit selects the optimal acquisition method based on, for example, feedback provided by the user in the past. For example, the acquisition unit can prioritize the use of a specific acquisition means based on the user's past feedback. The acquisition unit can also analyze the user's past feedback and customize the acquisition method. For example, the acquisition unit can analyze past ratings, comments, and survey results and adjust the acquisition means. This makes it possible to optimize the acquisition method based on the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the drawing. For example, the analysis unit performs a detailed analysis for an important drawing. For example, the analysis unit can adjust the level of detail of the analysis based on the priority of the project or the scale of the building. The analysis unit can also perform a standard analysis for a general drawing. For example, the analysis unit can adjust the number of analysis items and the depth of the analysis. The analysis unit can also perform a simplified analysis for a simple drawing. For example, the analysis unit can adjust the level of detail of the analysis to perform an efficient analysis. This makes it possible to optimize the level of detail of the analysis depending on the importance of the drawing.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the drawing. For example, in the case of a residential drawing, the analysis unit applies an analysis algorithm specifically for residential buildings. For example, in the case of a commercial facility drawing, the analysis unit can apply an analysis algorithm specifically for commercial facilities. Furthermore, in the case of an industrial facility drawing, the analysis unit can also apply an analysis algorithm specifically for industrial facilities. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the category of the drawing. This makes it possible to apply the optimal analysis algorithm depending on the category of the drawing.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit can preferentially use a specific analysis method based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit can improve the accuracy of the analysis based on past analysis data and evaluations of the analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the drawings. For example, the analysis unit prioritizes analysis of urgent drawings. For example, the analysis unit can prioritize analysis of drawings with an approaching submission deadline. In addition, the analysis unit can postpone analysis of drawings with a distant submission deadline. For example, the analysis unit can determine the priority of analysis based on the project deadline, submission date, etc. This makes it possible to optimize the priority of analysis according to the submission date of the drawings.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of drawings. For example, the analysis unit can prioritize analysis of important drawings. For example, the analysis unit can prioritize analysis of highly relevant drawings. The analysis unit can also postpone analysis of less relevant drawings. For example, the analysis unit can adjust the order of analysis based on the relevance of projects or the relevance of buildings. This makes it possible to optimize the order of analysis according to the relevance of drawings.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. For example, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terms. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that eliminate technical terms as much as possible. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the analysis results to be optimized according to the user's level of expertise.

[0046] The generation unit can adjust the level of detail of the generated data based on the importance of the calculated area during generation. For example, the generation unit generates detailed digital data for an important area. For example, the generation unit can generate standard digital data for a general area. The generation unit can also generate simplified digital data for a simple area. For example, the generation unit can adjust the level of detail of the generated data based on the priority of the project or the size of the building. This makes it possible to optimize the level of detail of the generated data depending on the importance of the calculated area.

[0047] During generation, the generation unit can apply different generation algorithms depending on the type of painting robot. For example, in the case of a specific painting robot, the generation unit applies a generation algorithm dedicated to that robot. For example, in the case of a general-purpose painting robot, the generation unit can apply a standard generation algorithm. In addition, in the case of a high-performance painting robot, the generation unit can also apply a detailed generation algorithm. For example, the generation unit can select and apply an optimal generation algorithm based on the functions and performance of the painting robot. This makes it possible to apply an optimal generation algorithm depending on the type of painting robot.

[0048] During generation, the generation unit can improve the accuracy of the generated data by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. For example, the generation unit can preferentially use a specific generation method from the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of the generated data. For example, the generation unit can improve the accuracy of the generated data based on evaluations of the past generation data and generation results. This makes it possible to improve the accuracy of the generated data based on the user's past generation results.

[0049] The generation unit can determine the priority of the generated data at the time of generation based on the submission time of the painting. For example, in the case of urgent painting, the generation unit can prioritize generating digital data. For example, the generation unit can prioritize generating digital data for painting with an upcoming submission deadline. The generation unit can also postpone generating digital data for painting with a distant submission deadline. For example, the generation unit can determine the priority of the generated data based on the project deadline, submission date, etc. This makes it possible to optimize the priority of the generated data depending on the submission time of the painting.

[0050] The generation unit can adjust the order of generated data based on the relevance of the paints during generation. For example, the generation unit can generate digital data preferentially for important paints. For example, the generation unit can generate digital data preferentially for highly relevant paints. Furthermore, the generation unit can also postpone generating digital data for less relevant paints. For example, the generation unit can adjust the order of generated data based on the relevance of the project or the relevance of the building. This makes it possible to optimize the order of generated data according to the relevance of the paints.

[0051] The generation unit may adjust the use of technical terms in the generated data according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit may generate digital data that uses a lot of technical terms. For example, if the user has general knowledge, the generation unit may generate digital data that uses less technical terms. Furthermore, if the user is a beginner, the generation unit may generate digital data that minimizes technical terms. For example, the generation unit may adjust the use of technical terms in the generated data according to the user's level of expertise. This allows the generated data to be optimized according to the user's level of expertise.

[0052] During control, the control unit can analyze the past operation history of the painting robot and select the optimal control method. The control unit, for example, selects the most efficient control method from the past operation history of the painting robot. For example, the control unit can analyze the past operation history of the painting robot and preferentially use a specific operation pattern. The control unit can also adjust the control algorithm based on the past operation history of the painting robot. For example, the control unit can analyze past operation data and operation success rates to select the optimal control method. This makes it possible to select the optimal control method based on the past operation history of the painting robot.

[0053] During control, the control unit can customize the control means based on the type and performance of the painting robot. For example, in the case of a specific painting robot, the control unit uses a control means dedicated to that robot. For example, in the case of a general-purpose painting robot, the control unit can use a standard control means. In addition, in the case of a high-performance painting robot, the control unit can also use a detailed control means. For example, the control unit can select and apply the optimal control means based on the function and performance of the painting robot. This makes it possible to apply the optimal control means depending on the type and performance of the painting robot.

[0054] The control unit can improve the control method by reflecting user feedback during control. The control unit can adjust the control method based on, for example, feedback provided by the user. For example, the control unit can prioritize the use of specific control means based on the user feedback. The control unit can also analyze user feedback and improve the control method. For example, the control unit can analyze past ratings, comments, and survey results and adjust the control means. This makes it possible to optimize the control method based on user feedback.

[0055] During control, the control unit can select the optimal control method by taking into account the geographical location information of the painting robot. For example, if the painting robot is in a specific area, the control unit selects the optimal control method for that area. For example, if the painting robot is moving, the control unit can select the control method most suitable for the current location. Furthermore, if the painting robot is involved in a specific construction project, the control unit can select the optimal control method for that project. For example, the control unit can obtain the geographical location information of the painting robot using GPS data or a location information service and select the optimal control method based on that information. This allows the optimal control method to be selected based on the geographical location information of the painting robot.

[0056] During control, the control unit can analyze the social media activity of the painting robot and suggest a control measure. For example, the control unit can suggest a control measure related to a painting project shared by the painting robot on social media. For example, the control unit can analyze the content of posts on social media by the painting robot and suggest a related control measure. The control unit can also suggest a related control measure by referring to the activity of the painting robot's friends on social media. For example, the control unit can select a related control measure based on the social media activity. This makes it possible to suggest an optimal control measure based on the social media activity of the painting robot.

[0057] During control, the control unit can customize the control method by reflecting past feedback from the painting robot. The control unit selects the optimal control method based on, for example, feedback provided by the painting robot in the past. For example, the control unit can preferentially use a specific control means based on the past feedback from the painting robot. The control unit can also analyze the past feedback from the painting robot and customize the control method. For example, the control unit can analyze past evaluations, comments, and survey results and adjust the control means. This makes it possible to optimize the control method based on the past feedback from the painting robot.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When acquiring drawing data, the acquisition unit can analyze the user's past acquisition history and select the optimal acquisition method. For example, it can prioritize acquisition of drawing data formats that the user has used in the past. It can also suggest the most efficient acquisition method based on the user's past acquisition history. Furthermore, it can analyze the quality of drawing data that the user has acquired in the past and select the optimal acquisition method. This makes it possible to select the optimal acquisition method based on the user's past acquisition history.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the drawing. For example, for important drawings, a detailed analysis can be performed. For general drawings, a standard analysis can be performed. Furthermore, for simple drawings, a simplified analysis can be performed. This makes it possible to optimize the level of detail of the analysis depending on the importance of the drawing.

[0061] During generation, the generation unit can apply different generation algorithms depending on the type of painting robot. For example, in the case of a specific painting robot, a generation algorithm dedicated to that robot can be applied. In addition, in the case of a general-purpose painting robot, a standard generation algorithm can be applied. Furthermore, in the case of a high-performance painting robot, a detailed generation algorithm can be applied. This makes it possible to apply the optimal generation algorithm depending on the type of painting robot.

[0062] During control, the control unit can analyze the past operation history of the painting robot and select the optimal control method. For example, the most efficient control method can be selected from the past operation history of the painting robot. Also, the past operation history of the painting robot can be analyzed to preferentially use specific operation patterns. Furthermore, the control algorithm can be adjusted based on the past operation history of the painting robot. This makes it possible to select the optimal control method based on the past operation history of the painting robot.

[0063] During control, the control unit can select the optimal control method taking into account the geographical location information of the painting robot. For example, if the painting robot is in a specific area, the control unit can select the optimal control method for that area. Also, if the painting robot is moving, the control unit can select the control method most suitable for the current location. Furthermore, if the painting robot is involved in a specific construction project, the control unit can also select the optimal control method for that project. This makes it possible to select the optimal control method based on the geographical location information of the painting robot.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The acquisition unit acquires digital drawing data. The digital drawing data may be, for example, JPEG, PNG, CAD data, etc., but is not limited to these. The acquisition unit may acquire scanned image data or CAD data. Step 2: The analysis unit analyzes the drawing data acquired by the acquisition unit, identifies the exterior wall and roof sections, and calculates their respective areas. The analysis unit uses AI to recognize the outline of the exterior wall on the drawing and calculates its area. It can also use machine learning algorithms to recognize the outline of the roof and calculate its area. Step 3: The generation unit generates digital data that instructs the painting robot which parts to paint and in what order, based on the areas calculated by the analysis unit. The generation unit generates digital data that instructs the painting order and painting area. Step 4: The control unit instructs the painting robot to paint based on the digital data generated by the generation unit. The control unit instructs the painting robot on the painting sequence and method.

[0066] (Example 2) A system according to an embodiment of the present invention calculates the area of ​​exterior walls and roofs from drawings and digitizes painting instructions for painting robots. This system acquires digital drawing data, analyzes it using AI to calculate the area of ​​the exterior walls and roofs, generates digital data instructing the painting robot on how to paint, and the painting robot follows the instructions. For example, when acquiring digital drawing data, the drawings are input in digital format, such as scanned image data or CAD data. Next, AI analyzes the input drawing data, identifies the exterior wall and roof sections, and calculates their respective areas. Based on the calculated areas, digital data instructing the painting robot on how to paint is generated, and this digital data is transmitted to the painting robot. The painting robot then paints based on the received digital data. This allows the area of ​​the exterior walls and roofs to be accurately calculated from the drawings and the painting instructions to be digitized for painting robots. This system improves the efficiency and accuracy of painting work. For example, when painting large buildings, accurate painting by painting robots can be expected to reduce work time and improve quality.

[0067] A painting system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a control unit. The acquisition unit acquires digital drawing data. Examples of digital drawing data include, but are not limited to, JPEG, PNG, and CAD data. The acquisition unit can acquire, for example, scanned image data or CAD data. The analysis unit uses AI to analyze the drawing data acquired by the acquisition unit, identify the exterior wall and roof portions, and calculate their respective areas. For example, the analysis unit can use deep learning to recognize the outline of the exterior wall on the drawing and calculate its area. The analysis unit can also use a machine learning algorithm to recognize the outline of the roof and calculate its area. The generation unit generates digital data based on the calculated areas that instructs the painting robot which parts to paint and in what order. For example, the generation unit generates digital data that instructs the painting order and the painting area. The control unit causes the painting robot to paint based on the digital data generated by the generation unit. For example, the control unit instructs the painting robot on the painting order and method. This allows the system to perform a comprehensive process from acquiring drawing data to controlling the painting robot.

[0068] The acquisition unit can acquire digital drawing data such as scanned image data or CAD data. The scanned image data may include, but is not limited to, various resolutions and file formats. For example, the acquisition unit can scan a drawing using a scanner and save it as image data. Furthermore, when acquiring CAD data, the acquisition unit can use file formats such as DXF and DWG. This allows acquisition of drawing data in a variety of formats.

[0069] The analysis unit can use AI to identify the exterior wall and roof portions on the drawing and calculate their respective areas. For example, the analysis unit can use deep learning to recognize the outline of the exterior wall on the drawing and calculate its area. For example, the analysis unit can use an image analysis algorithm to identify the outline of the exterior wall and count the number of pixels to calculate the area. The analysis unit can also use a machine learning algorithm to recognize the outline of the roof and calculate its area. For example, the analysis unit can analyze vector data to calculate the area of ​​the roof. This makes it possible to accurately calculate the areas of the exterior walls and roof using AI.

[0070] The generation unit can generate digital data instructing the painting robot which parts to paint and in what order based on the calculated area. The generation unit generates digital data instructing, for example, the painting order or the painting range. For example, the generation unit can generate digital data instructing the painting robot which parts to paint and in what order based on the area of ​​an exterior wall. The generation unit can also generate digital data instructing the painting robot which parts to paint and in what order based on the area of ​​a roof. This allows the painting robot's operation to be efficiently instructed.

[0071] The control unit allows the painting robot to paint based on the received digital data. The control unit, for example, instructs the painting robot on the painting sequence and method. For example, when painting a specific part of an exterior wall, the control unit can instruct the painting robot to execute the painting sequence and method according to the digital data. Furthermore, when painting a specific part of a roof, the control unit can also execute the painting sequence and method according to the digital data. This allows the painting robot to paint as instructed.

[0072] The acquisition unit can estimate the user's emotions and adjust the timing of drawing data acquisition based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit can quickly acquire drawing data to reduce the user's burden. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, when the user is relaxed, the acquisition unit can slowly acquire drawing data to prompt the user to confirm in detail. For example, the acquisition unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when the user is in a hurry, the acquisition unit can immediately acquire drawing data to enable rapid processing. For example, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the timing of drawing data acquisition to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When acquiring drawing data, the acquisition unit can analyze the user's past acquisition history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of drawing data formats that the user has used in the past. For example, the acquisition unit can suggest the most efficient acquisition method based on the user's past acquisition history. The acquisition unit can also analyze the quality of drawing data acquired by the user in the past and select the optimal acquisition method. For example, the acquisition unit can analyze past acquisition data and acquisition frequency to select the optimal acquisition method. This makes it possible to select the optimal acquisition method based on the user's past acquisition history.

[0074] When acquiring the drawing data, the acquisition unit can filter the drawing data based on the type and scale of the building. For example, in the case of a large building, the acquisition unit preferentially acquires detailed drawing data. For example, the acquisition unit can filter and acquire appropriate drawing data based on the type and scale of the building. In addition, in the case of a small building, the acquisition unit can also acquire simplified drawing data. For example, the acquisition unit can filter and acquire appropriate drawing data depending on the type of specific building, such as a residential building or a commercial facility. This makes it possible to acquire appropriate drawing data depending on the type and scale of the building.

[0075] When acquiring drawing data, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user uses voice input, the acquisition unit can acquire the drawing data using voice recognition technology. For example, if the user uses text input, the acquisition unit can acquire the drawing data using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can also acquire the drawing data using image recognition technology. For example, the acquisition unit can select the optimal acquisition means depending on the user's input method. This makes it possible to select the optimal acquisition means for drawing data depending on the user's input method.

[0076] The acquisition unit can estimate the user's emotions and determine the priority of the drawing data to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring important drawing data. For example, when the user is relaxed, the acquisition unit can prioritize acquiring detailed drawing data. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the minimum amount of drawing data necessary. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the acquisition unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the priority of the drawing data to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When acquiring drawing data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the acquisition unit can prioritize acquiring drawing data related to that area. For example, if the user is moving, the acquisition unit can prioritize acquiring drawing data closest to the user's current location. Furthermore, if the user is involved in a specific construction project, the acquisition unit can prioritize acquiring drawing data related to that project. For example, the acquisition unit can acquire the user's geographical location information using GPS data or a location information service and select highly relevant data based on that information. This makes it possible to acquire highly relevant drawing data based on the user's geographical location information.

[0078] When acquiring the drawing data, the acquisition unit can analyze the user's social media activities and acquire related data. For example, the acquisition unit can acquire drawing data related to a construction project shared by the user on social media. For example, the acquisition unit can analyze the content of the user's social media posts and acquire related drawing data. The acquisition unit can also acquire related drawing data by referring to the activities of the user's friends on social media. For example, the acquisition unit can select related data based on the social media activities. This makes it possible to acquire related drawing data based on the user's social media activities.

[0079] When acquiring drawing data, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit selects the optimal acquisition method based on, for example, feedback provided by the user in the past. For example, the acquisition unit can prioritize the use of a specific acquisition means based on the user's past feedback. The acquisition unit can also analyze the user's past feedback and customize the acquisition method. For example, the acquisition unit can analyze past ratings, comments, and survey results and adjust the acquisition means. This makes it possible to optimize the acquisition method based on the user's past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the key points. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows the presentation method of the analysis to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the drawing. For example, the analysis unit performs a detailed analysis for an important drawing. For example, the analysis unit can adjust the level of detail of the analysis based on the priority of the project or the scale of the building. The analysis unit can also perform a standard analysis for a general drawing. For example, the analysis unit can adjust the number of analysis items and the depth of the analysis. The analysis unit can also perform a simplified analysis for a simple drawing. For example, the analysis unit can adjust the level of detail of the analysis to perform an efficient analysis. This makes it possible to optimize the level of detail of the analysis depending on the importance of the drawing.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the drawing. For example, in the case of a residential drawing, the analysis unit applies an analysis algorithm specifically for residential buildings. For example, in the case of a commercial facility drawing, the analysis unit can apply an analysis algorithm specifically for commercial facilities. Furthermore, in the case of an industrial facility drawing, the analysis unit can also apply an analysis algorithm specifically for industrial facilities. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the category of the drawing. This makes it possible to apply the optimal analysis algorithm depending on the category of the drawing.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit can preferentially use a specific analysis method based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit can improve the accuracy of the analysis based on past analysis data and evaluations of the analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform an analysis that adds visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows the length of the analysis to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the drawings. For example, the analysis unit prioritizes analysis of urgent drawings. For example, the analysis unit can prioritize analysis of drawings with an approaching submission deadline. In addition, the analysis unit can postpone analysis of drawings with a distant submission deadline. For example, the analysis unit can determine the priority of analysis based on the project deadline, submission date, etc. This makes it possible to optimize the priority of analysis according to the submission date of the drawings.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of drawings. For example, the analysis unit can prioritize analysis of important drawings. For example, the analysis unit can prioritize analysis of highly relevant drawings. The analysis unit can also postpone analysis of less relevant drawings. For example, the analysis unit can adjust the order of analysis based on the relevance of projects or the relevance of buildings. This makes it possible to optimize the order of analysis according to the relevance of drawings.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. For example, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terms. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that eliminate technical terms as much as possible. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the analysis results to be optimized according to the user's level of expertise.

[0088] The generation unit can estimate the user's emotions and adjust the representation of the generated digital data based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates digital data that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate digital data that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate digital data that adds visually stimulating effects. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the generation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the representation of the digital data to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The generation unit can adjust the level of detail of the generated data based on the importance of the calculated area during generation. For example, the generation unit generates detailed digital data for an important area. For example, the generation unit can generate standard digital data for a general area. The generation unit can also generate simplified digital data for a simple area. For example, the generation unit can adjust the level of detail of the generated data based on the priority of the project or the size of the building. This makes it possible to optimize the level of detail of the generated data depending on the importance of the calculated area.

[0090] During generation, the generation unit can apply different generation algorithms depending on the type of painting robot. For example, in the case of a specific painting robot, the generation unit applies a generation algorithm dedicated to that robot. For example, in the case of a general-purpose painting robot, the generation unit can apply a standard generation algorithm. In addition, in the case of a high-performance painting robot, the generation unit can also apply a detailed generation algorithm. For example, the generation unit can select and apply an optimal generation algorithm based on the functions and performance of the painting robot. This makes it possible to apply an optimal generation algorithm depending on the type of painting robot.

[0091] During generation, the generation unit can improve the accuracy of the generated data by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. For example, the generation unit can preferentially use a specific generation method from the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of the generated data. For example, the generation unit can improve the accuracy of the generated data based on evaluations of the past generation data and generation results. This makes it possible to improve the accuracy of the generated data based on the user's past generation results.

[0092] The generation unit can estimate the user's emotions and adjust the length of the generated data based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point digital data. For example, if the user is relaxed, the generation unit can generate longer digital data including detailed explanations. Furthermore, if the user is excited, the generation unit can generate digital data with visually stimulating effects. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the generation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the length of the generated data to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The generation unit can determine the priority of the generated data at the time of generation based on the submission time of the painting. For example, in the case of urgent painting, the generation unit can prioritize generating digital data. For example, the generation unit can prioritize generating digital data for painting with an upcoming submission deadline. The generation unit can also postpone generating digital data for painting with a distant submission deadline. For example, the generation unit can determine the priority of the generated data based on the project deadline, submission date, etc. This makes it possible to optimize the priority of the generated data depending on the submission time of the painting.

[0094] The generation unit can adjust the order of generated data based on the relevance of the paints during generation. For example, the generation unit can generate digital data preferentially for important paints. For example, the generation unit can generate digital data preferentially for highly relevant paints. Furthermore, the generation unit can also postpone generating digital data for less relevant paints. For example, the generation unit can adjust the order of generated data based on the relevance of the project or the relevance of the building. This makes it possible to optimize the order of generated data according to the relevance of the paints.

[0095] The generation unit may adjust the use of technical terms in the generated data according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit may generate digital data that uses a lot of technical terms. For example, if the user has general knowledge, the generation unit may generate digital data that uses less technical terms. Furthermore, if the user is a beginner, the generation unit may generate digital data that minimizes technical terms. For example, the generation unit may adjust the use of technical terms in the generated data according to the user's level of expertise. This allows the generated data to be optimized according to the user's level of expertise.

[0096] The control unit can estimate the user's emotions and adjust the operation of the painting robot based on the estimated user emotions. For example, if the user is nervous, the control unit can move the painting robot slowly to give the user a sense of security. For example, if the user is relaxed, the control unit can move the painting robot at a normal speed. Furthermore, if the user is in a hurry, the control unit can also speed up the painting robot's operation. For example, the control unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, the control unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the control unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the operation of the painting robot to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] During control, the control unit can analyze the past operation history of the painting robot and select the optimal control method. The control unit, for example, selects the most efficient control method from the past operation history of the painting robot. For example, the control unit can analyze the past operation history of the painting robot and preferentially use a specific operation pattern. The control unit can also adjust the control algorithm based on the past operation history of the painting robot. For example, the control unit can analyze past operation data and operation success rates to select the optimal control method. This makes it possible to select the optimal control method based on the past operation history of the painting robot.

[0098] During control, the control unit can customize the control means based on the type and performance of the painting robot. For example, in the case of a specific painting robot, the control unit uses a control means dedicated to that robot. For example, in the case of a general-purpose painting robot, the control unit can use a standard control means. In addition, in the case of a high-performance painting robot, the control unit can also use a detailed control means. For example, the control unit can select and apply the optimal control means based on the function and performance of the painting robot. This makes it possible to apply the optimal control means depending on the type and performance of the painting robot.

[0099] The control unit can improve the control method by reflecting user feedback during control. The control unit can adjust the control method based on, for example, feedback provided by the user. For example, the control unit can prioritize the use of specific control means based on the user feedback. The control unit can also analyze user feedback and improve the control method. For example, the control unit can analyze past ratings, comments, and survey results and adjust the control means. This makes it possible to optimize the control method based on user feedback.

[0100] The control unit can estimate the user's emotions and determine the operation priorities of the painting robot based on the estimated user emotions. For example, if the user is feeling stressed, the control unit can prioritize important painting tasks. For example, if the user is relaxed, the control unit can prioritize detailed painting tasks. Furthermore, if the user is in a hurry, the control unit can prioritize the minimum necessary painting tasks. For example, the control unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, the control unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the control unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the painting robot's operation priorities to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] During control, the control unit can select the optimal control method by taking into account the geographical location information of the painting robot. For example, if the painting robot is in a specific area, the control unit selects the optimal control method for that area. For example, if the painting robot is moving, the control unit can select the control method most suitable for the current location. Furthermore, if the painting robot is involved in a specific construction project, the control unit can select the optimal control method for that project. For example, the control unit can obtain the geographical location information of the painting robot using GPS data or a location information service and select the optimal control method based on that information. This allows the optimal control method to be selected based on the geographical location information of the painting robot.

[0102] During control, the control unit can analyze the social media activity of the painting robot and suggest a control measure. For example, the control unit can suggest a control measure related to a painting project shared by the painting robot on social media. For example, the control unit can analyze the content of posts on social media by the painting robot and suggest a related control measure. The control unit can also suggest a related control measure by referring to the activity of the painting robot's friends on social media. For example, the control unit can select a related control measure based on the social media activity. This makes it possible to suggest an optimal control measure based on the social media activity of the painting robot.

[0103] During control, the control unit can customize the control method by reflecting past feedback from the painting robot. The control unit selects the optimal control method based on, for example, feedback provided by the painting robot in the past. For example, the control unit can preferentially use a specific control means based on the past feedback from the painting robot. The control unit can also analyze the past feedback from the painting robot and customize the control method. For example, the control unit can analyze past evaluations, comments, and survey results and adjust the control means. This makes it possible to optimize the control method based on the past feedback from the painting robot. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, generation unit, and control unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire digital drawing data using the camera 42 or the reception device 38 of the smart device 14. The acquisition unit can also be realized by the identification processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the drawing data using AI by the identification processing unit 290 of the data processing device 12, identifies the exterior wall and roof portions, and calculates their respective areas. For example, the generation unit generates digital data instructing a painting robot on a painting method based on the areas calculated by the identification processing unit 290 of the data processing device 12. For example, the control unit can instruct the painting robot to paint based on the digital data generated by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, generation unit, and control unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire digital drawing data using the camera 42 or microphone 238 of the smart glasses 214. The acquisition unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the drawing data using AI by the specific processing unit 290 of the data processing device 12, identifies the exterior wall and roof portions, and calculates their respective areas. For example, the generation unit generates digital data instructing a painting robot on a painting method based on the areas calculated by the specific processing unit 290 of the data processing device 12. For example, the control unit can instruct the painting robot to paint based on the digital data generated by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, analysis unit, generation unit, and control unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit can acquire digital drawing data using the camera 42 or microphone 238 of the headset terminal 314. The acquisition unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the drawing data using AI by the specific processing unit 290 of the data processing device 12, identifies the exterior wall and roof portions, and calculates their respective areas. For example, the generation unit generates digital data instructing a painting robot on a painting method based on the areas calculated by the specific processing unit 290 of the data processing device 12. For example, the control unit can instruct the painting robot to paint based on the digital data generated by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, analysis unit, generation unit, and control unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire digital drawing data using the camera 42 or microphone 238 of the robot 414. The acquisition unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the drawing data using AI by the specific processing unit 290 of the data processing device 12, identifies the exterior wall and roof portions, and calculates their respective areas. For example, the generation unit generates digital data instructing the painting robot on a painting method based on the areas calculated by the specific processing unit 290 of the data processing device 12. For example, the control unit can instruct the painting robot to paint based on the digital data generated by the control unit 46A of the robot 414.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the drawing data based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition of the drawing data can be performed quickly, reducing the burden on the user. Alternatively, if the user is relaxed, the acquisition of the drawing data can be performed slowly, encouraging detailed confirmation. Furthermore, if the user is in a hurry, the acquisition of the drawing data can be performed immediately, enabling rapid processing. This makes it possible to optimize the timing of acquiring the drawing data according to the user's emotions.

[0106] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. This makes it possible to optimize the way the analysis is presented according to the user's emotions.

[0107] The generation unit can estimate the user's emotions and adjust the representation method of the generated digital data based on the estimated user's emotions. For example, if the user is relaxed, digital data that progresses at a leisurely pace can be generated. If the user is in a hurry, digital data that emphasizes the shortest route can be generated. Furthermore, if the user is excited, digital data that adds visually stimulating effects can be generated. In this way, the representation method of the digital data can be optimized according to the user's emotions.

[0108] The control unit can estimate the user's emotions and adjust the operation of the painting robot based on the estimated user's emotions. For example, if the user is nervous, the painting robot can move slowly to provide a sense of security. If the user is relaxed, the painting robot can move at a normal speed. Furthermore, if the user is in a hurry, the painting robot can move quickly. This allows the operation of the painting robot to be optimized according to the user's emotions.

[0109] The control unit can estimate the user's emotions and determine the operation priorities of the painting robot based on the estimated user's emotions. For example, if the user is feeling stressed, important painting work can be prioritized. If the user is relaxed, detailed painting work can be prioritized. Furthermore, if the user is in a hurry, the minimum necessary painting work can be prioritized. This makes it possible to optimize the operation priorities of the painting robot according to the user's emotions.

[0110] When acquiring drawing data, the acquisition unit can analyze the user's past acquisition history and select the optimal acquisition method. For example, it can prioritize acquisition of drawing data formats that the user has used in the past. It can also suggest the most efficient acquisition method based on the user's past acquisition history. Furthermore, it can analyze the quality of drawing data that the user has acquired in the past and select the optimal acquisition method. This makes it possible to select the optimal acquisition method based on the user's past acquisition history.

[0111] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the drawing. For example, for important drawings, a detailed analysis can be performed. For general drawings, a standard analysis can be performed. Furthermore, for simple drawings, a simplified analysis can be performed. This makes it possible to optimize the level of detail of the analysis depending on the importance of the drawing.

[0112] During generation, the generation unit can apply different generation algorithms depending on the type of painting robot. For example, in the case of a specific painting robot, a generation algorithm dedicated to that robot can be applied. In addition, in the case of a general-purpose painting robot, a standard generation algorithm can be applied. Furthermore, in the case of a high-performance painting robot, a detailed generation algorithm can be applied. This makes it possible to apply the optimal generation algorithm depending on the type of painting robot.

[0113] During control, the control unit can analyze the past operation history of the painting robot and select the optimal control method. For example, the most efficient control method can be selected from the past operation history of the painting robot. Also, the past operation history of the painting robot can be analyzed to preferentially use specific operation patterns. Furthermore, the control algorithm can be adjusted based on the past operation history of the painting robot. This makes it possible to select the optimal control method based on the past operation history of the painting robot.

[0114] During control, the control unit can select the optimal control method taking into account the geographical location information of the painting robot. For example, if the painting robot is in a specific area, the control unit can select the optimal control method for that area. Also, if the painting robot is moving, the control unit can select the control method most suitable for the current location. Furthermore, if the painting robot is involved in a specific construction project, the control unit can also select the optimal control method for that project. This makes it possible to select the optimal control method based on the geographical location information of the painting robot.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The acquisition unit acquires digital drawing data. The digital drawing data may be, for example, JPEG, PNG, CAD data, etc., but is not limited to these. The acquisition unit may acquire scanned image data or CAD data. Step 2: The analysis unit analyzes the drawing data acquired by the acquisition unit, identifies the exterior wall and roof sections, and calculates their respective areas. The analysis unit uses AI to recognize the outline of the exterior wall on the drawing and calculates its area. It can also use machine learning algorithms to recognize the outline of the roof and calculate its area. Step 3: The generation unit generates digital data that instructs the painting robot which parts to paint and in what order, based on the areas calculated by the analysis unit. The generation unit generates digital data that instructs the painting order and painting area. Step 4: The control unit instructs the painting robot to paint based on the digital data generated by the generation unit. The control unit instructs the painting robot on the painting sequence and method.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

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

[0124] The 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.

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

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

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

[0128] Fig. 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.

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

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

[0131] In the 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.

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

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

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

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

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

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

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

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

[0151] The data processing system 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring digital drawing data; an analysis unit that analyzes the drawing data acquired by the acquisition unit and calculates the areas of the exterior walls and the roof; a generating unit that generates digital data instructing a painting robot on a painting method based on the area calculated by the analyzing unit; a control unit that performs painting based on the digital data generated by the generation unit. A system characterized by:

2. The acquisition unit Acquire digital drawing data from scanned image data or CAD data 2. The system of claim 1.

3. The analysis unit AI identifies the exterior walls and roof on the drawing and calculates their respective areas 2. The system of claim 1.

4. The generation unit Based on the calculated area, digital data is generated that instructs the painting robot which parts to paint and in what order.

2. The system of claim 1.

5. The control unit The painting robot paints based on the received digital data.

2. The system of claim 1.

6. The acquisition unit Estimates the user's emotions and adjusts the timing of acquiring drawing data based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit When acquiring drawing data, analyze the user's past acquisition history and select the appropriate acquisition method.

2. The system of claim 1.

8. The acquisition unit When retrieving drawing data, filter it based on building type and size.

2. The system of claim 1.

9. The acquisition unit When acquiring drawing data, select the optimal acquisition method according to the user's input method.

2. The system of claim 1.

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