system

Abstract

The system according to this embodiment aims to automatically generate digital content that is consistent with the real world and to customize it according to the user's preferences. [Solution] The system according to the embodiment comprises a scanning unit, an analysis unit, a generation unit, and a coordination unit. The scanning unit scans the real space. The analysis unit analyzes the data scanned by the scanning unit. The generation unit generates digital content based on the data analyzed by the analysis unit. The coordination unit coordinates the digital content generated by the generation unit according to the user's preferences.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the cost for generating digital content that matches the real space is high and the deviation from the real world is an issue.

[0005] The system according to the embodiment aims to automatically generate digital content that is consistent with the real space and coordinate it according to the user's preference.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, an analysis unit, a generation unit, and a coordination unit. The scanning unit scans the real world. The analysis unit analyzes the data scanned by the scanning unit. The generation unit generates digital content based on the data analyzed by the analysis unit. The coordination unit coordinates the digital content generated by the generation unit according to the user's preferences. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate digital content that is consistent with the real world and can be customized to the user's preferences. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The agent service "iSPACE" according to an embodiment of the present invention is a system that utilizes a generation AI to automatically generate a Mixed Reality (MR) space that matches the real world and customizes it to the user's preferences. This system begins when the user wears a VR device and scans the real world. The generation AI analyzes the scanned data of the real world and automatically generates digital content that is consistent with that space. The generated digital content is customized to the user's preferences, with either a Japanese or Western style. Furthermore, interactive space settings are possible through two-way conversation with iSPACE. For example, the user wears a VR device and scans the real world. At this time, the spatial measurement function of the VR device is used to acquire detailed data of the real world. For example, information such as the layout of an office and the arrangement of furniture can be scanned. Next, the generation AI analyzes the scanned data of the real world. Based on the data of the real world, the generation AI automatically generates digital content that is consistent with that space. For example, digital content can be generated with a Japanese or Western style to match the layout of an office. The generated digital content is customized to the user's preferences. Users can interactively configure spatial settings through two-way conversations with iSPACE. For example, by giving instructions such as "Make this meeting room a Japanese-style room," the generating AI creates digital content based on those instructions, providing an MR space that matches the real world. This system allows users to virtualize and freely customize their real space. For example, when changing the interior of an office, users can add virtual PC screens and peripherals while avoiding the risk of bumping into real objects. Furthermore, even if the real space changes, it can recognize the space in real time and transform any object. By using this service, the cost of office implementation can be significantly reduced. For example, the cost per tsubo (approximately 3.3 square meters) for office interiors is said to be over 300,000 yen, but by using this service, the real space can be virtualized and freely customized, thus reducing costs.This allows iSPACE to virtualize the user's real-world space and customize it freely.

[0029] The iSPACE system according to this embodiment comprises a scanning unit, an analysis unit, a generation unit, and a coordination unit. The scanning unit scans the real space. The scanning unit acquires detailed data of the real space, for example, using the spatial measurement function of a VR device. The scanning unit can scan information such as the layout of an office or the arrangement of furniture. The scanning unit can also acquire dimensions and shapes of the real space, for example, using laser scanning technology. The scanning unit can also acquire image data of the real space, for example, using a camera. The analysis unit analyzes the data scanned by the scanning unit. The analysis unit generates a 3D model of the real space based on the scanned data, for example. The analysis unit can also analyze the layout of the real space based on the scanned data, for example. The analysis unit can also analyze the materials and colors of the real space based on the scanned data, for example. The generation unit generates digital content based on the data analyzed by the analysis unit. The generation unit generates digital content, for example, according to the layout of the real space. The generation unit can also generate digital content according to the materials and colors of the real space, for example. The generation unit can, for example, generate digital content to match the dimensions and shape of a real-world space. Some or all of the above-described processes in the generation unit are performed using a generation AI. The generation AI takes scanned data as input and outputs digital content. The generation AI is given a prompt, for example, "Generate digital content to match the layout of the real-world space." The generation AI generates digital content based on the scanned data and provides its output. The coordination unit coordinates the digital content generated by the generation unit to the user's preferences. The coordination unit coordinates the digital content to match the user's preferences, for example, with Japanese or Western styles. The coordination unit can also adjust the colors and designs to match the user's preferences. The coordination unit can also adjust the placement and layout to match the user's preferences.This allows the iSPACE system to scan and analyze real-world spaces, generate digital content, and customize it to the user's preferences.

[0030] The scanning unit scans the real world. For example, it uses the spatial measurement capabilities of a VR device to acquire detailed data about the real world. Specifically, it uses multiple sensors and cameras mounted on the VR device to capture three-dimensional information of the space with high precision. This allows for the accurate acquisition of all details of the space, such as office layout, furniture placement, wall and floor dimensions, and even ceiling height. The scanning unit can also acquire dimensions and shapes of the real world using laser scanning technology. A laser scanner accurately measures the distance to an object by emitting a laser beam and measuring its reflection time. This technology makes it possible to acquire three-dimensional data of the space with millimeter-level accuracy. Furthermore, the scanning unit can acquire image data of the real world using a camera. By using a high-resolution camera, it can also capture detailed visual information such as the color, texture, and light reflection of the space. This allows the scanning unit to collect comprehensive data, including not only the physical dimensions of the space but also its visual features. This data is transmitted in real time to a central database and used in subsequent analysis and generation processes. By combining these diverse technologies, the scanning unit can create detailed digital replicas of real-world spaces, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the data scanned by the scanning unit. For example, the analysis unit generates a 3D model of the real space based on the scanned data. Specifically, it processes the scanned data as point cloud data and converts it into a triangular mesh to construct a detailed 3D model of the real space. This 3D model includes not only the dimensions and shape of the space, but also texture and color information, enabling a highly realistic reproduction. The analysis unit can also analyze the layout of the real space based on the scanned data. Using the information extracted from the scanned data, it identifies the placement of furniture and equipment, the width of passageways, the location of doors and windows, etc., and gains a detailed understanding of how the space is being used. Furthermore, the analysis unit can also analyze the materials and colors of the real space based on the scanned data. Using image analysis technology, it identifies the materials and colors of walls, floors, and furniture and reflects this in the digital model. This allows the analysis unit to analyze the physical and visual characteristics of the real space in detail and provide the information necessary for subsequent generation processes. Based on these analysis results, the analysis unit can also propose improvements to the efficiency of space utilization and design. For example, by optimizing furniture placement, we can propose ways to improve the efficiency of space utilization. This allows the analysis unit to support a detailed understanding and optimization of the real-world space, thereby improving the overall system performance.

[0032] The generation unit generates digital content based on data analyzed by the analysis unit. For example, the generation unit generates digital content to match the layout of the real world. Specifically, it generates digital content such as furniture, decorations, and lighting to be placed in the virtual space based on 3D models and layout information provided by the analysis unit. The generation unit can also generate digital content to match the materials and colors of the real world. Based on material and color information, it applies appropriate textures and colors to objects in the virtual space to create a visual reproduction that matches the real world. The generation unit can also generate digital content to match the dimensions and shapes of the real world. Based on dimension and shape information, it adjusts the size and placement of objects in the virtual space to achieve a layout that matches the real world. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes scanned data as input and outputs digital content. For example, the generation AI is given the prompt, "Generate digital content to match the layout of the real world." For example, the generation AI generates digital content based on the scanned data and provides its output. The generation AI uses deep learning technology to learn features extracted from scanned data and generate optimal digital content. This allows the generation unit to produce digital content with high accuracy and efficiency, and to customize it according to user requirements.

[0033] The Coordination Unit coordinates the digital content generated by the Generation Unit to match the user's preferences. For example, the Coordination Unit can coordinate digital content in a Japanese or Western style to match the user's preferences. Specifically, it selects an appropriate design style based on user input and past preference data and applies it to objects in the virtual space. The Coordination Unit can also adjust colors and designs to match the user's preferences. Based on the color palette and design theme specified by the user, it adjusts the colors and designs of objects in the virtual space to create a unified space. The Coordination Unit can also adjust placement and layout to match the user's preferences. Based on user instructions and feedback, it optimizes the placement and layout of objects in the virtual space to provide a user-friendly and attractive space. Furthermore, the Coordination Unit can adjust the overall atmosphere by paying attention to details such as lighting and decorations according to the user's preferences. This allows the Coordination Unit to customize according to the individual needs and preferences of the user, providing a more satisfying digital space. The Coordination Unit can also collect user feedback and continuously improve the accuracy and effectiveness of the coordination. This allows the coordination unit to provide users with an optimal digital space and improve the overall user experience of the system.

[0034] The conversation unit can interactively configure the spatial settings through two-way conversation. The conversation unit can, for example, understand user instructions using speech recognition technology. The conversation unit can also, for example, analyze user instructions using natural language processing technology. The conversation unit can also, for example, converse with the user using a dialogue system. This allows the conversation unit to interactively configure the spatial settings through two-way conversation. Some or all of the above-described processes in the conversation unit may be performed using AI or not. For example, the conversation unit can acquire user instructions using speech recognition technology, analyze those instructions using natural language processing technology, and interactively configure the spatial settings.

[0035] The transformation unit can recognize space in real time and transform any object. For example, the transformation unit can acquire data of the real space in real time and transform objects based on that data. For example, the transformation unit can also recognize the layout of the real space in real time and transform objects based on that layout. For example, the transformation unit can also recognize the material and color of the real space in real time and transform objects based on that material and color. In this way, the transformation unit can recognize space in real time and transform any object. Some or all of the above processing in the transformation unit may be performed using AI or not. For example, the transformation unit may acquire data of the real space in real time, input that data into the AI, and have the AI ​​perform the object transformation.

[0036] The scanning unit can acquire detailed data of the real world using the spatial measurement function of the VR device. For example, the scanning unit can acquire the dimensions and shape of the real world using the spatial measurement function of the VR device. The scanning unit can also acquire the layout of the real world using the spatial measurement function of the VR device. The scanning unit can also acquire the materials and colors of the real world using the spatial measurement function of the VR device. As a result, the scanning unit can acquire detailed data of the real world using the spatial measurement function of the VR device. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the data acquired using the spatial measurement function of the VR device into the AI ​​and have the AI ​​perform a detailed analysis of the data.

[0037] The generation unit can automatically generate digital content consistent with the real space based on data from that space. For example, the generation unit generates digital content to match the layout of the real space. The generation unit can also generate digital content to match the materials and colors of the real space. The generation unit can also generate digital content to match the dimensions and shapes of the real space. In this way, the generation unit can automatically generate digital content consistent with the real space based on data from that space. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes data from the real space as input and outputs digital content consistent with that space. For example, the generation AI is given a prompt such as "Please generate digital content to match the layout of the real space." The generation AI generates digital content based on the data from the real space and provides the output.

[0038] The coordination unit can coordinate digital content in Japanese or Western styles to match the user's preferences. For example, the coordination unit can adjust colors and designs to match the user's preferences. The coordination unit can also adjust placement and layout to match the user's preferences. The coordination unit can also adjust the style of decorations and furniture to match the user's preferences. In this way, the coordination unit can coordinate digital content in Japanese or Western styles to match the user's preferences. Some or all of the above processes in the coordination unit may be performed using AI or not. For example, the coordination unit inputs data on the user's preferences into the AI ​​and has the AI ​​perform the coordination of the digital content.

[0039] The scanning unit can analyze the user's past scanning history and select the optimal scanning method during a scan. For example, the scanning unit can propose the optimal scanning pattern based on data from spaces previously scanned by the user. The scanning unit can also analyze the accuracy of data previously scanned by the user and adjust the scanning method as needed. For example, the scanning unit can adjust the scanning range and angle considering the characteristics of spaces previously scanned by the user. In this way, the scanning unit can select the optimal scanning method by analyzing the user's past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history into the AI ​​and have the AI ​​select the optimal scanning method.

[0040] The scanning unit can adjust its scan range based on the user's current activity during scanning. For example, if the user is sitting, the scanning unit will prioritize scanning the area visible from the user's seated position. If the user is walking, the scanning unit can also adjust its scan range along the user's walking path. If the user is working, the scanning unit can also focus its scan on the work area. This allows the scanning unit to perform more appropriate scans by adjusting its scan range based on the user's current activity. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit may input the user's current activity into the AI ​​and have the AI ​​adjust the scan range.

[0041] The scanning unit can prioritize scanning highly relevant areas by considering the user's geographical location during scanning. For example, if the user is in an office, the scanning unit will prioritize scanning important areas within the office. If the user is at home, the scanning unit can also prioritize scanning major rooms within the home. If the user is in a public place, the scanning unit can adjust its scanning range by considering the surrounding environment. This allows the scanning unit to prioritize scanning highly relevant areas by considering the user's geographical location. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's geographical location information into the AI ​​and have the AI ​​perform a scan of highly relevant areas.

[0042] The scanning unit can analyze the user's social media activity during scanning and scan relevant areas. For example, the scanning unit may prioritize scanning locations where the user frequently posts on social media. The scanning unit may also scan relevant areas based on the user's social media check-in history. For example, the scanning unit may prioritize scanning locations where the user has many social media followers. In this way, the scanning unit can scan relevant areas by analyzing the user's social media activity. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit may input the user's social media activity data into the AI ​​and have the AI ​​perform the scan of relevant areas.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the scanned data during the analysis. For example, the analysis unit can analyze data from important areas in detail to provide highly accurate information. For example, the analysis unit can also analyze data from less important areas in a simplified manner to provide only the minimum necessary information. For example, the analysis unit can determine the priority of the analysis according to its importance and process the data efficiently. This allows the analysis unit to perform more efficient analysis by adjusting the level of detail of the analysis based on the importance of the scanned data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the scanned data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the scanned data during analysis. For example, the analysis unit can apply an office-specific analysis algorithm to office layout data. For example, the analysis unit can also apply a furniture-specific analysis algorithm to furniture placement data. For example, the analysis unit can also apply a lighting-specific analysis algorithm to lighting placement data. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the scanned data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the scanned data into the AI ​​and have the AI ​​apply different analysis algorithms.

[0045] The analysis unit can adjust the order of analysis based on the acquisition timing of the scan data during analysis. For example, the analysis unit can prioritize the analysis of the most recent scan data to provide real-time information. The analysis unit can also prioritize processing the latest data, for example, by delaying the processing of older scan data. The analysis unit can also efficiently process data by adjusting the order of analysis according to the acquisition timing. As a result, the analysis unit can perform more efficient analysis by adjusting the order of analysis based on the acquisition timing of the scan data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the acquisition timing of the scan data into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the scan data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly provide important information. The analysis unit can also process data efficiently by delaying the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data to provide optimal information. This allows the analysis unit to perform more efficient analysis by adjusting the order of analysis based on the relevance of the scan data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the scan data into the AI ​​and have the AI ​​adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the digital content it generates based on the importance of the scanned data during generation. For example, the generation unit can generate detailed digital content for important areas to provide highly accurate information. For example, the generation unit can also generate simplified digital content for less important areas to provide only the minimum necessary information. For example, the generation unit can determine the generation priority according to importance to efficiently generate digital content. This enables efficient content generation by adjusting the level of detail of the digital content based on the importance of the scanned data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes the importance of the scanned data as input and outputs the level of detail of the digital content. For example, the generation AI is given the prompt, "Adjust the level of detail of the digital content based on the importance of the scanned data." For example, the generation AI adjusts the level of detail of the digital content based on the importance of the scanned data and provides the output.

[0048] The generation unit can apply different generation algorithms depending on the category of the scanned data during generation. For example, the generation unit applies an office-specific generation algorithm to office layout data. The generation unit can also apply a furniture-specific generation algorithm to furniture placement data. The generation unit can also apply a lighting-specific generation algorithm to lighting placement data. This allows the generation unit to generate optimal content by applying different generation algorithms depending on the category of the scanned data. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI takes the category of the scanned data as input and outputs a generation algorithm. The generation AI is given a prompt, for example, "Apply a different generation algorithm depending on the category of the scanned data." The generation AI applies a generation algorithm based on the category of the scanned data and provides its output.

[0049] The generation unit can determine the priority of digital content to be generated based on the acquisition date of the scan data during generation. For example, the generation unit can prioritize generating digital content based on the latest scan data. The generation unit can also prioritize generating the latest digital content, for example, by delaying the generation of older scan data. The generation unit can also efficiently generate digital content by adjusting the generation priority according to the acquisition date. This enables efficient content generation by the generation unit determining the priority of digital content based on the acquisition date of the scan data. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI takes the acquisition date of the scan data as input and outputs the priority of digital content. For example, the generation AI is given the prompt, "Please determine the priority of digital content based on the acquisition date of the scan data." The generation AI determines the priority of digital content based on the acquisition date of the scan data and provides the output.

[0050] The generation unit can adjust the order of digital content generated based on the relevance of the scanned data during generation. For example, the generation unit can prioritize generating digital content based on highly relevant data. The generation unit can also efficiently generate digital content by delaying the generation of less relevant data. For example, the generation unit can adjust the generation order according to the relevance of the data to provide optimal digital content. This enables efficient content generation by adjusting the order of digital content based on the relevance of the scanned data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes the relevance of the scanned data as input and outputs the order of the digital content. For example, the generation AI is given the prompt, "Please adjust the order of the digital content based on the relevance of the scanned data." The generation AI adjusts the order of the digital content based on the relevance of the scanned data and provides the output.

[0051] The styling unit can analyze the user's past styling history to select the optimal styling method when creating a style. For example, the styling unit can analyze the trends of styles the user has chosen in the past and suggest similar styles. For example, the styling unit can suggest the optimal styling based on the colors and designs the user has liked in the past. For example, the styling unit can make suggestions based on the season and events from the user's past styling history. In this way, the styling unit can select the optimal styling method by analyzing the user's past styling history. Some or all of the above processes in the styling unit may be performed using AI or not. For example, the styling unit can input the user's past styling history into the AI ​​and have the AI ​​select the optimal styling method.

[0052] The coordination unit can customize the coordination methods based on the user's current living situation. For example, if the user is working from home, the coordination unit will suggest a coordination that is best suited for a home office. If the user is raising children, the coordination unit can also suggest a coordination that takes children's safety into consideration. If the user has pets, the coordination unit can also suggest a pet-friendly coordination. In this way, the coordination unit can provide more appropriate coordination by customizing the coordination methods based on the user's current living situation. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input the user's current living situation into the AI ​​and have the AI ​​perform the customization of the coordination methods.

[0053] The coordination unit can select the optimal coordination method by considering the user's geographical location information during the coordination process. For example, if the user lives in a cold region, the coordination unit may suggest a coordination with warm colors. If the user lives in a tropical region, the coordination unit may also suggest a coordination with cool colors. If the user lives in an urban area, the coordination unit may also suggest a coordination with a modern design. In this way, the coordination unit can select the optimal coordination method by considering the user's geographical location information. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit may input the user's geographical location information into the AI ​​and have the AI ​​select the optimal coordination method.

[0054] The coordination unit can analyze the user's social media activity and propose coordination methods during the coordination process. For example, the coordination unit can propose coordination based on the style the user frequently posts on social media. For example, the coordination unit can also propose coordination based on the style that has many followers on the user's social media. For example, the coordination unit can propose relevant coordination based on the user's social media check-in history. In this way, the coordination unit can propose the optimal coordination method by analyzing the user's social media activity. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input the user's social media activity data into AI and have the AI ​​propose coordination methods.

[0055] The conversation unit can provide optimal conversation content by referring to the user's past conversation history during a conversation. For example, the conversation unit can suggest relevant conversations based on what the user has said in the past. For example, the conversation unit can also prioritize providing topics of interest based on the user's past conversation history. For example, the conversation unit can analyze the user's past conversation history and suggest the best way to proceed with the conversation. In this way, the conversation unit can provide optimal conversation content by referring to the user's past conversation history. Some or all of the above processing in the conversation unit may be performed using AI or not. For example, the conversation unit can input the user's past conversation history into the AI ​​and have the AI ​​perform the task of providing optimal conversation content.

[0056] The conversation unit can provide optimal conversation content by considering the user's device information during a conversation. For example, if the user is using a smartphone, the conversation unit can provide conversation content that is adapted to the screen size. If the user is using a tablet, the conversation unit can also provide conversation content optimized for a larger screen. If the user is using a smartwatch, the conversation unit can also provide concise and highly visible conversation content. In this way, the conversation unit can provide optimal conversation content by considering the user's device information. Some or all of the above processing in the conversation unit may be performed using AI or not. For example, the conversation unit can input the user's device information into the AI ​​and have the AI ​​perform the task of providing optimal conversation content.

[0057] The conversion unit can select the optimal conversion method by referring to the user's past conversion history during conversion. For example, the conversion unit can analyze the trends of conversions the user has performed in the past and suggest similar methods. For example, the conversion unit can also suggest the optimal conversion based on the conversion methods the user has preferred in the past. For example, the conversion unit can make suggestions based on specific conditions from the user's past conversion history. In this way, the conversion unit can select the optimal conversion method by referring to the user's past conversion history. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can input the user's past conversion history into AI and have the AI ​​select the optimal conversion method.

[0058] The conversion unit can select the optimal conversion method by considering the user's device information during conversion. For example, if the user is using a smartphone, the conversion unit provides a conversion method that matches the screen size. For example, if the user is using a tablet, the conversion unit can also provide a conversion method optimized for a larger screen. For example, if the user is using a smartwatch, the conversion unit can also provide a concise and highly visible conversion method. In this way, the conversion unit can select the optimal conversion method by considering the user's device information. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit inputs the user's device information into the AI ​​and has the AI ​​select the optimal conversion method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The scanning unit can monitor the user's health status and adjust the frequency and range of scans accordingly. For example, if the user is tired, the scanning unit reduces the scanning frequency to lessen the user's burden. If the user is healthy and active, the scanning unit increases the scanning frequency to acquire more detailed data. Furthermore, if the user has a specific health problem, the unit can focus the scan on areas related to that problem. This allows the scanning unit to perform more appropriate scans by adjusting the frequency and range of scans according to the user's health status.

[0061] The analytics unit can analyze users' past behavior patterns and prioritize analysis based on predicted behavior. For example, it can predict the time a user arrives at the office each morning and prioritize the analysis of office layout data accordingly. If a user holds meetings on specific days of the week, it can also prioritize the analysis of data for those meeting rooms. Furthermore, it can prioritize the analysis of data for areas frequently used by the user, providing information quickly. In this way, the analytics unit can determine the priority of analysis based on predicted behavior by analyzing users' past behavior patterns.

[0062] The generation unit can customize digital content based on the user's hobbies and interests. For example, if the user is interested in art, the generation unit can generate digital content that includes artwork. If the user is interested in sports, it can also generate sports-related digital content. Furthermore, if the user likes a particular movie or music, it can generate digital content based on that theme. In this way, the generation unit can provide more personalized content by customizing digital content based on the user's hobbies and interests.

[0063] The coordination department can propose interior designs tailored to the user's lifestyle. For example, if the user is an outdoorsy type, they can propose interiors that evoke a sense of nature. If the user is an indoorsy type, they can propose designs that prioritize a comfortable indoor space. Furthermore, if the user is a busy business person, they can propose designs that provide an efficient work environment. In this way, the coordination department can provide a more suitable space by proposing designs that match the user's lifestyle.

[0064] The conversational unit can customize the conversation content to match the user's language and culture. For example, if the user speaks Japanese, it will provide a conversation in Japanese. If the user speaks English, it can also provide a conversation in English. Furthermore, if the user belongs to a specific culture, it can provide conversation content that is considerate of that culture. In this way, the conversational unit can provide more appropriate communication by customizing the conversation content to match the user's language and culture.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The scanning unit scans the real world. The scanning unit acquires detailed data of the real world, for example, using the spatial measurement function of a VR device. The scanning unit can scan information such as the layout of an office and the arrangement of furniture. The scanning unit can also acquire dimensions and shapes of the real world using laser scanning technology. The scanning unit can also acquire image data of the real world using a camera. Step 2: The analysis unit analyzes the data scanned by the scanning unit. The analysis unit generates a 3D model of the real space based on the scanned data. The analysis unit can also analyze the layout of the real space based on the scanned data. The analysis unit can also analyze the materials and colors of the real space based on the scanned data. Step 3: The generation unit generates digital content based on the data analyzed by the analysis unit. The generation unit generates digital content to match the layout of the real space. The generation unit can also generate digital content to match the materials and colors of the real space. The generation unit can also generate digital content to match the dimensions and shapes of the real space. Some or all of the above processes in the generation unit are performed using a generation AI. The generation AI takes scanned data as input and outputs digital content. The generation AI is prompted with the input, "Generate digital content to match the layout of the real space." The generation AI generates digital content based on the scanned data and provides its output. Step 4: The Coordination Unit coordinates the digital content generated by the Generation Unit to the user's preferences. The Coordination Unit coordinates the digital content in Japanese or Western styles according to the user's preferences. The Coordination Unit can also adjust the colors and designs according to the user's preferences. The Coordination Unit can also adjust the placement and layout according to the user's preferences.

[0067] (Example of form 2) The agent service "iSPACE" according to an embodiment of the present invention is a system that utilizes a generation AI to automatically generate a Mixed Reality (MR) space that matches the real world and customizes it to the user's preferences. This system begins when the user wears a VR device and scans the real world. The generation AI analyzes the scanned data of the real world and automatically generates digital content that is consistent with that space. The generated digital content is customized to the user's preferences, with either a Japanese or Western style. Furthermore, interactive space settings are possible through two-way conversation with iSPACE. For example, the user wears a VR device and scans the real world. At this time, the spatial measurement function of the VR device is used to acquire detailed data of the real world. For example, information such as the layout of an office and the arrangement of furniture can be scanned. Next, the generation AI analyzes the scanned data of the real world. Based on the data of the real world, the generation AI automatically generates digital content that is consistent with that space. For example, digital content can be generated with a Japanese or Western style to match the layout of an office. The generated digital content is customized to the user's preferences. Users can interactively configure spatial settings through two-way conversations with iSPACE. For example, by giving instructions such as "Make this meeting room a Japanese-style room," the generating AI creates digital content based on those instructions, providing an MR space that matches the real world. This system allows users to virtualize and freely customize their real space. For example, when changing the interior of an office, users can add virtual PC screens and peripherals while avoiding the risk of bumping into real objects. Furthermore, even if the real space changes, it can recognize the space in real time and transform any object. By using this service, the cost of office implementation can be significantly reduced. For example, the cost per tsubo (approximately 3.3 square meters) for office interiors is said to be over 300,000 yen, but by using this service, the real space can be virtualized and freely customized, thus reducing costs.This allows iSPACE to virtualize the user's real-world space and customize it freely.

[0068] The iSPACE system according to this embodiment comprises a scanning unit, an analysis unit, a generation unit, and a coordination unit. The scanning unit scans the real space. The scanning unit acquires detailed data of the real space, for example, using the spatial measurement function of a VR device. The scanning unit can scan information such as the layout of an office or the arrangement of furniture. The scanning unit can also acquire dimensions and shapes of the real space, for example, using laser scanning technology. The scanning unit can also acquire image data of the real space, for example, using a camera. The analysis unit analyzes the data scanned by the scanning unit. The analysis unit generates a 3D model of the real space based on the scanned data, for example. The analysis unit can also analyze the layout of the real space based on the scanned data, for example. The analysis unit can also analyze the materials and colors of the real space based on the scanned data, for example. The generation unit generates digital content based on the data analyzed by the analysis unit. The generation unit generates digital content, for example, according to the layout of the real space. The generation unit can also generate digital content according to the materials and colors of the real space, for example. The generation unit can, for example, generate digital content to match the dimensions and shape of a real-world space. Some or all of the above-described processes in the generation unit are performed using a generation AI. The generation AI takes scanned data as input and outputs digital content. The generation AI is given a prompt, for example, "Generate digital content to match the layout of the real-world space." The generation AI generates digital content based on the scanned data and provides its output. The coordination unit coordinates the digital content generated by the generation unit to the user's preferences. The coordination unit coordinates the digital content to match the user's preferences, for example, with Japanese or Western styles. The coordination unit can also adjust the colors and designs to match the user's preferences. The coordination unit can also adjust the placement and layout to match the user's preferences.This allows the iSPACE system to scan and analyze real-world spaces, generate digital content, and customize it to the user's preferences.

[0069] The scanning unit scans the real world. For example, it uses the spatial measurement capabilities of a VR device to acquire detailed data about the real world. Specifically, it uses multiple sensors and cameras mounted on the VR device to capture three-dimensional information of the space with high precision. This allows for the accurate acquisition of all details of the space, such as office layout, furniture placement, wall and floor dimensions, and even ceiling height. The scanning unit can also acquire dimensions and shapes of the real world using laser scanning technology. A laser scanner accurately measures the distance to an object by emitting a laser beam and measuring its reflection time. This technology makes it possible to acquire three-dimensional data of the space with millimeter-level accuracy. Furthermore, the scanning unit can acquire image data of the real world using a camera. By using a high-resolution camera, it can also capture detailed visual information such as the color, texture, and light reflection of the space. This allows the scanning unit to collect comprehensive data, including not only the physical dimensions of the space but also its visual features. This data is transmitted in real time to a central database and used in subsequent analysis and generation processes. By combining these diverse technologies, the scanning unit can create detailed digital replicas of real-world spaces, improving the overall accuracy and reliability of the system.

[0070] The analysis unit analyzes the data scanned by the scanning unit. For example, the analysis unit generates a 3D model of the real space based on the scanned data. Specifically, it processes the scanned data as point cloud data and converts it into a triangular mesh to construct a detailed 3D model of the real space. This 3D model includes not only the dimensions and shape of the space, but also texture and color information, enabling a highly realistic reproduction. The analysis unit can also analyze the layout of the real space based on the scanned data. Using the information extracted from the scanned data, it identifies the placement of furniture and equipment, the width of passageways, the location of doors and windows, etc., and gains a detailed understanding of how the space is being used. Furthermore, the analysis unit can also analyze the materials and colors of the real space based on the scanned data. Using image analysis technology, it identifies the materials and colors of walls, floors, and furniture and reflects this in the digital model. This allows the analysis unit to analyze the physical and visual characteristics of the real space in detail and provide the information necessary for subsequent generation processes. Based on these analysis results, the analysis unit can also propose improvements to the efficiency of space utilization and design. For example, by optimizing furniture placement, we can propose ways to improve the efficiency of space utilization. This allows the analysis unit to support a detailed understanding and optimization of the real-world space, thereby improving the overall system performance.

[0071] The generation unit generates digital content based on data analyzed by the analysis unit. For example, the generation unit generates digital content to match the layout of the real world. Specifically, it generates digital content such as furniture, decorations, and lighting to be placed in the virtual space based on 3D models and layout information provided by the analysis unit. The generation unit can also generate digital content to match the materials and colors of the real world. Based on material and color information, it applies appropriate textures and colors to objects in the virtual space to create a visual reproduction that matches the real world. The generation unit can also generate digital content to match the dimensions and shapes of the real world. Based on dimension and shape information, it adjusts the size and placement of objects in the virtual space to achieve a layout that matches the real world. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes scanned data as input and outputs digital content. For example, the generation AI is given the prompt, "Generate digital content to match the layout of the real world." For example, the generation AI generates digital content based on the scanned data and provides its output. The generation AI uses deep learning technology to learn features extracted from scanned data and generate optimal digital content. This allows the generation unit to produce digital content with high accuracy and efficiency, and to customize it according to user requirements.

[0072] The Coordination Unit coordinates the digital content generated by the Generation Unit to match the user's preferences. For example, the Coordination Unit can coordinate digital content in a Japanese or Western style to match the user's preferences. Specifically, it selects an appropriate design style based on user input and past preference data and applies it to objects in the virtual space. The Coordination Unit can also adjust colors and designs to match the user's preferences. Based on the color palette and design theme specified by the user, it adjusts the colors and designs of objects in the virtual space to create a unified space. The Coordination Unit can also adjust placement and layout to match the user's preferences. Based on user instructions and feedback, it optimizes the placement and layout of objects in the virtual space to provide a user-friendly and attractive space. Furthermore, the Coordination Unit can adjust the overall atmosphere by paying attention to details such as lighting and decorations according to the user's preferences. This allows the Coordination Unit to customize according to the individual needs and preferences of the user, providing a more satisfying digital space. The Coordination Unit can also collect user feedback and continuously improve the accuracy and effectiveness of the coordination. This allows the coordination unit to provide users with an optimal digital space and improve the overall user experience of the system.

[0073] The conversation unit can interactively configure the spatial settings through two-way conversation. The conversation unit can, for example, understand user instructions using speech recognition technology. The conversation unit can also, for example, analyze user instructions using natural language processing technology. The conversation unit can also, for example, converse with the user using a dialogue system. This allows the conversation unit to interactively configure the spatial settings through two-way conversation. Some or all of the above-described processes in the conversation unit may be performed using AI or not. For example, the conversation unit can acquire user instructions using speech recognition technology, analyze those instructions using natural language processing technology, and interactively configure the spatial settings.

[0074] The transformation unit can recognize space in real time and transform any object. For example, the transformation unit can acquire data of the real space in real time and transform objects based on that data. For example, the transformation unit can also recognize the layout of the real space in real time and transform objects based on that layout. For example, the transformation unit can also recognize the material and color of the real space in real time and transform objects based on that material and color. In this way, the transformation unit can recognize space in real time and transform any object. Some or all of the above processing in the transformation unit may be performed using AI or not. For example, the transformation unit may acquire data of the real space in real time, input that data into the AI, and have the AI ​​perform the object transformation.

[0075] The scanning unit can acquire detailed data of the real world using the spatial measurement function of the VR device. For example, the scanning unit can acquire the dimensions and shape of the real world using the spatial measurement function of the VR device. The scanning unit can also acquire the layout of the real world using the spatial measurement function of the VR device. The scanning unit can also acquire the materials and colors of the real world using the spatial measurement function of the VR device. As a result, the scanning unit can acquire detailed data of the real world using the spatial measurement function of the VR device. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the data acquired using the spatial measurement function of the VR device into the AI ​​and have the AI ​​perform a detailed analysis of the data.

[0076] The generation unit can automatically generate digital content consistent with the real space based on data from that space. For example, the generation unit generates digital content to match the layout of the real space. The generation unit can also generate digital content to match the materials and colors of the real space. The generation unit can also generate digital content to match the dimensions and shapes of the real space. In this way, the generation unit can automatically generate digital content consistent with the real space based on data from that space. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes data from the real space as input and outputs digital content consistent with that space. For example, the generation AI is given a prompt such as "Please generate digital content to match the layout of the real space." The generation AI generates digital content based on the data from the real space and provides the output.

[0077] The coordination unit can coordinate digital content in Japanese or Western styles to match the user's preferences. For example, the coordination unit can adjust colors and designs to match the user's preferences. The coordination unit can also adjust placement and layout to match the user's preferences. The coordination unit can also adjust the style of decorations and furniture to match the user's preferences. In this way, the coordination unit can coordinate digital content in Japanese or Western styles to match the user's preferences. Some or all of the above processes in the coordination unit may be performed using AI or not. For example, the coordination unit inputs data on the user's preferences into the AI ​​and has the AI ​​perform the coordination of the digital content.

[0078] The scanning unit can estimate the user's emotions and adjust the timing of the scan based on the estimated emotions. For example, if the user is relaxed, the scanning unit can perform a slow scan to acquire detailed data. If the user is in a hurry, the scanning unit can also perform a rapid scan to acquire only the minimum necessary data. If the user is excited, the scanning unit can interrupt the scan and wait until the user calms down. This allows the scanning unit to perform a more appropriate scan by adjusting the timing of the scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of the scan.

[0079] The scanning unit can analyze the user's past scanning history and select the optimal scanning method during a scan. For example, the scanning unit can propose the optimal scanning pattern based on data from spaces previously scanned by the user. The scanning unit can also analyze the accuracy of data previously scanned by the user and adjust the scanning method as needed. For example, the scanning unit can adjust the scanning range and angle considering the characteristics of spaces previously scanned by the user. In this way, the scanning unit can select the optimal scanning method by analyzing the user's past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history into the AI ​​and have the AI ​​select the optimal scanning method.

[0080] The scanning unit can adjust its scan range based on the user's current activity during scanning. For example, if the user is sitting, the scanning unit will prioritize scanning the area visible from the user's seated position. If the user is walking, the scanning unit can also adjust its scan range along the user's walking path. If the user is working, the scanning unit can also focus its scan on the work area. This allows the scanning unit to perform more appropriate scans by adjusting its scan range based on the user's current activity. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit may input the user's current activity into the AI ​​and have the AI ​​adjust the scan range.

[0081] The scanning unit can estimate the user's emotions and determine the priority of objects to scan based on the estimated emotions. For example, if the user is relaxed, the scanning unit can perform a detailed scan and acquire overall data. If the user is in a hurry, the scanning unit can prioritize scanning important areas. If the user is excited, the scanning unit can interrupt the scan and wait until the user calms down. This allows the scanning unit to perform a more appropriate scan by determining the priority of objects to scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of objects to scan.

[0082] The scanning unit can prioritize scanning highly relevant areas by considering the user's geographical location during scanning. For example, if the user is in an office, the scanning unit will prioritize scanning important areas within the office. If the user is at home, the scanning unit can also prioritize scanning major rooms within the home. If the user is in a public place, the scanning unit can adjust its scanning range by considering the surrounding environment. This allows the scanning unit to prioritize scanning highly relevant areas by considering the user's geographical location. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's geographical location information into the AI ​​and have the AI ​​perform a scan of highly relevant areas.

[0083] The scanning unit can analyze the user's social media activity during scanning and scan relevant areas. For example, the scanning unit may prioritize scanning locations where the user frequently posts on social media. The scanning unit may also scan relevant areas based on the user's social media check-in history. For example, the scanning unit may prioritize scanning locations where the user has many social media followers. In this way, the scanning unit can scan relevant areas by analyzing the user's social media activity. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit may input the user's social media activity data into the AI ​​and have the AI ​​perform the scan of relevant areas.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate data. If the user is in a hurry, the analysis unit can also perform a rapid analysis and provide only the minimum necessary data. If the user is excited, the analysis unit can interrupt the analysis and wait until the user calms down. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the analysis method.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the scanned data during the analysis. For example, the analysis unit can analyze data from important areas in detail to provide highly accurate information. For example, the analysis unit can also analyze data from less important areas in a simplified manner to provide only the minimum necessary information. For example, the analysis unit can determine the priority of the analysis according to its importance and process the data efficiently. This allows the analysis unit to perform more efficient analysis by adjusting the level of detail of the analysis based on the importance of the scanned data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the scanned data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the scanned data during analysis. For example, the analysis unit can apply an office-specific analysis algorithm to office layout data. For example, the analysis unit can also apply a furniture-specific analysis algorithm to furniture placement data. For example, the analysis unit can also apply a lighting-specific analysis algorithm to lighting placement data. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the scanned data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the scanned data into the AI ​​and have the AI ​​apply different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize a detailed analysis. If the user is in a hurry, the analysis unit may also prioritize a rapid analysis. If the user is agitated, the analysis unit may interrupt the analysis and wait until the user calms down. This allows the analysis unit to perform more appropriate analysis by prioritizing the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit may input user emotion data into the AI ​​and have the AI ​​determine the priority of the analysis.

[0088] The analysis unit can adjust the order of analysis based on the acquisition timing of the scan data during analysis. For example, the analysis unit can prioritize the analysis of the most recent scan data to provide real-time information. The analysis unit can also prioritize processing the latest data, for example, by delaying the processing of older scan data. The analysis unit can also efficiently process data by adjusting the order of analysis according to the acquisition timing. As a result, the analysis unit can perform more efficient analysis by adjusting the order of analysis based on the acquisition timing of the scan data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the acquisition timing of the scan data into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the scan data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly provide important information. The analysis unit can also process data efficiently by delaying the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data to provide optimal information. This allows the analysis unit to perform more efficient analysis by adjusting the order of analysis based on the relevance of the scan data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the scan data into the AI ​​and have the AI ​​adjust the order of analysis.

[0090] The generation unit can estimate the user's emotions and adjust the presentation of the generated digital content based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate digital content with calming colors. For example, if the user is in a hurry, the generation unit can also generate simple and highly visible digital content. For example, if the user is excited, the generation unit can also generate visually stimulating digital content. In this way, the generation unit can provide more appropriate content by adjusting the presentation of the digital content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation AI takes user emotion data as input and outputs a presentation of the digital content. For example, the generation AI is input with the prompt, "Adjust the presentation of the digital content based on the user's emotions." Generative AI, for example, adjusts the way digital content is presented based on user emotion data and provides the output.

[0091] The generation unit can adjust the level of detail of the digital content it generates based on the importance of the scanned data during generation. For example, the generation unit can generate detailed digital content for important areas to provide highly accurate information. For example, the generation unit can also generate simplified digital content for less important areas to provide only the minimum necessary information. For example, the generation unit can determine the generation priority according to importance to efficiently generate digital content. This enables efficient content generation by adjusting the level of detail of the digital content based on the importance of the scanned data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes the importance of the scanned data as input and outputs the level of detail of the digital content. For example, the generation AI is given the prompt, "Adjust the level of detail of the digital content based on the importance of the scanned data." For example, the generation AI adjusts the level of detail of the digital content based on the importance of the scanned data and provides the output.

[0092] The generation unit can apply different generation algorithms depending on the category of the scanned data during generation. For example, the generation unit applies an office-specific generation algorithm to office layout data. The generation unit can also apply a furniture-specific generation algorithm to furniture placement data. The generation unit can also apply a lighting-specific generation algorithm to lighting placement data. This allows the generation unit to generate optimal content by applying different generation algorithms depending on the category of the scanned data. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI takes the category of the scanned data as input and outputs a generation algorithm. The generation AI is given a prompt, for example, "Apply a different generation algorithm depending on the category of the scanned data." The generation AI applies a generation algorithm based on the category of the scanned data and provides its output.

[0093] The generation unit can estimate the user's emotions and adjust the length of the digital content it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate short, concise digital content. If the user is relaxed, the generation unit can also generate longer digital content with detailed explanations. If the user is excited, the generation unit can also generate digital content with visually stimulating effects. This allows the generation unit to provide more appropriate content by adjusting the length of the digital content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation AI takes user emotion data as input and outputs the length of the digital content. For example, the generation AI is input with the prompt, "Adjust the length of the digital content based on the user's emotions." Generative AI, for example, adjusts the length of digital content based on user emotion data and provides the output.

[0094] The generation unit can determine the priority of digital content to be generated based on the acquisition date of the scan data during generation. For example, the generation unit can prioritize generating digital content based on the latest scan data. The generation unit can also prioritize generating the latest digital content, for example, by delaying the generation of older scan data. The generation unit can also efficiently generate digital content by adjusting the generation priority according to the acquisition date. This enables efficient content generation by the generation unit determining the priority of digital content based on the acquisition date of the scan data. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI takes the acquisition date of the scan data as input and outputs the priority of digital content. For example, the generation AI is given the prompt, "Please determine the priority of digital content based on the acquisition date of the scan data." The generation AI determines the priority of digital content based on the acquisition date of the scan data and provides the output.

[0095] The generation unit can adjust the order of digital content generated based on the relevance of the scanned data during generation. For example, the generation unit can prioritize generating digital content based on highly relevant data. The generation unit can also efficiently generate digital content by delaying the generation of less relevant data. For example, the generation unit can adjust the generation order according to the relevance of the data to provide optimal digital content. This enables efficient content generation by adjusting the order of digital content based on the relevance of the scanned data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation AI takes the relevance of the scanned data as input and outputs the order of the digital content. For example, the generation AI is given the prompt, "Please adjust the order of the digital content based on the relevance of the scanned data." The generation AI adjusts the order of the digital content based on the relevance of the scanned data and provides the output.

[0096] The coordination unit can estimate the user's emotions and adjust the coordination method based on the estimated emotions. For example, if the user is relaxed, the coordination unit may suggest a calm color scheme. If the user is in a hurry, the coordination unit may suggest a simple and quick coordination. If the user is excited, the coordination unit may suggest a visually stimulating coordination. This allows the coordination unit to provide more appropriate coordination by adjusting the coordination method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit may input user emotion data into an AI and have the AI ​​adjust the coordination method.

[0097] The styling unit can analyze the user's past styling history to select the optimal styling method when creating a style. For example, the styling unit can analyze the trends of styles the user has chosen in the past and suggest similar styles. For example, the styling unit can suggest the optimal styling based on the colors and designs the user has liked in the past. For example, the styling unit can make suggestions based on the season and events from the user's past styling history. In this way, the styling unit can select the optimal styling method by analyzing the user's past styling history. Some or all of the above processes in the styling unit may be performed using AI or not. For example, the styling unit can input the user's past styling history into the AI ​​and have the AI ​​select the optimal styling method.

[0098] The coordination unit can customize the coordination methods based on the user's current living situation. For example, if the user is working from home, the coordination unit will suggest a coordination that is best suited for a home office. If the user is raising children, the coordination unit can also suggest a coordination that takes children's safety into consideration. If the user has pets, the coordination unit can also suggest a pet-friendly coordination. In this way, the coordination unit can provide more appropriate coordination by customizing the coordination methods based on the user's current living situation. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input the user's current living situation into the AI ​​and have the AI ​​perform the customization of the coordination methods.

[0099] The coordination unit can estimate the user's emotions and determine coordination priorities based on the estimated emotions. For example, if the user is relaxed, the coordination unit may prioritize detailed coordination. If the user is in a hurry, the coordination unit may also prioritize quick coordination. If the user is excited, the coordination unit may interrupt the coordination and wait until the user calms down. This allows the coordination unit to provide more appropriate coordination by determining coordination priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit may input user emotion data into an AI and have the AI ​​determine the coordination priorities.

[0100] The coordination unit can select the optimal coordination method by considering the user's geographical location information during the coordination process. For example, if the user lives in a cold region, the coordination unit may suggest a coordination with warm colors. If the user lives in a tropical region, the coordination unit may also suggest a coordination with cool colors. If the user lives in an urban area, the coordination unit may also suggest a coordination with a modern design. In this way, the coordination unit can select the optimal coordination method by considering the user's geographical location information. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit may input the user's geographical location information into the AI ​​and have the AI ​​select the optimal coordination method.

[0101] The coordination unit can analyze the user's social media activity and propose coordination methods during the coordination process. For example, the coordination unit can propose coordination based on the style the user frequently posts on social media. For example, the coordination unit can also propose coordination based on the style that has many followers on the user's social media. For example, the coordination unit can propose relevant coordination based on the user's social media check-in history. In this way, the coordination unit can propose the optimal coordination method by analyzing the user's social media activity. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input the user's social media activity data into AI and have the AI ​​propose coordination methods.

[0102] The conversational unit can estimate the user's emotions and adjust the content of the conversation based on the estimated emotions. For example, if the user is relaxed, the conversational unit will proceed in a calm tone. For example, if the user is in a hurry, the conversational unit can also conduct a quick and concise conversation. For example, if the user is agitated, the conversational unit can interrupt the conversation and wait until the user calms down. In this way, the conversational unit can conduct a more appropriate conversation by adjusting the content of the conversation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversational unit may be performed using AI or not. For example, the conversational unit can input user emotion data into an AI and have the AI ​​adjust the content of the conversation.

[0103] The conversation unit can provide optimal conversation content by referring to the user's past conversation history during a conversation. For example, the conversation unit can suggest relevant conversations based on what the user has said in the past. For example, the conversation unit can also prioritize providing topics of interest based on the user's past conversation history. For example, the conversation unit can analyze the user's past conversation history and suggest the best way to proceed with the conversation. In this way, the conversation unit can provide optimal conversation content by referring to the user's past conversation history. Some or all of the above processing in the conversation unit may be performed using AI or not. For example, the conversation unit can input the user's past conversation history into the AI ​​and have the AI ​​perform the task of providing optimal conversation content.

[0104] The conversation unit can estimate the user's emotions and determine conversation priorities based on those estimated emotions. For example, if the user is relaxed, the conversation unit may prioritize detailed conversation. If the user is in a hurry, the conversation unit may also prioritize quick conversation. If the user is agitated, the conversation unit may interrupt the conversation and wait until the user calms down. This allows the conversation unit to have more appropriate conversations by prioritizing conversations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using AI or not. For example, the conversation unit may input user emotion data into an AI and have the AI ​​determine the conversation priorities.

[0105] The conversation unit can provide optimal conversation content by considering the user's device information during a conversation. For example, if the user is using a smartphone, the conversation unit can provide conversation content that is adapted to the screen size. If the user is using a tablet, the conversation unit can also provide conversation content optimized for a larger screen. If the user is using a smartwatch, the conversation unit can also provide concise and highly visible conversation content. In this way, the conversation unit can provide optimal conversation content by considering the user's device information. Some or all of the above processing in the conversation unit may be performed using AI or not. For example, the conversation unit can input the user's device information into the AI ​​and have the AI ​​perform the task of providing optimal conversation content.

[0106] The conversion unit can estimate the user's emotions and adjust the conversion method based on the estimated emotions. For example, if the user is relaxed, the conversion unit can perform the conversion at a slow pace. For example, if the user is in a hurry, the conversion unit can perform the conversion quickly. For example, if the user is excited, the conversion unit can interrupt the conversion and wait until the user calms down. This allows the conversion unit to perform a more appropriate conversion by adjusting the conversion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can input the user's emotion data into the AI ​​and have the AI ​​adjust the conversion method.

[0107] The conversion unit can select the optimal conversion method by referring to the user's past conversion history during conversion. For example, the conversion unit can analyze the trends of conversions the user has performed in the past and suggest similar methods. For example, the conversion unit can also suggest the optimal conversion based on the conversion methods the user has preferred in the past. For example, the conversion unit can make suggestions based on specific conditions from the user's past conversion history. In this way, the conversion unit can select the optimal conversion method by referring to the user's past conversion history. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can input the user's past conversion history into AI and have the AI ​​select the optimal conversion method.

[0108] The translation unit can estimate the user's emotions and determine the priority of the translation based on the estimated emotions. For example, if the user is relaxed, the translation unit may prioritize detailed translations. If the user is in a hurry, the translation unit may also prioritize quick translations. If the user is excited, the translation unit may interrupt the translation and wait until the user calms down. This allows the translation unit to perform more appropriate translations by determining the priority of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit may input user emotion data into an AI and have the AI ​​determine the priority of the translations.

[0109] The conversion unit can select the optimal conversion method by considering the user's device information during conversion. For example, if the user is using a smartphone, the conversion unit provides a conversion method that matches the screen size. For example, if the user is using a tablet, the conversion unit can also provide a conversion method optimized for a larger screen. For example, if the user is using a smartwatch, the conversion unit can also provide a concise and highly visible conversion method. In this way, the conversion unit can select the optimal conversion method by considering the user's device information. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit inputs the user's device information into the AI ​​and has the AI ​​select the optimal conversion method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The scanning unit can monitor the user's health status and adjust the frequency and range of scans accordingly. For example, if the user is tired, the scanning unit reduces the scanning frequency to lessen the user's burden. If the user is healthy and active, the scanning unit increases the scanning frequency to acquire more detailed data. Furthermore, if the user has a specific health problem, the unit can focus the scan on areas related to that problem. This allows the scanning unit to perform more appropriate scans by adjusting the frequency and range of scans according to the user's health status.

[0112] The analytics unit can analyze users' past behavior patterns and prioritize analysis based on predicted behavior. For example, it can predict the time a user arrives at the office each morning and prioritize the analysis of office layout data accordingly. If a user holds meetings on specific days of the week, it can also prioritize the analysis of data for those meeting rooms. Furthermore, it can prioritize the analysis of data for areas frequently used by the user, providing information quickly. In this way, the analytics unit can determine the priority of analysis based on predicted behavior by analyzing users' past behavior patterns.

[0113] The generation unit can customize digital content based on the user's hobbies and interests. For example, if the user is interested in art, the generation unit can generate digital content that includes artwork. If the user is interested in sports, it can also generate sports-related digital content. Furthermore, if the user likes a particular movie or music, it can generate digital content based on that theme. In this way, the generation unit can provide more personalized content by customizing digital content based on the user's hobbies and interests.

[0114] The coordination department can propose interior designs tailored to the user's lifestyle. For example, if the user is an outdoorsy type, they can propose interiors that evoke a sense of nature. If the user is an indoorsy type, they can propose designs that prioritize a comfortable indoor space. Furthermore, if the user is a busy business person, they can propose designs that provide an efficient work environment. In this way, the coordination department can provide a more suitable space by proposing designs that match the user's lifestyle.

[0115] The conversational unit can customize the conversation content to match the user's language and culture. For example, if the user speaks Japanese, it will provide a conversation in Japanese. If the user speaks English, it can also provide a conversation in English. Furthermore, if the user belongs to a specific culture, it can provide conversation content that is considerate of that culture. In this way, the conversational unit can provide more appropriate communication by customizing the conversation content to match the user's language and culture.

[0116] The scanning unit can estimate the user's emotions and adjust the scan accuracy based on that estimation. For example, if the user is relaxed, the scanning unit performs a high-precision scan and acquires detailed data. If the user is in a hurry, the scanning unit can perform a quick scan and acquire only the minimum necessary data. If the user is agitated, the scanning unit can interrupt the scan and wait until the user calms down. In this way, the scanning unit can perform a more appropriate scan by adjusting the scan accuracy according to the user's emotions.

[0117] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate data. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide only the minimum necessary data. If the user is agitated, the analysis unit can suspend the analysis and wait until the user calms down. In this way, the analysis unit can perform more appropriate analysis by adjusting the timing of the analysis according to the user's emotions.

[0118] The generation unit can estimate the user's emotions and adjust the style of the digital content it generates based on those emotions. For example, if the user is relaxed, it can generate digital content with calming colors. If the user is in a hurry, it can generate simple and highly visible digital content. If the user is excited, it can generate visually stimulating digital content. In this way, the generation unit can provide more appropriate content by adjusting the style of the digital content according to the user's emotions.

[0119] The styling unit can estimate the user's emotions and suggest outfits based on those emotions. For example, if the user is relaxed, it can suggest outfits with calming colors. If the user is in a hurry, it can suggest simple and quick outfits. If the user is excited, it can suggest visually stimulating outfits. In this way, the styling unit can suggest outfits that are more appropriate by responding to the user's emotions.

[0120] The conversational unit can estimate the user's emotions and adjust the tone of the conversation based on those estimates. For example, if the user is relaxed, it will proceed with the conversation in a calm tone. If the user is in a hurry, it can also engage in a quick and concise conversation. Furthermore, if the user is agitated, it can interrupt the conversation and wait until the user calms down. In this way, the conversational unit can enable more appropriate communication by adjusting the tone of the conversation according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The scanning unit scans the real world. The scanning unit acquires detailed data of the real world, for example, using the spatial measurement function of a VR device. The scanning unit can scan information such as the layout of an office and the arrangement of furniture. The scanning unit can also acquire dimensions and shapes of the real world using laser scanning technology. The scanning unit can also acquire image data of the real world using a camera. Step 2: The analysis unit analyzes the data scanned by the scanning unit. The analysis unit generates a 3D model of the real space based on the scanned data. The analysis unit can also analyze the layout of the real space based on the scanned data. The analysis unit can also analyze the materials and colors of the real space based on the scanned data. Step 3: The generation unit generates digital content based on the data analyzed by the analysis unit. The generation unit generates digital content to match the layout of the real space. The generation unit can also generate digital content to match the materials and colors of the real space. The generation unit can also generate digital content to match the dimensions and shapes of the real space. Some or all of the above processes in the generation unit are performed using a generation AI. The generation AI takes scanned data as input and outputs digital content. The generation AI is prompted with the input, "Generate digital content to match the layout of the real space." The generation AI generates digital content based on the scanned data and provides its output. Step 4: The Coordination Unit coordinates the digital content generated by the Generation Unit to the user's preferences. The Coordination Unit coordinates the digital content in Japanese or Western styles according to the user's preferences. The Coordination Unit can also adjust the colors and designs according to the user's preferences. The Coordination Unit can also adjust the placement and layout according to the user's preferences.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, and coordination unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit acquires detailed data of the real space using the camera 42 and spatial measurement function of the smart device 14. The analysis unit analyzes the scanned data using the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the real space. The generation unit generates digital content based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The coordination unit coordinates the digital content generated by the control unit 46A of the smart device 14 according to the user's preferences. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, and coordination unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit acquires detailed data of the real world using the camera 42 and spatial measurement function of the smart glasses 214. The analysis unit analyzes the scanned data using, for example, the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the real world. The generation unit generates digital content based on the data analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The coordination unit coordinates the digital content generated by, for example, the control unit 46A of the smart glasses 214 according to the user's preferences. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, and coordination unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit acquires detailed data of the real space using the camera 42 and spatial measurement function of the headset terminal 314. The analysis unit analyzes the scanned data using the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the real space. The generation unit generates digital content based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The coordination unit coordinates the digital content generated by the control unit 46A of the headset terminal 314 according to the user's preferences. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the scanning unit, analysis unit, generation unit, and coordination unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the scanning unit acquires detailed data of the real space using the camera 42 and spatial measurement functions of the robot 414. The analysis unit analyzes the scanned data by, for example, the specific processing unit 290 of the data processing unit 12 and generates a 3D model of the real space. The generation unit generates digital content based on the data analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The coordination unit coordinates the digital content generated by, for example, the control unit 46A of the robot 414 according to the user's preferences. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A scanning unit that scans the real world, An analysis unit that analyzes the data scanned by the aforementioned scanning unit, A generation unit that generates digital content based on the data analyzed by the analysis unit, The system includes a coordination unit that coordinates the digital content generated by the generation unit to suit the user's preferences. A system characterized by the following features. (Note 2) It features a conversation section that allows for interactive spatial configuration through two-way conversation. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a transformation unit that recognizes space in real time and transforms arbitrary objects. The system described in Appendix 1, characterized by the features described herein. (Note 4) The scanning unit is We use the spatial measurement function of VR devices to acquire detailed data on the real world. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on data from the real world, it automatically generates digital content that is consistent with that space. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned coordination unit is Coordinate digital content with Japanese or Western styles to match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is During scanning, the system analyzes the user's past scan history and selects the optimal scanning method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is During scanning, the scan range is adjusted based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and determines the priority of what to scan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is During scanning, the system prioritizes scanning areas that are more relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is During the scan, the system analyzes the user's social media activity and scans relevant areas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the scan data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the scan data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on when the scan data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the scan data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates user emotions and adjusts the way digital content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail of the generated digital content is adjusted based on the importance of the scanned data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the scanned data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the digital content generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of the digital content to be generated is determined based on when the scanned data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of the generated digital content is adjusted based on the relevance of the scanned data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned coordination unit is It estimates the user's emotions and adjusts the coordination method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned coordination unit is When creating a coordinated outfit, the system analyzes the user's past outfit history to select the optimal coordination method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned coordination unit is When creating an outfit, the method of creating the outfit is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coordination unit is It estimates the user's emotions and determines the priority of the coordination based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned coordination unit is When coordinating outfits, the system selects the optimal coordination method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned coordination unit is When creating a coordinated outfit, we analyze the user's social media activity and suggest ways to coordinate it. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned conversation section is, It estimates the user's emotions and adjusts the conversation content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned conversation section is, During a conversation, the system provides optimal conversation content by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned conversation section is, It estimates the user's emotions and determines conversation priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned conversation section is, During conversations, the system takes the user's device information into consideration to provide the most appropriate conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 35) The conversion unit is It estimates the user's emotions and adjusts the conversion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The conversion unit is During conversion, the system selects the optimal conversion method by referring to the user's past conversion history. The system described in Appendix 1, characterized by the features described herein. (Note 37) The conversion unit is It estimates the user's emotions and determines the priority of conversions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The conversion unit is During conversion, the optimal conversion method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A scanning unit that scans the real world, An analysis unit that analyzes the data scanned by the aforementioned scanning unit, A generation unit that generates digital content based on the data analyzed by the analysis unit, The system includes a coordination unit that coordinates the digital content generated by the generation unit to suit the user's preferences. A system characterized by the following features.

2. It features a conversation section that allows for interactive spatial configuration through two-way conversation. The system according to feature 1.

3. It features a transformation unit that recognizes space in real time and transforms arbitrary objects. The system according to feature 1.

4. The scanning unit is We use the spatial measurement function of VR devices to acquire detailed data on the real world. The system according to feature 1.

5. The generating unit is Based on data from the real world, it automatically generates digital content that is consistent with that space. The system according to feature 1.

6. The aforementioned coordination unit is Coordinate digital content with Japanese or Western styles to match the user's preferences. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is During scanning, the system analyzes the user's past scan history and selects the optimal scanning method. The system according to feature 1.

9. The scanning unit is During scanning, the scan range is adjusted based on the user's current activity. The system according to feature 1.

10. The scanning unit is It estimates the user's emotions and determines the priority of what to scan based on the estimated user emotions. The system according to feature 1.

Images