system

Abstract

The system according to the embodiment aims to generate a 3D model based on original data and enable visual confirmation. [Solution] A system according to an embodiment includes an upload unit, an analysis unit, a generation unit, and a display unit. The upload unit uploads data. The analysis unit analyzes the data uploaded by the upload unit. The generation unit generates a 3D model based on the data analyzed by the analysis unit. The display unit displays the 3D model generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of making it difficult to generate 3D models based on proprietary data and visually check them.

[0005] The system according to the embodiment aims to generate a 3D model based on original data and enable visual confirmation. [Means for solving the problem]

[0006] A system according to an embodiment includes an upload unit, an analysis unit, a generation unit, and a display unit. The upload unit uploads data. The analysis unit analyzes the data uploaded by the upload unit. The generation unit generates a 3D model based on the data analyzed by the analysis unit. The display unit displays the 3D model generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a 3D model based on original data and make it possible to visually check it. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 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 3D AR content generation system according to an embodiment of the present invention is a platform for generating and reviewing 3D AR content using proprietary data owned by companies and government agencies. This system can perform a comprehensive process, from uploading data to analyzing it, generating 3D models, and displaying them. For example, in the construction industry, viewing a completed building model in 3D makes it easier to confirm and revise designs. In government agencies, viewing urban plans on 3D maps makes it easier to grasp the overall picture of the plan. Furthermore, the system can be used in other fields, such as education and medicine, for example, for classes using 3D models of historical buildings or training using 3D models of the human body. This allows the 3D AR content generation system to quickly and accurately generate and review 3D models using proprietary data owned by companies and government agencies.

[0029] A 3D AR content generation system according to an embodiment includes an upload unit, an analysis unit, a generation unit, and a display unit. The upload unit uploads proprietary data held by companies and government agencies to a platform. The upload unit can accept data in various formats, such as text data, image data, and sensor data. The analysis unit analyzes the uploaded data using AI and generates a 3D model. The analysis unit analyzes the data using machine learning algorithms, such as deep learning and neural networks. The generation unit generates a 3D model based on the data analyzed by the analysis unit. The generation unit generates the 3D model in a format such as a polygon model or a volume model. The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model in three dimensions on a device, such as an AR device or a smartphone, so that all members can view it. This allows the 3D AR content generation system according to an embodiment to perform a consistent process from data upload to analysis, 3D model generation, and display.

[0030] The analysis unit can analyze the uploaded data using AI and generate a 3D model. The analysis unit analyzes the data using machine learning algorithms such as deep learning and neural networks. For example, by using deep learning, it is possible to extract features from large amounts of data and perform highly accurate analysis. Furthermore, by using neural networks, it is possible to learn complex data patterns and improve the accuracy of analysis. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the type and format of data and perform the analysis. For example, it applies an image analysis algorithm to image data and a natural language processing algorithm to text data. In this way, the analysis unit can analyze the uploaded data using AI and generate a 3D model.

[0031] The generation unit can display the generated 3D model on an AR device or a smartphone. For example, the generation unit displays the generated 3D model on the AR device or smartphone. For example, AR devices include HoloLens (registered trademark) and Magic Leap (registered trademark), and these devices can be used to display the 3D model in three dimensions. Furthermore, smartphones include iPhone (registered trademark) and Android (registered trademark) devices, and these devices can be used to easily display the 3D model. Furthermore, the generation unit can provide an interface for displaying the generated 3D model on various devices. For example, the generation unit selects an optimal display method depending on the screen size and resolution of the AR device or smartphone, and displays the 3D model. In this way, the generation unit can display the generated 3D model on the AR device or smartphone.

[0032] The display unit can display the generated 3D model in three dimensions so that all members can view it. The display unit, for example, uses technologies such as stereography and holography to display the generated 3D model in three dimensions. For example, stereography can be used to display images from two different viewpoints, creating a three-dimensional visual effect. Holography can also be used to display the 3D model in three dimensions by utilizing the interference of light. Furthermore, the display unit can provide an interface that allows all members to view the generated 3D model. For example, the display unit selects an optimal display method depending on the screen size and resolution of the AR device or smartphone, and displays the 3D model. In this way, the display unit can display the generated 3D model in three dimensions so that all members can view it.

[0033] The upload unit can upload proprietary data held by companies or government agencies to the platform. The upload unit uploads, for example, proprietary data held by companies or government agencies to the platform. For example, it can accept various types of data, such as internal company data and statistical data from government agencies. Furthermore, the upload unit can select the optimal upload method depending on the type and format of the data. For example, for large volumes of data, it can select a method of dividing and uploading the data. Furthermore, for image data, it can select a method of compressing and uploading the data. This allows the upload unit to efficiently upload proprietary data held by companies or government agencies.

[0034] The analysis unit can also be used in the education or medical fields, and can generate 3D models of historical buildings or 3D models of the human body. For example, in the education field, the analysis unit generates 3D models of historical buildings. For example, data of historical buildings is uploaded, and the analysis unit analyzes the data to generate a 3D model. Also, in the medical field, the analysis unit generates 3D models of the human body. For example, medical data is uploaded, and the analysis unit analyzes the data to generate a 3D model. Furthermore, in the education or medical field, the analysis unit can provide an interface for conducting training or lessons using the generated 3D models. For example, in the education field, lessons can be conducted using 3D models of historical buildings. Also, in the medical field, 3D models of the human body can be used for surgical simulations or anatomical education. In this way, the analysis unit can generate 3D models that can be used in the education or medical fields.

[0035] The upload unit can select an appropriate upload method depending on the type and format of data. The upload unit selects the optimal upload method depending on, for example, the type and format of data. For example, in the case of large volumes of data, a method of dividing and uploading the data can be selected. In addition, in the case of image data, a method of compressing and uploading the data can be selected. Furthermore, in the case of text data, the upload unit can select a method of directly uploading the data. This allows the upload unit to upload data using the optimal method depending on the type and format of the data.

[0036] The upload unit can determine the priority of uploading based on the importance of the data. The upload unit can, for example, determine the priority of uploading based on the importance of the data. For example, data with high importance is uploaded first. Data with low importance can be uploaded later. Furthermore, the upload unit can upload multiple pieces of data simultaneously according to the importance. This allows the upload unit to upload data with high importance first.

[0037] The uploading unit can prioritize uploading highly relevant data based on the user's geographical location information. The uploading unit, for example, prioritizes uploading highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, data related to that area can be prioritized for uploading. Furthermore, when the user is moving, data related to the user's current location can be prioritized for uploading. Furthermore, when the user is in a specific location, data related to that location can be prioritized for uploading. This allows the uploading unit to prioritize uploading highly relevant data based on the user's geographical location information.

[0038] The upload unit can analyze the user's social media activities and upload relevant data. For example, the upload unit can analyze the user's social media activities and upload relevant data. For example, the upload unit can prioritize uploading data shared by the user on social media. Also, the upload unit can prioritize uploading data mentioned by the user on social media. Furthermore, the upload unit can extract and upload highly relevant data from the user's social media activities. This allows the upload unit to upload relevant data based on the user's social media activities.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, data with high importance can be analyzed in detail. Data with low importance can be analyzed simply. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data.

[0040] The analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies the most appropriate analysis algorithm depending on the category of data. For example, an image analysis algorithm can be applied to image data, and a natural language processing algorithm can be applied to text data. Furthermore, a statistical analysis algorithm can be applied to numerical data. This allows the analysis unit to apply the most appropriate analysis algorithm depending on the category of data.

[0041] The analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit determines the priority of analysis based on, for example, the time of data submission. For example, data submitted earlier is analyzed preferentially. Data submitted later can be analyzed later. Furthermore, the analysis unit can adjust the priority of analysis in stages depending on the time of submission. This allows the analysis unit to determine the priority of analysis depending on the time of data submission.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. For example, highly relevant data is analyzed preferentially. Also, less relevant data can be analyzed later. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance. This allows the analysis unit to adjust the order of analysis according to the relevance of the data.

[0043] The generation unit can adjust the level of detail of the 3D model based on the importance of the data. The generation unit adjusts the level of detail of the 3D model based on, for example, the importance of the data. For example, a detailed 3D model can be generated for data with high importance. Also, a simplified 3D model can be generated for data with low importance. Furthermore, the generation unit can adjust the level of detail of the 3D model in stages according to the importance. This allows the generation unit to adjust the level of detail of the 3D model according to the importance of the data.

[0044] The generation unit can apply different generation algorithms depending on the category of data. For example, the generation unit applies the optimal generation algorithm depending on the category of data. For example, a generation algorithm for architecture can be applied to architecture data, and a generation algorithm for medical data can be applied to medical data. Also, a generation algorithm for education can be applied to educational data. This allows the generation unit to apply the optimal generation algorithm depending on the category of data.

[0045] The generation unit can determine the generation priority of 3D models based on the time of data submission. The generation unit determines the generation priority of 3D models based on, for example, the time of data submission. For example, data submitted earlier can be generated preferentially. Data submitted later can be generated later. Furthermore, the generation unit can adjust the generation priority in stages depending on the time of submission. This allows the generation unit to determine the generation priority of 3D models depending on the time of data submission.

[0046] The generation unit can adjust the generation order of 3D models based on the relevance of the data. The generation unit adjusts the generation order of 3D models based on, for example, the relevance of the data. For example, highly relevant data is generated preferentially. Furthermore, less relevant data can be generated later. Furthermore, the generation unit can adjust the generation order in stages according to the relevance. This allows the generation unit to adjust the generation order of 3D models according to the relevance of the data.

[0047] The display unit can select an appropriate display method by referring to the user's past operation history. The display unit, for example, selects the optimal display method by referring to the user's past operation history. For example, the display unit preferentially provides display methods that the user has used in the past. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide a display method with high visibility. This allows the display unit to select the optimal display method based on the user's past operation history.

[0048] The display unit can select an appropriate display method based on the user's device information. The display unit selects the optimal display method, for example, taking the user's device information into consideration. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. This allows the display unit to select the optimal display method based on the user's device information.

[0049] The display unit can select an appropriate display method based on the user's geographical location information. The display unit selects the optimal display method, for example, taking the user's geographical location information into consideration. For example, when the user is in a specific area, information related to that area can be displayed preferentially. Furthermore, when the user is moving, information related to the user's current location can be displayed preferentially. Furthermore, when the user is in a specific location, information related to that location can be displayed preferentially. This allows the display unit to select the optimal display method based on the user's geographical location information.

[0050] The display unit can analyze the user's social media activity and display related 3D models. For example, the display unit can analyze the user's social media activity and display related 3D models. For example, the display unit can display related 3D models based on information shared by the user on social media. Also, the display unit can display related 3D models based on information mentioned by the user on social media. Furthermore, the display unit can extract and display highly relevant 3D models from the user's social media activity. This allows the display unit to display related 3D models based on the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The upload unit can analyze the user's past upload history and suggest the optimal data format and upload method. For example, for a user who has frequently uploaded large amounts of data in the past, it can suggest a method of compressing the data before uploading. Also, for a user who uploads a lot of image data, it can provide a function to automatically adjust the image resolution. Furthermore, the upload unit can also suggest the optimal upload timing based on the user's past upload history. In this way, the upload unit can support more efficient data uploads based on the user's past behavior.

[0053] The analysis unit can evaluate the reliability of data and prioritize analysis of highly reliable data. For example, reliability can be evaluated based on information such as the source of the data, the date and time of creation, and the creator. The analysis unit can also check the consistency and integrity of the data and lower the analysis priority of data with low reliability. Furthermore, the analysis unit can provide feedback on the reliability evaluation results to the user and encourage them to improve data quality. This allows the analysis unit to prioritize analysis of highly reliable data and generate more accurate 3D models.

[0054] The generation unit can adjust the method of generating the 3D model according to the performance of the user's device. For example, a detailed 3D model can be generated for a user using a high-performance device. A simplified 3D model can be generated for a user using a low-performance device. Furthermore, the generation unit can adjust the method of generating the 3D model according to the remaining battery level and network connection status of the device. This allows the generation unit to provide an optimal 3D model according to the performance of the user's device.

[0055] The display unit can adjust the display method of the 3D model based on the user's visual characteristics. For example, a display method that adjusts color contrast can be provided to a user with color vision deficiency. Also, a display method that increases the font size or icon size can be provided to a user with poor eyesight. Furthermore, the display unit can adjust the rotation speed and zoom level of the 3D model according to the user's visual characteristics. This allows the display unit to provide the optimal display method according to the user's visual characteristics.

[0056] The upload unit can adjust the data upload method based on the user's internet connection speed. For example, if the connection speed is slow, it can select a method of uploading data in parts. Alternatively, if the connection speed is fast, it can select a method of uploading data all at once. Furthermore, the upload unit can adjust the data compression rate according to the connection speed to shorten the upload time. In this way, the upload unit can provide the optimal data upload method according to the user's internet connection speed.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The upload unit uploads proprietary data held by companies and government agencies to the platform. The upload unit can accept data in various formats, such as text data, image data, and sensor data. Step 2: The analysis unit analyzes the uploaded data using AI. The analysis unit analyzes the data using machine learning algorithms such as deep learning and neural networks. Step 3: The generator generates a 3D model based on the data analyzed by the analyzer. The generator generates the 3D model in the form of, for example, a polygon model or a volume model. Step 4: The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model in three dimensions on a device such as an AR device or a smartphone so that all members can see it.

[0059] (Example 2) A 3D AR content generation system according to an embodiment of the present invention is a platform for generating and reviewing 3D AR content using proprietary data owned by companies and government agencies. This system can perform a comprehensive process, from uploading data to analyzing it, generating 3D models, and displaying them. For example, in the construction industry, viewing a completed building model in 3D makes it easier to confirm and revise designs. In government agencies, viewing urban plans on 3D maps makes it easier to grasp the overall picture of the plan. Furthermore, the system can be used in other fields, such as education and medicine, for example, for classes using 3D models of historical buildings or training using 3D models of the human body. This allows the 3D AR content generation system to quickly and accurately generate and review 3D models using proprietary data owned by companies and government agencies.

[0060] A 3D AR content generation system according to an embodiment includes an upload unit, an analysis unit, a generation unit, and a display unit. The upload unit uploads proprietary data held by companies and government agencies to a platform. The upload unit can accept data in various formats, such as text data, image data, and sensor data. The analysis unit analyzes the uploaded data using AI and generates a 3D model. The analysis unit analyzes the data using machine learning algorithms, such as deep learning and neural networks. The generation unit generates a 3D model based on the data analyzed by the analysis unit. The generation unit generates the 3D model in a format such as a polygon model or a volume model. The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model in three dimensions on a device, such as an AR device or a smartphone, so that all members can view it. This allows the 3D AR content generation system according to an embodiment to perform a consistent process from data upload to analysis, 3D model generation, and display.

[0061] The analysis unit can analyze the uploaded data using AI and generate a 3D model. The analysis unit analyzes the data using machine learning algorithms such as deep learning and neural networks. For example, by using deep learning, it is possible to extract features from large amounts of data and perform highly accurate analysis. Furthermore, by using neural networks, it is possible to learn complex data patterns and improve the accuracy of analysis. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the type and format of data and perform the analysis. For example, it applies an image analysis algorithm to image data and a natural language processing algorithm to text data. In this way, the analysis unit can analyze the uploaded data using AI and generate a 3D model.

[0062] The generation unit can display the generated 3D model on an AR device or a smartphone. The generation unit, for example, displays the generated 3D model on an AR device or a smartphone. For example, AR devices include HoloLens and Magic Leap, and these devices can be used to display the 3D model in three dimensions. Furthermore, smartphones include iPhones and Android devices, and these devices can be used to easily display the 3D model. Furthermore, the generation unit can provide an interface for displaying the generated 3D model on various devices. For example, the generation unit selects an optimal display method depending on the screen size and resolution of the AR device or smartphone, and displays the 3D model. In this way, the generation unit can display the generated 3D model on the AR device or smartphone.

[0063] The display unit can display the generated 3D model in three dimensions so that all members can view it. The display unit, for example, uses technologies such as stereography and holography to display the generated 3D model in three dimensions. For example, stereography can be used to display images from two different viewpoints, creating a three-dimensional visual effect. Holography can also be used to display the 3D model in three dimensions by utilizing the interference of light. Furthermore, the display unit can provide an interface that allows all members to view the generated 3D model. For example, the display unit selects an optimal display method depending on the screen size and resolution of the AR device or smartphone, and displays the 3D model. In this way, the display unit can display the generated 3D model in three dimensions so that all members can view it.

[0064] The upload unit can upload proprietary data held by companies or government agencies to the platform. The upload unit uploads, for example, proprietary data held by companies or government agencies to the platform. For example, it can accept various types of data, such as internal company data and statistical data from government agencies. Furthermore, the upload unit can select the optimal upload method depending on the type and format of the data. For example, for large volumes of data, it can select a method of dividing and uploading the data. Furthermore, for image data, it can select a method of compressing and uploading the data. This allows the upload unit to efficiently upload proprietary data held by companies or government agencies.

[0065] The analysis unit can also be used in the education or medical fields, and can generate 3D models of historical buildings or 3D models of the human body. For example, in the education field, the analysis unit generates 3D models of historical buildings. For example, data of historical buildings is uploaded, and the analysis unit analyzes the data to generate a 3D model. Also, in the medical field, the analysis unit generates 3D models of the human body. For example, medical data is uploaded, and the analysis unit analyzes the data to generate a 3D model. Furthermore, in the education or medical field, the analysis unit can provide an interface for conducting training or lessons using the generated 3D models. For example, in the education field, lessons can be conducted using 3D models of historical buildings. Also, in the medical field, 3D models of the human body can be used for surgical simulations or anatomical education. In this way, the analysis unit can generate 3D models that can be used in the education or medical fields.

[0066] The upload unit can estimate the user's emotions and adjust the timing of data upload based on the estimated user's emotions. The upload unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, by using facial expression recognition, the emotion can be estimated from the user's facial expression. Furthermore, by using voice analysis, the emotion can be estimated from the tone and speed of the user's voice. Furthermore, the upload unit can adjust the timing of data upload based on the estimated user's emotions. For example, if the user is feeling stressed, the upload timing can be delayed until the user is relaxed. Furthermore, if the user is in a hurry, the process can be prioritized to speed up the upload. This allows the upload unit to upload data at the optimal timing according to the user's emotions.

[0067] The upload unit can select an appropriate upload method depending on the type and format of data. The upload unit selects the optimal upload method depending on, for example, the type and format of data. For example, in the case of large volumes of data, a method of dividing and uploading the data can be selected. In addition, in the case of image data, a method of compressing and uploading the data can be selected. Furthermore, in the case of text data, the upload unit can select a method of directly uploading the data. This allows the upload unit to upload data using the optimal method depending on the type and format of the data.

[0068] The upload unit can determine the priority of uploading based on the importance of the data. The upload unit can, for example, determine the priority of uploading based on the importance of the data. For example, data with high importance is uploaded first. Data with low importance can be uploaded later. Furthermore, the upload unit can upload multiple pieces of data simultaneously according to the importance. This allows the upload unit to upload data with high importance first.

[0069] The upload unit can estimate the user's emotions and determine the priority of data to be uploaded based on the estimated user's emotions. The upload unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, by using facial expression recognition, it is possible to estimate the user's emotions from the user's facial expressions. Furthermore, by using voice analysis, it is possible to estimate the user's emotions from the tone and speed of the user's voice. Furthermore, the upload unit can determine the priority of data to be uploaded based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize uploading data of high importance. Furthermore, if the user is relaxed, it can upload data with normal priority. In this way, the upload unit can determine the priority of data according to the user's emotions.

[0070] The uploading unit can prioritize uploading highly relevant data based on the user's geographical location information. The uploading unit, for example, prioritizes uploading highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, data related to that area can be prioritized for uploading. Furthermore, when the user is moving, data related to the user's current location can be prioritized for uploading. Furthermore, when the user is in a specific location, data related to that location can be prioritized for uploading. This allows the uploading unit to prioritize uploading highly relevant data based on the user's geographical location information.

[0071] The upload unit can analyze the user's social media activities and upload relevant data. For example, the upload unit can analyze the user's social media activities and upload relevant data. For example, the upload unit can prioritize uploading data shared by the user on social media. Also, the upload unit can prioritize uploading data mentioned by the user on social media. Furthermore, the upload unit can extract and upload highly relevant data from the user's social media activities. This allows the upload unit to upload relevant data based on the user's social media activities.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, by using facial expression recognition, emotions can be estimated from the user's facial expressions. Furthermore, by using voice analysis, emotions can be estimated from the tone and speed of the user's voice. Furthermore, the analysis unit can adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. On the other hand, if the user is in a hurry, concise analysis results that focus on the main points can be provided. This allows the analysis unit to adjust the way the analysis is presented according to the user's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, data with high importance can be analyzed in detail. Data with low importance can be analyzed simply. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data.

[0074] The analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies the most appropriate analysis algorithm depending on the category of data. For example, an image analysis algorithm can be applied to image data, and a natural language processing algorithm can be applied to text data. Furthermore, a statistical analysis algorithm can be applied to numerical data. This allows the analysis unit to apply the most appropriate analysis algorithm depending on the category of data.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, by using facial expression recognition, emotions can be estimated from the user's facial expressions. Furthermore, by using voice analysis, emotions can be estimated from the tone and speed of the user's voice. Furthermore, the analysis unit can adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point analysis result can be provided. On the other hand, if the user is relaxed, a detailed analysis result can be provided. This allows the analysis unit to adjust the length of the analysis according to the user's emotions.

[0076] The analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit determines the priority of analysis based on, for example, the time of data submission. For example, data submitted earlier is analyzed preferentially. Data submitted later can be analyzed later. Furthermore, the analysis unit can adjust the priority of analysis in stages depending on the time of submission. This allows the analysis unit to determine the priority of analysis depending on the time of data submission.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. For example, highly relevant data is analyzed preferentially. Also, less relevant data can be analyzed later. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance. This allows the analysis unit to adjust the order of analysis according to the relevance of the data.

[0078] The generation unit can estimate the user's emotion and adjust the generation method of the 3D model based on the estimated user's emotion. The generation unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, by using facial expression recognition, the emotion can be estimated from the user's facial expression. Furthermore, by using voice analysis, the emotion can be estimated from the tone and speed of the user's voice. Furthermore, the generation unit can adjust the generation method of the 3D model based on the estimated user's emotion. For example, if the user is relaxed, a detailed 3D model can be generated. On the other hand, if the user is in a hurry, a simplified 3D model can be generated. This allows the generation unit to adjust the generation method of the 3D model according to the user's emotion.

[0079] The generation unit can adjust the level of detail of the 3D model based on the importance of the data. The generation unit adjusts the level of detail of the 3D model based on, for example, the importance of the data. For example, a detailed 3D model can be generated for data with high importance. Also, a simplified 3D model can be generated for data with low importance. Furthermore, the generation unit can adjust the level of detail of the 3D model in stages according to the importance. This allows the generation unit to adjust the level of detail of the 3D model according to the importance of the data.

[0080] The generation unit can apply different generation algorithms depending on the category of data. For example, the generation unit applies the optimal generation algorithm depending on the category of data. For example, a generation algorithm for architecture can be applied to architecture data, and a generation algorithm for medical data can be applied to medical data. Also, a generation algorithm for education can be applied to educational data. This allows the generation unit to apply the optimal generation algorithm depending on the category of data.

[0081] The generation unit can estimate the user's emotions and adjust the generation order of 3D models based on the estimated user's emotions. The generation unit uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, facial expression recognition can be used to estimate emotions from the user's facial expressions. Voice analysis can also be used to estimate emotions from the tone and speed of the user's voice. Furthermore, the generation unit can adjust the generation order of 3D models based on the estimated user's emotions. For example, if the user is in a hurry, generation can start from the most important parts. On the other hand, if the user is relaxed, generation can be performed in the normal order. This allows the generation unit to adjust the generation order of 3D models according to the user's emotions.

[0082] The generation unit can determine the generation priority of 3D models based on the time of data submission. The generation unit determines the generation priority of 3D models based on, for example, the time of data submission. For example, data submitted earlier can be generated preferentially. Data submitted later can be generated later. Furthermore, the generation unit can adjust the generation priority in stages depending on the time of submission. This allows the generation unit to determine the generation priority of 3D models depending on the time of data submission.

[0083] The generation unit can adjust the generation order of 3D models based on the relevance of the data. The generation unit adjusts the generation order of 3D models based on, for example, the relevance of the data. For example, highly relevant data is generated preferentially. Furthermore, less relevant data can be generated later. Furthermore, the generation unit can adjust the generation order in stages according to the relevance. This allows the generation unit to adjust the generation order of 3D models according to the relevance of the data.

[0084] The display unit can estimate the user's emotion and adjust the display method of the 3D model based on the estimated user's emotion. The display unit uses, for example, technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, by using facial expression recognition, it is possible to estimate the emotion from the user's facial expression. Furthermore, by using voice analysis, it is possible to estimate the emotion from the tone and speed of the user's voice. Furthermore, the display unit can adjust the display method of the 3D model based on the estimated user's emotion. For example, if the user is nervous, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, it can provide a display method that includes detailed information. This allows the display unit to adjust the display method of the 3D model according to the user's emotion.

[0085] The display unit can select an appropriate display method by referring to the user's past operation history. The display unit, for example, selects the optimal display method by referring to the user's past operation history. For example, the display unit preferentially provides display methods that the user has used in the past. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide a display method with high visibility. This allows the display unit to select the optimal display method based on the user's past operation history.

[0086] The display unit can select an appropriate display method based on the user's device information. The display unit selects the optimal display method, for example, taking the user's device information into consideration. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. This allows the display unit to select the optimal display method based on the user's device information.

[0087] The display unit can estimate the user's emotions and adjust the display order of 3D models based on the estimated user's emotions. The display unit uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, facial expression recognition can be used to estimate emotions from the user's facial expressions. Voice analysis can also be used to estimate emotions from the tone and speed of the user's voice. Furthermore, the display unit can adjust the display order of 3D models based on the estimated user's emotions. For example, if the user is in a hurry, the most important parts can be displayed first. On the other hand, if the user is relaxed, the 3D models can be displayed in the normal order. This allows the display unit to adjust the display order of 3D models according to the user's emotions.

[0088] The display unit can select an appropriate display method based on the user's geographical location information. The display unit selects the optimal display method, for example, taking the user's geographical location information into consideration. For example, when the user is in a specific area, information related to that area can be displayed preferentially. Furthermore, when the user is moving, information related to the user's current location can be displayed preferentially. Furthermore, when the user is in a specific location, information related to that location can be displayed preferentially. This allows the display unit to select the optimal display method based on the user's geographical location information.

[0089] The display unit can analyze the user's social media activity and display related 3D models. For example, the display unit can analyze the user's social media activity and display related 3D models. For example, the display unit can display related 3D models based on information shared by the user on social media. Also, the display unit can display related 3D models based on information mentioned by the user on social media. Furthermore, the display unit can extract and display highly relevant 3D models from the user's social media activity. This allows the display unit to display related 3D models based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the smart device 14 and uploads proprietary data held by companies and government agencies to the platform. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded data using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model based on the analyzed data. The display unit is realized, for example, by the output device 40 of the smart device 14 and displays the generated 3D model. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the smart glasses 214 and uploads proprietary data held by companies and government agencies to the platform. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded data using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model based on the analyzed data. The display unit is realized, for example, by the speaker 240 of the smart glasses 214 and displays the generated 3D model. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the headset type terminal 314 and uploads proprietary data held by companies and government agencies to the platform. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded data using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model based on the analyzed data. The display unit is realized, for example, by the display 343 of the headset type terminal 314 and displays the generated 3D model. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the upload unit is realized by the control unit 46A of the robot 414 and uploads proprietary data held by companies and government agencies to the platform. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded data using AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model based on the analyzed data. The display unit is realized, for example, by the control target 443 of the robot 414 and displays the generated 3D model.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The upload unit can analyze the user's past upload history and suggest the optimal data format and upload method. For example, for a user who has frequently uploaded large amounts of data in the past, it can suggest a method of compressing the data before uploading. Also, for a user who uploads a lot of image data, it can provide a function to automatically adjust the image resolution. Furthermore, the upload unit can also suggest the optimal upload timing based on the user's past upload history. In this way, the upload unit can support more efficient data uploads based on the user's past behavior.

[0092] The analysis unit can evaluate the reliability of data and prioritize analysis of highly reliable data. For example, reliability can be evaluated based on information such as the source of the data, the date and time of creation, and the creator. The analysis unit can also check the consistency and integrity of the data and lower the analysis priority of data with low reliability. Furthermore, the analysis unit can provide feedback on the reliability evaluation results to the user and encourage them to improve data quality. This allows the analysis unit to prioritize analysis of highly reliable data and generate more accurate 3D models.

[0093] The generation unit can adjust the method of generating the 3D model according to the performance of the user's device. For example, a detailed 3D model can be generated for a user using a high-performance device. A simplified 3D model can be generated for a user using a low-performance device. Furthermore, the generation unit can adjust the method of generating the 3D model according to the remaining battery level and network connection status of the device. This allows the generation unit to provide an optimal 3D model according to the performance of the user's device.

[0094] The display unit can adjust the display method of the 3D model based on the user's visual characteristics. For example, a display method that adjusts color contrast can be provided to a user with color vision deficiency. Also, a display method that increases the font size or icon size can be provided to a user with poor eyesight. Furthermore, the display unit can adjust the rotation speed and zoom level of the 3D model according to the user's visual characteristics. This allows the display unit to provide the optimal display method according to the user's visual characteristics.

[0095] The upload unit can adjust the data upload method based on the user's internet connection speed. For example, if the connection speed is slow, it can select a method of uploading data in parts. Alternatively, if the connection speed is fast, it can select a method of uploading data all at once. Furthermore, the upload unit can adjust the data compression rate according to the connection speed to shorten the upload time. In this way, the upload unit can provide the optimal data upload method according to the user's internet connection speed.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a report that briefly summarizes the analysis results can be provided. On the other hand, if the user is relaxed, a report that includes detailed analysis results can be provided. Furthermore, the analysis unit can adjust the display order of the analysis results and the points to emphasize according to the user's emotions. This allows the analysis unit to provide optimal analysis results according to the user's emotions.

[0097] The generation unit can estimate the user's emotions and adjust the generation speed of the 3D model based on the estimated user's emotions. For example, if the user is in a hurry, the generation speed can be increased to provide a simplified 3D model. Alternatively, if the user is relaxed, the generation speed can be decreased to provide a detailed 3D model. Furthermore, the generation unit can display the progress of the generation process in real time according to the user's emotions, giving the user a sense of security. This allows the generation unit to provide an optimal 3D model generation experience according to the user's emotions.

[0098] The display unit can estimate the user's emotions and adjust the colors of the 3D model based on the estimated user's emotions. For example, if the user is relaxed, the 3D model can be displayed using warm colors. On the other hand, if the user is concentrating, the 3D model can be displayed using cool colors. Furthermore, the display unit can adjust the brightness and contrast of the 3D model according to the user's emotions. This allows the display unit to display the 3D model in optimal colors according to the user's emotions.

[0099] The upload unit can estimate the user's emotions and adjust the data upload method based on the estimated user emotions. For example, if the user is feeling stressed, the upload process can be simplified to reduce the burden on the user. Alternatively, if the user is feeling relaxed, detailed upload options can be provided to allow the user to configure detailed settings. Furthermore, the upload unit can visually display the progress of the upload according to the user's emotions to give the user a sense of security. This allows the upload unit to provide an optimal data upload experience according to the user's emotions.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis feedback method based on the estimated user emotions. For example, if the user is feeling stressed, positive feedback can be emphasized to increase the user's motivation. Alternatively, if the user is relaxed, detailed feedback can be provided to allow the user to deeply understand the analysis results. Furthermore, the analysis unit can adjust the timing and frequency of feedback according to the user's emotions. This allows the analysis unit to provide optimal feedback according to the user's emotions.

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

[0102] Step 1: The upload unit uploads proprietary data held by companies and government agencies to the platform. The upload unit can accept data in various formats, such as text data, image data, and sensor data. Step 2: The analysis unit analyzes the uploaded data using AI. The analysis unit analyzes the data using machine learning algorithms such as deep learning and neural networks. Step 3: The generator generates a 3D model based on the data analyzed by the analyzer. The generator generates the 3D model in the form of, for example, a polygon model or a volume model. Step 4: The display unit displays the 3D model generated by the generation unit. The display unit displays the 3D model in three dimensions on a device such as an AR device or a smartphone so that all members can see it.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 upload unit for uploading data; an analysis unit that analyzes the data uploaded by the upload unit; a generation unit that generates a 3D model based on the data analyzed by the analysis unit; a display unit that displays the 3D model generated by the generation unit; and Equipped with A system characterized by:

2. The analysis unit Uploaded data is analyzed using AI to generate a 3D model The system of claim 1 .

3. The generation unit View the generated 3D model on an AR device or smartphone The system of claim 1 .

4. The display unit The generated 3D model is displayed in three dimensions so that all members can see it. The system of claim 1 .

5. The upload unit Companies or government agencies can upload their own data to the platform The system of claim 1 .

6. The analysis unit It can also be used in the education or medical fields to generate 3D models of historical buildings or 3D models of the human body. The system of claim 1 .

7. The upload unit Inferring user emotions and adjusting the timing of data uploads based on the estimated user emotions The system of claim 1 .

8. The upload unit Choose the appropriate upload method depending on the type and format of your data The system of claim 1 .

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