system

Abstract

The system according to this embodiment aims to improve productivity by generating high-quality videos and programs based on user conditions and advice. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a modification unit, a provision unit, and a simulation unit. The reception unit receives conditions and advice from the user. The generation unit generates videos and programs based on the conditions and advice received by the reception unit. The modification unit detects and corrects bugs in the program generated by the generation unit. The provision unit provides the videos and programs generated by the generation unit. The simulation unit simulates the operation of the program modified by the modification unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating high-quality videos and programs based on user conditions and advice and detecting and fixing bugs is complex and there is a limit to improving productivity.

[0005] The system according to the embodiment aims to generate high-quality videos and programs based on user conditions and advice and improve productivity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a modification unit, a provision unit, and a simulation unit. The reception unit receives conditions and advice from the user. The generation unit generates videos and programs based on the conditions and advice received by the reception unit. The modification unit detects and corrects bugs in the program generated by the generation unit. The provision unit provides the videos and programs generated by the generation unit. The simulation unit simulates the operation of the program modified by the modification unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate high-quality videos and programs based on user conditions and advice, thereby improving productivity. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] ​​​​​​​​​​​​​​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The game production support system according to an embodiment of the present invention is a system that improves the productivity of game production by utilizing generative AI. This game production support system allows users to input various conditions and advice into the generative AI, enabling the generation of high-quality videos and programs, as well as bug detection and correction. This improves the productivity of game production and reduces the costs of technology, personnel, and marketing required for production. For example, a user inputs detailed conditions such as the game's concept, story, and character settings into the generative AI. This information is input into the generative AI, which then generates game videos and programs based on the input conditions and advice. The generative AI generates game scenes with specific themes and programs scenes in which the protagonist plays a specific role. Furthermore, the generative AI detects and corrects bugs in the generated programs. The generative AI detects bugs such as characters passing through walls or games crashing under specific conditions, and automatically corrects these bugs. This improves game quality and reduces the time and effort required for bug fixing. Users can generate high-quality game content simply by inputting conditions and advice into the generative AI. Additionally, because the generative AI detects and corrects bugs, the time and effort required for bug fixing can be reduced. This reduces the costs associated with the technology, personnel, and marketing required for game development. For example, game programmers can simultaneously work on multiple projects simply by giving instructions to a generation AI. This improves the efficiency of game development and enhances competitiveness in the highly competitive game market. As a result, the game development support system can improve the productivity of game development by generating high-quality videos and programs based on user requirements and advice, and by detecting and fixing bugs.

[0029] The game production support system according to this embodiment comprises a reception unit, a generation unit, a modification unit, a provision unit, and a simulation unit. The reception unit receives conditions and advice from the user. Conditions and advice from the user include, but are not limited to, detailed conditions such as the game's concept, story, and character settings. The reception unit, for example, stores the conditions and advice entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to generate videos and programs based on the conditions and advice received by the reception unit. The generation unit, for example, generates game scenes with a specific theme and programs scenes in which the protagonist plays a specific role. The generation unit uses a generation AI to analyze the input conditions and generates game videos and programs based on them. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate the game's story. The generation unit can also generate game scenes using a multimodal generation AI. The generation unit can also program the actions of game characters using a generation AI. The modification unit detects and modifies bugs in the program generated by the generation unit. The modification unit uses generation AI to analyze the generated program and detect bugs. For example, the modification unit detects bugs such as characters passing through walls or bugs that cause the game to crash under certain conditions. The modification unit automatically fixes the detected bugs. The modification unit uses generation AI to correct the bugs. The provision unit provides the videos and programs generated by the generation unit to users. For example, the provision unit provides the generated videos and programs to users through web applications or mobile applications. The provision unit can also send the generated videos and programs via email. The provision unit can also print the generated videos and programs using a printer and provide them. The simulation unit simulates the operation of the program modified by the modification unit. The simulation unit uses generation AI to simulate the operation of the modified program. For example, the simulation unit simulates the operation of the modified program in a virtual environment and verifies its operation.The simulation unit can also simulate the operation of the modified program in an actual game environment. This allows the game production support system according to the embodiment to generate high-quality videos and programs based on user conditions and advice, and to detect and fix bugs, thereby improving the productivity of game production.

[0030] The reception unit receives conditions and suggestions from users. These conditions and suggestions include, but are not limited to, detailed conditions such as the game's concept, story, and character settings. The reception unit stores the conditions and suggestions entered by users in a database and provides them to the generation unit. Specifically, the reception unit provides an interface for user input, allowing users to specify details such as the game's theme, character personalities, story development, and gameplay mechanics. Users can provide conditions and suggestions through text input, selection of options, image uploads, etc. The reception unit analyzes these inputs in real time and stores them in the database in an appropriate format. Furthermore, the reception unit allows users to refer to conditions and suggestions they have entered in the past, enabling them to reuse or modify previous settings. This allows users to efficiently provide conditions and suggestions and smoothly proceed through the initial stages of game development.

[0031] The generation unit uses generation AI to generate videos and programs based on conditions and suggestions received by the reception unit. For example, the generation unit can generate game scenes with a specific theme and program scenes in which the protagonist plays a specific role. The generation unit uses generation AI to analyze the input conditions and generates game videos and programs based on them. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate the game's story. For example, it generates detailed scenarios and dialogues based on a story outline provided by the user. The generation unit can also generate game scenes using multimodal generation AI. For example, it generates background images and character animations based on user-specified environment settings. Furthermore, the generation unit can also program the actions of game characters using generation AI. For example, it generates animation sequences to make character movements and actions appear natural. The generation unit automates these generation processes, enabling it to quickly and accurately generate content based on user-specified conditions. This allows the generation unit to efficiently provide high-quality game content that meets user demands.

[0032] The modification unit detects and fixes bugs in programs generated by the generation unit. The modification unit uses generation AI to analyze the generated programs and detect bugs. Specifically, the modification unit detects bugs such as characters passing through walls or bugs that cause the game to crash under certain conditions. The modification unit automatically fixes the detected bugs. For example, the generation AI analyzes the program code and identifies the part causing the bug. Next, the generation AI automatically generates code to fix the bug and applies it to the program. The modification unit can not only fix bugs using the generation AI but also verify the operation of the modified program. For example, the modification unit runs the modified program in a test environment to confirm that the bug does not recur. Furthermore, the modification unit can accept feedback from users and make additional bug fixes and improvements. This allows the modification unit to maintain the quality of the generated programs and provide users with a high-quality gaming experience.

[0033] The provider unit provides users with videos and programs generated by the generator unit. For example, the provider unit provides generated videos and programs to users through web applications or mobile applications. Specifically, the provider unit uploads the generated content to a cloud server, making it accessible to users. Users can view and download the generated content through web browsers or mobile apps. The provider unit can also send generated videos and programs via email. For example, it can send a link to the generated content to an email address specified by the user, allowing for easy access. Furthermore, the provider unit can print and provide generated videos and programs. For example, it can print game character designs or scene sketches and provide them to users. This allows the provider unit to deliver generated content to users in diverse ways and ensure easy access. Additionally, the provider unit can collect user feedback to improve delivery methods and develop new delivery methods. This enables the provider unit to deliver content to users quickly and flexibly, improving user satisfaction.

[0034] The simulation unit simulates the operation of the program modified by the modification unit. The simulation unit uses a generation AI to simulate the operation of the modified program. Specifically, the simulation unit simulates the operation of the modified program in a virtual environment and verifies its operation. For example, the simulation unit reproduces character movements and scene progression in the virtual environment to verify that the program operates as intended. The simulation unit can also simulate the operation of the modified program in an actual game environment. For example, the simulation unit runs the modified program on actual game hardware and verifies its operation. This allows the simulation unit to confirm that the modified program operates correctly in the actual environment and provide users with a high-quality gaming experience. Furthermore, the simulation unit can save the simulation results in a database and use them for future modifications and improvements. This allows the simulation unit to maintain the quality of the modified program and improve the reliability of the entire system.

[0035] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can automatically display conditions and suggestions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions and suggestions to be used during specific time periods based on the user's past input history. This improves user convenience by selecting the optimal reception method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal reception method.

[0036] The reception unit can filter the received conditions and advice based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving conditions and advice related to the user's current projects. The reception unit can also filter and display highly relevant conditions and advice based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate conditions and advice according to the progress of the user's projects. In this way, highly relevant information can be provided by filtering conditions and advice based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving conditions and advice. For example, if the user is in a specific region, the reception unit will prioritize receiving conditions and advice related to that region. The reception unit can also filter and display highly relevant conditions and advice based on the user's current location. Furthermore, if the user is on the move, the reception unit can prioritize receiving conditions and advice appropriate to their current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0038] The reception desk can analyze the user's social media activity and receive relevant information when receiving conditions or advice. For example, the reception desk can prioritize receiving relevant conditions and advice based on information shared by the user on social media. It can also prioritize receiving conditions and advice provided by the user's social media followers and friends. Furthermore, the reception desk can analyze the user's social media activity history and filter and display highly relevant conditions and advice. This allows the reception desk to receive highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0039] The generation unit can adjust the level of detail of the generated content based on the importance of the conditions and advice during the generation process. For example, the generation unit can generate detailed videos or programs based on important conditions and advice. It can also generate simplified videos or programs based on lower-priority conditions and advice. Furthermore, the generation unit can dynamically adjust the level of detail of the generated content according to the importance of the conditions and advice. This allows for efficient generation by adjusting the level of detail of the generated content based on the importance of the conditions and advice. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the level of detail of the generated content using a generation AI model that takes the importance of conditions and advice as input and outputs the level of detail of the generated content.

[0040] The generation unit can apply different generation algorithms depending on the game category during generation. For example, in the case of an action game, the generation unit can apply an algorithm that generates dynamic scenes in real time. It can also apply an algorithm that generates logical puzzles in the case of a puzzle game. Furthermore, in the case of an RPG game, the generation unit can apply a generation algorithm specialized in storytelling. This allows for the generation of more appropriate content by applying a generation algorithm tailored to the game category. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the game category as input and outputs an appropriate generation algorithm.

[0041] The generation unit can determine the generation priority based on the timing of the submission of conditions and advice during the generation process. For example, the generation unit can prioritize processing and generating conditions and advice submitted early. It can also prioritize processing and generating conditions and advice of high urgency. Furthermore, the generation unit can dynamically adjust the generation priority according to the submission timing. This enables efficient generation by determining the generation priority based on the submission timing of conditions and advice. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can determine the generation priority using a generation AI model that takes the submission timing of conditions and advice as input and outputs the generation priority.

[0042] The generation unit can adjust the generation order based on the relevance of conditions and advice during generation. For example, the generation unit can prioritize processing and generating conditions and advice that are highly relevant. It can also postpone the generation of less relevant conditions and advice. Furthermore, the generation unit can dynamically adjust the generation order according to the relevance of conditions and advice. This allows for efficient generation by adjusting the generation order based on the relevance of conditions and advice. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the generation order using a generation AI model that takes the relevance of conditions and advice as input and outputs the generation order.

[0043] The repair unit can optimize its repair algorithm by referring to past bug data during the repair process. For example, the repair unit can quickly detect and repair similar bugs based on past bug data. It can also analyze past bug data and apply the optimal repair algorithm. Furthermore, the repair unit can determine repair priorities by referring to past bug data. This allows for the optimization of the repair algorithm and efficient repairs by referring to past bug data. Some or all of the above processes in the repair unit are performed using a generative AI. For example, the repair unit can input past bug data into the generative AI and have the generative AI perform the optimization of the repair algorithm.

[0044] The modification unit can apply different modification methods depending on the game category during the modification process. For example, in the case of action games, the modification unit can apply a method to detect and fix dynamic bugs in real time. In the case of puzzle games, the modification unit can also apply a method to detect and fix logical bugs. Furthermore, in the case of RPG games, the modification unit can apply a method to detect and fix bugs related to storytelling. This allows for more appropriate modifications by applying modification methods according to the game category. Some or all of the above processing in the modification unit is performed using generative AI. For example, the modification unit can apply modification methods using a generative AI model that takes the game category as input and outputs an appropriate modification method.

[0045] The repair unit can determine the priority of repairs based on when the bug occurred. For example, the repair unit will prioritize fixing recently occurring bugs. It can also prioritize fixing bugs that have been left unaddressed for a long time. Furthermore, the repair unit can dynamically adjust the repair priority according to when the bug occurred. This enables efficient repairs by determining the repair priority based on when the bug occurred. Some or all of the above processes in the repair unit are performed using generative AI. For example, the repair unit can determine the repair priority using a generative AI model that takes the time the bug occurred as input and outputs the repair priority.

[0046] The modification unit can adjust the order of bug modifications based on their relevance. For example, it can prioritize fixing highly relevant bugs. It can also postpone fixing less relevant bugs. Furthermore, the modification unit can dynamically adjust the order of modifications according to the relevance of the bugs. This allows for efficient modifications by adjusting the order of modifications based on the relevance of the bugs. Some or all of the above processes in the modification unit are performed using generative AI. For example, the modification unit can adjust the order of modifications using a generative AI model that takes the relevance of bugs as input and outputs the order of modifications.

[0047] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may prioritize providing display methods that the user has frequently used in the past. The service provider can also suggest the optimal display method based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and prioritize providing highly relevant information. This improves user convenience by selecting the optimal service delivery method based on past usage history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past usage data into a generating AI and have the generating AI select the optimal service delivery method.

[0048] The information provider can filter information based on the user's current projects and areas of interest at the time of delivery. For example, the provider can prioritize providing information related to the user's current ongoing projects. The provider can also filter and display highly relevant information based on the user's areas of interest. Furthermore, the provider can suggest appropriate information according to the progress of the user's projects. In this way, highly relevant information can be provided by filtering information based on the user's projects and areas of interest. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0049] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider will prioritize providing information related to that region. The information provider can also filter and display highly relevant information based on the user's current location. Furthermore, if the user is on the move, the information provider can prioritize providing information appropriate to their current location. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's location data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0050] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing relevant information based on information shared by the user on social media. It can also prioritize providing information provided by the user's social media followers and friends. Furthermore, the service provider can analyze the user's social media activity history and filter and display highly relevant information. This allows the service provider to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0051] The simulation unit can optimize the simulation algorithm by referring to past simulation data during the simulation. For example, the simulation unit can quickly execute similar simulations based on past simulation data. The simulation unit can also analyze past simulation data and apply the optimal simulation algorithm. Furthermore, the simulation unit can determine the priority of simulations by referring to past simulation data. This allows for the optimization of the simulation algorithm by referring to past simulation data, enabling efficient simulations. Some or all of the above processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the simulation algorithm.

[0052] The simulation unit can apply different simulation methods depending on the game category during simulation. For example, in the case of an action game, the simulation unit can apply a method that performs a dynamic simulation in real time. In the case of a puzzle game, the simulation unit can also apply a method that performs a logical simulation. Furthermore, in the case of an RPG game, the simulation unit can apply a method that performs a simulation related to storytelling. By applying a simulation method appropriate to the game category, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can apply a simulation method using a generative AI model that takes the game category as input and outputs an appropriate simulation method.

[0053] The simulation unit can determine the simulation priority based on the timing of modifications during the simulation. For example, the simulation unit can prioritize simulating recently modified programs. It can also prioritize simulating programs that have been left untouched for a long period of time. Furthermore, the simulation unit can dynamically adjust the simulation priority according to the timing of modifications. This enables efficient simulation by determining the simulation priority based on the timing of modifications. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the timing of modifications into a generating AI and have the generating AI execute the simulation priority.

[0054] The simulation unit can adjust the simulation order based on the relevance of the modifications during the simulation. For example, the simulation unit can prioritize simulating modifications that are highly relevant. It can also postpone simulating modifications that are less relevant. Furthermore, the simulation unit can dynamically adjust the simulation order according to the relevance of the modifications. This allows for efficient simulation by adjusting the simulation order based on the relevance of the modifications. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the relevance of the modifications into a generating AI and have the generating AI execute the simulation order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past input history and select the most suitable reception method. For example, it can automatically display conditions and suggestions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions and suggestions that the user will use during specific time periods based on their past input history. By selecting the most suitable reception method based on past input history, the system can improve user convenience.

[0057] The generation unit can adjust the level of detail in the generated content based on the importance of the conditions and advice given during the generation process. For example, it can generate detailed videos or programs based on important conditions and advice. It can also generate simplified videos or programs based on lower-priority conditions and advice. Furthermore, it can dynamically adjust the level of detail in the generated content according to the importance of the conditions and advice. This allows for efficient generation by adjusting the level of detail based on the importance of the conditions and advice.

[0058] The repair department can optimize its repair algorithm by referring to past bug data during the repair process. For example, it can quickly detect and repair similar bugs based on past bug data. It can also analyze past bug data and apply the optimal repair algorithm. Furthermore, it can determine the priority of repairs by referring to past bug data. As a result, by referring to past bug data, the repair algorithm can be optimized, enabling efficient repairs.

[0059] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize providing display methods that the user has frequently used in the past. It can also suggest the optimal display method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and prioritize providing highly relevant information. By selecting the optimal service delivery method based on past usage history, the service provider can improve user convenience.

[0060] The simulation unit can apply different simulation methods depending on the game category during simulation. For example, in the case of an action game, a method that performs dynamic simulations in real time can be applied. In the case of a puzzle game, a method that performs logical simulations can be applied. Furthermore, in the case of an RPG game, a method that performs simulations related to storytelling can be applied. This allows for more appropriate simulations by applying simulation methods according to the game category.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception unit receives conditions and suggestions from users. These conditions and suggestions may include detailed information such as the game's concept, story, and character settings. The reception unit stores the conditions and suggestions entered by users in a database and provides them to the generation unit. Step 2: The generation unit uses a generation AI to generate videos and programs based on the conditions and suggestions received by the reception unit. For example, it generates game scenes with a specific theme and programs scenes in which the protagonist plays a specific role. The generation unit uses a text generation AI (e.g., LLM) and a multimodal generation AI to generate the game's story, scenes, and character actions. Step 3: The modification unit detects and fixes bugs in the program generated by the generation unit. The modification unit analyzes the program generated using the generation AI and detects bugs. For example, it detects and automatically fixes bugs such as characters passing through walls or bugs that cause the game to crash under certain conditions. Step 4: The provider unit provides the video or program generated by the generator unit to the user. The provider unit can provide the generated video or program to the user through a web application or mobile application, or it can also send it by email or print it out. Step 5: The simulation unit simulates the operation of the program modified by the modification unit. The simulation unit uses generated AI to simulate the operation of the modified program in a virtual environment or an actual game environment and verify its operation.

[0063] (Example of form 2) The game production support system according to an embodiment of the present invention is a system that improves the productivity of game production by utilizing generative AI. This game production support system allows users to input various conditions and advice into the generative AI, enabling the generation of high-quality videos and programs, as well as bug detection and correction. This improves the productivity of game production and reduces the costs of technology, personnel, and marketing required for production. For example, a user inputs detailed conditions such as the game's concept, story, and character settings into the generative AI. This information is input into the generative AI, which then generates game videos and programs based on the input conditions and advice. The generative AI generates game scenes with specific themes and programs scenes in which the protagonist plays a specific role. Furthermore, the generative AI detects and corrects bugs in the generated programs. The generative AI detects bugs such as characters passing through walls or games crashing under specific conditions, and automatically corrects these bugs. This improves game quality and reduces the time and effort required for bug fixing. Users can generate high-quality game content simply by inputting conditions and advice into the generative AI. Additionally, because the generative AI detects and corrects bugs, the time and effort required for bug fixing can be reduced. This reduces the costs associated with the technology, personnel, and marketing required for game development. For example, game programmers can simultaneously work on multiple projects simply by giving instructions to a generation AI. This improves the efficiency of game development and enhances competitiveness in the highly competitive game market. As a result, the game development support system can improve the productivity of game development by generating high-quality videos and programs based on user requirements and advice, and by detecting and fixing bugs.

[0064] The game production support system according to this embodiment comprises a reception unit, a generation unit, a modification unit, a provision unit, and a simulation unit. The reception unit receives conditions and advice from the user. Conditions and advice from the user include, but are not limited to, detailed conditions such as the game's concept, story, and character settings. The reception unit, for example, stores the conditions and advice entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to generate videos and programs based on the conditions and advice received by the reception unit. The generation unit, for example, generates game scenes with a specific theme and programs scenes in which the protagonist plays a specific role. The generation unit uses a generation AI to analyze the input conditions and generates game videos and programs based on them. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate the game's story. The generation unit can also generate game scenes using a multimodal generation AI. The generation unit can also program the actions of game characters using a generation AI. The modification unit detects and modifies bugs in the program generated by the generation unit. The modification unit uses generation AI to analyze the generated program and detect bugs. For example, the modification unit detects bugs such as characters passing through walls or bugs that cause the game to crash under certain conditions. The modification unit automatically fixes the detected bugs. The modification unit uses generation AI to correct the bugs. The provision unit provides the videos and programs generated by the generation unit to users. For example, the provision unit provides the generated videos and programs to users through web applications or mobile applications. The provision unit can also send the generated videos and programs via email. The provision unit can also print the generated videos and programs using a printer and provide them. The simulation unit simulates the operation of the program modified by the modification unit. The simulation unit uses generation AI to simulate the operation of the modified program. For example, the simulation unit simulates the operation of the modified program in a virtual environment and verifies its operation.The simulation unit can also simulate the operation of the modified program in an actual game environment. This allows the game production support system according to the embodiment to generate high-quality videos and programs based on user conditions and advice, and to detect and fix bugs, thereby improving the productivity of game production.

[0065] The reception unit receives conditions and suggestions from users. These conditions and suggestions include, but are not limited to, detailed conditions such as the game's concept, story, and character settings. The reception unit stores the conditions and suggestions entered by users in a database and provides them to the generation unit. Specifically, the reception unit provides an interface for user input, allowing users to specify details such as the game's theme, character personalities, story development, and gameplay mechanics. Users can provide conditions and suggestions through text input, selection of options, image uploads, etc. The reception unit analyzes these inputs in real time and stores them in the database in an appropriate format. Furthermore, the reception unit allows users to refer to conditions and suggestions they have entered in the past, enabling them to reuse or modify previous settings. This allows users to efficiently provide conditions and suggestions and smoothly proceed through the initial stages of game development.

[0066] The generation unit uses generation AI to generate videos and programs based on conditions and suggestions received by the reception unit. For example, the generation unit can generate game scenes with a specific theme and program scenes in which the protagonist plays a specific role. The generation unit uses generation AI to analyze the input conditions and generates game videos and programs based on them. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate the game's story. For example, it generates detailed scenarios and dialogues based on a story outline provided by the user. The generation unit can also generate game scenes using multimodal generation AI. For example, it generates background images and character animations based on user-specified environment settings. Furthermore, the generation unit can also program the actions of game characters using generation AI. For example, it generates animation sequences to make character movements and actions appear natural. The generation unit automates these generation processes, enabling it to quickly and accurately generate content based on user-specified conditions. This allows the generation unit to efficiently provide high-quality game content that meets user demands.

[0067] The modification unit detects and fixes bugs in programs generated by the generation unit. The modification unit uses generation AI to analyze the generated programs and detect bugs. Specifically, the modification unit detects bugs such as characters passing through walls or bugs that cause the game to crash under certain conditions. The modification unit automatically fixes the detected bugs. For example, the generation AI analyzes the program code and identifies the part causing the bug. Next, the generation AI automatically generates code to fix the bug and applies it to the program. The modification unit can not only fix bugs using the generation AI but also verify the operation of the modified program. For example, the modification unit runs the modified program in a test environment to confirm that the bug does not recur. Furthermore, the modification unit can accept feedback from users and make additional bug fixes and improvements. This allows the modification unit to maintain the quality of the generated programs and provide users with a high-quality gaming experience.

[0068] The provider unit provides users with videos and programs generated by the generator unit. For example, the provider unit provides generated videos and programs to users through web applications or mobile applications. Specifically, the provider unit uploads the generated content to a cloud server, making it accessible to users. Users can view and download the generated content through web browsers or mobile apps. The provider unit can also send generated videos and programs via email. For example, it can send a link to the generated content to an email address specified by the user, allowing for easy access. Furthermore, the provider unit can print and provide generated videos and programs. For example, it can print game character designs or scene sketches and provide them to users. This allows the provider unit to deliver generated content to users in diverse ways and ensure easy access. Additionally, the provider unit can collect user feedback to improve delivery methods and develop new delivery methods. This enables the provider unit to deliver content to users quickly and flexibly, improving user satisfaction.

[0069] The simulation unit simulates the operation of the program modified by the modification unit. The simulation unit uses a generation AI to simulate the operation of the modified program. Specifically, the simulation unit simulates the operation of the modified program in a virtual environment and verifies its operation. For example, the simulation unit reproduces character movements and scene progression in the virtual environment to verify that the program operates as intended. The simulation unit can also simulate the operation of the modified program in an actual game environment. For example, the simulation unit runs the modified program on actual game hardware and verifies its operation. This allows the simulation unit to confirm that the modified program operates correctly in the actual environment and provide users with a high-quality gaming experience. Furthermore, the simulation unit can save the simulation results in a database and use them for future modifications and improvements. This allows the simulation unit to maintain the quality of the modified program and improve the reliability of the entire system.

[0070] The reception desk can estimate the user's emotions and adjust how conditions and advice are received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of conditions and advice. This allows for a more appropriate interface by adjusting how conditions and advice are received according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can automatically display conditions and suggestions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions and suggestions to be used during specific time periods based on the user's past input history. This improves user convenience by selecting the optimal reception method based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal reception method.

[0072] The reception unit can filter the received conditions and advice based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving conditions and advice related to the user's current projects. The reception unit can also filter and display highly relevant conditions and advice based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate conditions and advice according to the progress of the user's projects. In this way, highly relevant information can be provided by filtering conditions and advice based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0073] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of the conditions and advice to accept. For example, if the user is nervous, the reception desk will prioritize important conditions and advice. If the user is relaxed, the reception desk may also prioritize detailed conditions and advice. Furthermore, if the user is in a hurry, the reception desk may prioritize conditions and advice that can be processed quickly. This allows for the prioritization of more appropriate information by determining the priority of conditions and advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving conditions and advice. For example, if the user is in a specific region, the reception unit will prioritize receiving conditions and advice related to that region. The reception unit can also filter and display highly relevant conditions and advice based on the user's current location. Furthermore, if the user is on the move, the reception unit can prioritize receiving conditions and advice appropriate to their current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0075] The reception desk can analyze the user's social media activity and receive relevant information when receiving conditions or advice. For example, the reception desk can prioritize receiving relevant conditions and advice based on information shared by the user on social media. It can also prioritize receiving conditions and advice provided by the user's social media followers and friends. Furthermore, the reception desk can analyze the user's social media activity history and filter and display highly relevant conditions and advice. This allows the reception desk to receive highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0076] The generation unit can estimate the user's emotions and adjust the presentation of the generated videos and programs based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. In this way, by adjusting the presentation of videos and programs according to the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] The generation unit can adjust the level of detail of the generated content based on the importance of the conditions and advice during the generation process. For example, the generation unit can generate detailed videos or programs based on important conditions and advice. It can also generate simplified videos or programs based on lower-priority conditions and advice. Furthermore, the generation unit can dynamically adjust the level of detail of the generated content according to the importance of the conditions and advice. This allows for efficient generation by adjusting the level of detail of the generated content based on the importance of the conditions and advice. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the level of detail of the generated content using a generation AI model that takes the importance of conditions and advice as input and outputs the level of detail of the generated content.

[0078] The generation unit can apply different generation algorithms depending on the game category during generation. For example, in the case of an action game, the generation unit can apply an algorithm that generates dynamic scenes in real time. It can also apply an algorithm that generates logical puzzles in the case of a puzzle game. Furthermore, in the case of an RPG game, the generation unit can apply a generation algorithm specialized in storytelling. This allows for the generation of more appropriate content by applying a generation algorithm tailored to the game category. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the game category as input and outputs an appropriate generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the videos and programs it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. This allows for the generation of more appropriate content by adjusting the length of videos and programs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The generation unit can determine the generation priority based on the timing of the submission of conditions and advice during the generation process. For example, the generation unit can prioritize processing and generating conditions and advice submitted early. It can also prioritize processing and generating conditions and advice of high urgency. Furthermore, the generation unit can dynamically adjust the generation priority according to the submission timing. This enables efficient generation by determining the generation priority based on the submission timing of conditions and advice. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can determine the generation priority using a generation AI model that takes the submission timing of conditions and advice as input and outputs the generation priority.

[0081] The generation unit can adjust the generation order based on the relevance of conditions and advice during generation. For example, the generation unit can prioritize processing and generating conditions and advice that are highly relevant. It can also postpone the generation of less relevant conditions and advice. Furthermore, the generation unit can dynamically adjust the generation order according to the relevance of conditions and advice. This allows for efficient generation by adjusting the generation order based on the relevance of conditions and advice. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the generation order using a generation AI model that takes the relevance of conditions and advice as input and outputs the generation order.

[0082] The remediation unit can estimate the user's emotions and adjust the bug detection and remediation methods based on the estimated emotions. For example, if the user is stressed, the remediation unit can quickly detect bugs and provide simple remediation methods. If the user is relaxed, the remediation unit can also provide detailed bug reports and suggest remediation methods. Furthermore, if the user is in a hurry, the remediation unit can prioritize detecting and remediating the most important bugs. This allows for more appropriate remediation by adjusting the bug detection and remediation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the remediation unit is performed using generative AI. For example, the remediation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The repair unit can optimize its repair algorithm by referring to past bug data during the repair process. For example, the repair unit can quickly detect and repair similar bugs based on past bug data. It can also analyze past bug data and apply the optimal repair algorithm. Furthermore, the repair unit can determine repair priorities by referring to past bug data. This allows for the optimization of the repair algorithm and efficient repairs by referring to past bug data. Some or all of the above processes in the repair unit are performed using a generative AI. For example, the repair unit can input past bug data into the generative AI and have the generative AI perform the optimization of the repair algorithm.

[0084] The modification unit can apply different modification methods depending on the game category during the modification process. For example, in the case of action games, the modification unit can apply a method to detect and fix dynamic bugs in real time. In the case of puzzle games, the modification unit can also apply a method to detect and fix logical bugs. Furthermore, in the case of RPG games, the modification unit can apply a method to detect and fix bugs related to storytelling. This allows for more appropriate modifications by applying modification methods according to the game category. Some or all of the above processing in the modification unit is performed using generative AI. For example, the modification unit can apply modification methods using a generative AI model that takes the game category as input and outputs an appropriate modification method.

[0085] The modification unit can estimate the user's emotions and determine the priority of modifications based on those emotions. For example, if the user is stressed, the modification unit will prioritize fixing critical bugs. If the user is relaxed, the modification unit can also provide a detailed bug report and determine the priority of modifications. Furthermore, if the user is in a hurry, the modification unit can prioritize fixing bugs that can be fixed quickly. This allows for more appropriate modifications by prioritizing modifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the modification unit is performed using generative AI. For example, the modification unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The repair unit can determine the priority of repairs based on when the bug occurred. For example, the repair unit will prioritize fixing recently occurring bugs. It can also prioritize fixing bugs that have been left unaddressed for a long time. Furthermore, the repair unit can dynamically adjust the repair priority according to when the bug occurred. This enables efficient repairs by determining the repair priority based on when the bug occurred. Some or all of the above processes in the repair unit are performed using generative AI. For example, the repair unit can determine the repair priority using a generative AI model that takes the time the bug occurred as input and outputs the repair priority.

[0087] The modification unit can adjust the order of bug modifications based on their relevance. For example, it can prioritize fixing highly relevant bugs. It can also postpone fixing less relevant bugs. Furthermore, the modification unit can dynamically adjust the order of modifications according to the relevance of the bugs. This allows for efficient modifications by adjusting the order of modifications based on the relevance of the bugs. Some or all of the above processes in the modification unit are performed using generative AI. For example, the modification unit can adjust the order of modifications using a generative AI model that takes the relevance of bugs as input and outputs the order of modifications.

[0088] The service provider can estimate the user's emotions and adjust the display method of the videos and programs based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may prioritize providing display methods that the user has frequently used in the past. The service provider can also suggest the optimal display method based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and prioritize providing highly relevant information. This improves user convenience by selecting the optimal service delivery method based on past usage history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past usage data into a generating AI and have the generating AI select the optimal service delivery method.

[0090] The information provider can filter information based on the user's current projects and areas of interest at the time of delivery. For example, the provider can prioritize providing information related to the user's current ongoing projects. The provider can also filter and display highly relevant information based on the user's areas of interest. Furthermore, the provider can suggest appropriate information according to the progress of the user's projects. In this way, highly relevant information can be provided by filtering information based on the user's projects and areas of interest. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0091] The service provider can estimate the user's emotions and prioritize the videos and programs offered based on those emotions. For example, if the user is tense, the service provider will prioritize providing important information. If the user is relaxed, the service provider can also prioritize providing detailed information. Furthermore, if the user is in a hurry, the service provider can prioritize providing information that can be processed quickly. This allows for the prioritization of more relevant information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider is performed using generative AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider will prioritize providing information related to that region. The information provider can also filter and display highly relevant information based on the user's current location. Furthermore, if the user is on the move, the information provider can prioritize providing information appropriate to their current location. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's location data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0093] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing relevant information based on information shared by the user on social media. It can also prioritize providing information provided by the user's social media followers and friends. Furthermore, the service provider can analyze the user's social media activity history and filter and display highly relevant information. This allows the service provider to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0094] The simulation unit can estimate the user's emotions and adjust the simulation display method based on the estimated emotions. For example, if the user is tense, the simulation unit can provide a simple and highly visible display method. If the user is relaxed, the simulation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the simulation unit can provide a concise display method. By adjusting the display method according to the user's emotions, more appropriate simulation results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the simulation unit is performed using generative AI. For example, the simulation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The simulation unit can optimize the simulation algorithm by referring to past simulation data during the simulation. For example, the simulation unit can quickly execute similar simulations based on past simulation data. The simulation unit can also analyze past simulation data and apply the optimal simulation algorithm. Furthermore, the simulation unit can determine the priority of simulations by referring to past simulation data. This allows for the optimization of the simulation algorithm by referring to past simulation data, enabling efficient simulations. Some or all of the above processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input past simulation data into a generating AI and have the generating AI perform the optimization of the simulation algorithm.

[0096] The simulation unit can apply different simulation methods depending on the game category during simulation. For example, in the case of an action game, the simulation unit can apply a method that performs a dynamic simulation in real time. In the case of a puzzle game, the simulation unit can also apply a method that performs a logical simulation. Furthermore, in the case of an RPG game, the simulation unit can apply a method that performs a simulation related to storytelling. By applying a simulation method appropriate to the game category, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can apply a simulation method using a generative AI model that takes the game category as input and outputs an appropriate simulation method.

[0097] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is tense, the simulation unit will prioritize important simulations. It can also prioritize detailed simulations if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can prioritize simulations that can be executed quickly. This allows for more appropriate simulations by prioritizing simulations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit are performed using generative AI. For example, the simulation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0098] The simulation unit can determine the simulation priority based on the timing of modifications during the simulation. For example, the simulation unit can prioritize simulating recently modified programs. It can also prioritize simulating programs that have been left untouched for a long period of time. Furthermore, the simulation unit can dynamically adjust the simulation priority according to the timing of modifications. This enables efficient simulation by determining the simulation priority based on the timing of modifications. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the timing of modifications into a generating AI and have the generating AI execute the simulation priority.

[0099] The simulation unit can adjust the simulation order based on the relevance of the modifications during the simulation. For example, the simulation unit can prioritize simulating modifications that are highly relevant. It can also postpone simulating modifications that are less relevant. Furthermore, the simulation unit can dynamically adjust the simulation order according to the relevance of the modifications. This allows for efficient simulation by adjusting the simulation order based on the relevance of the modifications. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can input the relevance of the modifications into a generating AI and have the generating AI execute the simulation order.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception system can estimate the user's emotions and adjust how conditions and advice are received based on those estimations. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of conditions and advice. This allows for a more appropriate interface by adjusting how conditions and advice are received according to the user's emotions.

[0102] The generation unit can estimate the user's emotions and adjust the presentation of the generated videos and programs based on those estimated emotions. For example, if the user is relaxed, it can generate a video that progresses at a leisurely pace. If the user is in a hurry, it can generate a video that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a video with visually stimulating effects. By adjusting the presentation of videos and programs according to the user's emotions, it is possible to generate more appropriate content.

[0103] The remediation unit can estimate the user's emotions and adjust the bug detection and remediation methods based on those estimates. For example, if the user is stressed, it can quickly detect bugs and provide simple remediation methods. If the user is relaxed, it can provide detailed bug reports and suggest remediation methods. Furthermore, if the user is in a hurry, it can prioritize detecting and remediating the most important bugs. This allows for more appropriate remediation by adjusting bug detection and remediation methods according to the user's emotions.

[0104] The service provider can estimate the user's emotions and adjust the display method of videos and programs based on those estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information.

[0105] The simulation unit can estimate the user's emotions and adjust the simulation display method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it is possible to provide more appropriate simulation results.

[0106] The reception desk can analyze a user's past input history and select the most suitable reception method. For example, it can automatically display conditions and suggestions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions and suggestions that the user will use during specific time periods based on their past input history. By selecting the most suitable reception method based on past input history, the system can improve user convenience.

[0107] The generation unit can adjust the level of detail in the generated content based on the importance of the conditions and advice given during the generation process. For example, it can generate detailed videos or programs based on important conditions and advice. It can also generate simplified videos or programs based on lower-priority conditions and advice. Furthermore, it can dynamically adjust the level of detail in the generated content according to the importance of the conditions and advice. This allows for efficient generation by adjusting the level of detail based on the importance of the conditions and advice.

[0108] The repair department can optimize its repair algorithm by referring to past bug data during the repair process. For example, it can quickly detect and repair similar bugs based on past bug data. It can also analyze past bug data and apply the optimal repair algorithm. Furthermore, it can determine the priority of repairs by referring to past bug data. As a result, by referring to past bug data, the repair algorithm can be optimized, enabling efficient repairs.

[0109] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize providing display methods that the user has frequently used in the past. It can also suggest the optimal display method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and prioritize providing highly relevant information. By selecting the optimal service delivery method based on past usage history, the service provider can improve user convenience.

[0110] The simulation unit can apply different simulation methods depending on the game category during simulation. For example, in the case of an action game, a method that performs dynamic simulations in real time can be applied. In the case of a puzzle game, a method that performs logical simulations can be applied. Furthermore, in the case of an RPG game, a method that performs simulations related to storytelling can be applied. This allows for more appropriate simulations by applying simulation methods according to the game category.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception unit receives conditions and suggestions from users. These conditions and suggestions may include detailed information such as the game's concept, story, and character settings. The reception unit stores the conditions and suggestions entered by users in a database and provides them to the generation unit. Step 2: The generation unit uses a generation AI to generate videos and programs based on the conditions and suggestions received by the reception unit. For example, it generates game scenes with a specific theme and programs scenes in which the protagonist plays a specific role. The generation unit uses a text generation AI (e.g., LLM) and a multimodal generation AI to generate the game's story, scenes, and character actions. Step 3: The modification unit detects and fixes bugs in the program generated by the generation unit. The modification unit analyzes the program generated using the generation AI and detects bugs. For example, it detects and automatically fixes bugs such as characters passing through walls or bugs that cause the game to crash under certain conditions. Step 4: The provider unit provides the video or program generated by the generator unit to the user. The provider unit can provide the generated video or program to the user through a web application or mobile application, or it can also send it by email or print it out. Step 5: The simulation unit simulates the operation of the program modified by the modification unit. The simulation unit uses generated AI to simulate the operation of the modified program in a virtual environment or an actual game environment and verify its operation.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, modification unit, provision unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives conditions and advice from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates videos and programs using generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects and modifies bugs in the generated program. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated videos and programs to the user. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates the operation of the modified program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, modification unit, provision unit, and simulation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives conditions and advice from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates videos and programs using generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects and modifies bugs in the generated program. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated videos and programs to the user. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates the operation of the modified program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, modification unit, provision unit, and simulation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives conditions and advice from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates videos and programs using generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects and modifies bugs in the generated program. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated videos and programs to the user. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates the operation of the modified program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, modification unit, provision unit, and simulation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives conditions and advice from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates videos and programs using generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects and modifies bugs in the generated program. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated videos and programs to the user. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates the operation of the modified program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception desk that accepts requests and suggestions from users, A generation unit that generates videos and programs based on the conditions and advice received by the reception unit, A modification unit that detects and corrects bugs in the program generated by the generation unit, A providing unit that provides the video and program generated by the generation unit, The system includes a simulation unit that simulates the operation of the program modified by the modification unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the way conditions and advice are accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past input history and select the optimal acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving requests for conditions or advice, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of acceptable conditions and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving requests for conditions or advice, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving conditions or advice, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts the way videos and programs are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During generation, adjust the level of detail based on the importance of the conditions and advice. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, different generation algorithms are applied depending on the game category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the length of the generated videos and programs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the generation priority is determined based on the conditions and the timing of advice submission. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the generation order is adjusted based on the relevance of conditions and advice. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned modified section is We estimate user sentiment and adjust bug detection and remediation methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned modified section is During the rework process, the rework algorithm is optimized by referring to past bug data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned modified section is During the refurbishment process, different refurbishment methods will be applied to each game category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned modified section is The system estimates user sentiment and determines the priority of improvements based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned modified section is During the rework process, the priority of the rework will be determined based on when the bug occurred. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned modified section is During the update process, adjust the order of updates based on the relevance of the bugs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how videos and programs are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the videos and programs to be offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, During simulation, the simulation algorithm is optimized by referring to past simulation data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, During simulation, different simulation methods are applied for each game category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of the simulation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, During simulation, the simulation priorities are determined based on when the modifications will occur. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned simulation unit, During simulation, adjust the simulation order based on the relevance of the modifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts requests and suggestions from users, A generation unit that generates videos and programs based on the conditions and advice received by the reception unit, A modification unit that detects and corrects bugs in the program generated by the generation unit, A providing unit that provides the video and program generated by the generation unit, The system includes a simulation unit that simulates the operation of the program modified by the modification unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the way conditions and advice are accepted based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past input history and select the optimal acceptance method. The system according to feature 1.

4. The aforementioned reception unit is When receiving requests for conditions or advice, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of acceptable conditions and advice based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving requests for conditions or advice, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving conditions or advice, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the way videos and programs are expressed based on those estimated emotions. The system according to feature 1.

9. The generating unit is During generation, adjust the level of detail based on the importance of the conditions and advice. The system according to feature 1.

10. The generating unit is During generation, different generation algorithms are applied depending on the game category. The system according to feature 1.

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