system

Abstract

The system according to this embodiment aims to evaluate the user's activities that contribute to the SDGs and provide game elements based on that evaluation. [Solution] The system according to the embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives video submissions taken by users. The determination unit analyzes the video received by the reception unit and determines its sustainability level. The generation unit generates monsters based on the sustainability level determined by the determination unit. The provision unit provides the monsters generated by the generation unit to the users.

Description

Technical Field

[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 as a 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 a mechanism for evaluating activities related to the user's SDGs and utilizing them as game elements is not sufficiently provided.

[0005] The system according to the embodiment aims to evaluate activities related to the user's SDGs and provide game elements based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives video submissions from users. The determination unit analyzes the video received by the reception unit and determines its sustainability level. The generation unit generates monsters based on the sustainability level determined by the determination unit. The provision unit provides the monsters generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the user's activities that contribute to the SDGs and provide game elements based on that evaluation. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.[[ID=I5]]

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

[0019] The smart device 14 comprises a computer 36, a 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 system according to an embodiment of the present invention is a system in which users post videos of their daily "activities that contribute to the SDGs," and a generating AI determines the sustainability level. This system is a game that generates monsters according to the determined sustainability level. Rare monsters appear depending on activities that make a significant contribution to the SDGs and the accumulation of daily activities. For example, a user posts a video of their daily "activities that contribute to the SDGs." For example, they might film a video of recycling activities, energy saving, or tree planting, and upload it to the system. This video is input to the generating AI. Next, the generating AI analyzes the posted video and determines the sustainability level. The generating AI analyzes the content of the video and evaluates how much it contributes to the SDGs. For example, if a video of recycling activities is posted, it determines how much that activity contributes to the environment. A monster is generated according to the determined sustainability level. For example, if the sustainability level is high, a rare monster is generated. Conversely, if the sustainability level is low, a low-rarity monster is generated. Furthermore, rare monsters appear in response to activities that significantly contribute to the SDGs, as well as the accumulation of daily activities. For example, users who regularly engage in recycling activities may have a special rare monster generated. This system allows users to contribute to the SDGs while having fun. Through the game, users can see how much their activities contribute to the SDGs, further increasing their motivation. In addition, by sharing the generated monsters with other users, they can spread awareness of the SDGs. For example, if a user posts a video of their recycling activity and the generation AI determines that the activity is "Very High," a monster with a rarity of 80 may be generated. This monster has characteristics such as power 1500 and speed 800, and can compete with other users. In this way, the system allows users to contribute to the SDGs while having fun by posting videos of their daily "activities connected to the SDGs" and having the generation AI determine the sustainability level.This allows the system to evaluate user activity and promote activities that contribute to the SDGs.

[0029] The system according to this embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit accepts video submissions from users. The reception unit provides, for example, an interface for users to upload videos they have filmed. The reception unit checks the format and size of the video and can accept it in an appropriate format. For example, the reception unit can check the resolution and file format of the video and convert it to an appropriate format. The reception unit can also provide guidelines regarding the content of videos submitted by users. For example, the reception unit can provide criteria regarding the length and content of videos to enable users to submit appropriate videos. The determination unit uses a generation AI to analyze the videos accepted by the reception unit and determine their sustainability level. For example, the determination unit evaluates how much the generation AI contributes to the SDGs by analyzing the content of the video. The generation AI uses technologies such as deep learning and neural networks to analyze the content of the video in detail. For example, the generation AI identifies recycling activities, energy conservation, tree planting activities, etc., within the video and evaluates the environmental impact of these activities. The generation AI quantifies the sustainability level based on the video content and outputs the evaluation result. The generation unit generates monsters based on the sustainability level determined by the judgment unit. The generation unit generates monsters according to the sustainability level, for example, using the generation AI. The generation AI determines the appearance and characteristics of the monsters and generates monsters to provide to the user. For example, if the sustainability level is high, the generation AI generates monsters with high rarity. Conversely, if the sustainability level is low, it generates monsters with low rarity. The generation unit determines the characteristics and abilities of the generated monsters and provides them to the user. The provision unit provides the monsters generated by the generation unit to the user. The provision unit, for example, adds the generated monsters to the user's account, making the monsters available to the user. The provision unit can also include a function for sharing the generated monsters with other users. For example, the provision unit provides an option to share the generated monsters on social media or via email.As a result, the system according to this embodiment can promote activities that contribute to the SDGs by analyzing videos taken by users, generating and providing monsters based on their sustainability level, and so on.

[0030] The reception desk accepts video submissions from users. For example, the reception desk provides an interface for users to upload videos. Specifically, it designs an intuitive user interface to allow users to easily upload videos. For instance, it uses drag-and-drop functionality and file selection dialogs to make it easier for users to select video files from their devices. The reception desk can also check the video format and size and accept submissions in the appropriate format. For example, it can check the video resolution and file format and convert them to an appropriate format. Specifically, if a video is too high resolution or in an unsupported file format, the reception desk will have a function to automatically compress or convert the video to an appropriate format. Furthermore, the reception desk can provide guidelines regarding the content of videos submitted by users. For example, it can provide standards regarding video length and content to ensure users submit appropriate videos. Specifically, it provides checklists to ensure that videos are within a certain length range and do not contain specific content (e.g., violent scenes or inappropriate language). This allows the reception desk to create an environment where users can easily and appropriately submit videos, maintaining the overall quality of the system.

[0031] The judgment unit uses a generation AI to analyze videos received by the reception unit and determine their sustainability level. For example, the judgment unit uses the generation AI to analyze the content of the video and evaluate how much it contributes to the SDGs. The generation AI uses technologies such as deep learning and neural networks to analyze the video content in detail. Specifically, the generation AI identifies recycling activities, energy conservation, tree planting activities, etc., within the video and evaluates the environmental impact of these activities. For example, the generation AI recognizes objects and scenes within the video and evaluates how relevant they are to the SDGs goals. Based on the video content, the generation AI quantifies the sustainability level and outputs the evaluation result. Specifically, the generation AI assigns a score to each element in the video and calculates the sustainability level by summarizing these scores. For example, a video containing many recycling activities will be given a high score, while a video containing elements that have a negative impact on the environment will be given a low score. This allows the judgment unit to analyze the video content in detail and evaluate the sustainability level based on objective criteria. Furthermore, the evaluation unit can provide feedback on the evaluation results to the user, offering reference information to help the user improve their activities. For example, by presenting specific areas for improvement and recommendations based on the evaluation results, it can encourage users to engage in more sustainable activities.

[0032] The generation unit generates monsters based on the sustainability level determined by the judgment unit. The generation unit uses, for example, a generation AI to generate monsters according to the sustainability level. The generation AI determines the appearance and characteristics of the monsters and generates the monsters to be provided to the users. Specifically, if the sustainability level is high, the generation AI generates monsters of high rarity. For example, a user who posts a video with a very high sustainability level will be provided with a rare monster with special abilities. Conversely, if the sustainability level is low, it generates monsters of low rarity. For example, a user who posts a video with a low sustainability level will be provided with a common monster with only basic abilities. The generation unit determines the characteristics and abilities of the generated monsters and provides them to the users. Specifically, it determines the monsters' appearance (color and shape), abilities (attack power and defense power), characteristics (special abilities and skills), etc. This allows the generation unit to provide individually customized monsters according to the sustainability level of the videos posted by the users. Furthermore, the generation unit saves the data of the generated monsters so that users can access it later. For example, by logging into their account, users can view monsters they have previously generated and compare them with those of other users. This allows the generation system to provide users with continuous motivation and promote sustainable activities.

[0033] The provider unit provides users with monsters generated by the generator unit. For example, the provider unit adds the generated monsters to the user's account, making them available for use. Specifically, the provider unit ensures that generated monsters are automatically added when the user logs into their account. The provider unit can also provide features for sharing generated monsters with other users. For example, the provider unit provides options for sharing generated monsters via social media or email. Specifically, it provides buttons or links for users to post information about the generated monsters on social media or email them to friends. This allows users to share their achievements with others and promote sustainable practices. Furthermore, the provider unit can offer games and activities using the generated monsters. For example, users can use the generated monsters to compete against other users or cooperate to complete missions. This allows the provider unit to provide users with an enjoyable experience and promote sustainable practices. Additionally, the provider unit can collect user feedback to improve the system. For example, users can send feedback and requests regarding generated monsters to the provider unit, enabling continuous improvement of the system's functions and content. This allows the service provider to offer high-quality services to users and support sustainable activities.

[0034] The judgment unit can analyze the content of a video using a generative AI and determine its sustainability level. For example, the judgment unit uses the generative AI to analyze the video content and evaluate how much it contributes to the SDGs. The generative AI uses technologies such as deep learning and neural networks to analyze the video content in detail. For example, the generative AI identifies recycling activities, energy conservation, and tree planting activities in the video and evaluates the environmental impact of these activities. Based on the video content, the generative AI quantifies the sustainability level and outputs the evaluation result. In this way, by using the generative AI, the content of a video can be accurately analyzed and its sustainability level can be determined.

[0035] The generation unit can generate monsters based on sustainability levels using a generation AI. For example, the generation unit uses the generation AI to generate monsters according to the sustainability level. The generation AI determines the appearance and characteristics of the monsters and generates the monsters to be provided to the user. For example, if the sustainability level is high, the generation AI will generate monsters of high rarity. Conversely, if the sustainability level is low, it will generate monsters of low rarity. The generation unit determines the characteristics and abilities of the generated monsters and provides them to the user. In this way, by using the generation AI, it is possible to generate monsters based on sustainability levels.

[0036] The service provider can provide the generated monsters to users. For example, the service provider can add the generated monsters to the user's account, making them available for use. The service provider can also provide features for sharing the generated monsters with other users. For example, the service provider can provide options for sharing the generated monsters on social media or via email. This allows users to contribute to the SDGs while having fun by providing them with the generated monsters. Some or all of the processes described above in the service provider may be performed using AI, for example, or not using AI.

[0037] The service provider may include features for sharing the generated monsters with other users. For example, the service provider may provide options for sharing the generated monsters via social media or email. The service provider may also provide an interface that allows users to easily share the generated monsters with other users. For example, the service provider may include a share button, allowing users to share monsters with a single click. This allows for raising awareness of the SDGs by sharing the generated monsters with other users. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI.

[0038] The reception desk can provide guidelines regarding the format and content of videos submitted by users. For example, the reception desk can set standards for video length and content to help users submit appropriate videos. The reception desk can check the format and size of videos and accept them in the appropriate format. For example, the reception desk can check the resolution and file format of videos and convert them to the appropriate format. The reception desk can also provide guidelines regarding the content of videos submitted by users. For example, the reception desk can set standards for video length and content to help users submit appropriate videos. By providing guidelines regarding the format and content of videos submitted by users, appropriate video submissions can be promoted. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The reception desk can analyze a user's past posting history and select the optimal posting method. For example, the reception desk can suggest a similar method based on a user's past successful posting methods. The reception desk can also identify the time of day when a user received the best response from their past posting history and encourage them to post during that time. The reception desk can also analyze the tags and keywords a user has used in the past and suggest the most suitable tags and keywords. In this way, by analyzing a user's past posting history, the reception desk can suggest the optimal posting method. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0040] The reception desk can filter videos based on the user's current activities and areas of interest when they are posted. For example, the reception desk may suggest prioritizing the posting of videos related to the user's current activities. The reception desk can also filter relevant videos based on the user's areas of interest and encourage posting. The reception desk can also analyze the user's current activities in real time and suggest the most suitable content for posting. This allows for the promotion of optimal video posting by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0041] The reception desk can prioritize accepting videos that are highly relevant to the user's location when they submit a video, taking into account the user's geographical location. For example, if a user is in a specific region, the reception desk may suggest that they prioritize submitting videos related to that region. The reception desk can also prioritize accepting videos related to local events or activities based on the user's current location. The reception desk can also analyze the user's geographical location and suggest the most suitable content to submit. This allows the reception desk to prioritize accepting videos that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0042] The reception desk can analyze a user's social media activity when a video is posted and accept relevant videos. For example, the reception desk may suggest prioritizing the posting of relevant videos based on what the user has shared on social media. The reception desk can also analyze a user's social media activity and accept videos based on the posts that received the most engagement. The reception desk can also analyze a user's areas of interest on social media and accept relevant videos. In this way, relevant videos can be accepted by analyzing a 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 using AI.

[0043] The judgment unit can apply different judgment algorithms based on the content of the video during the judgment process. For example, the judgment unit can apply an algorithm that evaluates the effectiveness of recycling to a video of recycling activities. The judgment unit can also apply an algorithm that evaluates the amount of energy saved to a video of energy saving. The judgment unit can also apply an algorithm that evaluates the environmental effects of tree planting to a video of tree planting activities. By applying different judgment algorithms based on the content of the video, it becomes possible to accurately determine the sustainability level. Some or all of the above processing in the judgment unit may be performed using, for example, generative AI, or without using generative AI.

[0044] The judgment unit can improve the accuracy of its judgment by considering the video's shooting environment and background information during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to environmental data of the video's shooting location. The judgment unit can also improve the accuracy of its judgment by considering the video's background information (e.g., weather, time of day). The judgment unit can also improve the accuracy of its judgment by referring to information about the video's shooting device. In this way, the accuracy of the judgment can be improved by considering the video's shooting environment and background information. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0045] The judgment unit can determine the priority of judgments based on when the videos were shot. For example, the judgment unit may prioritize the most recent videos and provide real-time feedback. The judgment unit may also prioritize videos shot during a specific event period. The judgment unit may also prioritize videos posted during times when users frequently post. This allows for real-time feedback by determining the priority of judgments based on when the videos were shot. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0046] The judgment unit can improve the accuracy of its judgment by referring to relevant literature and data related to the video during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to academic papers related to the content of the video. The judgment unit can also improve the accuracy of its judgment by referring to statistical data related to the content of the video. The judgment unit can also improve the accuracy of its judgment by referring to past judgment results related to the content of the video. In this way, the accuracy of the judgment can be improved by referring to relevant literature and data related to the video. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0047] The generation unit can apply different generation algorithms based on the sustainability level during generation. For example, if the sustainability level is high, the generation unit can apply an algorithm that generates high-rarity monsters. If the sustainability level is low, the generation unit can also apply an algorithm that generates common monsters. If the sustainability level is moderate, the generation unit can also apply an algorithm that generates rare monsters when certain conditions are met. In this way, appropriate monsters can be generated by applying different generation algorithms based on the sustainability level. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0048] The generation unit can customize the characteristics of monsters during generation by considering the user's past activity history. For example, the generation unit can generate monsters with specific characteristics based on the user's past activities. The generation unit can also generate monsters with specific skills from the user's past activity history. The generation unit can also analyze the user's past activity history and generate monsters with optimal characteristics. This allows the generation of monsters with optimal characteristics by considering the user's past activity history. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0049] The generation unit can determine the characteristics of monsters by considering the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit will generate monsters with characteristics related to that region. The generation unit can also generate monsters that reflect the characteristics of a region based on the user's current location. The generation unit can also analyze the user's geographical location information and generate monsters with optimal characteristics. This allows for the generation of monsters with optimal characteristics by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0050] The generation unit can analyze the user's social media activity during generation to customize the monster's characteristics. For example, the generation unit can generate a monster with specific characteristics based on content shared by the user on social media. The generation unit can also analyze the user's social media activity and generate a monster based on the most well-received posts. The generation unit can also analyze the user's areas of interest on social media and generate a monster with relevant characteristics. In this way, by analyzing the user's social media activity, it is possible to generate a monster with optimal characteristics. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0051] The delivery unit can select the optimal delivery method by referring to the user's past monster acquisition history at the time of delivery. For example, the delivery unit can provide relevant monsters based on the characteristics of monsters the user has acquired in the past. The delivery unit can also select the delivery method that has received the best response from the user's past acquisition history. The delivery unit can also analyze the user's past acquisition history and propose the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past monster acquisition history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0052] The delivery unit can customize the method of providing monsters based on the user's current activity status at the time of delivery. For example, the delivery unit can provide monsters related to the activities the user is currently engaged in. The delivery unit can also analyze the user's current activity status in real time and suggest the optimal method of delivery. The delivery unit can also provide monsters when certain conditions are met based on the user's current activity status. In this way, the optimal method of delivery can be provided by customizing the method of providing monsters based on the user's current activity status. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0053] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider will provide monsters related to that region. The service provider can also provide monsters that reflect the characteristics of a region based on the user's current location. The service provider can also analyze the user's geographical location information and propose the optimal service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0054] The delivery unit can analyze the user's social media activity at the time of delivery and propose a method for delivering monsters. For example, the delivery unit can deliver relevant monsters based on content shared by the user on social media. The delivery unit can also analyze the user's social media activity and propose the delivery method that received the best response. The delivery unit can also analyze the user's areas of interest on social media and deliver relevant monsters. In this way, by analyzing the user's social media activity, the optimal method for delivering monsters can be proposed. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[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 system can automatically tag users with relevant SDGs goals based on the content of their submitted videos. For example, if a video about recycling is submitted, the reception system can tag it with "Goal 12: Responsible Consumption and Production." If a video about energy conservation is submitted, it can be tagged with "Goal 7: Affordable and Clean Energy." If a video about tree planting is submitted, it can be tagged with "Goal 15: Life on Land." This allows users to easily understand which SDGs goals their activities relate to and raise their awareness of the SDGs. Some or all of the above processing in the reception system may be performed using AI, for example, or not.

[0057] The generation unit can customize the characteristics of monsters during generation by considering the user's past activity history. For example, it can generate monsters with specific characteristics based on the user's past activities. It can also generate monsters with specific skills based on the user's past activity history. It can analyze the user's past activity history and generate monsters with optimal characteristics. In this way, by considering the user's past activity history, it is possible to generate monsters with optimal characteristics. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0058] The reception desk can analyze a user's past posting history and select the optimal posting method. For example, it can suggest a similar method based on the user's past successful posting methods. It can also identify the time slots that received the best response from the user's past posting history and encourage posting during those times. It can also analyze the tags and keywords the user has used in the past and suggest the most suitable tags and keywords. In this way, by analyzing the user's past posting history, the optimal posting method can be suggested. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0059] The judgment unit can apply different judgment algorithms based on the content of the video during the judgment process. For example, a video of recycling activities can be given an algorithm that evaluates the effectiveness of recycling. A video of energy saving can be given an algorithm that evaluates the amount of energy saved. A video of tree planting activities can be given an algorithm that evaluates the environmental effects of tree planting. By applying different judgment algorithms based on the content of the video, it becomes possible to accurately determine the sustainability level. Some or all of the above-described processing in the judgment unit may be performed using, for example, generative AI, or without using generative AI.

[0060] The delivery unit can select the optimal delivery method by referring to the user's past monster acquisition history at the time of delivery. For example, it can provide relevant monsters based on the characteristics of monsters the user has acquired in the past. It can also select the delivery method that received the best response from the user's past acquisition history. It can also analyze the user's past acquisition history and propose the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past monster acquisition history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0061] The judgment unit can improve the accuracy of its judgment by considering the video's shooting environment and background information during the judgment process. For example, it can improve the accuracy of its judgment by referring to environmental data of the video's shooting location. It can also improve the accuracy of its judgment by considering the video's background information (e.g., weather, time of day). It can also improve the accuracy of its judgment by referring to information about the video's shooting device. In this way, the accuracy of the judgment can be improved by considering the video's shooting environment and background information. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts video submissions from users. For example, the reception desk can provide an interface for users to upload videos, check the video format and size, and accept them in the appropriate format. Furthermore, it can check the video resolution and file format and convert them to the appropriate format. The reception desk also provides guidelines on video content, indicating standards for video length and content, to help users submit appropriate videos. Step 2: The judgment unit uses a generation AI to analyze the video received by the reception unit and determine its sustainability level. The judgment unit evaluates how much the video contributes to the SDGs by analyzing the content of the video using the generation AI. The generation AI uses technologies such as deep learning and neural networks to identify recycling activities, energy conservation, tree planting activities, etc., within the video and evaluates the environmental impact of these activities. Based on the content of the video, it quantifies the sustainability level and outputs the evaluation result. Step 3: The generation unit generates monsters based on the sustainability level determined by the judgment unit. The generation unit uses a generation AI to generate monsters according to the sustainability level. The generation AI determines the appearance and characteristics of the monsters, generating high-rarity monsters if the sustainability level is high, and low-rarity monsters if it is low. The characteristics and abilities of the generated monsters are determined and provided to the user. Step 4: The provider unit provides the monsters generated by the generator unit to the user. The provider unit adds the generated monsters to the user's account, making them available for the user to use. Furthermore, it includes a function for sharing the generated monsters with other users and provides options for sharing via social media or email.

[0064] (Example of form 2) The system according to an embodiment of the present invention is a system in which users post videos of their daily "activities that contribute to the SDGs," and a generating AI determines the sustainability level. This system is a game that generates monsters according to the determined sustainability level. Rare monsters appear depending on activities that make a significant contribution to the SDGs and the accumulation of daily activities. For example, a user posts a video of their daily "activities that contribute to the SDGs." For example, they might film a video of recycling activities, energy saving, or tree planting, and upload it to the system. This video is input to the generating AI. Next, the generating AI analyzes the posted video and determines the sustainability level. The generating AI analyzes the content of the video and evaluates how much it contributes to the SDGs. For example, if a video of recycling activities is posted, it determines how much that activity contributes to the environment. A monster is generated according to the determined sustainability level. For example, if the sustainability level is high, a rare monster is generated. Conversely, if the sustainability level is low, a low-rarity monster is generated. Furthermore, rare monsters appear in response to activities that significantly contribute to the SDGs, as well as the accumulation of daily activities. For example, users who regularly engage in recycling activities may have a special rare monster generated. This system allows users to contribute to the SDGs while having fun. Through the game, users can see how much their activities contribute to the SDGs, further increasing their motivation. In addition, by sharing the generated monsters with other users, they can spread awareness of the SDGs. For example, if a user posts a video of their recycling activity and the generation AI determines that the activity is "Very High," a monster with a rarity of 80 may be generated. This monster has characteristics such as power 1500 and speed 800, and can compete with other users. In this way, the system allows users to contribute to the SDGs while having fun by posting videos of their daily "activities connected to the SDGs" and having the generation AI determine the sustainability level.This allows the system to evaluate user activity and promote activities that contribute to the SDGs.

[0065] The system according to this embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit accepts video submissions from users. The reception unit provides, for example, an interface for users to upload videos they have filmed. The reception unit checks the format and size of the video and can accept it in an appropriate format. For example, the reception unit can check the resolution and file format of the video and convert it to an appropriate format. The reception unit can also provide guidelines regarding the content of videos submitted by users. For example, the reception unit can provide criteria regarding the length and content of videos to enable users to submit appropriate videos. The determination unit uses a generation AI to analyze the videos accepted by the reception unit and determine their sustainability level. For example, the determination unit evaluates how much the generation AI contributes to the SDGs by analyzing the content of the video. The generation AI uses technologies such as deep learning and neural networks to analyze the content of the video in detail. For example, the generation AI identifies recycling activities, energy conservation, tree planting activities, etc., within the video and evaluates the environmental impact of these activities. The generation AI quantifies the sustainability level based on the video content and outputs the evaluation result. The generation unit generates monsters based on the sustainability level determined by the judgment unit. The generation unit generates monsters according to the sustainability level, for example, using the generation AI. The generation AI determines the appearance and characteristics of the monsters and generates monsters to provide to the user. For example, if the sustainability level is high, the generation AI generates monsters with high rarity. Conversely, if the sustainability level is low, it generates monsters with low rarity. The generation unit determines the characteristics and abilities of the generated monsters and provides them to the user. The provision unit provides the monsters generated by the generation unit to the user. The provision unit, for example, adds the generated monsters to the user's account, making the monsters available to the user. The provision unit can also include a function for sharing the generated monsters with other users. For example, the provision unit provides an option to share the generated monsters on social media or via email.As a result, the system according to this embodiment can promote activities that contribute to the SDGs by analyzing videos taken by users, generating and providing monsters based on their sustainability level, and so on.

[0066] The reception desk accepts video submissions from users. For example, the reception desk provides an interface for users to upload videos. Specifically, it designs an intuitive user interface to allow users to easily upload videos. For instance, it uses drag-and-drop functionality and file selection dialogs to make it easier for users to select video files from their devices. The reception desk can also check the video format and size and accept submissions in the appropriate format. For example, it can check the video resolution and file format and convert them to an appropriate format. Specifically, if a video is too high resolution or in an unsupported file format, the reception desk will have a function to automatically compress or convert the video to an appropriate format. Furthermore, the reception desk can provide guidelines regarding the content of videos submitted by users. For example, it can provide standards regarding video length and content to ensure users submit appropriate videos. Specifically, it provides checklists to ensure that videos are within a certain length range and do not contain specific content (e.g., violent scenes or inappropriate language). This allows the reception desk to create an environment where users can easily and appropriately submit videos, maintaining the overall quality of the system.

[0067] The judgment unit uses a generation AI to analyze videos received by the reception unit and determine their sustainability level. For example, the judgment unit uses the generation AI to analyze the content of the video and evaluate how much it contributes to the SDGs. The generation AI uses technologies such as deep learning and neural networks to analyze the video content in detail. Specifically, the generation AI identifies recycling activities, energy conservation, tree planting activities, etc., within the video and evaluates the environmental impact of these activities. For example, the generation AI recognizes objects and scenes within the video and evaluates how relevant they are to the SDGs goals. Based on the video content, the generation AI quantifies the sustainability level and outputs the evaluation result. Specifically, the generation AI assigns a score to each element in the video and calculates the sustainability level by summarizing these scores. For example, a video containing many recycling activities will be given a high score, while a video containing elements that have a negative impact on the environment will be given a low score. This allows the judgment unit to analyze the video content in detail and evaluate the sustainability level based on objective criteria. Furthermore, the evaluation unit can provide feedback on the evaluation results to the user, offering reference information to help the user improve their activities. For example, by presenting specific areas for improvement and recommendations based on the evaluation results, it can encourage users to engage in more sustainable activities.

[0068] The generation unit generates monsters based on the sustainability level determined by the judgment unit. The generation unit uses, for example, a generation AI to generate monsters according to the sustainability level. The generation AI determines the appearance and characteristics of the monsters and generates the monsters to be provided to the users. Specifically, if the sustainability level is high, the generation AI generates monsters of high rarity. For example, a user who posts a video with a very high sustainability level will be provided with a rare monster with special abilities. Conversely, if the sustainability level is low, it generates monsters of low rarity. For example, a user who posts a video with a low sustainability level will be provided with a common monster with only basic abilities. The generation unit determines the characteristics and abilities of the generated monsters and provides them to the users. Specifically, it determines the monsters' appearance (color and shape), abilities (attack power and defense power), characteristics (special abilities and skills), etc. This allows the generation unit to provide individually customized monsters according to the sustainability level of the videos posted by the users. Furthermore, the generation unit saves the data of the generated monsters so that users can access it later. For example, by logging into their account, users can view monsters they have previously generated and compare them with those of other users. This allows the generation system to provide users with continuous motivation and promote sustainable activities.

[0069] The provider unit provides users with monsters generated by the generator unit. For example, the provider unit adds the generated monsters to the user's account, making them available for use. Specifically, the provider unit ensures that generated monsters are automatically added when the user logs into their account. The provider unit can also provide features for sharing generated monsters with other users. For example, the provider unit provides options for sharing generated monsters via social media or email. Specifically, it provides buttons or links for users to post information about the generated monsters on social media or email them to friends. This allows users to share their achievements with others and promote sustainable practices. Furthermore, the provider unit can offer games and activities using the generated monsters. For example, users can use the generated monsters to compete against other users or cooperate to complete missions. This allows the provider unit to provide users with an enjoyable experience and promote sustainable practices. Additionally, the provider unit can collect user feedback to improve the system. For example, users can send feedback and requests regarding generated monsters to the provider unit, enabling continuous improvement of the system's functions and content. This allows the service provider to offer high-quality services to users and support sustainable activities.

[0070] The judgment unit can analyze the content of a video using a generative AI and determine its sustainability level. For example, the judgment unit uses the generative AI to analyze the video content and evaluate how much it contributes to the SDGs. The generative AI uses technologies such as deep learning and neural networks to analyze the video content in detail. For example, the generative AI identifies recycling activities, energy conservation, and tree planting activities in the video and evaluates the environmental impact of these activities. Based on the video content, the generative AI quantifies the sustainability level and outputs the evaluation result. In this way, by using the generative AI, the content of a video can be accurately analyzed and its sustainability level can be determined.

[0071] The generation unit can generate monsters based on sustainability levels using a generation AI. For example, the generation unit uses the generation AI to generate monsters according to the sustainability level. The generation AI determines the appearance and characteristics of the monsters and generates the monsters to be provided to the user. For example, if the sustainability level is high, the generation AI will generate monsters of high rarity. Conversely, if the sustainability level is low, it will generate monsters of low rarity. The generation unit determines the characteristics and abilities of the generated monsters and provides them to the user. In this way, by using the generation AI, it is possible to generate monsters based on sustainability levels.

[0072] The service provider can provide the generated monsters to users. For example, the service provider can add the generated monsters to the user's account, making them available for use. The service provider can also provide features for sharing the generated monsters with other users. For example, the service provider can provide options for sharing the generated monsters on social media or via email. This allows users to contribute to the SDGs while having fun by providing them with the generated monsters. Some or all of the processes described above in the service provider may be performed using AI, for example, or not using AI.

[0073] The service provider may include features for sharing the generated monsters with other users. For example, the service provider may provide options for sharing the generated monsters via social media or email. The service provider may also provide an interface that allows users to easily share the generated monsters with other users. For example, the service provider may include a share button, allowing users to share monsters with a single click. This allows for raising awareness of the SDGs by sharing the generated monsters with other users. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI.

[0074] The reception desk can provide guidelines regarding the format and content of videos submitted by users. For example, the reception desk can set standards for video length and content to help users submit appropriate videos. The reception desk can check the format and size of videos and accept them in the appropriate format. For example, the reception desk can check the resolution and file format of videos and convert them to the appropriate format. The reception desk can also provide guidelines regarding the content of videos submitted by users. For example, the reception desk can set standards for video length and content to help users submit appropriate videos. By providing guidelines regarding the format and content of videos submitted by users, appropriate video submissions can be promoted. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0075] The reception desk can estimate the user's emotions and adjust the timing of video posting based on the estimated emotions. For example, if the user is relaxed, the reception desk can suggest the optimal posting time, allowing the user to post videos without stress. If the user is busy, the reception desk can also set a reminder to post later and notify them at the appropriate time. If the user is excited, the reception desk can also provide a simple interface to allow them to post immediately. This allows users to post videos without stress by adjusting the timing of video posting based on their 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, for example, or without AI.

[0076] The reception desk can analyze a user's past posting history and select the optimal posting method. For example, the reception desk can suggest a similar method based on a user's past successful posting methods. The reception desk can also identify the time of day when a user received the best response from their past posting history and encourage them to post during that time. The reception desk can also analyze the tags and keywords a user has used in the past and suggest the most suitable tags and keywords. In this way, by analyzing a user's past posting history, the reception desk can suggest the optimal posting method. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0077] The reception desk can filter videos based on the user's current activities and areas of interest when they are posted. For example, the reception desk may suggest prioritizing the posting of videos related to the user's current activities. The reception desk can also filter relevant videos based on the user's areas of interest and encourage posting. The reception desk can also analyze the user's current activities in real time and suggest the most suitable content for posting. This allows for the promotion of optimal video posting by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0078] The reception desk can estimate the user's emotions and determine the priority of videos to post based on the estimated emotions. For example, if the user is relaxed, the reception desk may suggest prioritizing the posting of high-importance videos. If the user is busy, the reception desk may also prioritize suggesting videos that can be posted easily. If the user is excited, the reception desk may also prioritize suggesting videos that can be posted immediately. This allows users to post the most suitable videos by prioritizing videos based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0079] The reception desk can prioritize accepting videos that are highly relevant to the user's location when they submit a video, taking into account the user's geographical location. For example, if a user is in a specific region, the reception desk may suggest that they prioritize submitting videos related to that region. The reception desk can also prioritize accepting videos related to local events or activities based on the user's current location. The reception desk can also analyze the user's geographical location and suggest the most suitable content to submit. This allows the reception desk to prioritize accepting videos that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0080] The reception desk can analyze a user's social media activity when a video is posted and accept relevant videos. For example, the reception desk may suggest prioritizing the posting of relevant videos based on what the user has shared on social media. The reception desk can also analyze a user's social media activity and accept videos based on the posts that received the most engagement. The reception desk can also analyze a user's areas of interest on social media and accept relevant videos. In this way, relevant videos can be accepted by analyzing a 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 using AI.

[0081] The judgment unit can estimate the user's emotions and adjust the sustainability level criteria based on the estimated user emotions. For example, if the user is relaxed, the judgment unit can apply detailed criteria to determine an accurate sustainability level. If the user is in a hurry, the judgment unit can also apply simplified criteria to quickly determine the sustainability level. If the user is excited, the judgment unit can also provide a judgment result with visually stimulating effects. This allows for accurate judgment by adjusting the sustainability level criteria based on 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 judgment unit may be performed using AI, for example, or without AI.

[0082] The judgment unit can apply different judgment algorithms based on the content of the video during the judgment process. For example, the judgment unit can apply an algorithm that evaluates the effectiveness of recycling to a video of recycling activities. The judgment unit can also apply an algorithm that evaluates the amount of energy saved to a video of energy saving. The judgment unit can also apply an algorithm that evaluates the environmental effects of tree planting to a video of tree planting activities. By applying different judgment algorithms based on the content of the video, it becomes possible to accurately determine the sustainability level. Some or all of the above processing in the judgment unit may be performed using, for example, generative AI, or without using generative AI.

[0083] The judgment unit can improve the accuracy of its judgment by considering the video's shooting environment and background information during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to environmental data of the video's shooting location. The judgment unit can also improve the accuracy of its judgment by considering the video's background information (e.g., weather, time of day). The judgment unit can also improve the accuracy of its judgment by referring to information about the video's shooting device. In this way, the accuracy of the judgment can be improved by considering the video's shooting environment and background information. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0084] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit provides a simple and highly visible display method. If the user is relaxed, the judgment unit can also provide a display method that includes detailed information. If the user is in a hurry, the judgment unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the judgment result based on the user's emotions, the optimal display method can be provided to the user. 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 judgment unit may be performed using AI, for example, or without using AI.

[0085] The judgment unit can determine the priority of judgments based on when the videos were shot. For example, the judgment unit may prioritize the most recent videos and provide real-time feedback. The judgment unit may also prioritize videos shot during a specific event period. The judgment unit may also prioritize videos posted during times when users frequently post. This allows for real-time feedback by determining the priority of judgments based on when the videos were shot. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0086] The judgment unit can improve the accuracy of its judgment by referring to relevant literature and data related to the video during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to academic papers related to the content of the video. The judgment unit can also improve the accuracy of its judgment by referring to statistical data related to the content of the video. The judgment unit can also improve the accuracy of its judgment by referring to past judgment results related to the content of the video. In this way, the accuracy of the judgment can be improved by referring to relevant literature and data related to the video. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0087] The generation unit can estimate the user's emotions and adjust the characteristics of the monster it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a monster with calm characteristics. If the user is excited, the generation unit can also generate a monster with aggressive characteristics. If the user is tired, the generation unit can also generate a monster with high recovery ability. In this way, by adjusting the characteristics of the monster based on the user's emotions, it is possible to generate the optimal monster for the user. 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 a generation AI, for example, or without a generation AI.

[0088] The generation unit can apply different generation algorithms based on the sustainability level during generation. For example, if the sustainability level is high, the generation unit can apply an algorithm that generates high-rarity monsters. If the sustainability level is low, the generation unit can also apply an algorithm that generates common monsters. If the sustainability level is moderate, the generation unit can also apply an algorithm that generates rare monsters when certain conditions are met. In this way, appropriate monsters can be generated by applying different generation algorithms based on the sustainability level. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0089] The generation unit can customize the characteristics of monsters during generation by considering the user's past activity history. For example, the generation unit can generate monsters with specific characteristics based on the user's past activities. The generation unit can also generate monsters with specific skills from the user's past activity history. The generation unit can also analyze the user's past activity history and generate monsters with optimal characteristics. This allows the generation of monsters with optimal characteristics by considering the user's past activity history. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0090] The generation unit can estimate the user's emotions and adjust how the generated monsters are displayed based on the estimated emotions. For example, if the user is relaxed, the generation unit may display the monsters in calm colors. If the user is excited, the generation unit may also display the monsters in vivid colors. If the user is tired, the generation unit may also display the monsters in highly visible colors. By adjusting how the monsters are displayed based on the user's emotions, the optimal display method can be provided to the user. 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 a generation AI, for example, or without a generation AI.

[0091] The generation unit can determine the characteristics of monsters by considering the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit will generate monsters with characteristics related to that region. The generation unit can also generate monsters that reflect the characteristics of a region based on the user's current location. The generation unit can also analyze the user's geographical location information and generate monsters with optimal characteristics. This allows for the generation of monsters with optimal characteristics by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0092] The generation unit can analyze the user's social media activity during generation to customize the monster's characteristics. For example, the generation unit can generate a monster with specific characteristics based on content shared by the user on social media. The generation unit can also analyze the user's social media activity and generate a monster based on the most well-received posts. The generation unit can also analyze the user's areas of interest on social media and generate a monster with relevant characteristics. In this way, by analyzing the user's social media activity, it is possible to generate a monster with optimal characteristics. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0093] The serving unit can estimate the user's emotions and adjust the way the monster is served based on the estimated emotions. For example, if the user is relaxed, the serving unit may serve the monster with calming music. If the user is excited, the serving unit may also serve the monster with visually stimulating effects. If the user is tired, the serving unit may also serve the monster in a simple and highly visible way. In this way, by adjusting the way the monster is served based on the user's emotions, the optimal serving method can be provided to the user. 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 serving unit may be performed using AI, for example, or not using AI.

[0094] The delivery unit can select the optimal delivery method by referring to the user's past monster acquisition history at the time of delivery. For example, the delivery unit can provide relevant monsters based on the characteristics of monsters the user has acquired in the past. The delivery unit can also select the delivery method that has received the best response from the user's past acquisition history. The delivery unit can also analyze the user's past acquisition history and propose the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past monster acquisition history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0095] The delivery unit can customize the method of providing monsters based on the user's current activity status at the time of delivery. For example, the delivery unit can provide monsters related to the activities the user is currently engaged in. The delivery unit can also analyze the user's current activity status in real time and suggest the optimal method of delivery. The delivery unit can also provide monsters when certain conditions are met based on the user's current activity status. In this way, the optimal method of delivery can be provided by customizing the method of providing monsters based on the user's current activity status. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0096] The service provider can estimate the user's emotions and determine the priority of monster provision based on the estimated user emotions. For example, if the user is relaxed, the service provider will prioritize providing high-importance monsters. If the user is busy, the service provider may also prioritize providing easily obtainable monsters. If the user is excited, the service provider may also prioritize providing monsters that can be obtained immediately. In this way, by determining the priority of monster provision based on the user's emotions, the service provider can provide the user with the most suitable monsters. 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 may be performed using AI, for example, or not using AI.

[0097] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider will provide monsters related to that region. The service provider can also provide monsters that reflect the characteristics of a region based on the user's current location. The service provider can also analyze the user's geographical location information and propose the optimal service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0098] The delivery unit can analyze the user's social media activity at the time of delivery and propose a method for delivering monsters. For example, the delivery unit can deliver relevant monsters based on content shared by the user on social media. The delivery unit can also analyze the user's social media activity and propose the delivery method that received the best response. The delivery unit can also analyze the user's areas of interest on social media and deliver relevant monsters. In this way, by analyzing the user's social media activity, the optimal method for delivering monsters can be proposed. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception system can automatically tag users with relevant SDGs goals based on the content of their submitted videos. For example, if a video about recycling is submitted, the reception system can tag it with "Goal 12: Responsible Consumption and Production." If a video about energy conservation is submitted, it can be tagged with "Goal 7: Affordable and Clean Energy." If a video about tree planting is submitted, it can be tagged with "Goal 15: Life on Land." This allows users to easily understand which SDGs goals their activities relate to and raise their awareness of the SDGs. Some or all of the above processing in the reception system may be performed using AI, for example, or not.

[0101] The judgment unit can estimate the user's emotions and adjust the sustainability level criteria based on the estimated user emotions. For example, if the user is relaxed, detailed criteria can be applied to determine the accurate sustainability level. If the user is in a hurry, simplified criteria can be applied to quickly determine the sustainability level. If the user is excited, the judgment result can be provided with visually stimulating effects. This allows for accurate judgment by adjusting the sustainability level criteria based on 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 judgment unit may be performed using AI, for example, or without AI.

[0102] The generation unit can customize the characteristics of monsters during generation by considering the user's past activity history. For example, it can generate monsters with specific characteristics based on the user's past activities. It can also generate monsters with specific skills based on the user's past activity history. It can analyze the user's past activity history and generate monsters with optimal characteristics. In this way, by considering the user's past activity history, it is possible to generate monsters with optimal characteristics. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0103] The serving unit can estimate the user's emotions and adjust the way the monster is served based on the estimated emotions. For example, if the user is relaxed, the monster may be served with calming music. If the user is excited, the monster may be served with visually stimulating effects. If the user is tired, the monster may be served in a simple and highly visible way. In this way, by adjusting the way the monster is served based on the user's emotions, the optimal serving method can be provided to the user. 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 processing described above in the serving unit may be performed using AI, for example, or not using AI.

[0104] The reception desk can analyze a user's past posting history and select the optimal posting method. For example, it can suggest a similar method based on the user's past successful posting methods. It can also identify the time slots that received the best response from the user's past posting history and encourage posting during those times. It can also analyze the tags and keywords the user has used in the past and suggest the most suitable tags and keywords. In this way, by analyzing the user's past posting history, the optimal posting method can be suggested. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0105] The judgment unit can apply different judgment algorithms based on the content of the video during the judgment process. For example, a video of recycling activities can be given an algorithm that evaluates the effectiveness of recycling. A video of energy saving can be given an algorithm that evaluates the amount of energy saved. A video of tree planting activities can be given an algorithm that evaluates the environmental effects of tree planting. By applying different judgment algorithms based on the content of the video, it becomes possible to accurately determine the sustainability level. Some or all of the above-described processing in the judgment unit may be performed using, for example, generative AI, or without using generative AI.

[0106] The generation unit can estimate the user's emotions and adjust the characteristics of the monster it generates based on the estimated user emotions. For example, if the user is relaxed, it can generate a monster with calm characteristics. If the user is excited, it can also generate a monster with aggressive characteristics. If the user is tired, it can also generate a monster with high recovery ability. In this way, by adjusting the monster's characteristics based on the user's emotions, it is possible to generate the optimal monster for the user. 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 a generation AI, for example, or without a generation AI.

[0107] The delivery unit can select the optimal delivery method by referring to the user's past monster acquisition history at the time of delivery. For example, it can provide relevant monsters based on the characteristics of monsters the user has acquired in the past. It can also select the delivery method that received the best response from the user's past acquisition history. It can also analyze the user's past acquisition history and propose the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past monster acquisition history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0108] The reception desk can estimate the user's emotions and adjust the timing of video posting based on those emotions. For example, if the user is relaxed, it can suggest the optimal posting time, allowing the user to post videos without stress. If the user is busy, it can set a reminder to post later and notify them at the appropriate time. If the user is excited, it can provide a simple interface to allow them to post immediately. This allows users to post videos without stress by adjusting the timing of video posting based on their 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, for example, or without AI.

[0109] The judgment unit can improve the accuracy of its judgment by considering the video's shooting environment and background information during the judgment process. For example, it can improve the accuracy of its judgment by referring to environmental data of the video's shooting location. It can also improve the accuracy of its judgment by considering the video's background information (e.g., weather, time of day). It can also improve the accuracy of its judgment by referring to information about the video's shooting device. In this way, the accuracy of the judgment can be improved by considering the video's shooting environment and background information. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without using a generative AI.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk accepts video submissions from users. For example, the reception desk can provide an interface for users to upload videos, check the video format and size, and accept them in the appropriate format. Furthermore, it can check the video resolution and file format and convert them to the appropriate format. The reception desk also provides guidelines on video content, indicating standards for video length and content, to help users submit appropriate videos. Step 2: The judgment unit uses a generation AI to analyze the video received by the reception unit and determine its sustainability level. The judgment unit evaluates how much the video contributes to the SDGs by analyzing the content of the video using the generation AI. The generation AI uses technologies such as deep learning and neural networks to identify recycling activities, energy conservation, tree planting activities, etc., within the video and evaluates the environmental impact of these activities. Based on the content of the video, it quantifies the sustainability level and outputs the evaluation result. Step 3: The generation unit generates monsters based on the sustainability level determined by the judgment unit. The generation unit uses a generation AI to generate monsters according to the sustainability level. The generation AI determines the appearance and characteristics of the monsters, generating high-rarity monsters if the sustainability level is high, and low-rarity monsters if it is low. The characteristics and abilities of the generated monsters are determined and provided to the user. Step 4: The provider unit provides the monsters generated by the generator unit to the user. The provider unit adds the generated monsters to the user's account, making them available for the user to use. Furthermore, it includes a function for sharing the generated monsters with other users and provides options for sharing via social media or email.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts the submission of videos taken by the user. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the video using generation AI to determine the sustainability level. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a monster based on the determined sustainability level. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated monster to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts the submission of videos taken by the user. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the video using generation AI to determine the sustainability level. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a monster based on the determined sustainability level. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated monster to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision 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 accepts the submission of videos taken by the user. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video using a generation AI to determine the sustainability level. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a monster based on the determined sustainability level. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated monster to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision unit, is implemented, for example, by 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 accepts the submission of videos taken by the user. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the video using a generation AI to determine the sustainability level. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a monster based on the determined sustainability level. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated monster to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that accepts video submissions from users, A determination unit analyzes the video received by the reception unit and determines the sustainability level, A generation unit that generates monsters based on the sustainability level determined by the determination unit, The system includes a providing unit that provides the monsters generated by the generation unit to the user. A system characterized by the following features. (Note 2) The determination unit, The AI ​​generates the content of the video and determines its sustainability level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates monsters based on their sustainability level. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated monsters to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It includes a feature to share the monsters that are generated with other users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is This provides guidelines regarding the format and content of videos uploaded by users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates user sentiment and adjusts the timing of video uploads based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past posting history and select the optimal posting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading videos, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates user sentiment and prioritizes which videos to post based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users upload videos, the system prioritizes accepting videos that are highly relevant to their location, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a video is posted, the system analyzes the user's social media activity and accepts relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, We estimate user emotions and adjust the sustainability level criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, When making a judgment, a different judgment algorithm is applied based on the content of the video. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a judgment, the accuracy of the judgment is improved by taking into account the video's shooting environment and background information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, When making a judgment, the priority of the judgment will be determined based on when the video was filmed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, During the assessment process, we refer to relevant literature and data related to the video to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the characteristics of the monsters generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different generation algorithms are applied based on the sustainability level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During creation, the monster's characteristics are customized by taking into account the user's past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how monsters are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the monster's characteristics are determined by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the user's social media activity is analyzed to customize the monster's characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how monsters are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a monster, the system will refer to the user's past monster acquisition history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing monsters, the method of providing them will be customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of monster provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the monster, we analyze the user's social media activity and suggest a method for providing the monster. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 video submissions from users, A determination unit analyzes the video received by the reception unit and determines the sustainability level, A generation unit that generates monsters based on the sustainability level determined by the determination unit, The system includes a providing unit that provides the monsters generated by the generation unit to the user. A system characterized by the following features.

2. The determination unit, The AI ​​generates the content of the video and determines its sustainability level. The system according to feature 1.

3. The generating unit is The AI ​​generates monsters based on their sustainability level. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated monsters to the user. The system according to feature 1.

5. The aforementioned supply unit is, It includes a feature to share the monsters that are generated with other users. The system according to feature 1.

6. The aforementioned reception unit is This provides guidelines regarding the format and content of videos uploaded by users. The system according to feature 1.

7. The aforementioned reception unit is It estimates user sentiment and adjusts the timing of video uploads based on the estimated user sentiment. The system according to feature 1.

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

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