system

Abstract

The system according to this embodiment aims to provide an environment in which children can develop an interest in natural science and learn while stimulating their imagination. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and an evaluation unit. The analysis unit analyzes digital encyclopedia data. The generation unit generates organisms based on the information analyzed by the analysis unit. The evaluation unit evaluates the organisms generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 learning materials for natural science are boring and not attractive, and there is a lack of a learning place that stimulates imagination.

[0005] The system according to the embodiment aims to provide an environment in which children can learn while being interested in natural science and stimulating their imagination.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and an evaluation unit. The analysis unit analyzes digital encyclopedia data. The generation unit generates an organism based on the information analyzed by the analysis unit. The evaluation unit evaluates the organism generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can provide an environment in which children can develop an interest in natural science and learn while stimulating their imagination. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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.

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An educational platform according to an embodiment of the present invention is a system that utilizes generative AI to make learning natural science fun and interesting. This system analyzes digital encyclopedia data and extracts and analyzes necessary information. Next, it uses an existing game engine to combine organisms selected by children and generate 3D models and animations. Furthermore, it introduces a theme-based story mode and scoring system, where the AI ​​evaluates the organism that best fits the theme and awards points. This mechanism allows children to interact with organisms in a game-like manner and become more proactive in learning about nature and science. In addition, through the process of combining organisms, children's imagination and scientific knowledge grow, and their scientific literacy is strengthened. For example, when analyzing digital encyclopedia data with AI, it refers to existing online animal and plant encyclopedias (e.g., Wikipedia and GBIF), and the AI ​​collects and analyzes information on the ecology and characteristics of specific organisms. Next, when generating organisms using an existing game engine, it combines organisms selected by children and generates 3D models and animations. For example, it generates a new organism by combining a lion and an eagle, and creates a 3D model and animation of that organism. Furthermore, when introducing a theme-based story mode and scoring system, a theme such as "the fastest creature" is presented, and the AI ​​evaluates the creature that best fits the theme and awards points. This allows children to generate creatures that align with the theme and compete based on the results. This mechanism allows children to interact with creatures in a game-like way and become more proactive in learning about nature and science. In addition, through the process of crossbreeding creatures, children's imagination and scientific knowledge grow, and their scientific literacy is strengthened. For example, by learning the characteristics of different creatures and combining them to create new creatures, children can cultivate scientific thinking skills. In this way, the educational platform can make learning natural science fun and interesting.

[0029] The educational platform according to this embodiment comprises an analysis unit, a generation unit, and an evaluation unit. The analysis unit analyzes digital encyclopedia data. The analysis unit needs to clarify, for example, the specific types and formats of the digital encyclopedia data. For example, this includes image data, text data, and audio data. The analysis unit analyzes the digital encyclopedia data using methods such as image analysis, text analysis, and audio analysis. The generation unit generates organisms based on the information analyzed by the analysis unit. The generation unit generates 3D models and animations by combining organisms selected by children, for example, using an existing game engine. The generation unit uses game engines such as Unity or Unreal Engine. The generation unit generates organisms using methods such as genetic algorithms and random generation. The evaluation unit evaluates the organisms generated by the generation unit. The evaluation unit evaluates the organisms generated based on a theme, for example, and assigns points. The evaluation unit evaluates the organisms using methods such as scientific evaluation and user evaluation. As a result, the educational platform according to this embodiment can make learning natural science fun and interesting.

[0030] The analysis unit analyzes digital encyclopedia data. The analysis unit needs to clarify the specific types and formats of the digital encyclopedia data, such as image data, text data, and audio data. Image data includes photographs and illustrations of organisms, which are analyzed using image recognition technology. Specifically, it extracts the features of organisms within the images and performs classification and identification. Text data includes descriptions, characteristics, and classification information of organisms, which are analyzed using natural language processing technology. For example, it extracts keywords from text data and organizes related information. Audio data includes animal sounds and explanatory audio, which are analyzed using speech recognition technology. The audio data extracts features from the audio waveform and converts the audio content into text. The analysis unit integrates this data to provide comprehensive information about the organisms. Furthermore, the analysis unit can learn data patterns using machine learning algorithms, enabling highly accurate analysis of new data. This allows the analysis unit to efficiently and accurately analyze digital encyclopedia data and provide foundational information for educational platforms.

[0031] The generation unit generates organisms based on information analyzed by the analysis unit. For example, the generation unit uses an existing game engine to combine organisms chosen by children and generate 3D models and animations. Specifically, it uses game engines such as Unity or Unreal Engine to create 3D models of organisms and add animations. The generation unit uses methods such as genetic algorithms and random generation to combine the characteristics of organisms and generate new organisms. Genetic algorithms are a method of mimicking the genetic information of organisms and generating offspring that inherit the characteristics of their parents. This allows children to create new organisms by combining the characteristics of different organisms. The generation unit uses physical simulations and animation techniques to add realistic textures and movements to the generated 3D models of organisms. For example, it uses shaders and particle systems to realistically reproduce the texture of the organism's skin and the movement of its hair. It also uses bone animation and inverse kinematics to make the organism's movements look natural. In this way, the generation unit can generate realistic and engaging organisms that children can learn from while having fun.

[0032] The evaluation unit evaluates the organisms generated by the generation unit. For example, the evaluation unit evaluates organisms generated based on a given theme and assigns points to them. Specifically, it evaluates organisms using methods such as scientific evaluation and user evaluation. Scientific evaluation assesses whether the characteristics and behaviors of the organisms are based on actual biological knowledge. For example, it verifies whether the ecology and behavior of the generated organisms match those of real organisms. User evaluation assesses how much children enjoy and are interested in the generated organisms. For example, it collects data such as how long children observed the generated organisms and how many operations they performed, and incorporates this into the evaluation. Based on these evaluation results, the evaluation unit assigns points to the generated organisms, visualizing the children's learning progress. Furthermore, the evaluation unit provides the evaluation results as feedback to the generation unit, which helps improve the generation algorithm and generate new organisms. This allows the evaluation unit to improve the quality of the generated organisms and enhance children's motivation to learn.

[0033] The analysis unit can refer to existing online plant and animal encyclopedias and extract and analyze the necessary information. The analysis unit can refer to specific websites or databases, for example. The analysis unit can refer to online plant and animal encyclopedias such as Wikipedia or GBIF, for example. The analysis unit can extract and analyze information such as species name, ecology, and characteristics. This allows for the efficient extraction and analysis of necessary information by referring to existing online plant and animal encyclopedias. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from online plant and animal encyclopedias into a generating AI and have the generating AI perform the extraction and analysis of the necessary information.

[0034] The generation unit can use an existing game engine to combine creatures chosen by children and generate 3D models and animations. The generation unit uses game engines such as Unity or Unreal Engine. The generation unit generates creatures using methods such as genetic algorithms or random generation. For example, the generation unit can generate a new creature by combining a lion and an eagle, and create a 3D model and animation of that creature. This allows children to visually enjoy the creatures they choose by using an existing game engine. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input data of creatures generated using a game engine into a generation AI, and have the generation AI execute the generation of 3D models and animations.

[0035] The evaluation unit can evaluate organisms generated based on a theme and award points. For example, if the theme is "fastest creature," the evaluation unit will evaluate the organism that best fits that theme and award points. The evaluation unit evaluates organisms using methods such as scientific evaluation or user evaluation. The evaluation unit needs to clearly define evaluation criteria and point types. This allows for increased motivation to learn by evaluating organisms based on a theme and awarding points. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input data on the generated organisms into a generating AI and have the generating AI perform the evaluation and point awarding.

[0036] The evaluation unit can analyze the characteristics of the generated organisms and perform scientific evaluations. For example, the evaluation unit analyzes the characteristics of the generated organisms, such as their morphology, behavior, and ecology. The evaluation unit evaluates the organisms using methods such as scientific evaluation or user evaluation. The evaluation unit needs to clarify evaluation criteria and evaluation methods, for example. This allows for an improvement in the quality of learning by scientifically evaluating the characteristics of the generated organisms. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the characteristics data of the generated organisms into a generating AI and have the generating AI perform a scientific evaluation.

[0037] The analysis unit can analyze information about the ecology and characteristics of specific organisms in detail, thereby improving the accuracy of the information provided to the user. For example, the analysis unit can collect and analyze detailed data on the ecology of specific organisms. For example, the analysis unit can collect and analyze detailed data on the characteristics of specific organisms. For example, the analysis unit can collect and analyze detailed data on the behavioral patterns of specific organisms. By doing so, the accuracy of the information provided can be improved by analyzing information about the ecology and characteristics of specific organisms in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the ecology and characteristics of specific organisms into a generating AI and have the generating AI perform a detailed analysis of the information.

[0038] The analysis unit can extract optimal information by referring to the user's past learning history. For example, the analysis unit can analyze the user's past learning history and extract relevant information. For example, the analysis unit can analyze the user's past learning history and extract information that is of interest. For example, the analysis unit can analyze the user's past learning history and extract information that deepens understanding. In this way, by referring to the user's past learning history, optimal information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past learning history data into a generating AI and have the generating AI perform the extraction of optimal information.

[0039] The analysis unit can prioritize the analysis of region-specific biological information based on the user's geographical location information. For example, the analysis unit prioritizes the analysis of region-specific biological information based on the user's geographical location information. For example, the analysis unit prioritizes the analysis of region-specific ecosystem information based on the user's geographical location information. For example, the analysis unit prioritizes the analysis of region-specific biological characteristics information based on the user's geographical location information. By prioritizing the analysis of region-specific biological information based on the user's geographical location information, the analysis unit can provide region-specific information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of region-specific biological information.

[0040] The analysis unit can analyze a user's social media activity and extract relevant biological information. For example, the analysis unit can analyze a user's social media activity and extract interesting biological information. For example, the analysis unit can analyze a user's social media activity and extract relevant biological information. For example, the analysis unit can analyze a user's social media activity and extract biological information to deepen understanding. In this way, relevant biological information can be provided by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the extraction of relevant biological information.

[0041] The generation unit can optimize algorithms that combine the characteristics of different organisms. For example, the generation unit optimizes algorithms that combine the characteristics of different organisms to generate realistic organisms. For example, the generation unit optimizes algorithms that combine the characteristics of different organisms to generate interesting organisms. For example, the generation unit optimizes algorithms that combine the characteristics of different organisms to generate organisms with detailed characteristics. In this way, more realistic organisms can be generated by optimizing algorithms that combine the characteristics of different organisms. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input characteristic data of different organisms into a generation AI and have the generation AI perform algorithm optimization.

[0042] The generation unit can generate the optimal organism by referring to the user's past generation history. For example, the generation unit can analyze the user's past generation history and generate relevant organisms. For example, the generation unit can analyze the user's past generation history and generate organisms that pique interest. For example, the generation unit can analyze the user's past generation history and generate organisms with detailed characteristics. In this way, the optimal organism can be generated by referring to the user's past generation history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past generation history data into a generation AI and have the generation AI perform the generation of the optimal organism.

[0043] The generation unit can prioritize the generation of region-specific organisms based on the user's geographical location information. For example, the generation unit prioritizes the generation of region-specific organisms based on the user's geographical location information. For example, the generation unit generates organisms suitable for region-specific ecosystems based on the user's geographical location information. For example, the generation unit generates organisms with region-specific characteristics based on the user's geographical location information. In this way, by generating region-specific organisms based on the user's geographical location information, it is possible to provide organisms tailored to the region. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the generation of region-specific organisms.

[0044] The generation unit can analyze the user's social media activity and generate relevant organisms. For example, the generation unit analyzes the user's social media activity and generates organisms that are of interest. For example, the generation unit analyzes the user's social media activity and generates relevant organisms. For example, the generation unit analyzes the user's social media activity and generates organisms with detailed characteristics. In this way, relevant organisms can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant organisms.

[0045] The evaluation unit can perform a detailed analysis of the characteristics of the generated organism and conduct a scientific evaluation. For example, the evaluation unit can perform a detailed analysis of the characteristics of the generated organism and conduct a scientific evaluation. For example, the evaluation unit can perform a detailed analysis of the behavioral patterns of the generated organism and conduct a scientific evaluation. For example, the evaluation unit can perform a detailed analysis of the ecology of the generated organism and conduct a scientific evaluation. This makes a scientific evaluation possible by analyzing the characteristics of the generated organism in detail. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the characteristics data of the generated organism into a generating AI and have the generating AI perform a scientific evaluation.

[0046] The evaluation unit can perform optimal evaluations by referring to the user's past evaluation history. For example, the evaluation unit analyzes the user's past evaluation history and performs relevant evaluations. For example, the evaluation unit analyzes the user's past evaluation history and performs interesting evaluations. For example, the evaluation unit analyzes the user's past evaluation history and performs detailed evaluations. In this way, optimal evaluations can be performed by referring to the user's past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past evaluation history data into a generating AI and have the generating AI perform optimal evaluations.

[0047] The evaluation unit can apply region-specific evaluation criteria based on the user's geographical location information. For example, the evaluation unit applies region-specific evaluation criteria based on the user's geographical location information. For example, the evaluation unit applies evaluation criteria suitable for the region's specific ecosystem based on the user's geographical location information. For example, the evaluation unit applies evaluation criteria with region-specific characteristics based on the user's geographical location information. This makes it possible to perform region-specific evaluations by applying region-specific evaluation criteria based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information data into a generating AI and have the generating AI execute the application of region-specific evaluation criteria.

[0048] The evaluation unit can analyze a user's social media activity and perform relevant evaluations. For example, the evaluation unit can analyze a user's social media activity and perform an interesting evaluation. For example, the evaluation unit can analyze a user's social media activity and perform a relevant evaluation. For example, the evaluation unit can analyze a user's social media activity and perform a detailed evaluation. In this way, relevant evaluations can be performed by analyzing a user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user social media activity data into a generating AI and have the generating AI perform relevant evaluations.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can monitor the user's learning progress in real time and adjust the content of the information analyzed according to the user's learning progress. For example, if a user has a deep understanding of a particular topic, it can prioritize analyzing more advanced information related to that topic. Conversely, if a user has a shallow understanding of a particular topic, it can prioritize analyzing basic information related to that topic. Furthermore, it can gradually adjust the difficulty level of the information analyzed according to the user's learning progress. This allows for the provision of optimal information tailored to the user's learning progress, thereby enhancing the effectiveness of learning.

[0051] The generation unit can generate new organisms based on the user's learning history, taking into account the characteristics of organisms generated in the past. For example, it can analyze the characteristics of organisms the user has generated in the past and determine the characteristics of new organisms based on that analysis. It can also generate new organisms by combining the characteristics of organisms the user has generated in the past. Furthermore, based on the user's learning history, it can predict organisms that the user might be interested in and generate those organisms. This allows the system to leverage the user's learning history to generate more interesting organisms, thereby increasing learning motivation.

[0052] The evaluation unit can adjust the evaluation method according to the user's learning style. For example, if the user has a visual learning style, it can provide a visually easy-to-understand evaluation method. If the user has an auditory learning style, it can provide an evaluation method using audio. Furthermore, if the user has an experiential learning style, it can provide a method of evaluation by actually operating the device. By providing an evaluation method that matches the user's learning style, the effectiveness of learning can be enhanced.

[0053] The analysis unit can adjust the content of the information it analyzes according to the user's learning objectives. For example, if the user's goal is to pass a specific exam, it can prioritize the analysis of information related to that exam. Similarly, if the user's goal is to acquire a specific skill, it can prioritize the analysis of information related to that skill. Furthermore, it can adjust the scope and depth of the information analyzed according to the user's learning objectives. This allows the system to provide optimal information tailored to the user's learning objectives, thereby enhancing the effectiveness of their learning.

[0054] The generation unit can adjust the characteristics of the organisms it generates according to the user's learning environment. For example, if the user is learning outdoors, it can generate organisms suited to the natural environment. If the user is learning indoors, it can generate organisms suited to the indoor environment. Furthermore, by adjusting the characteristics of the generated organisms according to the user's learning environment, the effectiveness of learning can be enhanced. This allows for the generation of optimal organisms tailored to the user's learning environment, thereby increasing learning motivation.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The analysis unit analyzes the digital encyclopedia data. The analysis unit clarifies the specific types and formats of data such as image data, text data, and audio data, and analyzes the digital encyclopedia data using methods such as image analysis, text analysis, and audio analysis. Step 2: The generation unit generates organisms based on the information analyzed by the analysis unit. The generation unit uses an existing game engine (e.g., Unity or Unreal Engine) to combine the organisms chosen by the children and generate 3D models and animations. The generation unit generates organisms using methods such as genetic algorithms and random generation. Step 3: The evaluation unit evaluates the organisms generated by the generation unit. The evaluation unit evaluates the organisms generated based on the theme and assigns points. The evaluation unit evaluates the organisms using methods such as scientific evaluation and user evaluation.

[0057] (Example of form 2) An educational platform according to an embodiment of the present invention is a system that utilizes generative AI to make learning natural science fun and interesting. This system analyzes digital encyclopedia data and extracts and analyzes necessary information. Next, it uses an existing game engine to combine organisms selected by children and generate 3D models and animations. Furthermore, it introduces a theme-based story mode and scoring system, where the AI ​​evaluates the organism that best fits the theme and awards points. This mechanism allows children to interact with organisms in a game-like manner and become more proactive in learning about nature and science. In addition, through the process of combining organisms, children's imagination and scientific knowledge grow, and their scientific literacy is strengthened. For example, when analyzing digital encyclopedia data with AI, it refers to existing online animal and plant encyclopedias (e.g., Wikipedia and GBIF), and the AI ​​collects and analyzes information on the ecology and characteristics of specific organisms. Next, when generating organisms using an existing game engine, it uses Unity or Unreal Engine to combine organisms selected by children and generate 3D models and animations. For example, it generates a new organism by combining a lion and an eagle, and creates a 3D model and animation of that organism. Furthermore, when introducing a theme-based story mode and scoring system, a theme such as "the fastest creature" is presented, and the AI ​​evaluates the creature that best fits the theme and awards points. This allows children to generate creatures that align with the theme and compete based on the results. This mechanism allows children to interact with creatures in a game-like way and become more proactive in learning about nature and science. In addition, through the process of crossbreeding creatures, children's imagination and scientific knowledge grow, and their scientific literacy is strengthened. For example, by learning the characteristics of different creatures and combining them to create new creatures, children can cultivate scientific thinking skills. In this way, the educational platform can make learning natural science fun and interesting.

[0058] The educational platform according to this embodiment comprises an analysis unit, a generation unit, and an evaluation unit. The analysis unit analyzes digital encyclopedia data. The analysis unit needs to clarify, for example, the specific types and formats of the digital encyclopedia data. For example, this includes image data, text data, and audio data. The analysis unit analyzes the digital encyclopedia data using methods such as image analysis, text analysis, and audio analysis. The generation unit generates organisms based on the information analyzed by the analysis unit. The generation unit generates 3D models and animations by combining organisms selected by children, for example, using an existing game engine. The generation unit uses game engines such as Unity or Unreal Engine. The generation unit generates organisms using methods such as genetic algorithms and random generation. The evaluation unit evaluates the organisms generated by the generation unit. The evaluation unit evaluates the organisms generated based on a theme, for example, and assigns points. The evaluation unit evaluates the organisms using methods such as scientific evaluation and user evaluation. As a result, the educational platform according to this embodiment can make learning natural science fun and interesting.

[0059] The analysis unit analyzes digital encyclopedia data. The analysis unit needs to clarify the specific types and formats of the digital encyclopedia data, such as image data, text data, and audio data. Image data includes photographs and illustrations of organisms, which are analyzed using image recognition technology. Specifically, it extracts the features of organisms within the images and performs classification and identification. Text data includes descriptions, characteristics, and classification information of organisms, which are analyzed using natural language processing technology. For example, it extracts keywords from text data and organizes related information. Audio data includes animal sounds and explanatory audio, which are analyzed using speech recognition technology. The audio data extracts features from the audio waveform and converts the audio content into text. The analysis unit integrates this data to provide comprehensive information about the organisms. Furthermore, the analysis unit can learn data patterns using machine learning algorithms, enabling highly accurate analysis of new data. This allows the analysis unit to efficiently and accurately analyze digital encyclopedia data and provide foundational information for educational platforms.

[0060] The generation unit generates organisms based on information analyzed by the analysis unit. For example, the generation unit uses an existing game engine to combine organisms chosen by children and generate 3D models and animations. Specifically, it uses game engines such as Unity or Unreal Engine to create 3D models of organisms and add animations. The generation unit uses methods such as genetic algorithms and random generation to combine the characteristics of organisms and generate new organisms. Genetic algorithms are a method of mimicking the genetic information of organisms and generating offspring that inherit the characteristics of their parents. This allows children to create new organisms by combining the characteristics of different organisms. The generation unit uses physical simulations and animation techniques to add realistic textures and movements to the generated 3D models of organisms. For example, it uses shaders and particle systems to realistically reproduce the texture of the organism's skin and the movement of its hair. It also uses bone animation and inverse kinematics to make the organism's movements look natural. In this way, the generation unit can generate realistic and engaging organisms that children can learn from while having fun.

[0061] The evaluation unit evaluates the organisms generated by the generation unit. For example, the evaluation unit evaluates organisms generated based on a given theme and assigns points to them. Specifically, it evaluates organisms using methods such as scientific evaluation and user evaluation. Scientific evaluation assesses whether the characteristics and behaviors of the organisms are based on actual biological knowledge. For example, it verifies whether the ecology and behavior of the generated organisms match those of real organisms. User evaluation assesses how much children enjoy and are interested in the generated organisms. For example, it collects data such as how long children observed the generated organisms and how many operations they performed, and incorporates this into the evaluation. Based on these evaluation results, the evaluation unit assigns points to the generated organisms, visualizing the children's learning progress. Furthermore, the evaluation unit provides the evaluation results as feedback to the generation unit, which helps improve the generation algorithm and generate new organisms. This allows the evaluation unit to improve the quality of the generated organisms and enhance children's motivation to learn.

[0062] The analysis unit can refer to existing online plant and animal encyclopedias and extract and analyze the necessary information. The analysis unit can refer to specific websites or databases, for example. The analysis unit can refer to online plant and animal encyclopedias such as Wikipedia or GBIF, for example. The analysis unit can extract and analyze information such as species name, ecology, and characteristics. This allows for the efficient extraction and analysis of necessary information by referring to existing online plant and animal encyclopedias. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from online plant and animal encyclopedias into a generating AI and have the generating AI perform the extraction and analysis of the necessary information.

[0063] The generation unit can use an existing game engine to combine creatures chosen by children and generate 3D models and animations. The generation unit uses game engines such as Unity or Unreal Engine. The generation unit generates creatures using methods such as genetic algorithms or random generation. For example, the generation unit can generate a new creature by combining a lion and an eagle, and create a 3D model and animation of that creature. This allows children to visually enjoy the creatures they choose by using an existing game engine. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input data of creatures generated using a game engine into a generation AI, and have the generation AI execute the generation of 3D models and animations.

[0064] The evaluation unit can evaluate organisms generated based on a theme and award points. For example, if the theme is "fastest creature," the evaluation unit will evaluate the organism that best fits that theme and award points. The evaluation unit evaluates organisms using methods such as scientific evaluation or user evaluation. The evaluation unit needs to clearly define evaluation criteria and point types. This allows for increased motivation to learn by evaluating organisms based on a theme and awarding points. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input data on the generated organisms into a generating AI and have the generating AI perform the evaluation and point awarding.

[0065] The evaluation unit can analyze the characteristics of the generated organisms and perform scientific evaluations. For example, the evaluation unit analyzes the characteristics of the generated organisms, such as their morphology, behavior, and ecology. The evaluation unit evaluates the organisms using methods such as scientific evaluation or user evaluation. The evaluation unit needs to clarify evaluation criteria and evaluation methods, for example. This allows for an improvement in the quality of learning by scientifically evaluating the characteristics of the generated organisms. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the characteristics data of the generated organisms into a generating AI and have the generating AI perform a scientific evaluation.

[0066] The analysis unit can estimate the user's emotions and determine the priority of information to analyze based on the estimated user emotions. For example, if the user is excited, the analysis unit will prioritize analyzing information that is of interest. For example, if the user is bored, the analysis unit will prioritize analyzing new information that is of interest. For example, if the user is focused, the analysis unit will prioritize analyzing detailed information. In this way, by prioritizing information based on the user's emotions, more interesting information can be provided. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0067] The analysis unit can analyze information about the ecology and characteristics of specific organisms in detail, thereby improving the accuracy of the information provided to the user. For example, the analysis unit can collect and analyze detailed data on the ecology of specific organisms. For example, the analysis unit can collect and analyze detailed data on the characteristics of specific organisms. For example, the analysis unit can collect and analyze detailed data on the behavioral patterns of specific organisms. By doing so, the accuracy of the information provided can be improved by analyzing information about the ecology and characteristics of specific organisms in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the ecology and characteristics of specific organisms into a generating AI and have the generating AI perform a detailed analysis of the information.

[0068] The analysis unit can extract optimal information by referring to the user's past learning history. For example, the analysis unit can analyze the user's past learning history and extract relevant information. For example, the analysis unit can analyze the user's past learning history and extract information that is of interest. For example, the analysis unit can analyze the user's past learning history and extract information that deepens understanding. In this way, by referring to the user's past learning history, optimal information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past learning history data into a generating AI and have the generating AI perform the extraction of optimal information.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit provides a visually stimulating display method. For example, if the user is bored, the analysis unit provides a visually engaging display method. For example, if the user is focused, the analysis unit provides a display method that includes detailed information. By adjusting the display method based on the user's emotions, a more engaging display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0070] The analysis unit can prioritize the analysis of region-specific biological information based on the user's geographical location information. For example, the analysis unit prioritizes the analysis of region-specific biological information based on the user's geographical location information. For example, the analysis unit prioritizes the analysis of region-specific ecosystem information based on the user's geographical location information. For example, the analysis unit prioritizes the analysis of region-specific biological characteristics information based on the user's geographical location information. By prioritizing the analysis of region-specific biological information based on the user's geographical location information, the analysis unit can provide region-specific information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of region-specific biological information.

[0071] The analysis unit can analyze a user's social media activity and extract relevant biological information. For example, the analysis unit can analyze a user's social media activity and extract interesting biological information. For example, the analysis unit can analyze a user's social media activity and extract relevant biological information. For example, the analysis unit can analyze a user's social media activity and extract biological information to deepen understanding. In this way, relevant biological information can be provided by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the extraction of relevant biological information.

[0072] The generation unit can estimate the user's emotions and adjust the characteristics of the creatures it generates based on the estimated user emotions. For example, if the user is excited, the generation unit generates visually stimulating creatures. For example, if the user is bored, the generation unit generates interesting creatures. For example, if the user is focused, the generation unit generates creatures with detailed characteristics. In this way, by adjusting the characteristics of the creatures based on the user's emotions, it is possible to generate more interesting creatures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the creature's characteristics.

[0073] The generation unit can optimize algorithms that combine the characteristics of different organisms. For example, the generation unit optimizes algorithms that combine the characteristics of different organisms to generate realistic organisms. For example, the generation unit optimizes algorithms that combine the characteristics of different organisms to generate interesting organisms. For example, the generation unit optimizes algorithms that combine the characteristics of different organisms to generate organisms with detailed characteristics. In this way, more realistic organisms can be generated by optimizing algorithms that combine the characteristics of different organisms. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input characteristic data of different organisms into a generation AI and have the generation AI perform algorithm optimization.

[0074] The generation unit can generate the optimal organism by referring to the user's past generation history. For example, the generation unit can analyze the user's past generation history and generate relevant organisms. For example, the generation unit can analyze the user's past generation history and generate organisms that pique interest. For example, the generation unit can analyze the user's past generation history and generate organisms with detailed characteristics. In this way, the optimal organism can be generated by referring to the user's past generation history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past generation history data into a generation AI and have the generation AI perform the generation of the optimal organism.

[0075] The generation unit can estimate the user's emotions and adjust the appearance of the generated creatures based on the estimated user emotions. For example, if the user is excited, the generation unit will generate creatures with a visually stimulating appearance. For example, if the user is bored, the generation unit will generate creatures with an intriguing appearance. For example, if the user is focused, the generation unit will generate creatures with a detailed appearance. In this way, by adjusting the appearance of creatures based on the user's emotions, more intriguing creatures can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the creature's appearance.

[0076] The generation unit can prioritize the generation of region-specific organisms based on the user's geographical location information. For example, the generation unit prioritizes the generation of region-specific organisms based on the user's geographical location information. For example, the generation unit generates organisms suitable for region-specific ecosystems based on the user's geographical location information. For example, the generation unit generates organisms with region-specific characteristics based on the user's geographical location information. In this way, by generating region-specific organisms based on the user's geographical location information, it is possible to provide organisms tailored to the region. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the generation of region-specific organisms.

[0077] The generation unit can analyze the user's social media activity and generate relevant organisms. For example, the generation unit analyzes the user's social media activity and generates organisms that are of interest. For example, the generation unit analyzes the user's social media activity and generates relevant organisms. For example, the generation unit analyzes the user's social media activity and generates organisms with detailed characteristics. In this way, relevant organisms can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant organisms.

[0078] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is excited, the evaluation unit provides visually stimulating evaluation criteria. For example, if the user is bored, the evaluation unit provides engaging evaluation criteria. For example, if the user is focused, the evaluation unit provides detailed evaluation criteria. This allows for more engaging evaluations by adjusting the evaluation 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 evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0079] The evaluation unit can perform a detailed analysis of the characteristics of the generated organism and conduct a scientific evaluation. For example, the evaluation unit can perform a detailed analysis of the characteristics of the generated organism and conduct a scientific evaluation. For example, the evaluation unit can perform a detailed analysis of the behavioral patterns of the generated organism and conduct a scientific evaluation. For example, the evaluation unit can perform a detailed analysis of the ecology of the generated organism and conduct a scientific evaluation. This makes a scientific evaluation possible by analyzing the characteristics of the generated organism in detail. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the characteristics data of the generated organism into a generating AI and have the generating AI perform a scientific evaluation.

[0080] The evaluation unit can perform optimal evaluations by referring to the user's past evaluation history. For example, the evaluation unit analyzes the user's past evaluation history and performs relevant evaluations. For example, the evaluation unit analyzes the user's past evaluation history and performs interesting evaluations. For example, the evaluation unit analyzes the user's past evaluation history and performs detailed evaluations. In this way, optimal evaluations can be performed by referring to the user's past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past evaluation history data into a generating AI and have the generating AI perform optimal evaluations.

[0081] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is excited, the evaluation unit provides a visually stimulating display method. For example, if the user is bored, the evaluation unit provides a visually engaging display method. For example, if the user is focused, the evaluation unit provides a display method that includes detailed information. By adjusting the display method of the evaluation results based on the user's emotions, a more engaging display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the evaluation results.

[0082] The evaluation unit can apply region-specific evaluation criteria based on the user's geographical location information. For example, the evaluation unit applies region-specific evaluation criteria based on the user's geographical location information. For example, the evaluation unit applies evaluation criteria suitable for the region's specific ecosystem based on the user's geographical location information. For example, the evaluation unit applies evaluation criteria with region-specific characteristics based on the user's geographical location information. This makes it possible to perform region-specific evaluations by applying region-specific evaluation criteria based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information data into a generating AI and have the generating AI execute the application of region-specific evaluation criteria.

[0083] The evaluation unit can analyze a user's social media activity and perform relevant evaluations. For example, the evaluation unit can analyze a user's social media activity and perform an interesting evaluation. For example, the evaluation unit can analyze a user's social media activity and perform a relevant evaluation. For example, the evaluation unit can analyze a user's social media activity and perform a detailed evaluation. In this way, relevant evaluations can be performed by analyzing a user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user social media activity data into a generating AI and have the generating AI perform relevant evaluations.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analysis unit can monitor the user's learning progress in real time and adjust the content of the information analyzed according to the user's learning progress. For example, if a user has a deep understanding of a particular topic, it can prioritize analyzing more advanced information related to that topic. Conversely, if a user has a shallow understanding of a particular topic, it can prioritize analyzing basic information related to that topic. Furthermore, it can gradually adjust the difficulty level of the information analyzed according to the user's learning progress. This allows for the provision of optimal information tailored to the user's learning progress, thereby enhancing the effectiveness of learning.

[0086] The generation unit can generate new organisms based on the user's learning history, taking into account the characteristics of organisms generated in the past. For example, it can analyze the characteristics of organisms the user has generated in the past and determine the characteristics of new organisms based on that analysis. It can also generate new organisms by combining the characteristics of organisms the user has generated in the past. Furthermore, based on the user's learning history, it can predict organisms that the user might be interested in and generate those organisms. This allows the system to leverage the user's learning history to generate more interesting organisms, thereby increasing learning motivation.

[0087] The evaluation unit can adjust the evaluation method according to the user's learning style. For example, if the user has a visual learning style, it can provide a visually easy-to-understand evaluation method. If the user has an auditory learning style, it can provide an evaluation method using audio. Furthermore, if the user has an experiential learning style, it can provide a method of evaluation by actually operating the device. By providing an evaluation method that matches the user's learning style, the effectiveness of learning can be enhanced.

[0088] The analysis unit can adjust the content of the information it analyzes according to the user's learning objectives. For example, if the user's goal is to pass a specific exam, it can prioritize the analysis of information related to that exam. Similarly, if the user's goal is to acquire a specific skill, it can prioritize the analysis of information related to that skill. Furthermore, it can adjust the scope and depth of the information analyzed according to the user's learning objectives. This allows the system to provide optimal information tailored to the user's learning objectives, thereby enhancing the effectiveness of their learning.

[0089] The generation unit can adjust the characteristics of the organisms it generates according to the user's learning environment. For example, if the user is learning outdoors, it can generate organisms suited to the natural environment. If the user is learning indoors, it can generate organisms suited to the indoor environment. Furthermore, by adjusting the characteristics of the generated organisms according to the user's learning environment, the effectiveness of learning can be enhanced. This allows for the generation of optimal organisms tailored to the user's learning environment, thereby increasing learning motivation.

[0090] The analysis unit can estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is excited, information can be presented in a visually stimulating way. If the user is bored, information can be presented in an engaging way. Furthermore, if the user is focused, information can be presented in a way that includes detailed information. By adjusting the information presentation method based on the user's emotions, more engaging information can be provided.

[0091] The generation unit can estimate the user's emotions and adjust the behavioral patterns of the generated creatures based on those estimated emotions. For example, if the user is excited, it can generate creatures with active behavioral patterns. If the user is bored, it can generate creatures with intriguing behavioral patterns. Furthermore, if the user is focused, it can generate creatures with detailed behavioral patterns. In this way, by adjusting the behavioral patterns of creatures based on the user's emotions, it is possible to generate more interesting creatures.

[0092] The evaluation unit can estimate the user's emotions and adjust the feedback method of the evaluation results based on the estimated user emotions. For example, if the user is excited, it can provide a visually stimulating feedback method. If the user is bored, it can provide an engaging feedback method. Furthermore, if the user is focused, it can provide a method that includes detailed feedback. In this way, by adjusting the feedback method based on the user's emotions, more effective feedback can be provided.

[0093] The analysis unit can estimate the user's emotions and adjust the depth of the information analyzed based on those emotions. For example, if the user is excited, it can provide detailed information. If the user is bored, it can provide information that is more engaging. Furthermore, if the user is focused, it can provide even deeper information. In this way, by adjusting the depth of information based on the user's emotions, it is possible to provide more engaging information.

[0094] The generation unit can estimate the user's emotions and adjust the voice of the creature it generates based on those emotions. For example, if the user is excited, it can generate a creature with an exciting voice. If the user is bored, it can generate a creature with an intriguing voice. Furthermore, if the user is focused, it can generate a creature with a detailed voice. In this way, by adjusting the voice of the creature based on the user's emotions, it is possible to generate a more engaging creature.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The analysis unit analyzes the digital encyclopedia data. The analysis unit clarifies the specific types and formats of data such as image data, text data, and audio data, and analyzes the digital encyclopedia data using methods such as image analysis, text analysis, and audio analysis. Step 2: The generation unit generates organisms based on the information analyzed by the analysis unit. The generation unit uses an existing game engine (e.g., Unity or Unreal Engine) to combine the organisms chosen by the children and generate 3D models and animations. The generation unit generates organisms using methods such as genetic algorithms and random generation. Step 3: The evaluation unit evaluates the organisms generated by the generation unit. The evaluation unit evaluates the organisms generated based on the theme and assigns points. The evaluation unit evaluates the organisms using methods such as scientific evaluation and user evaluation.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the analysis unit, generation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the digital encyclopedia data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates organisms based on the analyzed information. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the generated organisms. 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.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the analysis unit, generation unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the digital encyclopedia data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates organisms based on the analyzed information. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the generated organisms. 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.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

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

[0120] The 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.

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

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

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

[0124] Figure 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.

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

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

[0127] In the 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.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0131] The data processing system 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.

[0132] Each of the multiple elements described above, including the analysis unit, generation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the digital encyclopedia data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates organisms based on the analyzed information. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the generated organisms. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

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

[0136] The 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.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the analysis unit, generation unit, and evaluation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the digital encyclopedia data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates organisms based on the analyzed information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the generated organisms. 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The analysis unit analyzes the digital encyclopedia data, A generation unit that generates organisms based on the information analyzed by the aforementioned analysis unit, An evaluation unit for evaluating the organism produced by the generation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, Refer to existing online animal and plant encyclopedias and extract and analyze the necessary information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Using an existing game engine, children can combine creatures of their choice to generate 3D models and animations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluate the organisms generated based on the theme and award points. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Analyze the characteristics of the generated organisms and perform a scientific evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of information to analyze based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We analyze information about the ecology and characteristics of specific organisms in detail to improve the accuracy of the information we provide to users. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system extracts the most relevant information by referencing the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Based on the user's geographical location information, the system prioritizes the analysis of region-specific biological information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, Analyze users' social media activity and extract relevant biometric information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the characteristics of the organism generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Optimizing algorithms that combine the characteristics of different organisms. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It generates the optimal organism by referring to the user's past generation history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the appearance of the creatures it generates based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Based on the user's geographical location information, the game prioritizes generating region-specific organisms. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Analyze users' social media activity and generate relevant organisms. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, We will analyze the characteristics of the generated organisms in detail and conduct a scientific evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, We refer to the user's past rating history to provide the most optimal rating. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, Based on the user's geographical location information, region-specific evaluation criteria are applied. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, Analyze users' social media activity and perform relevant evaluations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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. The analysis unit analyzes the digital encyclopedia data, A generation unit that generates organisms based on the information analyzed by the aforementioned analysis unit, An evaluation unit for evaluating the organism produced by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Refer to existing online plant and animal encyclopedias and extract and analyze the necessary information. The system according to feature 1.

3. The generating unit is Using an existing game engine, children can combine creatures of their choice to generate 3D models and animations. The system according to feature 1.

4. The evaluation unit, Evaluate the organisms generated based on the theme and award points. The system according to feature 1.

5. The evaluation unit, Analyze the characteristics of the generated organisms and perform a scientific evaluation. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and determines the priority of information to analyze based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, We analyze information about the ecology and characteristics of specific organisms in detail to improve the accuracy of the information we provide to users. The system according to feature 1.

8. The aforementioned analysis unit, The system extracts the most relevant information by referencing the user's past learning history. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, Based on the user's geographical location information, the system prioritizes the analysis of region-specific biological information. The system according to feature 1.

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