System

Abstract

To provide a system that generates a virtual space based on a text prompt, and operates it through conversational items and voice dialogue.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, an analysis unit, and a display unit. The reception unit receives a text prompt. The generation unit generates a virtual space based on the text prompt received by the reception unit. The analysis unit analyzes conversational items or voice dialogue. The display unit displays the virtual space generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately address the generation of a virtual space based on text prompts and the manipulation of the space through conversational items and voice dialogue, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a virtual space based on text prompts and to operate it through conversational items and voice dialogue. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, a generation unit, an analysis unit, and a display unit. The reception unit receives a text prompt. The generation unit generates a virtual space based on the text prompt received by the reception unit. The analysis unit analyzes a conversation item or a voice dialogue. The display unit displays the virtual space generated by the generation unit. Effect of the Invention

[0007] The system of the embodiment generates a virtual space based on text prompts and can be manipulated through conversational items and voice dialogue. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The virtual space generation system according to the embodiment of the present invention is a system that generates a virtual space based on a text prompt and can be operated using conversational items and voice dialogue. In this system, a user inputs a text prompt, and a generation AI generates a virtual space based on the text prompt. Furthermore, by using conversational items and voice dialogue, the user can explore and operate in the virtual space. This technology is provided as a general-purpose item that works with the Metaverse platform. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space based on the prompt. The generated virtual space includes elements specified by the user (in this case, a forest and a castle). Next, the user explores and operates in the virtual space using conversational items and voice dialogue. For example, when a user gives a voice command such as "Show me the inside of the castle," the generation AI analyzes the command and displays the inside of the castle. Also, when a user gives a command such as "Put furniture in this room," the generation AI places the furniture based on the command. This technology allows users to intuitively operate the virtual space and build an infinitely expanding virtual world. In addition, this technology works with the Metaverse platform, making it possible to share and collaborate with other users to create virtual spaces. This allows the virtual space generation system to allow users to intuitively manipulate virtual spaces and create infinitely expanding virtual worlds.

[0029] The virtual space generation system according to the embodiment includes a reception unit, a generation unit, an analysis unit, and a display unit. The reception unit receives a text prompt input by a user. The text prompt includes, but is not limited to, a question format, an instruction format, and a free format. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the reception unit receives the prompt. The generation unit uses a generation AI to generate a virtual space based on the text prompt received by the reception unit. The generated virtual space includes, but is not limited to, a 3D model, an interactive element, and an environmental setting. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. The analysis unit analyzes the conversation item and the voice dialogue. The analysis unit analyzes the user's voice instruction, for example, using voice recognition technology or natural language processing technology. For example, when a user inputs a voice instruction such as "Show me the inside of the castle," the analysis unit analyzes the instruction and provides the generation unit with information for displaying the inside of the castle. The display unit displays the virtual space generated by the generation unit. The display unit can display the virtual space according to, for example, the orientation of the user's smartphone. The display unit can also display the virtual space according to the user's walking speed. As a result, the virtual space generation system according to the embodiment generates a virtual space based on a text prompt input by the user, and allows exploration and operation in the virtual space to be performed using conversation items and voice dialogue. Furthermore, since the virtual space generation system works in conjunction with the Metaverse platform, it is possible to share the virtual space with other users and jointly build a virtual space.

[0030] The reception unit receives a text prompt input by the user. Examples of the text prompt include, but are not limited to, a question format, an instruction format, and a free format. For example, when the user inputs a prompt such as "I want to build a castle in the forest," the reception unit receives the prompt. In order to accurately receive the text prompt input by the user, the reception unit analyzes the content of the prompt using natural language processing technology and understands the intention. For example, if the user inputs "I want to build a house on a hill where I can see the sunset," the reception unit extracts keywords such as "sunset," "hill," and "house" and analyzes the relationship between them. Furthermore, the reception unit can refer to past prompts and user history information in order to understand the context of the prompt input by the user. This allows the reception unit to more accurately grasp the user's intention and provide appropriate information to the generation unit. The reception unit also has a function of providing feedback to the prompt input by the user. For example, if the user inputs "I want to build a bigger castle," the reception unit asks an additional question such as "How big do you want it to be?" to further clarify the user's intention. This enables the reception unit to flexibly respond to user requests and provide information that enables the generation unit to generate a more accurate virtual space.

[0031] The generation unit uses the generation AI to generate a virtual space based on the text prompt received by the reception unit. The generated virtual space includes, for example, a 3D model, interactive elements, and environmental settings, but is not limited to such examples. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. The generation AI uses deep learning technology to analyze the user's prompt and selects an appropriate 3D model and environmental settings. For example, when a user inputs "I want to recreate an ancient temple," the generation AI generates a 3D model of the temple taking into account the ancient architectural style, decoration, and surrounding environment. In addition, the generation unit adds interactive elements so that the user can enjoy operation and exploration in the virtual space. For example, when a user gives an instruction such as "I want to open the castle gate," the generation unit generates an animation in which the gate opens in response to the instruction. In addition, the generation unit also has a function of adjusting the environmental settings of the virtual space. For example, when a user inputs "I want to make it a night forest," the generation unit changes the time setting of the virtual space to night and displays moonlight and a starry sky. This allows the generator to generate a variety of virtual spaces based on user prompts, making it possible to customize the virtual spaces in accordance with the user's requests.

[0032] The analysis unit analyzes the conversation item and the voice dialogue. The analysis unit analyzes the user's voice instruction using, for example, voice recognition technology or natural language processing technology. For example, when the user gives a voice instruction such as "Show me inside the castle," the analysis unit analyzes the instruction and provides the generation unit with information for displaying the inside of the castle. The analysis unit converts the user's voice into text using voice recognition technology and analyzes the text using natural language processing technology. For example, when the user gives a voice instruction such as "Make it brighter," the analysis unit understands the instruction "make it brighter" and provides the generation unit with information for changing the lighting settings of the virtual space. Furthermore, the analysis unit analyzes not only the user's voice instruction but also the operation of the conversation item. For example, the analysis unit analyzes the operation of the user clicking or dragging an object in the virtual space and instructs the generation unit to take an action according to the operation. This allows the analysis unit to accurately analyze the user's voice instruction and operation, and to smoothly perform interaction in the virtual space. In addition, the analysis unit can refer to past dialogue history and operation history in order to understand the user's intention more deeply. This allows the analysis unit to understand the user's preferences and trends and provide a more personalized experience.

[0033] The display unit displays the virtual space generated by the generation unit. The display unit can display the virtual space according to, for example, the orientation of the user's smartphone. The display unit can also display the virtual space according to the user's walking speed. The display unit adjusts the display of the virtual space in real time using sensor information from the user's device. For example, a gyro sensor or acceleration sensor from the smartphone is used to display visual changes according to the user's viewpoint and movement. This allows the user to freely explore the virtual space and enjoy a realistic experience. Furthermore, the display unit also has a function of adjusting the resolution and frame rate of the virtual space. For example, a high-performance device provides a smooth display with high resolution, and a low-performance device provides a smooth display by lowering the resolution. This allows the display unit to be compatible with various devices and provide optimal display quality. The display unit also has a function of managing the display order and priority of objects and effects in the virtual space. For example, if a user is paying attention to a specific object, the object is displayed with priority, and the display of other objects is delayed. This allows the display unit to optimize the user's visual experience and make operation and exploration in the virtual space more comfortable.

[0034] The analysis unit can analyze the user's voice instructions and execute operations in the virtual space. The analysis unit can analyze the user's voice instructions, for example, using voice recognition technology. For example, when the user gives a voice instruction such as "Show me inside the castle," the analysis unit analyzes the instruction and provides the generation unit with information for displaying the interior of the castle. In addition, when the user gives an instruction such as "Put furniture in this room," the analysis unit can also analyze the instruction and provide the generation unit with information for arranging the furniture. This enables operations in the virtual space based on the user's voice instructions. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's voice instructions to AI and have the AI ​​execute the analysis of the voice instructions.

[0035] The generation unit can generate a virtual space including a specified element based on the user's text prompt. The generation unit uses a generation AI to generate a virtual space based on the user's text prompt. For example, when the user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. The generated virtual space includes, for example, a 3D model, an interactive element, and an environmental setting, but is not limited to such examples. The generation unit uses, for example, a generation AI to generate a virtual space including an element specified by the user. This allows the virtual space to be generated based on the user's text prompt. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's text prompt to the generation AI and cause the generation AI to execute generation of the virtual space.

[0036] The display unit can display the virtual space based on the orientation of the user's smartphone. The display unit detects the orientation of the user's smartphone, for example, using a gyro sensor or an acceleration sensor. For example, when the user moves the smartphone up, down, left, or right, the display unit displays the virtual space according to the movement. In addition, when the user rotates the smartphone, the display unit can also display the virtual space according to the rotation. This makes it possible to display the virtual space according to the orientation of the user's smartphone. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data on the orientation of the smartphone to AI and cause AI to execute the display of the virtual space.

[0037] The display unit can display the virtual space based on the walking speed of the user. The display unit detects the walking speed of the user, for example, using a pedometer or GPS data. For example, when the user walks fast, the display unit displays the virtual space according to the speed. Also, when the user walks slowly, the display unit can display the virtual space according to the speed. This makes it possible to display the virtual space according to the walking speed of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data on the walking speed to AI and cause AI to execute the display of the virtual space.

[0038] The virtual space generation system includes a sharing unit for sharing the virtual space with other users. The sharing unit can perform, for example, real-time sharing or asynchronous sharing. For example, the sharing unit can share the virtual space generated by the user with other users in real time. The sharing unit can also save the virtual space generated by the user and share it with other users later. This allows the virtual space to be shared with other users. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input data of the virtual space to AI and cause AI to execute the sharing processing.

[0039] The virtual space generation system includes a cooperation unit for constructing a virtual space collaboratively. The cooperation unit provides, for example, a collaboration tool or a collaborative editing function. For example, the cooperation unit provides a collaboration tool that allows multiple users to edit a virtual space at the same time. The cooperation unit can also provide a collaborative editing function for users to collaboratively construct a virtual space. This allows users to collaboratively construct a virtual space. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooperation unit can input editing data of the virtual space to the AI ​​and cause the AI ​​to execute the collaborative editing processing.

[0040] The reception unit can analyze the user's past prompt history and select the optimal reception method. For example, the reception unit automatically displays prompts that the user has frequently used in the past as candidates. For example, the reception unit analyzes the pattern of prompts that the user has used in the past and suggests the optimal input method. The reception unit can also predict and suggest prompts to be used in a specific time period from the user's past prompt history. This makes it possible to select the optimal reception method based on the user's past prompt history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the past prompt history into an AI and cause the AI ​​to select the optimal reception method.

[0041] The reception unit may perform filtering based on the user's current field of interest when receiving the text prompt. The reception unit may preferentially receive related prompts based on, for example, keywords recently searched by the user. For example, the reception unit may suggest related prompts based on topics in which the user has shown interest in the past. The reception unit may also analyze the user's social media activities and filter prompts based on the user's current field of interest. This allows the text prompts to be filtered based on the user's current field of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input data on the user's field of interest to AI and cause the AI ​​to filter the prompts.

[0042] When receiving a text prompt, the reception unit can preferentially receive a highly relevant prompt in consideration of the geographical location information of the user. The reception unit acquires the geographical location information of the user, for example, using GPS data or a location information service. For example, when the user is in a specific area, the reception unit preferentially receives a prompt related to the area. Also, when the user is traveling, the reception unit can preferentially receive a prompt related to the travel destination. Also, when the user is at home, the reception unit can preferentially receive a prompt related to the vicinity of the home. This makes it possible to preferentially receive a highly relevant prompt based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information to AI and cause the AI ​​to perform filtering of the prompt.

[0043] The reception unit can analyze the user's social media activity and receive relevant prompts when receiving the text prompt. The reception unit preferentially receives relevant prompts based on, for example, the content recently shared by the user on social media. For example, the reception unit suggests relevant prompts based on topics the user follows on social media. The reception unit can also analyze the user's social media activity history and filter prompts based on current areas of interest. This makes it possible to receive relevant prompts based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input data on social media activity to AI and have the AI ​​perform filtering of prompts.

[0044] The generation unit can adjust the level of detail of the generation based on the importance of the prompt when generating the virtual space. For example, the generation unit generates a virtual space including detailed elements for a prompt with high importance. For example, the generation unit can generate a virtual space including concise elements for a prompt with low importance. The generation unit can also adjust the complexity of the virtual space to be generated according to the importance of the prompt. This allows the level of detail of the generation to be adjusted based on the importance of the prompt. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the importance of the prompt to the generation AI and cause the generation AI to adjust the level of detail of the generation of the virtual space.

[0045] The generation unit can apply different generation algorithms depending on the category of the prompt when generating the virtual space. For example, the generation unit applies an algorithm for generating a natural landscape to a prompt for a natural environment. For example, the generation unit can apply an algorithm for generating an urban landscape to a prompt for an urban environment. The generation unit can also apply a generation algorithm including fantasy elements to a prompt for a fantasy environment. This allows different generation algorithms to be applied depending on the category of the prompt. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the category of the prompt to the generation AI and cause the generation AI to apply the generation algorithm.

[0046] The generation unit can determine the priority of generation based on the submission time of the prompt when generating the virtual space. The generation unit, for example, prioritizes the most recently submitted prompt and generates the virtual space. For example, the generation unit can postpone the prompt submitted earlier and prioritize the most recent prompt. The generation unit can also adjust the order of the virtual spaces to be generated according to the submission time. This makes it possible to determine the priority of generation based on the submission time of the prompt. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the submission time of the prompt to the generation AI and cause the generation AI to determine the priority of generation.

[0047] The generation unit can adjust the order of generation based on the relevance of the prompts when generating the virtual space. For example, the generation unit prioritizes processing of highly relevant prompts to generate the virtual space. For example, the generation unit can postpone prompts with low relevance and prioritize important prompts. The generation unit can also adjust the order of the virtual space to be generated according to the relevance of the prompts. This makes it possible to adjust the order of generation based on the relevance of the prompts. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the relevance of the prompts to the generation AI and cause the generation AI to adjust the order of generation.

[0048] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction when analyzing the voice instruction. For example, the analysis unit performs a detailed analysis on a voice instruction with high importance and performs a highly accurate operation. For example, the analysis unit can perform a concise analysis on a voice instruction with low importance and perform a quick operation. The analysis unit can also adjust the level of detail of the analysis according to the importance of the voice instruction. This makes it possible to adjust the level of detail of the analysis based on the importance of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data on the importance of the instruction to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0049] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing the voice instruction. For example, the analysis unit applies an analysis algorithm dedicated to navigation to a voice instruction related to navigation. For example, the analysis unit can apply an analysis algorithm dedicated to operation to a voice instruction related to operation. Also, the analysis unit can apply an analysis algorithm dedicated to information search to a voice instruction related to information search. This makes it possible to apply different analysis algorithms depending on the category of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the category of the instruction to AI and cause AI to apply the analysis algorithm.

[0050] The analysis unit can determine the priority of the analysis based on the time of submission of the instruction when analyzing the voice instruction. For example, the analysis unit prioritizes the analysis of the most recently submitted voice instruction. For example, the analysis unit can postpone the voice instruction submitted earlier and prioritize the most recent voice instruction. The analysis unit can also adjust the order of analysis according to the submission time. This makes it possible to determine the priority of the analysis based on the time of submission of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data on the time of submission of the instruction to the AI ​​and cause the AI ​​to determine the priority of the analysis.

[0051] The analysis unit can adjust the order of analysis based on the relevance of the instructions when analyzing the voice instructions. For example, the analysis unit prioritizes analysis of highly relevant voice instructions. For example, the analysis unit can postpone less relevant voice instructions and prioritize important voice instructions. The analysis unit can also adjust the order of analysis according to the relevance of the voice instructions. This makes it possible to adjust the order of analysis based on the relevance of the instructions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the instructions to AI and cause AI to adjust the order of analysis.

[0052] The display unit can select the optimal display method by referring to the user's past operation history when displaying the virtual space. For example, the display unit automatically applies a display method that the user has used in the past. For example, the display unit suggests the optimal display method from the user's past operation history. The display unit can also provide a customized display method based on the display method that the user has used in the past. This allows the optimal display method to be selected based on the user's past operation history. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the past operation history to AI and cause the AI ​​to select the optimal display method.

[0053] The display unit can adjust the level of detail of the display based on the user's device information when displaying the virtual space. For example, when the user is using a high-resolution device, the display unit provides a detailed display. For example, when the user is using a low-resolution device, the display unit can provide a concise display. The display unit can also adjust the level of detail of the display according to the performance of the user's device. This allows the level of detail of the display to be adjusted based on the user's device information. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the device information to AI and cause the AI ​​to adjust the level of detail of the display.

[0054] The display unit can display highly relevant virtual spaces preferentially in consideration of the geographical location information of the user when displaying the virtual space. The display unit acquires the geographical location information of the user, for example, by using GPS data or a location information service. For example, when the user is in a specific area, the display unit can display a virtual space related to the area preferentially. In addition, when the user is traveling, the display unit can display a virtual space related to the travel destination preferentially. In addition, when the user is at home, the display unit can also display a virtual space related to the vicinity of the home preferentially. This makes it possible to display a highly relevant virtual space preferentially based on the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data of the geographical location information to AI and cause AI to execute the determination of the priority order of the virtual spaces.

[0055] The display unit can analyze the user's social media activity and display related virtual spaces when displaying the virtual space. The display unit preferentially displays related virtual spaces based on, for example, the content recently shared by the user on social media. For example, the display unit suggests related virtual spaces based on topics the user follows on social media. The display unit can also analyze the user's social media activity history and filter virtual spaces based on current areas of interest. This makes it possible to display related virtual spaces based on the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input data on social media activities to AI and have AI execute filtering of virtual spaces.

[0056] The sharing unit can select the optimal sharing method by referring to the past sharing history when sharing the virtual space. For example, the sharing unit automatically suggests a virtual space that the user has shared in the past. For example, the sharing unit suggests the optimal sharing method from the user's past sharing history. The sharing unit can also provide a customized sharing method based on the sharing method that the user has used in the past. This makes it possible to select the optimal sharing method based on the past sharing history. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input data of the past sharing history into an AI and cause the AI ​​to select the optimal sharing method.

[0057] The sharing unit can select the optimal sharing method in consideration of the attribute information of the other party when sharing the virtual space. For example, if the other party is a family member, the sharing unit can provide a private sharing method. For example, if the other party is a friend, the sharing unit can provide a sharing method via social media. In addition, if the other party is a business partner, the sharing unit can also provide a business-oriented sharing method. This makes it possible to select the optimal sharing method based on the attribute information of the other party. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input data of the other party's attribute information into AI and have the AI ​​select the optimal sharing method.

[0058] The cooperation unit can select the optimal cooperation method by referring to the past cooperation history when cooperating in the virtual space. For example, the cooperation unit automatically suggests a virtual space in which the user has cooperated in the past. For example, the cooperation unit suggests the optimal cooperation method from the user's past cooperation history. The cooperation unit can also provide a customized cooperation method based on the cooperation method used by the user in the past. This makes it possible to select the optimal cooperation method based on the past cooperation history. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooperation unit can input data of the past cooperation history into AI and cause the AI ​​to select the optimal cooperation method.

[0059] The cooperation unit can select the optimal cooperation method in consideration of the attribute information of the other party to cooperate in the virtual space. For example, if the other party to cooperate is a family member, the cooperation unit can provide a private cooperation method. For example, if the other party to cooperate is a friend, the cooperation unit can provide a cooperation method through social media. In addition, if the other party to cooperate is a business partner, the cooperation unit can also provide a business-oriented cooperation method. This makes it possible to select the optimal cooperation method based on the attribute information of the other party to cooperate. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooperation unit can input data of the other party's attribute information into AI and cause AI to select the optimal cooperation method.

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

[0061] The virtual space generation system may further include a learning unit that learns the user's past behavioral patterns. The learning unit, for example, analyzes what kind of virtual space the user has used in the past and suggests an optimal virtual space based on this. For example, if the user has used a natural environment in the past, the natural environment can be preferentially suggested the next time the user uses the system. Also, if the user has a tendency to perform a specific operation during a specific time period, a suggestion can be made that is tailored to that time period. This makes it possible to provide an optimal virtual space experience based on the user's past behavioral patterns.

[0062] The virtual space generation system may further include an environmental sound analysis unit that analyzes the user's real-time environmental sound. The environmental sound analysis unit, for example, analyzes the sounds around the user and adjusts the acoustic environment of the virtual space based on the results. For example, when the user is in a quiet environment, a quiet acoustic environment can be provided even in the virtual space. Also, when the user is in a noisy environment, the volume in the virtual space can be adjusted to allow the user to operate comfortably. This makes it possible to provide an optimal virtual space experience based on the user's real-time environmental sound.

[0063] The virtual space generation system may further include an eye gaze tracking unit that tracks the user's gaze. The eye gaze tracking unit, for example, detects which part the user is gazing at and adjusts the display of the virtual space based on this. For example, if the user is gazing at a specific object, detailed information about the object may be displayed. Also, if the user is gazing at a specific area for a long time, an operation related to the area may be suggested. This makes it possible to provide an optimal virtual space experience based on the user's gaze.

[0064] The virtual space generation system may further include a gesture recognition unit that recognizes a user's gesture. The gesture recognition unit detects, for example, the user's hand movement and performs an operation in the virtual space based on the detected movement. For example, when the user waves his / her hand, movement in the virtual space is started. Also, when the user performs a specific gesture, a specific operation may be performed. This enables intuitive operation based on the user's gesture, and provides a more natural virtual space experience.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit receives a text prompt input by a user. The text prompt may be in the form of a question, an instruction, or a free form. For example, if the user inputs a prompt such as "I want to build a castle in the forest," the prompt is received. Step 2: The generation unit uses the generation AI to generate a virtual space based on the text prompt received by the reception unit. The generated virtual space includes 3D models, interactive elements, and environmental settings. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. Step 3: The analysis unit analyzes the conversation items and the voice dialogue. The analysis unit uses voice recognition technology and natural language processing technology to analyze the user's voice instructions. For example, if the user gives a voice instruction such as "Show me the inside of the castle," the analysis unit analyzes the instruction and provides the generation unit with information to display the inside of the castle. Step 4: The display unit displays the virtual space generated by the generation unit. The display unit can display the virtual space according to the orientation of the user's smartphone. It can also display the virtual space according to the user's walking speed.

[0067] (Example 2) The virtual space generation system according to the embodiment of the present invention is a system that generates a virtual space based on a text prompt and can be operated using conversational items and voice dialogue. In this system, a user inputs a text prompt, and a generation AI generates a virtual space based on the text prompt. Furthermore, by using conversational items and voice dialogue, the user can explore and operate in the virtual space. This technology is provided as a general-purpose item that works with the Metaverse platform. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space based on the prompt. The generated virtual space includes elements specified by the user (in this case, a forest and a castle). Next, the user explores and operates in the virtual space using conversational items and voice dialogue. For example, when a user gives a voice command such as "Show me the inside of the castle," the generation AI analyzes the command and displays the inside of the castle. Also, when a user gives a command such as "Put furniture in this room," the generation AI places the furniture based on the command. This technology allows users to intuitively operate the virtual space and build an infinitely expanding virtual world. In addition, this technology works with the Metaverse platform, making it possible to share and collaborate with other users to create virtual spaces. This allows the virtual space generation system to allow users to intuitively manipulate virtual spaces and create infinitely expanding virtual worlds.

[0068] The virtual space generation system according to the embodiment includes a reception unit, a generation unit, an analysis unit, and a display unit. The reception unit receives a text prompt input by a user. The text prompt includes, but is not limited to, a question format, an instruction format, and a free format. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the reception unit receives the prompt. The generation unit uses a generation AI to generate a virtual space based on the text prompt received by the reception unit. The generated virtual space includes, but is not limited to, a 3D model, an interactive element, and an environmental setting. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. The analysis unit analyzes the conversation item and the voice dialogue. The analysis unit analyzes the user's voice instruction, for example, using voice recognition technology or natural language processing technology. For example, when a user inputs a voice instruction such as "Show me the inside of the castle," the analysis unit analyzes the instruction and provides the generation unit with information for displaying the inside of the castle. The display unit displays the virtual space generated by the generation unit. The display unit can display the virtual space according to, for example, the orientation of the user's smartphone. The display unit can also display the virtual space according to the user's walking speed. As a result, the virtual space generation system according to the embodiment generates a virtual space based on a text prompt input by the user, and allows exploration and operation in the virtual space to be performed using conversation items and voice dialogue. Furthermore, since the virtual space generation system works in conjunction with the Metaverse platform, it is possible to share the virtual space with other users and jointly build a virtual space.

[0069] The reception unit receives a text prompt input by the user. Examples of the text prompt include, but are not limited to, a question format, an instruction format, and a free format. For example, when the user inputs a prompt such as "I want to build a castle in the forest," the reception unit receives the prompt. In order to accurately receive the text prompt input by the user, the reception unit analyzes the content of the prompt using natural language processing technology and understands the intention. For example, if the user inputs "I want to build a house on a hill where I can see the sunset," the reception unit extracts keywords such as "sunset," "hill," and "house" and analyzes the relationship between them. Furthermore, the reception unit can refer to past prompts and user history information in order to understand the context of the prompt input by the user. This allows the reception unit to more accurately grasp the user's intention and provide appropriate information to the generation unit. The reception unit also has a function of providing feedback to the prompt input by the user. For example, if the user inputs "I want to build a bigger castle," the reception unit asks an additional question such as "How big do you want it to be?" to further clarify the user's intention. This enables the reception unit to flexibly respond to user requests and provide information that enables the generation unit to generate a more accurate virtual space.

[0070] The generation unit uses the generation AI to generate a virtual space based on the text prompt received by the reception unit. The generated virtual space includes, for example, a 3D model, interactive elements, and environmental settings, but is not limited to such examples. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. The generation AI uses deep learning technology to analyze the user's prompt and selects an appropriate 3D model and environmental settings. For example, when a user inputs "I want to recreate an ancient temple," the generation AI generates a 3D model of the temple taking into account the ancient architectural style, decoration, and surrounding environment. In addition, the generation unit adds interactive elements so that the user can enjoy operation and exploration in the virtual space. For example, when a user gives an instruction such as "I want to open the castle gate," the generation unit generates an animation in which the gate opens in response to the instruction. In addition, the generation unit also has a function of adjusting the environmental settings of the virtual space. For example, when a user inputs "I want to make it a night forest," the generation unit changes the time setting of the virtual space to night and displays moonlight and a starry sky. This allows the generator to generate a variety of virtual spaces based on user prompts, making it possible to customize the virtual spaces in accordance with the user's requests.

[0071] The analysis unit analyzes the conversation item and the voice dialogue. The analysis unit analyzes the user's voice instruction using, for example, voice recognition technology or natural language processing technology. For example, when the user gives a voice instruction such as "Show me inside the castle," the analysis unit analyzes the instruction and provides the generation unit with information for displaying the inside of the castle. The analysis unit converts the user's voice into text using voice recognition technology and analyzes the text using natural language processing technology. For example, when the user gives a voice instruction such as "Make it brighter," the analysis unit understands the instruction "make it brighter" and provides the generation unit with information for changing the lighting settings of the virtual space. Furthermore, the analysis unit analyzes not only the user's voice instruction but also the operation of the conversation item. For example, the analysis unit analyzes the operation of the user clicking or dragging an object in the virtual space and instructs the generation unit to take an action according to the operation. This allows the analysis unit to accurately analyze the user's voice instruction and operation, and to smoothly perform interaction in the virtual space. In addition, the analysis unit can refer to past dialogue history and operation history in order to understand the user's intention more deeply. This allows the analysis unit to understand the user's preferences and trends and provide a more personalized experience.

[0072] The display unit displays the virtual space generated by the generation unit. The display unit can display the virtual space according to, for example, the orientation of the user's smartphone. The display unit can also display the virtual space according to the user's walking speed. The display unit adjusts the display of the virtual space in real time using sensor information from the user's device. For example, a gyro sensor or acceleration sensor from the smartphone is used to display visual changes according to the user's viewpoint and movement. This allows the user to freely explore the virtual space and enjoy a realistic experience. Furthermore, the display unit also has a function of adjusting the resolution and frame rate of the virtual space. For example, a high-performance device provides a smooth display with high resolution, and a low-performance device provides a smooth display by lowering the resolution. This allows the display unit to be compatible with various devices and provide optimal display quality. The display unit also has a function of managing the display order and priority of objects and effects in the virtual space. For example, if a user is paying attention to a specific object, the object is displayed with priority, and the display of other objects is delayed. This allows the display unit to optimize the user's visual experience and make operation and exploration in the virtual space more comfortable.

[0073] The analysis unit can analyze the user's voice instructions and execute operations in the virtual space. The analysis unit can analyze the user's voice instructions, for example, using voice recognition technology. For example, when the user gives a voice instruction such as "Show me inside the castle," the analysis unit analyzes the instruction and provides the generation unit with information for displaying the interior of the castle. In addition, when the user gives an instruction such as "Put furniture in this room," the analysis unit can also analyze the instruction and provide the generation unit with information for arranging the furniture. This enables operations in the virtual space based on the user's voice instructions. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's voice instructions to AI and have the AI ​​execute the analysis of the voice instructions.

[0074] The generation unit can generate a virtual space including a specified element based on the user's text prompt. The generation unit uses a generation AI to generate a virtual space based on the user's text prompt. For example, when the user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. The generated virtual space includes, for example, a 3D model, an interactive element, and an environmental setting, but is not limited to such examples. The generation unit uses, for example, a generation AI to generate a virtual space including an element specified by the user. This allows the virtual space to be generated based on the user's text prompt. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's text prompt to the generation AI and cause the generation AI to execute generation of the virtual space.

[0075] The display unit can display the virtual space based on the orientation of the user's smartphone. The display unit detects the orientation of the user's smartphone, for example, using a gyro sensor or an acceleration sensor. For example, when the user moves the smartphone up, down, left, or right, the display unit displays the virtual space according to the movement. In addition, when the user rotates the smartphone, the display unit can also display the virtual space according to the rotation. This makes it possible to display the virtual space according to the orientation of the user's smartphone. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data on the orientation of the smartphone to AI and cause AI to execute the display of the virtual space.

[0076] The display unit can display the virtual space based on the walking speed of the user. The display unit detects the walking speed of the user, for example, using a pedometer or GPS data. For example, when the user walks fast, the display unit displays the virtual space according to the speed. Also, when the user walks slowly, the display unit can display the virtual space according to the speed. This makes it possible to display the virtual space according to the walking speed of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data on the walking speed to AI and cause AI to execute the display of the virtual space.

[0077] The virtual space generation system includes a sharing unit for sharing the virtual space with other users. The sharing unit can perform, for example, real-time sharing or asynchronous sharing. For example, the sharing unit can share the virtual space generated by the user with other users in real time. The sharing unit can also save the virtual space generated by the user and share it with other users later. This allows the virtual space to be shared with other users. A part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input data of the virtual space to AI and cause AI to execute the sharing processing.

[0078] The virtual space generation system includes a cooperation unit for constructing a virtual space collaboratively. The cooperation unit provides, for example, a collaboration tool or a collaborative editing function. For example, the cooperation unit provides a collaboration tool that allows multiple users to edit a virtual space at the same time. The cooperation unit can also provide a collaborative editing function for users to collaboratively construct a virtual space. This allows users to collaboratively construct a virtual space. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooperation unit can input editing data of the virtual space to the AI ​​and cause the AI ​​to execute the collaborative editing processing.

[0079] The reception unit can estimate the user's emotion and adjust the timing of receiving the text prompt based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, when the user is excited, the reception unit immediately receives the text prompt and quickly generates a virtual space. Also, when the user is relaxed, the reception unit can receive the text prompt at a slow pace and prompt detailed input. Also, when the user is stressed, the reception unit can provide a simple interface and minimize input procedures. This makes it possible to adjust the timing of receiving the text prompt based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the timing of receiving the text prompt.

[0080] The reception unit can analyze the user's past prompt history and select the optimal reception method. For example, the reception unit automatically displays prompts that the user has frequently used in the past as candidates. For example, the reception unit analyzes the pattern of prompts that the user has used in the past and suggests the optimal input method. The reception unit can also predict and suggest prompts to be used in a specific time period from the user's past prompt history. This makes it possible to select the optimal reception method based on the user's past prompt history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the past prompt history into an AI and cause the AI ​​to select the optimal reception method.

[0081] The reception unit may perform filtering based on the user's current field of interest when receiving the text prompt. The reception unit may preferentially receive related prompts based on, for example, keywords recently searched by the user. For example, the reception unit may suggest related prompts based on topics in which the user has shown interest in the past. The reception unit may also analyze the user's social media activities and filter prompts based on the user's current field of interest. This allows the text prompts to be filtered based on the user's current field of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input data on the user's field of interest to AI and cause the AI ​​to filter the prompts.

[0082] The reception unit can estimate the user's emotion and determine the priority of the prompts to be received based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, when the user is excited, the reception unit preferentially receives prompts with a high level of importance. In addition, when the user is relaxed, the reception unit can preferentially receive detailed prompts. In addition, when the user is stressed, the reception unit can also preferentially receive concise prompts. This allows the priority of the prompts to be determined based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the prompts.

[0083] When receiving a text prompt, the reception unit can preferentially receive a highly relevant prompt in consideration of the geographical location information of the user. The reception unit acquires the geographical location information of the user, for example, using GPS data or a location information service. For example, when the user is in a specific area, the reception unit preferentially receives a prompt related to the area. Also, when the user is traveling, the reception unit can preferentially receive a prompt related to the travel destination. Also, when the user is at home, the reception unit can preferentially receive a prompt related to the vicinity of the home. This makes it possible to preferentially receive a highly relevant prompt based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information to AI and cause the AI ​​to perform filtering of the prompt.

[0084] The reception unit can analyze the user's social media activity and receive relevant prompts when receiving the text prompt. The reception unit preferentially receives relevant prompts based on, for example, the content recently shared by the user on social media. For example, the reception unit suggests relevant prompts based on topics the user follows on social media. The reception unit can also analyze the user's social media activity history and filter prompts based on current areas of interest. This makes it possible to receive relevant prompts based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input data on social media activity to AI and have the AI ​​perform filtering of prompts.

[0085] The generation unit can estimate the user's emotion and adjust the expression method of the virtual space to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion, for example, by using facial expression recognition or voice analysis. For example, when the user is relaxed, the generation unit generates a virtual space including a calm landscape and music. Also, when the user is excited, the generation unit can generate a virtual space including dynamic and stimulating elements. Also, when the user is stressed, the generation unit can generate a virtual space including calm colors and a quiet environment. This makes it possible to adjust the expression method of the virtual space based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the virtual space.

[0086] The generation unit can adjust the level of detail of the generation based on the importance of the prompt when generating the virtual space. For example, the generation unit generates a virtual space including detailed elements for a prompt with high importance. For example, the generation unit can generate a virtual space including concise elements for a prompt with low importance. The generation unit can also adjust the complexity of the virtual space to be generated according to the importance of the prompt. This allows the level of detail of the generation to be adjusted based on the importance of the prompt. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the importance of the prompt to the generation AI and cause the generation AI to adjust the level of detail of the generation of the virtual space.

[0087] The generation unit can apply different generation algorithms depending on the category of the prompt when generating the virtual space. For example, the generation unit applies an algorithm for generating a natural landscape to a prompt for a natural environment. For example, the generation unit can apply an algorithm for generating an urban landscape to a prompt for an urban environment. The generation unit can also apply a generation algorithm including fantasy elements to a prompt for a fantasy environment. This allows different generation algorithms to be applied depending on the category of the prompt. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the category of the prompt to the generation AI and cause the generation AI to apply the generation algorithm.

[0088] The generation unit can estimate the user's emotion and adjust the length of the virtual space to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, when the user is in a hurry, the generation unit generates a virtual space that is completed in a short time. Also, when the user is relaxed, the generation unit can generate a virtual space that can be enjoyed for a long time. Also, when the user is excited, the generation unit can generate a virtual space of a moderate length. This allows the length of the virtual space to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the virtual space.

[0089] The generation unit can determine the priority of generation based on the submission time of the prompt when generating the virtual space. The generation unit, for example, prioritizes the most recently submitted prompt and generates the virtual space. For example, the generation unit can postpone the prompt submitted earlier and prioritize the most recent prompt. The generation unit can also adjust the order of the virtual spaces to be generated according to the submission time. This makes it possible to determine the priority of generation based on the submission time of the prompt. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the submission time of the prompt to the generation AI and cause the generation AI to determine the priority of generation.

[0090] The generation unit can adjust the order of generation based on the relevance of the prompts when generating the virtual space. For example, the generation unit prioritizes processing of highly relevant prompts to generate the virtual space. For example, the generation unit can postpone prompts with low relevance and prioritize important prompts. The generation unit can also adjust the order of the virtual space to be generated according to the relevance of the prompts. This makes it possible to adjust the order of generation based on the relevance of the prompts. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the relevance of the prompts to the generation AI and cause the generation AI to adjust the order of generation.

[0091] The analysis unit can estimate the user's emotion and adjust the analysis method of the voice instruction based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, when the user is relaxed, the analysis unit analyzes detailed voice instructions and performs highly accurate operations. In addition, when the user is in a hurry, the analysis unit can preferentially analyze concise voice instructions and perform operations quickly. In addition, when the user is stressed, the analysis unit can also preferentially analyze simple voice instructions and simplify operations. This makes it possible to adjust the analysis method of the voice instruction based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the analysis method of the voice instruction.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction when analyzing the voice instruction. For example, the analysis unit performs a detailed analysis on a voice instruction with high importance and performs a highly accurate operation. For example, the analysis unit can perform a concise analysis on a voice instruction with low importance and perform a quick operation. The analysis unit can also adjust the level of detail of the analysis according to the importance of the voice instruction. This makes it possible to adjust the level of detail of the analysis based on the importance of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data on the importance of the instruction to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing the voice instruction. For example, the analysis unit applies an analysis algorithm dedicated to navigation to a voice instruction related to navigation. For example, the analysis unit can apply an analysis algorithm dedicated to operation to a voice instruction related to operation. Also, the analysis unit can apply an analysis algorithm dedicated to information search to a voice instruction related to information search. This makes it possible to apply different analysis algorithms depending on the category of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the category of the instruction to AI and cause AI to apply the analysis algorithm.

[0094] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the analysis unit displays a detailed analysis result when the user is relaxed. Also, the analysis unit can display a concise analysis result when the user is in a hurry. Also, the analysis unit can display a visually easy-to-understand analysis result when the user is stressed. This allows the display method of the analysis result to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the display method of the analysis result.

[0095] The analysis unit can determine the priority of the analysis based on the time of submission of the instruction when analyzing the voice instruction. For example, the analysis unit prioritizes the analysis of the most recently submitted voice instruction. For example, the analysis unit can postpone the voice instruction submitted earlier and prioritize the most recent voice instruction. The analysis unit can also adjust the order of analysis according to the submission time. This makes it possible to determine the priority of the analysis based on the time of submission of the instruction. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data on the time of submission of the instruction to the AI ​​and cause the AI ​​to determine the priority of the analysis.

[0096] The analysis unit can adjust the order of analysis based on the relevance of the instructions when analyzing the voice instructions. For example, the analysis unit prioritizes analysis of highly relevant voice instructions. For example, the analysis unit can postpone less relevant voice instructions and prioritize important voice instructions. The analysis unit can also adjust the order of analysis according to the relevance of the voice instructions. This makes it possible to adjust the order of analysis based on the relevance of the instructions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the instructions to AI and cause AI to adjust the order of analysis.

[0097] The display unit can estimate the user's emotion and adjust the display method of the virtual space based on the estimated user's emotion. The display unit estimates the user's emotion, for example, by using facial expression recognition or voice analysis. For example, the display unit provides a display method including calm colors and music when the user is relaxed. Also, the display unit can provide a display method including vivid colors and dynamic effects when the user is excited. Also, the display unit can provide a display method including calm colors and a quiet environment when the user is stressed. This allows the display method of the virtual space to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed, for example, by using an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the display method of the virtual space.

[0098] The display unit can select the optimal display method by referring to the user's past operation history when displaying the virtual space. For example, the display unit automatically applies a display method that the user has used in the past. For example, the display unit suggests the optimal display method from the user's past operation history. The display unit can also provide a customized display method based on the display method that the user has used in the past. This allows the optimal display method to be selected based on the user's past operation history. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the past operation history to AI and cause the AI ​​to select the optimal display method.

[0099] The display unit can adjust the level of detail of the display based on the user's device information when displaying the virtual space. For example, when the user is using a high-resolution device, the display unit provides a detailed display. For example, when the user is using a low-resolution device, the display unit can provide a concise display. The display unit can also adjust the level of detail of the display according to the performance of the user's device. This allows the level of detail of the display to be adjusted based on the user's device information. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the device information to AI and cause the AI ​​to adjust the level of detail of the display.

[0100] The display unit can estimate the user's emotion and determine the priority of the virtual spaces to be displayed based on the estimated user's emotion. The display unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, when the user is excited, the display unit can preferentially display a dynamic and stimulating virtual space. Also, when the user is relaxed, the display unit can preferentially display a calm virtual space. Also, when the user is stressed, the display unit can preferentially display a calm virtual space. This allows the priority of the virtual spaces to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the virtual spaces.

[0101] The display unit can display highly relevant virtual spaces preferentially in consideration of the geographical location information of the user when displaying the virtual space. The display unit acquires the geographical location information of the user, for example, by using GPS data or a location information service. For example, when the user is in a specific area, the display unit can display a virtual space related to the area preferentially. In addition, when the user is traveling, the display unit can display a virtual space related to the travel destination preferentially. In addition, when the user is at home, the display unit can also display a virtual space related to the vicinity of the home preferentially. This makes it possible to display a highly relevant virtual space preferentially based on the geographical location information of the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data of the geographical location information to AI and cause AI to execute the determination of the priority order of the virtual spaces.

[0102] The display unit can analyze the user's social media activity and display related virtual spaces when displaying the virtual space. The display unit preferentially displays related virtual spaces based on, for example, the content recently shared by the user on social media. For example, the display unit suggests related virtual spaces based on topics the user follows on social media. The display unit can also analyze the user's social media activity history and filter virtual spaces based on current areas of interest. This makes it possible to display related virtual spaces based on the user's social media activity. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input data on social media activities to AI and have AI execute filtering of virtual spaces.

[0103] The sharing unit can estimate the user's emotion and select a virtual space to be shared based on the estimated user's emotion. The sharing unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the sharing unit shares a calm virtual space when the user is relaxed. Also, the sharing unit can share a dynamic and stimulating virtual space when the user is excited. Also, the sharing unit can share a calm virtual space when the user is stressed. This makes it possible to select a virtual space to be shared based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input the user's emotion data to the AI ​​and cause the AI ​​to select a virtual space.

[0104] The sharing unit can select the optimal sharing method by referring to the past sharing history when sharing the virtual space. For example, the sharing unit automatically suggests a virtual space that the user has shared in the past. For example, the sharing unit suggests the optimal sharing method from the user's past sharing history. The sharing unit can also provide a customized sharing method based on the sharing method that the user has used in the past. This makes it possible to select the optimal sharing method based on the past sharing history. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input data of the past sharing history into an AI and cause the AI ​​to select the optimal sharing method.

[0105] The sharing unit can estimate the user's emotion and adjust the frequency of sharing based on the estimated user's emotion. The sharing unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the sharing unit frequently shares the virtual space when the user is relaxed. Also, the sharing unit can share the virtual space at an appropriate frequency when the user is excited. Also, the sharing unit can reduce the frequency of sharing when the user is stressed. This allows the frequency of sharing to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the frequency of sharing.

[0106] The sharing unit can select the optimal sharing method in consideration of the attribute information of the other party when sharing the virtual space. For example, if the other party is a family member, the sharing unit can provide a private sharing method. For example, if the other party is a friend, the sharing unit can provide a sharing method via social media. In addition, if the other party is a business partner, the sharing unit can also provide a business-oriented sharing method. This makes it possible to select the optimal sharing method based on the attribute information of the other party. Part or all of the above-mentioned processing in the sharing unit may be performed, for example, using AI, or may be performed without using AI. For example, the sharing unit can input data of the other party's attribute information into AI and have the AI ​​select the optimal sharing method.

[0107] The cooperation unit can estimate the user's emotions and select a virtual space in which to cooperate based on the estimated user's emotions. The cooperation unit estimates the user's emotions, for example, using facial expression recognition or voice analysis. For example, the cooperation unit can suggest cooperation in a calm virtual space when the user is relaxed. Also, the cooperation unit can suggest cooperation in a dynamic and stimulating virtual space when the user is excited. Also, the cooperation unit can suggest cooperation in a calm virtual space when the user is stressed. This makes it possible to select a virtual space in which to cooperate based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the cooperation unit can input the user's emotion data into the AI ​​and cause the AI ​​to select a virtual space in which to cooperate.

[0108] The cooperation unit can select the optimal cooperation method by referring to the past cooperation history when cooperating in the virtual space. For example, the cooperation unit automatically suggests a virtual space in which the user has cooperated in the past. For example, the cooperation unit suggests the optimal cooperation method from the user's past cooperation history. The cooperation unit can also provide a customized cooperation method based on the cooperation method used by the user in the past. This makes it possible to select the optimal cooperation method based on the past cooperation history. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooperation unit can input data of the past cooperation history into AI and cause the AI ​​to select the optimal cooperation method.

[0109] The cooperation unit can estimate the user's emotion and adjust the frequency of cooperation based on the estimated user's emotion. The cooperation unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the cooperation unit frequently suggests cooperation when the user is relaxed. Also, the cooperation unit can suggest cooperation at a moderate frequency when the user is excited. Also, the cooperation unit can reduce the frequency of cooperation when the user is stressed. This allows the frequency of cooperation to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cooperation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cooperation unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the frequency of cooperation.

[0110] The cooperation unit can select the optimal cooperation method in consideration of the attribute information of the other party to cooperate in the virtual space. For example, if the other party to cooperate is a family member, the cooperation unit can provide a private cooperation method. For example, if the other party to cooperate is a friend, the cooperation unit can provide a cooperation method through social media. In addition, if the other party to cooperate is a business partner, the cooperation unit can also provide a business-oriented cooperation method. This makes it possible to select the optimal cooperation method based on the attribute information of the other party to cooperate. A part or all of the above-mentioned processing in the cooperation unit may be performed, for example, using AI, or may be performed without using AI. For example, the cooperation unit can input data of the other party's attribute information into AI and cause AI to select the optimal cooperation method.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The virtual space generation system may further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and stress level, and adjusts the generation and operation of the virtual space based on the measurement. For example, if the user's heart rate is high, a relaxing virtual space may be generated. Also, if the user is feeling stressed, simple operations may be provided preferentially. This makes it possible to provide an optimal virtual space experience according to the user's health condition.

[0113] The virtual space generation system may further include a learning unit that learns the user's past behavioral patterns. The learning unit, for example, analyzes what kind of virtual space the user has used in the past and suggests an optimal virtual space based on this. For example, if the user has used a natural environment in the past, the natural environment can be preferentially suggested the next time the user uses the system. Also, if the user has a tendency to perform a specific operation during a specific time period, a suggestion can be made that is tailored to that time period. This makes it possible to provide an optimal virtual space experience based on the user's past behavioral patterns.

[0114] The virtual space generation system may further include an environmental sound analysis unit that analyzes the user's real-time environmental sound. The environmental sound analysis unit, for example, analyzes the sounds around the user and adjusts the acoustic environment of the virtual space based on the results. For example, when the user is in a quiet environment, a quiet acoustic environment can be provided even in the virtual space. Also, when the user is in a noisy environment, the volume in the virtual space can be adjusted to allow the user to operate comfortably. This makes it possible to provide an optimal virtual space experience based on the user's real-time environmental sound.

[0115] The virtual space generation system may further include an eye gaze tracking unit that tracks the user's gaze. The eye gaze tracking unit, for example, detects which part the user is gazing at and adjusts the display of the virtual space based on this. For example, if the user is gazing at a specific object, detailed information about the object may be displayed. Also, if the user is gazing at a specific area for a long time, an operation related to the area may be suggested. This makes it possible to provide an optimal virtual space experience based on the user's gaze.

[0116] The virtual space generation system may further include a gesture recognition unit that recognizes a user's gesture. The gesture recognition unit detects, for example, the user's hand movement and performs an operation in the virtual space based on the detected movement. For example, when the user waves his / her hand, movement in the virtual space is started. Also, when the user performs a specific gesture, a specific operation may be performed. This enables intuitive operation based on the user's gesture, and provides a more natural virtual space experience.

[0117] The virtual space generation system can further estimate the user's emotions and adjust the behavior of the character in the virtual space based on the estimated user's emotions. For example, if the user is relaxed, the character in the virtual space can also behave calmly. If the user is excited, the character can also behave lively. If the user is stressed, the character can also behave in a comforting manner. This allows the behavior of the character in the virtual space to be adjusted based on the user's emotions, providing a more emotional experience.

[0118] The virtual space generation system can further estimate the user's emotions and adjust the music in the virtual space based on the estimated user's emotions. For example, if the user is relaxed, calm music can be played. If the user is excited, fast-paced music can be played. If the user is stressed, relaxing music can be played. In this way, the music in the virtual space can be adjusted based on the user's emotions to provide a more emotional experience.

[0119] The virtual space generation system can further estimate the user's emotions and adjust the weather in the virtual space based on the estimated user's emotions. For example, if the user is relaxed, a sunny virtual space can be generated. If the user is excited, dynamic weather such as a storm can be generated. If the user is stressed, a gentle rainy virtual space can be generated. In this way, the weather in the virtual space can be adjusted based on the user's emotions to provide a more emotional experience.

[0120] The virtual space generation system can further estimate the user's emotions and adjust the lighting in the virtual space based on the estimated user's emotions. For example, if the user is relaxed, soft lighting can be provided. If the user is excited, bright lighting can be provided. If the user is stressed, calm lighting can be provided. This allows the lighting in the virtual space to be adjusted based on the user's emotions, providing a more emotional experience.

[0121] The virtual space generation system can further estimate the user's emotion and adjust the interaction in the virtual space based on the estimated user's emotion. For example, if the user is relaxed, a gentle interaction can be provided. If the user is excited, a lively interaction can be provided. If the user is stressed, a simple interaction can be provided. In this way, the interaction in the virtual space can be adjusted based on the user's emotion to provide a more emotional experience.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The reception unit receives a text prompt input by a user. The text prompt may be in the form of a question, an instruction, or a free form. For example, if the user inputs a prompt such as "I want to build a castle in the forest," the prompt is received. Step 2: The generation unit uses the generation AI to generate a virtual space based on the text prompt received by the reception unit. The generated virtual space includes 3D models, interactive elements, and environmental settings. For example, when a user inputs a prompt such as "I want to build a castle in the forest," the generation AI generates a virtual space including a forest and a castle based on the prompt. Step 3: The analysis unit analyzes the conversation items and the voice dialogue. The analysis unit uses voice recognition technology and natural language processing technology to analyze the user's voice instructions. For example, if the user gives a voice instruction such as "Show me the inside of the castle," the analysis unit analyzes the instruction and provides the generation unit with information to display the inside of the castle. Step 4: The display unit displays the virtual space generated by the generation unit. The display unit can display the virtual space according to the orientation of the user's smartphone. It can also display the virtual space according to the user's walking speed.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0126] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, display unit, sharing unit, and cooperation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a text prompt input by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a virtual space using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's voice instruction using a voice recognition technology or a natural language processing technology. The display unit is realized by the output device 40 of the smart device 14 and displays the generated virtual space. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the virtual space with other users. The cooperation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for multiple users to jointly edit the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0135] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, display unit, sharing unit, and collaboration unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a text prompt input by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a virtual space using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's voice instruction using voice recognition technology or natural language processing technology. The display unit is realized by the output device 40 of the smart glasses 214 and displays the generated virtual space. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the virtual space with other users. The collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for multiple users to jointly edit the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, display unit, sharing unit, and collaboration unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a text prompt input by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a virtual space using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's voice instruction using a voice recognition technology or a natural language processing technology. The display unit is realized by the output device 40 of the headset type terminal 314 and displays the generated virtual space. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the virtual space with other users. The collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for multiple users to jointly edit the virtual space. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, display unit, sharing unit, and cooperation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a text prompt input by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a virtual space using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's voice instruction using a voice recognition technology or a natural language processing technology. The display unit is realized by the output device 40 of the robot 414 and displays the generated virtual space. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the virtual space with other users. The cooperation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for multiple users to jointly edit the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0178] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0182] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] In addition, 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 upon request from the data processing device 12.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0189] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0193] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0195] (Appendix 1) a reception unit for receiving a text prompt; a generator that generates a virtual space based on the text prompt received by the receiver; an analysis unit for analyzing a conversation item or a voice dialogue; a display unit that displays the virtual space generated by the generation unit; A system characterized by: (Appendix 2) The analysis unit is Analyzes the user's voice commands and executes operations within the virtual space 2. The system of claim 1, (Appendix 3) The generation unit is Generates a virtual space containing specified elements based on the user's text prompt 2. The system of claim 1, (Appendix 4) The display unit is Displaying a virtual space based on the orientation of the user's smartphone 2. The system of claim 1, (Appendix 5) The display unit is Displaying a virtual space based on the user's walking speed 2. The system of claim 1, (Appendix 6) The common part is Equipped with a sharing section for sharing the virtual space with other users 2. The system of claim 1, (Appendix 7) Equipping a collaboration department for jointly building virtual spaces 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and adjust the timing of receiving text prompts based on the estimated user's emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past prompt history and select the optimal reception method 2. The system of claim 1, (Appendix 10) The reception unit is When receiving a text prompt, filter it based on the user's current interests 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the user's emotions and prioritize prompts based on the estimated user's emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting text prompts, the system takes into account the user's geographic location to prioritize more relevant prompts. 2. The system of claim 1, (Appendix 13) The reception unit is When receiving a text prompt, analyze the user's social media activity and receive relevant prompts. 2. The system of claim 1, (Appendix 14) The generation unit is Estimate the user's emotions and adjust the way the virtual space is generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 15) The generation unit is When generating a virtual space, adjust the level of detail based on the importance of the prompt. 2. The system of claim 1, (Appendix 16) The generation unit is When generating a virtual space, different generation algorithms are applied depending on the category of the prompt. 2. The system of claim 1, (Appendix 17) The generation unit is The system estimates the user's emotions and adjusts the length of the virtual space to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 18) The generation unit is When generating virtual spaces, prioritize generation based on when prompts are submitted. 2. The system of claim 1, (Appendix 19) The generation unit is When generating virtual spaces, adjust the order of generation based on prompt relevance 2. The system of claim 1, (Appendix 20) The analysis unit is A method for estimating a user's emotion and adjusting a method for analyzing a voice instruction based on the estimated user's emotion 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing voice instructions, adjust the level of analysis detail based on the importance of the instructions. 2. The system of claim 1, (Appendix 22) The analysis unit is When analyzing voice commands, different analysis algorithms are applied depending on the category of the command. 2. The system of claim 1, (Appendix 23) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The analysis unit is When analyzing voice instructions, prioritize analysis based on when the instructions were submitted 2. The system of claim 1, (Appendix 25) The analysis unit is When analyzing voice instructions, adjust the order of analysis based on the relevance of the instructions 2. The system of claim 1, (Appendix 26) The display unit is Estimating a user's emotions and adjusting the display method of the virtual space based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The display unit is When displaying a virtual space, the system selects the optimal display method by referring to the user's past operation history. 2. The system of claim 1, (Appendix 28) The display unit is When displaying a virtual space, adjust the level of detail based on the user's device information 2. The system of claim 1, (Appendix 29) The display unit is A user's emotion is estimated, and a priority order for displaying virtual spaces is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 30) The display unit is When displaying virtual spaces, the system prioritizes displaying highly relevant virtual spaces by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The display unit is When viewing a virtual space, the system analyzes the user's social media activity and displays related virtual spaces. 2. The system of claim 1, (Appendix 32) The common part is Estimate the user's emotions and select a virtual space to share based on the estimated user's emotions. 2. The system of claim 1, (Appendix 33) The common part is When sharing a virtual space, select the optimal sharing method by referring to past sharing history 2. The system of claim 1, (Appendix 34) The common part is Infer user sentiment and adjust sharing frequency based on the estimated user sentiment 2. The system of claim 1, (Appendix 35) The common part is When sharing a virtual space, the optimal sharing method is selected taking into account the attribute information of the person sharing the space. 2. The system of claim 1, (Appendix 36) The Cooperation Department: The user's emotions are estimated, and a virtual space for cooperation is selected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 37) The Cooperation Department: When cooperating in virtual space, the optimal method of cooperation is selected by referring to past cooperation history. 2. The system of claim 1, (Appendix 38) The Cooperation Department: Inferring a user's emotions and adjusting the frequency of collaboration based on the estimated user's emotions 2. The system of claim 1, (Appendix 39) The Cooperation Department: When cooperating in virtual space, the optimal cooperation method is selected by taking into account the attribute information of the cooperating party. 2. The system of claim 1, [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving a text prompt; a generator that generates a virtual space based on the text prompt received by the receiver; an analysis unit for analyzing a conversation item or a voice dialogue; a display unit that displays the virtual space generated by the generation unit; A system characterized by:

2. The analysis unit is Analyzes the user's voice commands and executes operations within the virtual space 2. The system of claim 1 .

3. The generation unit is Generates a virtual space containing specified elements based on the user's text prompt 2. The system of claim 1 .

4. The display unit is Displaying a virtual space based on the orientation of the user's smartphone 2. The system of claim 1 .

5. The display unit is Displaying a virtual space based on the user's walking speed 2. The system of claim 1 .

6. Equipped with a sharing section for sharing the virtual space with other users 2. The system of claim 1 .

7. Equipping a collaboration department for jointly building virtual spaces 2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and adjust the timing of receiving text prompts based on the estimated user's emotions.

2. The system of claim 1 .

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