System

Abstract

An object of a system according to an embodiment is to easily develop an application for AR glasses or MR goggles without expert knowledge.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit receives an input in a chat format. The analysis unit analyzes the illustration on the basis of the information received by the reception unit. The generation unit generates a specification of the application based on the information analyzed by the analysis unit. The creation unit creates an application based on the specification generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, developing applications for AR glasses or MR goggles required specialized knowledge, making it difficult for general users to develop them easily.

[0005] The system according to the embodiment aims to enable easy development of applications for AR glasses and MR goggles without requiring specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit receives input in the form of a chat. The analysis unit analyzes an illustration based on the information received by the reception unit. The creation unit generates application specifications based on the information analyzed by the analysis unit. The creation unit creates an application based on the specifications generated by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows applications for AR glasses and MR goggles to be easily developed without any specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows anyone to easily develop applications using AR glasses or MR goggles. In this system, users input the details of the application they want to create in chat format and input illustrations through an interactive interface. A generation AI analyzes this information, automatically generates application specifications, and automatically creates an application that runs on AR glasses or MR goggles. This allows anyone to easily develop applications using AR glasses or MR goggles. For example, in the education field, teachers can easily create educational virtual reality apps and use them in their classes. In the entertainment field, creators can create their own virtual reality content and provide it to users. Furthermore, in the business field, companies can easily create apps to promote their products and provide them to customers.

[0029] An application development system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit receives information input by a user through an interactive interface. For example, a user may input a specific request, such as, "I want to create a virtual reality museum app." The analysis unit uses a generation AI to analyze an illustration provided by the user. For example, the generation AI analyzes the content of the illustration and specifies the design and functions of the application. The generation unit uses the generation AI to analyze the input information and illustration in chat format and generate application specifications. For example, the generation AI specifically determines the layout of exhibits and the operation method of the interface. The creation unit creates an application that runs on AR glasses or MR goggles based on the generated specifications. For example, the creation unit automatically generates application code based on the generated specifications to create a running application. This allows the application development system according to an embodiment to easily develop applications that use AR glasses or MR goggles through an interactive interface.

[0030] The reception unit can receive information input by a user through an interactive interface. Interactive interfaces include, but are not limited to, text chat, voice chat, and video chat. For example, the reception unit receives information input by a user through text chat. The reception unit can also receive information input through voice chat. Furthermore, the reception unit can also receive information input through video chat. This allows the reception unit to receive information input by a user through an interactive interface.

[0031] The analysis unit can analyze the illustration provided by the user using the generation AI. Examples of the generation AI include, but are not limited to, deep learning models and generation algorithms. The analysis unit can analyze the illustration provided by the user using the generation AI. For example, the generation AI analyzes the content of the illustration and specifies the design and functions of the application. The analysis unit can also use the generation AI to extract features of the illustration and reflect them in the application specifications. Furthermore, the analysis unit can use the generation AI to determine the layout of the application based on the content of the illustration. This allows the generation AI to analyze the illustration provided by the user.

[0032] The generation unit can use the generation AI to analyze the chat-style input information and illustrations and generate application specifications. The generation unit can, for example, use the generation AI to analyze the chat-style input information and illustrations and generate application specifications. For example, the generation AI can analyze the chat-style input information and generate basic application specifications. The generation unit can also use the generation AI to analyze the content of the illustrations and specify the design and functions of the application. Furthermore, the generation unit can also use the generation AI to integrate the chat-style input information and illustrations and generate detailed application specifications. This makes it possible for the generation AI to analyze the chat-style input information and illustrations and generate application specifications.

[0033] The creation unit can create an application that runs on AR glasses or MR goggles based on the generated specifications. Applications that run on AR glasses or MR goggles include, but are not limited to, educational applications, entertainment applications, and business applications. The creation unit can create an application that runs on AR glasses or MR goggles based on the generated specifications. For example, the creation unit can automatically generate application code based on the generated specifications to create a running application. The creation unit can also automatically generate an application design based on the generated specifications. Furthermore, the creation unit can automatically implement application functions based on the generated specifications. This makes it possible to create an application that runs on AR glasses or MR goggles based on the generated specifications.

[0034] The creation unit can provide the generated application to the user. Methods for providing the generated application to the user include, but are not limited to, downloading from a website, distribution from an app store, and direct installation. For example, the creation unit can provide the generated application so that it can be downloaded from a website. The creation unit can also distribute the generated application from an app store. Furthermore, the creation unit can also directly install the generated application. In this way, the generated application can be provided to the user.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays the contents of applications that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the contents of applications that will be used in a specific time period based on the user's past input history. In this way, the user's past input history can be analyzed and the optimal input method can be suggested.

[0036] The reception unit can filter the input content based on the user's current project or field of interest at the time of input. For example, the reception unit can preferentially display content related to the user's current project. The reception unit can also suggest content of related applications based on the user's field of interest. Furthermore, the reception unit can filter the input content based on fields in which the user has shown interest in the past. This makes it possible to filter the input content based on the user's current project or field of interest.

[0037] The reception unit can select the optimum input means depending on the user's input method at the time of input. For example, if the user selects voice input, the reception unit analyzes the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also analyze the input content using text analysis technology. Furthermore, if the user selects image input, the reception unit can also analyze the input content using image recognition technology. This makes it possible to select the optimum input means depending on the user's input method.

[0038] The reception unit can preferentially receive highly relevant information based on the user's geographical location information when inputting the information. For example, when the user is in a specific area, the reception unit can preferentially display the contents of applications related to that area. Furthermore, when the user is traveling, the reception unit can also suggest the contents of applications related to the travel destination. Furthermore, when the user is participating in a specific event, the reception unit can preferentially display the contents of applications related to the event. This makes it possible to preferentially receive highly relevant information based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity at the time of input and receive related information. The reception unit can, for example, suggest related application content based on content shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and receive related application content. Furthermore, the reception unit can also suggest related application content by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also preferentially display a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. This allows the input method to be customized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the illustration. For example, in the case of an important illustration, the analysis unit performs a detailed analysis down to the finest details. In addition, in the case of an illustration with low importance, the analysis unit can analyze only the main parts and provide results quickly. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the illustration. This makes it possible to adjust the level of detail of the analysis based on the importance of the illustration.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the illustration. For example, in the case of an illustration of a building, the analysis unit applies an analysis algorithm specifically for buildings. In addition, in the case of an illustration of a person, the analysis unit can also apply an analysis algorithm specifically for people. Furthermore, in the case of an illustration of a natural landscape, the analysis unit can also apply an analysis algorithm specifically for natural landscapes. This makes it possible to apply different analysis algorithms depending on the category of the illustration.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the analysis results of illustrations provided by the user in the past. The analysis unit can also preferentially apply a specific analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the illustration. For example, the analysis unit prioritizes analysis of illustrations submitted earlier. In addition, for illustrations submitted later, the analysis unit can analyze only the main parts and provide results quickly. Furthermore, the analysis unit can adjust the priority of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of the illustration.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the illustrations. For example, the analysis unit prioritizes analysis of highly relevant illustrations. In addition, for illustrations with low relevance, the analysis unit can analyze only the main parts and quickly provide results. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the illustrations. This makes it possible to adjust the order of analysis based on the relevance of the illustrations.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] When generating specifications, the generation unit can adjust the level of detail of the specifications based on the importance of the input information. For example, in the case of important input information, the generation unit generates a detailed specification. In addition, in the case of input information with low importance, the generation unit can also generate a concise specification including only the main parts. Furthermore, the generation unit can adjust the level of detail of the specifications according to the importance of the input information. In this way, the level of detail of the specifications can be adjusted based on the importance of the input information.

[0048] When generating specifications, the generation unit can apply different generation algorithms depending on the category of the application. For example, in the case of an educational application, the generation unit applies a generation algorithm dedicated to education. In addition, in the case of an entertainment application, the generation unit can also apply a generation algorithm dedicated to entertainment. Furthermore, in the case of a business application, the generation unit can also apply a generation algorithm dedicated to business. In this way, different generation algorithms can be applied depending on the category of the application.

[0049] When generating specifications, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit improves the accuracy of generation, for example, based on the generation results of specifications provided by the user in the past. The generation unit can also preferentially apply a specific generation algorithm based on the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and optimize the generation algorithm. This makes it possible to improve the accuracy of generation by referring to the user's past generation results.

[0050] When generating specifications, the generation unit can determine the generation priority based on the submission time of input information. For example, the generation unit generates input information that is submitted earlier with priority. In addition, the generation unit can also generate only the main part of input information that is submitted later with priority. Furthermore, the generation unit can adjust the generation priority based on the submission time. This makes it possible to determine the generation priority based on the submission time of input information.

[0051] When generating specifications, the generation unit can adjust the order of generation based on the relevance of the input information. For example, the generation unit preferentially generates highly relevant input information. In addition, in the case of input information with low relevance, the generation unit can also preferentially generate only the main part. Furthermore, the generation unit can also adjust the order of generation based on the relevance of the input information. In this way, the order of generation can be adjusted based on the relevance of the input information.

[0052] When generating a specification, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a specification that uses a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can generate a concise and easy-to-understand specification. Furthermore, the generation unit can adjust the way the specification is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the generation according to the user's level of expertise.

[0053] The creation unit can adjust the level of detail of the creation based on the importance of the generated specifications when creating an application. For example, in the case of important specifications, the creation unit creates an application including detailed settings. In addition, in the case of specifications with low importance, the creation unit can also create a simple application including only main functions. Furthermore, the creation unit can adjust the level of detail of the creation depending on the importance of the specifications. In this way, the level of detail of the creation can be adjusted based on the importance of the generated specifications.

[0054] When creating an application, the creation unit can apply different creation algorithms depending on the category of the application. For example, in the case of an educational application, the creation unit applies a creation algorithm specifically for education. In addition, in the case of an entertainment application, the creation unit can also apply a creation algorithm specifically for entertainment. Furthermore, in the case of a business application, the creation unit can also apply a creation algorithm specifically for business. In this way, different creation algorithms can be applied depending on the category of the application.

[0055] When creating an application, the creation unit can improve the accuracy of the creation by referring to the user's past creation results. The creation unit improves the accuracy of the creation, for example, based on the creation results of applications provided by the user in the past. The creation unit can also preferentially apply a specific creation algorithm based on the user's past creation results. Furthermore, the creation unit can analyze the user's past creation results and optimize the creation algorithm. This makes it possible to improve the accuracy of the creation by referring to the user's past creation results.

[0056] When creating an application, the creation unit can determine the priority of creation based on the submission date of the generated specifications. For example, the creation unit gives priority to creating specifications that are submitted earlier. In addition, the creation unit can also give priority to creating only the main parts of specifications that are submitted later. Furthermore, the creation unit can adjust the priority of creation based on the submission date. In this way, the priority of creation can be determined based on the submission date of the generated specifications.

[0057] When creating an application, the creation unit can adjust the order of creation based on the relevance of the generated specifications. For example, the creation unit creates specifications with high relevance with priority. In addition, in the case of specifications with low relevance, the creation unit can also create only the main parts with priority. Furthermore, the creation unit can adjust the order of creation based on the relevance of the specifications. In this way, the order of creation can be adjusted based on the relevance of the generated specifications.

[0058] The creation unit can adjust the use of technical terms in the creation of an application according to the user's level of expertise. For example, if the user has technical expertise, the creation unit creates an application that uses a lot of technical terms. Also, if the user does not have technical expertise, the creation unit can create a concise and easy-to-understand application. Furthermore, the creation unit can adjust the way the application is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the creation according to the user's level of expertise.

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

[0060] The reception unit can automatically suggest related past projects and reference materials based on the user's input. For example, if a user inputs, "I want to create a virtual reality museum app," the reception unit can suggest similar museum app projects that have been created in the past. Also, if a user inputs, "I want to create a virtual reality app for education," the reception unit can provide reference materials for related educational apps. Furthermore, if a user inputs, "I want to create a virtual reality app for entertainment," the reception unit can present successful examples in the entertainment field. This allows the user to develop applications more efficiently by utilizing past projects and reference materials.

[0061] The analysis unit can analyze the style of the illustration provided by the user and suggest a design template that matches the style. For example, if the user provides a hand-drawn style illustration, a hand-drawn style design template can be suggested. Also, if the user provides a digital art style illustration, a digital art style design template can be suggested. Furthermore, if the user provides a photographic style illustration, a photographic style design template can be suggested. This allows the user to select a design template that suits their illustration style and efficiently proceed with the design of their application.

[0062] During the testing phase of the generated application, the creation department can collect user feedback in real time and automatically reflect improvements to the application. For example, if a user gives feedback that the application is "difficult to use," the creation department can automatically make changes to improve operability. Also, if a user gives feedback that the design is "hard to read," the creation department can adjust the design. Furthermore, if a user gives feedback that a function is "missing," new functions can be added. This allows user feedback to be reflected quickly and the quality of the application to be improved.

[0063] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis based on the analysis results of illustrations provided by the user in the past. The analysis unit can also preferentially apply a specific analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0064] When generating specifications, the generation unit can adjust the level of detail of the specifications based on the importance of the input information. For example, in the case of important input information, a detailed specification is generated. In addition, in the case of input information with low importance, the generation unit can also generate a concise specification including only the main parts. Furthermore, the generation unit can adjust the level of detail of the specifications according to the importance of the input information. In this way, the level of detail of the specifications can be adjusted based on the importance of the input information.

[0065] When creating an application, the creation unit can determine the creation priority based on the submission time of the generated specifications. For example, the creation unit can give priority to creating specifications that are submitted early. In addition, the creation unit can also give priority to creating only the main parts of specifications that are submitted late. Furthermore, the creation unit can adjust the creation priority based on the submission time. In this way, the creation priority can be determined based on the submission time of the generated specifications.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives information entered by the user through an interactive interface. For example, the user can enter a specific request such as "I want to create a virtual reality museum app." Step 2: The analysis unit uses the generation AI to analyze the illustration provided by the user. For example, the generation AI analyzes the content of the illustration and concretizes the design and functions of the application. Step 3: The generation unit uses the generation AI to analyze the chat-style input information and illustrations and generate application specifications. For example, the generation AI determines specific details such as the layout of exhibits and how to operate the interface. Step 4: The creation unit creates an application that runs on the AR glasses or MR goggles based on the generated specifications. For example, the creation unit automatically generates application code based on the generated specifications to create a running application.

[0068] (Example 2) A system according to an embodiment of the present invention allows anyone to easily develop applications using AR glasses or MR goggles. In this system, users input the details of the application they want to create in chat format and input illustrations through an interactive interface. A generation AI analyzes this information, automatically generates application specifications, and automatically creates an application that runs on AR glasses or MR goggles. This allows anyone to easily develop applications using AR glasses or MR goggles. For example, in the education field, teachers can easily create educational virtual reality apps and use them in their classes. In the entertainment field, creators can create their own virtual reality content and provide it to users. Furthermore, in the business field, companies can easily create apps to promote their products and provide them to customers.

[0069] An application development system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a creation unit. The reception unit receives information input by a user through an interactive interface. For example, a user may input a specific request, such as, "I want to create a virtual reality museum app." The analysis unit uses a generation AI to analyze an illustration provided by the user. For example, the generation AI analyzes the content of the illustration and specifies the design and functions of the application. The generation unit uses the generation AI to analyze the input information and illustration in chat format and generate application specifications. For example, the generation AI specifically determines the layout of exhibits and the operation method of the interface. The creation unit creates an application that runs on AR glasses or MR goggles based on the generated specifications. For example, the creation unit automatically generates application code based on the generated specifications to create a running application. This allows the application development system according to an embodiment to easily develop applications that use AR glasses or MR goggles through an interactive interface.

[0070] The reception unit can receive information input by a user through an interactive interface. Interactive interfaces include, but are not limited to, text chat, voice chat, and video chat. For example, the reception unit receives information input by a user through text chat. The reception unit can also receive information input through voice chat. Furthermore, the reception unit can also receive information input through video chat. This allows the reception unit to receive information input by a user through an interactive interface.

[0071] The analysis unit can analyze the illustration provided by the user using the generation AI. Examples of the generation AI include, but are not limited to, deep learning models and generation algorithms. The analysis unit can analyze the illustration provided by the user using the generation AI. For example, the generation AI analyzes the content of the illustration and specifies the design and functions of the application. The analysis unit can also use the generation AI to extract features of the illustration and reflect them in the application specifications. Furthermore, the analysis unit can use the generation AI to determine the layout of the application based on the content of the illustration. This allows the generation AI to analyze the illustration provided by the user.

[0072] The generation unit can use the generation AI to analyze the chat-style input information and illustrations and generate application specifications. The generation unit can, for example, use the generation AI to analyze the chat-style input information and illustrations and generate application specifications. For example, the generation AI can analyze the chat-style input information and generate basic application specifications. The generation unit can also use the generation AI to analyze the content of the illustrations and specify the design and functions of the application. Furthermore, the generation unit can also use the generation AI to integrate the chat-style input information and illustrations and generate detailed application specifications. This makes it possible for the generation AI to analyze the chat-style input information and illustrations and generate application specifications.

[0073] The creation unit can create an application that runs on AR glasses or MR goggles based on the generated specifications. Applications that run on AR glasses or MR goggles include, but are not limited to, educational applications, entertainment applications, and business applications. The creation unit can create an application that runs on AR glasses or MR goggles based on the generated specifications. For example, the creation unit can automatically generate application code based on the generated specifications to create a running application. The creation unit can also automatically generate an application design based on the generated specifications. Furthermore, the creation unit can automatically implement application functions based on the generated specifications. This makes it possible to create an application that runs on AR glasses or MR goggles based on the generated specifications.

[0074] The creation unit can provide the generated application to the user. Methods for providing the generated application to the user include, but are not limited to, downloading from a website, distribution from an app store, and direct installation. For example, the creation unit can provide the generated application so that it can be downloaded from a website. The creation unit can also distribute the generated application from an app store. Furthermore, the creation unit can also directly install the generated application. In this way, the generated application can be provided to the user.

[0075] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. This allows the display method of the input interface to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays the contents of applications that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the contents of applications that will be used in a specific time period based on the user's past input history. In this way, the user's past input history can be analyzed and the optimal input method can be suggested.

[0077] The reception unit can filter the input content based on the user's current project or field of interest at the time of input. For example, the reception unit can preferentially display content related to the user's current project. The reception unit can also suggest content of related applications based on the user's field of interest. Furthermore, the reception unit can filter the input content based on fields in which the user has shown interest in the past. This makes it possible to filter the input content based on the user's current project or field of interest.

[0078] The reception unit can select the optimum input means depending on the user's input method at the time of input. For example, if the user selects voice input, the reception unit analyzes the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also analyze the input content using text analysis technology. Furthermore, if the user selects image input, the reception unit can also analyze the input content using image recognition technology. This makes it possible to select the optimum input means depending on the user's input method.

[0079] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This allows the design of the input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The reception unit can preferentially receive highly relevant information based on the user's geographical location information when inputting the information. For example, when the user is in a specific area, the reception unit can preferentially display the contents of applications related to that area. Furthermore, when the user is traveling, the reception unit can also suggest the contents of applications related to the travel destination. Furthermore, when the user is participating in a specific event, the reception unit can preferentially display the contents of applications related to the event. This makes it possible to preferentially receive highly relevant information based on the user's geographical location information.

[0081] The reception unit can analyze the user's social media activity at the time of input and receive related information. The reception unit can, for example, suggest related application content based on content shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and receive related application content. Furthermore, the reception unit can also suggest related application content by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also preferentially display a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. This allows the input method to be customized by reflecting the user's past feedback.

[0083] The analysis unit can estimate the user's emotions and adjust the illustration analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis, analyzing every detail of the illustration. Furthermore, if the user is in a hurry, the analysis unit can analyze only the main parts and provide results quickly. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This allows the illustration analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the illustration. For example, in the case of an important illustration, the analysis unit performs a detailed analysis down to the finest details. In addition, in the case of an illustration with low importance, the analysis unit can analyze only the main parts and provide results quickly. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the illustration. This makes it possible to adjust the level of detail of the analysis based on the importance of the illustration.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the illustration. For example, in the case of an illustration of a building, the analysis unit applies an analysis algorithm specifically for buildings. In addition, in the case of an illustration of a person, the analysis unit can also apply an analysis algorithm specifically for people. Furthermore, in the case of an illustration of a natural landscape, the analysis unit can also apply an analysis algorithm specifically for natural landscapes. This makes it possible to apply different analysis algorithms depending on the category of the illustration.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the analysis results of illustrations provided by the user in the past. The analysis unit can also preferentially apply a specific analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the illustration. For example, the analysis unit prioritizes analysis of illustrations submitted earlier. In addition, for illustrations submitted later, the analysis unit can analyze only the main parts and provide results quickly. Furthermore, the analysis unit can adjust the priority of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of the illustration.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the illustrations. For example, the analysis unit prioritizes analysis of highly relevant illustrations. In addition, for illustrations with low relevance, the analysis unit can analyze only the main parts and quickly provide results. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the illustrations. This makes it possible to adjust the order of analysis based on the relevance of the illustrations.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0091] The generation unit can estimate the user's emotions and adjust the method of generating specifications based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed specification. If the user is in a hurry, the generation unit can also generate a concise specification including only the main parts. Furthermore, if the user is excited, the generation unit can also generate a specification with a visually stimulating effect. This makes it possible to adjust the method of generating specifications according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] When generating specifications, the generation unit can adjust the level of detail of the specifications based on the importance of the input information. For example, in the case of important input information, the generation unit generates a detailed specification. In addition, in the case of input information with low importance, the generation unit can also generate a concise specification including only the main parts. Furthermore, the generation unit can adjust the level of detail of the specifications according to the importance of the input information. In this way, the level of detail of the specifications can be adjusted based on the importance of the input information.

[0093] When generating specifications, the generation unit can apply different generation algorithms depending on the category of the application. For example, in the case of an educational application, the generation unit applies a generation algorithm dedicated to education. In addition, in the case of an entertainment application, the generation unit can also apply a generation algorithm dedicated to entertainment. Furthermore, in the case of a business application, the generation unit can also apply a generation algorithm dedicated to business. In this way, different generation algorithms can be applied depending on the category of the application.

[0094] When generating specifications, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit improves the accuracy of generation, for example, based on the generation results of specifications provided by the user in the past. The generation unit can also preferentially apply a specific generation algorithm based on the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and optimize the generation algorithm. This makes it possible to improve the accuracy of generation by referring to the user's past generation results.

[0095] The generation unit can estimate the user's emotions and determine the priority of specification generation based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can prioritize generating the most important parts. Also, if the user is relaxed, the generation unit can generate a balanced overall specification. Furthermore, if the user is excited, the generation unit can prioritize generating visually stimulating parts. This makes it possible to determine the priority of specification generation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] When generating specifications, the generation unit can determine the generation priority based on the submission time of input information. For example, the generation unit generates input information that is submitted earlier with priority. In addition, the generation unit can also generate only the main part of input information that is submitted later with priority. Furthermore, the generation unit can adjust the generation priority based on the submission time. This makes it possible to determine the generation priority based on the submission time of input information.

[0097] When generating specifications, the generation unit can adjust the order of generation based on the relevance of the input information. For example, the generation unit preferentially generates highly relevant input information. In addition, in the case of input information with low relevance, the generation unit can also preferentially generate only the main part. Furthermore, the generation unit can also adjust the order of generation based on the relevance of the input information. In this way, the order of generation can be adjusted based on the relevance of the input information.

[0098] When generating a specification, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a specification that uses a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can generate a concise and easy-to-understand specification. Furthermore, the generation unit can adjust the way the specification is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the generation according to the user's level of expertise.

[0099] The creation unit can estimate the user's emotions and adjust the application creation method based on the estimated user emotions. For example, if the user is relaxed, the creation unit can create an application including detailed settings. If the user is in a hurry, the creation unit can also create a simple application including only key functions. Furthermore, if the user is excited, the creation unit can also create an application with visually stimulating effects. This makes it possible to adjust the application creation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The creation unit can adjust the level of detail of the creation based on the importance of the generated specifications when creating an application. For example, in the case of important specifications, the creation unit creates an application including detailed settings. In addition, in the case of specifications with low importance, the creation unit can also create a simple application including only main functions. Furthermore, the creation unit can adjust the level of detail of the creation depending on the importance of the specifications. In this way, the level of detail of the creation can be adjusted based on the importance of the generated specifications.

[0101] When creating an application, the creation unit can apply different creation algorithms depending on the category of the application. For example, in the case of an educational application, the creation unit applies a creation algorithm specifically for education. In addition, in the case of an entertainment application, the creation unit can also apply a creation algorithm specifically for entertainment. Furthermore, in the case of a business application, the creation unit can also apply a creation algorithm specifically for business. In this way, different creation algorithms can be applied depending on the category of the application.

[0102] When creating an application, the creation unit can improve the accuracy of the creation by referring to the user's past creation results. The creation unit improves the accuracy of the creation, for example, based on the creation results of applications provided by the user in the past. The creation unit can also preferentially apply a specific creation algorithm based on the user's past creation results. Furthermore, the creation unit can analyze the user's past creation results and optimize the creation algorithm. This makes it possible to improve the accuracy of the creation by referring to the user's past creation results.

[0103] The creation unit can estimate the user's emotions and determine the priority of applications to be created based on the estimated user emotions. For example, when the user is in a hurry, the creation unit prioritizes creating the most important applications. Furthermore, when the user is relaxed, the creation unit can create applications taking into consideration overall balance. Furthermore, when the user is excited, the creation unit can prioritize creating visually stimulating applications. This makes it possible to determine the priority of applications to be created according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When creating an application, the creation unit can determine the priority of creation based on the submission date of the generated specifications. For example, the creation unit gives priority to creating specifications that are submitted earlier. In addition, the creation unit can also give priority to creating only the main parts of specifications that are submitted later. Furthermore, the creation unit can adjust the priority of creation based on the submission date. In this way, the priority of creation can be determined based on the submission date of the generated specifications.

[0105] When creating an application, the creation unit can adjust the order of creation based on the relevance of the generated specifications. For example, the creation unit creates specifications with high relevance with priority. In addition, in the case of specifications with low relevance, the creation unit can also create only the main parts with priority. Furthermore, the creation unit can adjust the order of creation based on the relevance of the specifications. In this way, the order of creation can be adjusted based on the relevance of the generated specifications.

[0106] The creation unit can adjust the use of technical terms in the creation of an application according to the user's level of expertise. For example, if the user has technical expertise, the creation unit creates an application that uses a lot of technical terms. Also, if the user does not have technical expertise, the creation unit can create a concise and easy-to-understand application. Furthermore, the creation unit can adjust the way the application is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the creation according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and creation 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 receives user input information via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes illustrations provided by the user using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes chat-style input information and illustrations to generate application specifications. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an application that runs on AR glasses or MR goggles based on the generated specifications. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and creation 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 receives user input information through the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes illustrations provided by the user using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes chat-style input information and illustrations to generate application specifications. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an application that runs on AR glasses or MR goggles based on the generated specifications. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and creation 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 receives user input information via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes illustrations provided by the user using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes chat-style input information and illustrations to generate application specifications. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an application that runs on AR glasses or MR goggles based on the generated specifications. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input information via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes illustrations provided by the user using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes chat-style input information and illustrations to generate application specifications. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an application that runs on AR glasses or MR goggles based on the generated specifications.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can automatically suggest related past projects and reference materials based on the user's input. For example, if a user inputs, "I want to create a virtual reality museum app," the reception unit can suggest similar museum app projects that have been created in the past. Also, if a user inputs, "I want to create a virtual reality app for education," the reception unit can provide reference materials for related educational apps. Furthermore, if a user inputs, "I want to create a virtual reality app for entertainment," the reception unit can present successful examples in the entertainment field. This allows the user to develop applications more efficiently by utilizing past projects and reference materials.

[0109] The analysis unit can analyze the style of the illustration provided by the user and suggest a design template that matches the style. For example, if the user provides a hand-drawn style illustration, a hand-drawn style design template can be suggested. Also, if the user provides a digital art style illustration, a digital art style design template can be suggested. Furthermore, if the user provides a photographic style illustration, a photographic style design template can be suggested. This allows the user to select a design template that suits their illustration style and efficiently proceed with the design of their application.

[0110] The analysis unit can estimate the user's emotions and adjust the illustration analysis method based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis is performed to analyze every detail of the illustration. If the user is in a hurry, the analysis unit can analyze only the main parts and provide results quickly. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This makes it possible to adjust the illustration analysis method according to the user's emotions.

[0111] The generation unit can estimate the user's emotion and adjust the method of generating specifications based on the estimated user's emotion. For example, if the user is relaxed, a detailed specification is generated. If the user is in a hurry, the generation unit can also generate a concise specification including only the main parts. Furthermore, if the user is excited, the generation unit can also generate a specification with a visually stimulating effect. In this way, the method of generating specifications can be adjusted according to the user's emotion.

[0112] During the testing phase of the generated application, the creation department can collect user feedback in real time and automatically reflect improvements to the application. For example, if a user gives feedback that the application is "difficult to use," the creation department can automatically make changes to improve operability. Also, if a user gives feedback that the design is "hard to read," the creation department can adjust the design. Furthermore, if a user gives feedback that a function is "missing," new functions can be added. This allows user feedback to be reflected quickly and the quality of the application to be improved.

[0113] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. In this way, the display method of the input interface can be adjusted according to the user's emotion.

[0114] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis based on the analysis results of illustrations provided by the user in the past. The analysis unit can also preferentially apply a specific analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0115] When generating specifications, the generation unit can adjust the level of detail of the specifications based on the importance of the input information. For example, in the case of important input information, a detailed specification is generated. In addition, in the case of input information with low importance, the generation unit can also generate a concise specification including only the main parts. Furthermore, the generation unit can adjust the level of detail of the specifications according to the importance of the input information. In this way, the level of detail of the specifications can be adjusted based on the importance of the input information.

[0116] The creation unit can estimate the user's emotion and adjust the application creation method based on the estimated user's emotion. For example, if the user is relaxed, the creation unit can create an application including detailed settings. If the user is in a hurry, the creation unit can also create a simple application including only major functions. Furthermore, if the user is excited, the creation unit can also create an application with visually stimulating effects. In this way, the application creation method can be adjusted according to the user's emotion.

[0117] When creating an application, the creation unit can determine the creation priority based on the submission time of the generated specifications. For example, the creation unit can give priority to creating specifications that are submitted early. In addition, the creation unit can also give priority to creating only the main parts of specifications that are submitted late. Furthermore, the creation unit can adjust the creation priority based on the submission time. In this way, the creation priority can be determined based on the submission time of the generated specifications.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives information entered by the user through an interactive interface. For example, the user can enter a specific request such as "I want to create a virtual reality museum app." Step 2: The analysis unit uses the generation AI to analyze the illustration provided by the user. For example, the generation AI analyzes the content of the illustration and concretizes the design and functions of the application. Step 3: The generation unit uses the generation AI to analyze the chat-style input information and illustrations and generate application specifications. For example, the generation AI determines specific details such as the layout of exhibits and how to operate the interface. Step 4: The creation unit creates an application that runs on the AR glasses or MR goggles based on the generated specifications. For example, the creation unit automatically generates application code based on the generated specifications to create a running application.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the 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.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0154] 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.

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

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 chat-style input; an analysis unit that analyzes the illustration based on the information received by the reception unit; a generation unit that generates an application specification based on the information analyzed by the analysis unit; a creation unit that creates an application based on the specifications generated by the creation unit. A system characterized by:

2. The reception unit Accepts information entered by the user through an interactive interface 2. The system of claim 1.

3. The analysis unit Generative AI analyzes illustrations provided by users 2. The system of claim 1.

4. The generation unit Generative AI analyzes chat-style input information and illustrations to generate application specifications 2. The system of claim 1.

5. The creation unit Create an application that runs on AR glasses or MR goggles based on the generated specifications.

2. The system of claim 1.

6. The creation unit Providing the generated application to users 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit As you type, filter your input based on your current projects and interests 2. The system of claim 1.

10. The reception unit When inputting, select the most appropriate input method depending on the user's input method 2. The system of claim 1.

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