System

Abstract

An object of a system according to an embodiment is to efficiently and variously construct a virtual space using a generative AI.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a construction unit, and a provision unit. The reception unit receives a request from a user. The generation unit analyzes the request received by the reception unit and generates an element constituting the virtual space. The construction unit constructs a virtual space by combining the elements generated by the generation unit. The providing unit provides the user with the virtual space constructed by the constructing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies require manual generation of elements to build a virtual space, leaving room for improvement in terms of efficiency and diversity.

[0005] The system according to the embodiment aims to construct a virtual space efficiently and diversified using a generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a construction unit, and a provision unit. The reception unit receives a request from a user. The generation unit analyzes the request received by the reception unit and generates elements that constitute a virtual space. The construction unit constructs a virtual space by combining the elements generated by the generation unit. The provision unit provides the virtual space constructed by the construction unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and diversify virtual spaces using generation AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A virtual space generation system according to an embodiment of the present invention uses a generation AI to create a virtual space. In this system, a user inputs a scenario or elements they wish to experience in a virtual space. The generation AI analyzes the input, generates elements that make up the virtual space, and combines them to construct the virtual space and provide it to the user. For example, if a user inputs a request such as "I want to explore a medieval castle" or "I want to experience a futuristic city," the generation AI generates elements such as a scenario, landscape, creatures, buildings, music, and art, and combines them to construct a virtual space. This allows users to experience scenarios, landscapes, creatures, and architecture that would be difficult to realize in the real world. The virtual space generation system can also generate elements for practical learning and training in fields such as medicine, engineering, and aerospace. Examples include surgical simulations, new drug testing, the design and testing of new machines and structures, and simulations of spacecraft design and space missions. Furthermore, the system can be used for educational purposes such as language and cultural learning, historical reenactment, and geographical exploration. For example, historical events and locations can be recreated, allowing students to experience them as if they were there. This allows the virtual space generation system to provide diverse experiences, solving problems and providing new experiences in various industries and fields. This allows the virtual space generation system to efficiently and quickly generate virtual spaces using generative AI, providing diverse experiences. For example, users can experience scenarios, landscapes, living creatures, and architecture that are difficult to realize in the real world, satisfying the needs of people seeking creative expression. Furthermore, by providing practical learning and training in fields such as medicine, engineering, and aerospace, it can contribute to the improvement of professional skills and the development of new technologies. Furthermore, when used for educational purposes, it can help students gain a deeper understanding of history, geography, and culture, improving learning outcomes.

[0029] A virtual space generation system according to an embodiment includes a reception unit, a generation unit, a construction unit, and a provision unit. The reception unit receives requests from users. Requests may be in text, audio, or image formats, but are not limited to these examples. For example, a user may input a request such as "I want to explore a medieval castle" into the reception unit. The generation unit uses a generation AI to analyze the request received by the reception unit and generate elements constituting the virtual space. The generation unit generates elements such as a scenario, landscape, creatures, buildings, music, and art. For example, the generation AI may analyze a request such as "I want to explore a medieval castle" and generate a medieval castle scenario, landscape, creatures, buildings, etc. The generation unit can also use the generation AI to generate elements for practical learning and training in fields such as medicine, engineering, and aerospace. Examples of such elements include surgical simulations, new drug testing, the design and testing of new machines and structures, and simulations of spacecraft design and space missions. The construction unit combines the elements generated by the generation unit to construct a virtual space. The construction unit constructs a virtual space by combining, for example, generated scenarios, landscapes, creatures, buildings, music, art, etc. The provision unit provides the virtual space constructed by the construction unit to a user. The provision unit provides, for example, a virtual space that the user can freely explore and experience. In this way, the virtual space generation system according to the embodiment can generate and provide a virtual space based on a user request.

[0030] The generation unit can generate elements such as a scenario, scenery, creatures, structures, music, and art. The generation unit, for example, generates a scenario. Examples of scenarios include, but are not limited to, storyboards and event sequences. The generation unit, for example, generates a landscape. Examples of landscapes include, but are not limited to, natural landscapes and urban landscapes. The generation unit, for example, generates creatures. Examples of creatures include, but are not limited to, animals, plants, and fictional creatures. The generation unit, for example, generates structures. Examples of structures include, but are not limited to, buildings, bridges, and monuments. The generation unit, for example, generates music. Examples of music include, but are not limited to, background music and sound effects. The generation unit, for example, generates art. Examples of art include, but are not limited to, paintings, sculptures, and digital art. This allows for the generation of a variety of elements, thereby increasing the diversity of the virtual space. Some or all of the above-described 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 elements such as a scenario, landscape, living things, buildings, music, and art into the generation AI and output the elements generated by the generation AI.

[0031] The generation unit may generate elements for providing practical learning or training in the fields of medicine, engineering, and aerospace. The generation unit may generate elements for providing practical learning or training in the field of medicine, for example. Examples of medical elements include, but are not limited to, surgical simulations and diagnostic training. The generation unit may generate elements for providing practical learning or training in the field of engineering, for example. Examples of engineering elements include, but are not limited to, machine design simulations and engineering training. The generation unit may generate elements for providing practical learning or training in the field of aerospace, for example. Examples of aerospace elements include, but are not limited to, flight simulations and space exploration training. This allows elements for providing specialized learning or training to be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input elements of medicine, engineering, or aerospace into the generation AI and output elements generated by the generation AI.

[0032] The generation unit can generate elements for educational purposes, such as language and culture learning, historical reenactment, and geographical exploration. The generation unit generates elements for language and culture learning, for example. Examples of language and culture elements include, but are not limited to, language learning apps and cultural experience simulations. The generation unit generates elements for, for example, historical reenactment. Examples of historical elements include, but are not limited to, simulations of historical events and reenactments of historical buildings. The generation unit generates elements for, for example, geographical exploration. Examples of geographic elements include, but are not limited to, map simulations and geographic information systems. This can enhance learning effectiveness by generating elements for educational purposes. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input language, culture, history, and geography elements into a generation AI and output elements generated by the generation AI.

[0033] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit can automatically display scenarios and elements that the user has frequently requested in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest scenarios and elements to be used in a specific time period based on the user's past request history. This can improve user convenience by providing the optimal reception method based on the past request history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past request history data into a generation AI and have the generation AI select the optimal reception method.

[0034] When receiving a request, the reception unit can perform filtering based on the user's current interests and concerns. The reception unit can suggest related scenarios and elements based on, for example, the user's recent search keywords and browsing history. The reception unit can also analyze the content of posts from accounts the user follows on social media to suggest related requests. The reception unit can also suggest interesting scenarios and elements based on trends in online communities in which the user participates. This can improve the user experience by providing requests based on the user's interests and concerns. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's search history data and social media data into a generation AI and have the generation AI perform filtering.

[0035] When receiving a request, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can receive the request using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive the request using text analysis technology. Furthermore, if the user selects image input, the reception unit can also receive the request using image recognition technology. This can improve user convenience by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input voice data, text data, and image data into a generation AI and have the generation AI select the optimal reception means.

[0036] When receiving a request, the reception unit can prioritize receiving highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting scenarios and elements related to that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting scenarios and elements related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize suggesting scenarios and elements related to the event. This can improve the user experience by providing requests based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant requests.

[0037] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can suggest scenarios and elements related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related scenarios and elements. The reception unit can also suggest related scenarios and elements by referring to the activities of the user's friends on social media. This can improve the user experience by providing requests based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related requests.

[0038] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit can improve the request reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest specific elements or scenarios based on the user's past feedback. The reception unit can also analyze the user's feedback and suggest the optimal reception method. This can improve the user experience by providing a reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0039] The generation unit can adjust the level of detail of the generation based on the importance of the request during generation. For example, the generation unit generates detailed scenarios and elements for requests with high importance. The generation unit can also generate simplified scenarios and elements for requests with low importance. The generation unit can also adjust the number and types of elements to be generated depending on the importance. This can improve the user experience by providing a level of detail of the generation according to the importance of the request. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input request importance data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0040] The generation unit can apply different generation algorithms depending on the category of the request during generation. For example, in the case of scenario generation, the generation unit can apply a generation algorithm specialized for storytelling. In addition, in the case of landscape generation, the generation unit can also apply a generation algorithm specialized for natural landscapes. In addition, in the case of music generation, the generation unit can also apply a generation algorithm based on music theory. This makes it possible to improve the user experience by providing a generation algorithm according to the category of the request. Some 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 request category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0041] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of generation, for example, based on scenarios and elements that the user has previously preferred. The generation unit can also analyze the user's past feedback to improve the accuracy of generation. The generation unit can also learn the user's past generation results and generate elements with higher accuracy. This can improve the user experience by providing generation accuracy based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0042] The generation unit can determine the generation priority based on the time of request submission at the time of generation. For example, if a request is submitted early, the generation unit can prioritize generation. Also, if a request is submitted late, the generation unit can generate it with normal priority. Also, the generation unit can adjust the generation schedule according to the time of submission. This can improve the user experience by providing generation priority according to the time of request submission. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input request submission time data into the generation AI and have the generation AI determine the generation priority.

[0043] The generation unit can adjust the order of generation based on the relevance of the requests during generation. For example, the generation unit prioritizes the generation of highly relevant requests. The generation unit can also postpone the generation of less relevant requests. The generation unit can also dynamically adjust the order of generation based on the relevance. This can improve the user experience by providing an order of generation based on the relevance of the requests. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input relevance data of the requests into the generation AI and cause the generation AI to adjust the order of generation.

[0044] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit generates elements that use a lot of technical terminology for a user with high technical expertise. The generation unit can also generate elements that are explained in simple terms for a user with low technical expertise. The generation unit can also adjust the content of the generated elements according to the user's level of expertise. This can improve the user experience by providing technical terminology that matches the user's level of expertise. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0045] During construction, the construction unit can improve the accuracy of the construction by taking into account the interrelationships between the generated elements. The construction unit can, for example, construct a virtual space by taking into account the interrelationships between the generated scenario and scenery. The construction unit can also construct a virtual space by taking into account the interrelationships between the generated creatures and buildings. The construction unit can also construct a virtual space by taking into account the interrelationships between the generated music and art. This can improve the user experience by providing a construction that takes into account the interrelationships between the generated elements. Some or all of the above-described processing in the construction unit can be performed, for example, using AI, or can be performed without using AI. For example, the construction unit can input interrelationship data between the generated elements into the generation AI and cause the generation AI to improve the accuracy of the construction.

[0046] The construction unit can take attribute information of the generated elements into consideration when constructing the virtual space. The construction unit can construct the virtual space, for example, by taking into consideration the color and shape of the generated elements. The construction unit can also construct the virtual space by taking into consideration the music and sound effects of the generated elements. The construction unit can also construct the virtual space by taking into consideration the movement and animation of the generated elements. This can improve the user experience by providing a construction that takes into consideration the attribute information of the generated elements. Some or all of the above-described processing in the construction unit can be performed, for example, using AI or without using AI. For example, the construction unit can input attribute information data of the generated elements into the generation AI and cause the generation AI to execute the construction.

[0047] During construction, the construction unit can weight the construction based on the submission frequency of the generated elements. For example, the construction unit constructs a virtual space by preferentially using elements with a high submission frequency. The construction unit can also construct a virtual space by complementary use of elements with a low submission frequency. The construction unit can also adjust the importance of elements based on their submission frequency and construct a virtual space. This can improve the user experience by providing a construction based on the submission frequency of the generated elements. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or can be performed without using AI. For example, the construction unit can input submission frequency data of the generated elements to a generation AI and cause the generation AI to weight the construction.

[0048] The construction unit can perform construction while taking into account the geographical distribution of the generated elements. For example, the construction unit constructs a virtual space while taking into account the geographical positional relationships of the generated elements. The construction unit can also construct a virtual space by reflecting the geographical characteristics of the generated elements. The construction unit can also determine the layout of the virtual space based on the geographical distribution of the generated elements. This can improve the user experience by providing construction that takes into account the geographical distribution of the generated elements. Some or all of the above-described processing in the construction unit can be performed, for example, using AI or without AI. For example, the construction unit can input geographical distribution data of the generated elements to the generation AI and cause the generation AI to execute construction.

[0049] During construction, the construction unit can improve the accuracy of the construction by referring to literature related to the generated elements. The construction unit, for example, constructs a virtual space by referring to academic papers related to the generated elements. The construction unit can also construct a virtual space by referring to patent documents related to the generated elements. The construction unit can also construct a virtual space by referring to technical literature related to the generated elements. This can improve the user experience by providing a construction that references literature related to the generated elements. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or can be performed without using AI. For example, the construction unit can input literature data related to the generated elements into the generation AI and cause the generation AI to improve the accuracy of the construction.

[0050] The construction unit can take into consideration the market value of the generated elements when constructing the virtual space. For example, the construction unit can construct the virtual space by preferentially using elements with high market value. The construction unit can also construct the virtual space by complementary use of elements with low market value. The construction unit can also adjust the importance of elements based on market value and construct the virtual space. This can improve the user experience by providing a construction that takes into consideration the market value of the generated elements. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or can be performed without using AI. For example, the construction unit can input market value data of the generated elements into the generation AI and have the generation AI execute the construction.

[0051] When providing the display method, the providing unit can select an appropriate display method by referring to the user's past operation history. The providing unit can provide an optimal display method based on, for example, display methods that the user has preferred in the past. The providing unit can also analyze the user's past operation history and suggest an optimal display method. The providing unit can also provide an optimal display method by referring to the user's past feedback. This can improve the user experience by providing a display method based on the user's past operation history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's operation history data to the generation AI and cause the generation AI to select a display method.

[0052] The providing unit can customize the display content according to the user's current task when providing the display content. For example, if the user is relaxed, the providing unit can provide a display method using calm colors and music. Furthermore, if the user is excited, the providing unit can provide a display method using bright colors and up-tempo music. Furthermore, if the user is stressed, the providing unit can provide a display method using calm colors and relaxing music. This can improve the user experience by providing display content according to the user's current task. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's task data into the generation AI and cause the generation AI to customize the display content.

[0053] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit can improve the display method based on user feedback, for example. The providing unit can also analyze user feedback and propose an optimal display method. The providing unit can also customize the display method by referring to user feedback. This can improve the user experience by providing a providing method based on user feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0054] The providing unit can select an appropriate display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This can improve the user experience by providing a display method based on the user's device information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a display method.

[0055] The providing unit can make the display content multilingual according to the user's language setting when providing the display content. The providing unit can automatically set the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This can improve the user experience by providing multilingual display content based on the user's language setting. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0056] The providing unit can select an appropriate delivery method by taking into consideration the user's geographical location information when providing the information. For example, if the user is in a specific area, the providing unit can provide display content related to that area. Furthermore, if the user is traveling, the providing unit can also provide display content related to the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can also provide display content related to the event. This can improve the user experience by providing a delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select a delivery method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The generator can analyze the user's past generation results and increase the variety of elements to be generated. For example, it can generate new variations based on scenarios and elements that the user has previously preferred. It can also improve the quality of the generated elements by referring to the user's past feedback. Furthermore, it can learn from the user's past generation results and generate more diverse elements. This can improve the user experience by providing variations based on the user's past generation results.

[0059] The generation unit can reflect user feedback in real time in the elements to be generated. For example, the user can evaluate the generated elements in real time and adjust the elements based on the evaluation. The user can also input comments in real time and modify the elements based on the comments. Furthermore, the user can request changes to elements in real time and the elements can be generated in response to the requests. This allows for an improved user experience by reflecting feedback in real time.

[0060] The reception unit can automatically set the priority of requests based on the user's past request history. For example, it can prioritize scenarios or elements that the user frequently requests. It can also prioritize requests that the user has previously given high ratings. It can also prioritize requests that the user has previously requested but that have not yet been processed. This makes it possible to improve the user experience by providing priorities based on the user's past request history.

[0061] The reception unit can monitor the user's current activity status and adjust the method of receiving requests. For example, if the user is exercising, voice input can be given priority. Alternatively, if the user is doing desk work, text input can be given priority. Furthermore, if the user is relaxing, detailed options can be provided to enable customization of requests. This can improve the user experience by providing a method of receiving requests according to the user's activity status.

[0062] The generation unit can reflect the user's cultural background in the elements it generates. For example, if the user belongs to a particular cultural sphere, it can generate elements related to that culture. Also, if the user has a multicultural background, it can generate elements that combine multiple cultures. Furthermore, if the user is interested in a particular culture, it can generate elements related to that culture. This can improve the user experience by providing elements that correspond to the user's cultural background.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives a request from a user. The request may be in text, audio, or image format. For example, a user may input a request such as "I want to explore a medieval castle." Step 2: The generator uses AI to analyze the request received by the reception unit and generate elements that make up the virtual space. For example, it generates elements such as scenarios, landscapes, creatures, buildings, music, and art. It can also generate elements to provide practical learning and training in fields such as medicine, engineering, and aerospace. Step 3: The construction unit builds a virtual space by combining the elements generated by the generation unit, such as generated scenarios, landscapes, creatures, buildings, music, and art. Step 4: The providing unit provides the virtual space constructed by the constructing unit to the user, for example, providing a virtual space that the user can freely explore and experience.

[0065] (Example 2) A virtual space generation system according to an embodiment of the present invention uses a generation AI to create a virtual space. In this system, a user inputs a scenario or elements they wish to experience in a virtual space. The generation AI analyzes the input, generates elements that make up the virtual space, and combines them to construct the virtual space and provide it to the user. For example, if a user inputs a request such as "I want to explore a medieval castle" or "I want to experience a futuristic city," the generation AI generates elements such as a scenario, landscape, creatures, buildings, music, and art, and combines them to construct a virtual space. This allows users to experience scenarios, landscapes, creatures, and architecture that would be difficult to realize in the real world. The virtual space generation system can also generate elements for practical learning and training in fields such as medicine, engineering, and aerospace. Examples include surgical simulations, new drug testing, the design and testing of new machines and structures, and simulations of spacecraft design and space missions. Furthermore, the system can be used for educational purposes such as language and cultural learning, historical reenactment, and geographical exploration. For example, historical events and locations can be recreated, allowing students to experience them as if they were there. This allows the virtual space generation system to provide diverse experiences, solving problems and providing new experiences in various industries and fields. This allows the virtual space generation system to efficiently and quickly generate virtual spaces using generative AI, providing diverse experiences. For example, users can experience scenarios, landscapes, living creatures, and architecture that are difficult to realize in the real world, satisfying the needs of people seeking creative expression. Furthermore, by providing practical learning and training in fields such as medicine, engineering, and aerospace, it can contribute to the improvement of professional skills and the development of new technologies. Furthermore, when used for educational purposes, it can help students gain a deeper understanding of history, geography, and culture, improving learning outcomes.

[0066] A virtual space generation system according to an embodiment includes a reception unit, a generation unit, a construction unit, and a provision unit. The reception unit receives requests from users. Requests may be in text, audio, or image formats, but are not limited to these examples. For example, a user may input a request such as "I want to explore a medieval castle" into the reception unit. The generation unit uses a generation AI to analyze the request received by the reception unit and generate elements constituting the virtual space. The generation unit generates elements such as a scenario, landscape, creatures, buildings, music, and art. For example, the generation AI may analyze a request such as "I want to explore a medieval castle" and generate a medieval castle scenario, landscape, creatures, buildings, etc. The generation unit can also use the generation AI to generate elements for practical learning and training in fields such as medicine, engineering, and aerospace. Examples of such elements include surgical simulations, new drug testing, the design and testing of new machines and structures, and simulations of spacecraft design and space missions. The construction unit combines the elements generated by the generation unit to construct a virtual space. The construction unit constructs a virtual space by combining, for example, generated scenarios, landscapes, creatures, buildings, music, art, etc. The provision unit provides the virtual space constructed by the construction unit to a user. The provision unit provides, for example, a virtual space that the user can freely explore and experience. In this way, the virtual space generation system according to the embodiment can generate and provide a virtual space based on a user request.

[0067] The generation unit can generate elements such as a scenario, scenery, creatures, structures, music, and art. The generation unit, for example, generates a scenario. Examples of scenarios include, but are not limited to, storyboards and event sequences. The generation unit, for example, generates a landscape. Examples of landscapes include, but are not limited to, natural landscapes and urban landscapes. The generation unit, for example, generates creatures. Examples of creatures include, but are not limited to, animals, plants, and fictional creatures. The generation unit, for example, generates structures. Examples of structures include, but are not limited to, buildings, bridges, and monuments. The generation unit, for example, generates music. Examples of music include, but are not limited to, background music and sound effects. The generation unit, for example, generates art. Examples of art include, but are not limited to, paintings, sculptures, and digital art. This allows for the generation of a variety of elements, thereby increasing the diversity of the virtual space. Some or all of the above-described 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 elements such as a scenario, landscape, living things, buildings, music, and art into the generation AI and output the elements generated by the generation AI.

[0068] The generation unit may generate elements for providing practical learning or training in the fields of medicine, engineering, and aerospace. The generation unit may generate elements for providing practical learning or training in the field of medicine, for example. Examples of medical elements include, but are not limited to, surgical simulations and diagnostic training. The generation unit may generate elements for providing practical learning or training in the field of engineering, for example. Examples of engineering elements include, but are not limited to, machine design simulations and engineering training. The generation unit may generate elements for providing practical learning or training in the field of aerospace, for example. Examples of aerospace elements include, but are not limited to, flight simulations and space exploration training. This allows elements for providing specialized learning or training to be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input elements of medicine, engineering, or aerospace into the generation AI and output elements generated by the generation AI.

[0069] The generation unit can generate elements for educational purposes, such as language and culture learning, historical reenactment, and geographical exploration. The generation unit generates elements for language and culture learning, for example. Examples of language and culture elements include, but are not limited to, language learning apps and cultural experience simulations. The generation unit generates elements for, for example, historical reenactment. Examples of historical elements include, but are not limited to, simulations of historical events and reenactments of historical buildings. The generation unit generates elements for, for example, geographical exploration. Examples of geographic elements include, but are not limited to, map simulations and geographic information systems. This can enhance learning effectiveness by generating elements for educational purposes. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input language, culture, history, and geography elements into a generation AI and output elements generated by the generation AI.

[0070] The reception unit can estimate the user's emotions and adjust the request reception method based on the estimated user emotions. For example, if the user is excited, the reception unit can provide a simple and intuitive interface to allow the user to quickly input a request. Furthermore, if the user is relaxed, the reception unit can provide detailed options to enable the user to customize the request. Furthermore, if the user is stressed, the reception unit can provide a guided step-by-step input method to facilitate input of the request. This improves the user experience by providing a request reception method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit can automatically display scenarios and elements that the user has frequently requested in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest scenarios and elements to be used in a specific time period based on the user's past request history. This can improve user convenience by providing the optimal reception method based on the past request history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past request history data into a generation AI and have the generation AI select the optimal reception method.

[0072] When receiving a request, the reception unit can perform filtering based on the user's current interests and concerns. The reception unit can suggest related scenarios and elements based on, for example, the user's recent search keywords and browsing history. The reception unit can also analyze the content of posts from accounts the user follows on social media to suggest related requests. The reception unit can also suggest interesting scenarios and elements based on trends in online communities in which the user participates. This can improve the user experience by providing requests based on the user's interests and concerns. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's search history data and social media data into a generation AI and have the generation AI perform filtering.

[0073] When receiving a request, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can receive the request using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive the request using text analysis technology. Furthermore, if the user selects image input, the reception unit can also receive the request using image recognition technology. This can improve user convenience by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input voice data, text data, and image data into a generation AI and have the generation AI select the optimal reception means.

[0074] The reception unit can estimate the user's emotions and determine the priority of received requests based on the estimated user emotions. For example, if the user is excited, the reception unit can process the request with priority and quickly provide the virtual space. Furthermore, if the user is relaxed, the reception unit can process the request with normal priority. Furthermore, if the user is stressed, the reception unit can process the request with priority and quickly provide the virtual space. This improves the user experience by determining the priority of requests according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] When receiving a request, the reception unit can prioritize receiving highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting scenarios and elements related to that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting scenarios and elements related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize suggesting scenarios and elements related to the event. This can improve the user experience by providing requests based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant requests.

[0076] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can suggest scenarios and elements related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related scenarios and elements. The reception unit can also suggest related scenarios and elements by referring to the activities of the user's friends on social media. This can improve the user experience by providing requests based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related requests.

[0077] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit can improve the request reception method, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest specific elements or scenarios based on the user's past feedback. The reception unit can also analyze the user's feedback and suggest the optimal reception method. This can improve the user experience by providing a reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0078] The generation unit can estimate the user's emotions and adjust the expression method of the generated elements based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate elements using calm colors and music. Furthermore, if the user is excited, the generation unit can generate elements using bright colors and up-tempo music. Furthermore, if the user is stressed, the generation unit can generate elements using calm colors and relaxing music. This improves the user experience by providing an expression method of elements according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The generation unit can adjust the level of detail of the generation based on the importance of the request during generation. For example, the generation unit generates detailed scenarios and elements for requests with high importance. The generation unit can also generate simplified scenarios and elements for requests with low importance. The generation unit can also adjust the number and types of elements to be generated depending on the importance. This can improve the user experience by providing a level of detail of the generation according to the importance of the request. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input request importance data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0080] The generation unit can apply different generation algorithms depending on the category of the request during generation. For example, in the case of scenario generation, the generation unit can apply a generation algorithm specialized for storytelling. In addition, in the case of landscape generation, the generation unit can also apply a generation algorithm specialized for natural landscapes. In addition, in the case of music generation, the generation unit can also apply a generation algorithm based on music theory. This makes it possible to improve the user experience by providing a generation algorithm according to the category of the request. Some 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 request category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0081] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of generation, for example, based on scenarios and elements that the user has previously preferred. The generation unit can also analyze the user's past feedback to improve the accuracy of generation. The generation unit can also learn the user's past generation results and generate elements with higher accuracy. This can improve the user experience by providing generation accuracy based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0082] The generation unit can estimate the user's emotions and adjust the length of the generated elements based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point elements. If the user is relaxed, the generation unit can generate longer elements with detailed explanations. If the user is excited, the generation unit can generate elements with visually stimulating effects. This improves the user experience by providing elements with lengths that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The generation unit can determine the generation priority based on the time of request submission at the time of generation. For example, if a request is submitted early, the generation unit can prioritize generation. Also, if a request is submitted late, the generation unit can generate it with normal priority. Also, the generation unit can adjust the generation schedule according to the time of submission. This can improve the user experience by providing generation priority according to the time of request submission. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input request submission time data into the generation AI and have the generation AI determine the generation priority.

[0084] The generation unit can adjust the order of generation based on the relevance of the requests during generation. For example, the generation unit prioritizes the generation of highly relevant requests. The generation unit can also postpone the generation of less relevant requests. The generation unit can also dynamically adjust the order of generation based on the relevance. This can improve the user experience by providing an order of generation based on the relevance of the requests. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input relevance data of the requests into the generation AI and cause the generation AI to adjust the order of generation.

[0085] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit generates elements that use a lot of technical terminology for a user with high technical expertise. The generation unit can also generate elements that are explained in simple terms for a user with low technical expertise. The generation unit can also adjust the content of the generated elements according to the user's level of expertise. This can improve the user experience by providing technical terminology that matches the user's level of expertise. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0086] The construction unit can estimate the user's emotions and adjust construction criteria based on the estimated user emotions. For example, if the user is relaxed, the construction unit can construct a virtual space using calm colors and music. Furthermore, if the user is excited, the construction unit can construct a virtual space using vibrant colors and up-tempo music. Furthermore, if the user is stressed, the construction unit can construct a virtual space using calm colors and relaxing music. This improves the user experience by providing construction criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or without AI. For example, the construction unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0087] During construction, the construction unit can improve the accuracy of the construction by taking into account the interrelationships between the generated elements. The construction unit can, for example, construct a virtual space by taking into account the interrelationships between the generated scenario and scenery. The construction unit can also construct a virtual space by taking into account the interrelationships between the generated creatures and buildings. The construction unit can also construct a virtual space by taking into account the interrelationships between the generated music and art. This can improve the user experience by providing a construction that takes into account the interrelationships between the generated elements. Some or all of the above-described processing in the construction unit can be performed, for example, using AI, or can be performed without using AI. For example, the construction unit can input interrelationship data between the generated elements into the generation AI and cause the generation AI to improve the accuracy of the construction.

[0088] The construction unit can take attribute information of the generated elements into consideration when constructing the virtual space. The construction unit can construct the virtual space, for example, by taking into consideration the color and shape of the generated elements. The construction unit can also construct the virtual space by taking into consideration the music and sound effects of the generated elements. The construction unit can also construct the virtual space by taking into consideration the movement and animation of the generated elements. This can improve the user experience by providing a construction that takes into consideration the attribute information of the generated elements. Some or all of the above-described processing in the construction unit can be performed, for example, using AI or without using AI. For example, the construction unit can input attribute information data of the generated elements into the generation AI and cause the generation AI to execute the construction.

[0089] During construction, the construction unit can weight the construction based on the submission frequency of the generated elements. For example, the construction unit constructs a virtual space by preferentially using elements with a high submission frequency. The construction unit can also construct a virtual space by complementary use of elements with a low submission frequency. The construction unit can also adjust the importance of elements based on their submission frequency and construct a virtual space. This can improve the user experience by providing a construction based on the submission frequency of the generated elements. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or can be performed without using AI. For example, the construction unit can input submission frequency data of the generated elements to a generation AI and cause the generation AI to weight the construction.

[0090] The construction unit can estimate the user's emotions and adjust the order in which the construction results are displayed based on the estimated user emotions. For example, if the user is relaxed, the construction unit can display calm elements first. Furthermore, if the user is excited, the construction unit can display stimulating elements first. Furthermore, if the user is stressed, the construction unit can display calming elements first. This improves the user experience by providing a display order for the construction results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the construction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the construction unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] The construction unit can perform construction while taking into account the geographical distribution of the generated elements. For example, the construction unit constructs a virtual space while taking into account the geographical positional relationships of the generated elements. The construction unit can also construct a virtual space by reflecting the geographical characteristics of the generated elements. The construction unit can also determine the layout of the virtual space based on the geographical distribution of the generated elements. This can improve the user experience by providing construction that takes into account the geographical distribution of the generated elements. Some or all of the above-described processing in the construction unit can be performed, for example, using AI or without AI. For example, the construction unit can input geographical distribution data of the generated elements to the generation AI and cause the generation AI to execute construction.

[0092] During construction, the construction unit can improve the accuracy of the construction by referring to literature related to the generated elements. The construction unit, for example, constructs a virtual space by referring to academic papers related to the generated elements. The construction unit can also construct a virtual space by referring to patent documents related to the generated elements. The construction unit can also construct a virtual space by referring to technical literature related to the generated elements. This can improve the user experience by providing a construction that references literature related to the generated elements. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or can be performed without using AI. For example, the construction unit can input literature data related to the generated elements into the generation AI and cause the generation AI to improve the accuracy of the construction.

[0093] The construction unit can take into consideration the market value of the generated elements when constructing the virtual space. For example, the construction unit can construct the virtual space by preferentially using elements with high market value. The construction unit can also construct the virtual space by complementary use of elements with low market value. The construction unit can also adjust the importance of elements based on market value and construct the virtual space. This can improve the user experience by providing a construction that takes into consideration the market value of the generated elements. Some or all of the above-described processing in the construction unit can be performed using, for example, AI, or can be performed without using AI. For example, the construction unit can input market value data of the generated elements into the generation AI and have the generation AI execute the construction.

[0094] The providing unit can estimate the user's emotions and adjust the display method of the virtual space provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a display method using calm colors and music. Furthermore, if the user is excited, the providing unit can provide a display method using bright colors and up-tempo music. Furthermore, if the user is stressed, the providing unit can provide a display method using calm colors and relaxing music. This improves the user experience by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] When providing the display method, the providing unit can select an appropriate display method by referring to the user's past operation history. The providing unit can provide an optimal display method based on, for example, display methods that the user has preferred in the past. The providing unit can also analyze the user's past operation history and suggest an optimal display method. The providing unit can also provide an optimal display method by referring to the user's past feedback. This can improve the user experience by providing a display method based on the user's past operation history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's operation history data to the generation AI and cause the generation AI to select a display method.

[0096] The providing unit can customize the display content according to the user's current task when providing the display content. For example, if the user is relaxed, the providing unit can provide a display method using calm colors and music. Furthermore, if the user is excited, the providing unit can provide a display method using bright colors and up-tempo music. Furthermore, if the user is stressed, the providing unit can provide a display method using calm colors and relaxing music. This can improve the user experience by providing display content according to the user's current task. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's task data into the generation AI and cause the generation AI to customize the display content.

[0097] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit can improve the display method based on user feedback, for example. The providing unit can also analyze user feedback and propose an optimal display method. The providing unit can also customize the display method by referring to user feedback. This can improve the user experience by providing a providing method based on user feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0098] The providing unit can estimate the user's emotions and adjust the virtual space operation procedures provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide a display method using calm colors and music. Furthermore, if the user is excited, the providing unit can provide a display method using bright colors and upbeat music. Furthermore, if the user is stressed, the providing unit can provide a display method using calm colors and relaxing music. This improves the user experience by providing operation procedures according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The providing unit can select an appropriate display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This can improve the user experience by providing a display method based on the user's device information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a display method.

[0100] The providing unit can make the display content multilingual according to the user's language setting when providing the display content. The providing unit can automatically set the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This can improve the user experience by providing multilingual display content based on the user's language setting. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0101] The providing unit can select an appropriate delivery method by taking into consideration the user's geographical location information when providing the information. For example, if the user is in a specific area, the providing unit can provide display content related to that area. Furthermore, if the user is traveling, the providing unit can also provide display content related to the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can also provide display content related to the event. This can improve the user experience by providing a delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select a delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, construction unit, and provision 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 reception device 38 of the smart device 14 and receives a request from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI to generate elements that constitute the virtual space. The construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs a virtual space by combining the generated elements. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the constructed virtual space to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, construction unit, and provision 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 microphone 238 of the smart glasses 214 and receives a request from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI to generate elements that constitute the virtual space. The construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs a virtual space by combining the generated elements. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the constructed virtual space to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, construction unit, and provision 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 microphone 238 of the headset-type terminal 314 and receives a request from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI to generate elements that constitute the virtual space. The construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs a virtual space by combining the generated elements. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the constructed virtual space to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, construction unit, and provision 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 microphone 238 of the robot 414 and receives a request from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the request using a generation AI to generate elements that constitute the virtual space. The construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs a virtual space by combining the generated elements. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the constructed virtual space to the user.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The receiving unit can also acquire biometric information of the user and adjust the method of receiving the request. For example, the receiving unit can monitor the user's heart rate and electrodermal activity, and if the user is tense, provide an interface that helps the user relax. If the user is relaxed, the receiving unit can provide detailed options and enable customization of the request. Furthermore, if the user is excited, the receiving unit can provide a simple and intuitive interface that allows the user to quickly input a request. Thus, by providing a method of receiving a request based on the user's biometric information, the user experience can be improved.

[0104] The generator can analyze the user's past generation results and increase the variety of elements to be generated. For example, it can generate new variations based on scenarios and elements that the user has previously preferred. It can also improve the quality of the generated elements by referring to the user's past feedback. Furthermore, it can learn from the user's past generation results and generate more diverse elements. This can improve the user experience by providing variations based on the user's past generation results.

[0105] The generation unit can reflect user feedback in real time in the elements to be generated. For example, the user can evaluate the generated elements in real time and adjust the elements based on the evaluation. The user can also input comments in real time and modify the elements based on the comments. Furthermore, the user can request changes to elements in real time and the elements can be generated in response to the requests. This allows for an improved user experience by reflecting feedback in real time.

[0106] The generation unit can reflect the user's emotions in the generated elements. For example, if the user is relaxed, elements using calm colors and music can be generated. If the user is excited, elements using bright colors and up-tempo music can be generated. Furthermore, if the user is feeling stressed, elements using calm colors and relaxing music can be generated. This makes it possible to improve the user experience by providing elements that correspond to the user's emotions.

[0107] The receiving unit can analyze the user's voice tone and adjust the method for receiving a request. For example, if the user's voice tone is excited, a simple and intuitive interface can be provided to enable quick request input. Alternatively, if the user's voice tone is calm, detailed options can be provided to enable request customization. Furthermore, if the user's voice tone is tense, a guided step-by-step input method can be provided to make request input easier. This can improve the user experience by providing a method for receiving a request according to the user's voice tone.

[0108] The reception unit can automatically set the priority of requests based on the user's past request history. For example, it can prioritize scenarios or elements that the user frequently requests. It can also prioritize requests that the user has previously given high ratings. It can also prioritize requests that the user has previously requested but that have not yet been processed. This makes it possible to improve the user experience by providing priorities based on the user's past request history.

[0109] The reception unit can monitor the user's current activity status and adjust the method of receiving requests. For example, if the user is exercising, voice input can be given priority. Alternatively, if the user is doing desk work, text input can be given priority. Furthermore, if the user is relaxing, detailed options can be provided to enable customization of requests. This can improve the user experience by providing a method of receiving requests according to the user's activity status.

[0110] The generation unit can reflect the user's cultural background in the elements it generates. For example, if the user belongs to a particular cultural sphere, it can generate elements related to that culture. Also, if the user has a multicultural background, it can generate elements that combine multiple cultures. Furthermore, if the user is interested in a particular culture, it can generate elements related to that culture. This can improve the user experience by providing elements that correspond to the user's cultural background.

[0111] The reception unit can estimate the user's emotions and determine the priority of requests based on the estimated user emotions. For example, if the user is excited, the request can be processed with priority and the virtual space can be provided quickly. If the user is relaxed, the request can be processed with normal priority. Furthermore, if the user is stressed, the request can be processed with priority and the virtual space can be provided quickly. In this way, the user experience can be improved by determining the priority of requests according to the user's emotions.

[0112] The generator can consider the user's health condition when generating elements. For example, if the user is tired, it can generate elements that help them relax. If the user is energetic, it can generate stimulating elements. Furthermore, if the user is stressed, it can generate elements that help them relieve stress. This can improve the user experience by providing elements that correspond to the user's health condition.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives a request from a user. The request may be in text, audio, or image format. For example, a user may input a request such as "I want to explore a medieval castle." Step 2: The generator uses AI to analyze the request received by the reception unit and generate elements that make up the virtual space. For example, it generates elements such as scenarios, landscapes, creatures, buildings, music, and art. It can also generate elements to provide practical learning and training in fields such as medicine, engineering, and aerospace. Step 3: The construction unit builds a virtual space by combining the elements generated by the generation unit, such as generated scenarios, landscapes, creatures, buildings, music, and art. Step 4: The providing unit provides the virtual space constructed by the constructing unit to the user, for example, providing a virtual space that the user can freely explore and experience.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] 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.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] 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.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] 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.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 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 that receives requests from users; a generation unit that analyzes the request received by the reception unit and generates elements that constitute a virtual space; a construction unit that constructs a virtual space by combining the elements generated by the generation unit; a providing unit that provides the virtual space constructed by the construction unit to the user. A system characterized by:

2. The generation unit Generate scenarios, landscapes, creatures, buildings, music, and art elements 2. The system of claim 1.

3. The generation unit Generate elements to provide hands-on learning and training in the fields of medicine, engineering, and aerospace.

2. The system of claim 1.

4. The generation unit Generate elements for educational purposes of language and cultural learning, historical reenactment, and geographic exploration 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the way requests are accepted based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past request history and select the appropriate reception method 2. The system of claim 1.

7. The reception unit When a request is received, it is filtered based on the user's current interests.

2. The system of claim 1.

8. The reception unit When accepting a request, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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