System

Abstract

An object of the system according to the embodiment is to promote academic research and education to general people through provision of detailed information on ancient organisms and interactive experience.SOLUTION: A system includes a selection unit, an information providing unit, an interaction unit, a restoration unit, and a sharing unit. The selection unit allows a user to select a specific ancient organism. The information providing unit provides detailed information about characteristics and behavioral patterns of the ancient organism selected by the selection unit. The dialogue unit allows the user to have a dialogue or an interaction with an ancient creature on the basis of the information provided by the information providing unit. The restoration part restores the ancient creature by utilizing the generation AI. The sharing unit shares the model generated by the restoration unit and the simulation result with a researcher and an expert.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies provide limited information and experiences about ancient organisms, leaving room for improvement in academic research and public education.

[0005] The system of the embodiment aims to promote academic research and public education through detailed information and interactive experiences about ancient organisms. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, an information provision unit, a dialogue unit, a restoration unit, and a sharing unit. The selection unit allows the user to select a specific ancient organism. The information provision unit provides detailed information about the characteristics and behavioral patterns of the ancient organism selected by the selection unit. The dialogue unit allows the user to converse and interact with the ancient organism based on the information provided by the information provision unit. The restoration unit utilizes generation AI to restore the ancient organism. The sharing unit shares the models and simulation results generated by the restoration unit with researchers and experts. [Effects of the Invention]

[0007] Embodiments of the system can facilitate academic research and public education through detailed information and interactive experiences about ancient organisms. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generative AI to provide a user with an experience of conversing and interacting with a specific ancient creature. The system allows the user to select a specific ancient creature, and the generative AI provides realistic information about the creature's characteristics and behavioral patterns. The user can then converse and interact with the creature. This experience allows the user to enjoy being a part of the creature. Furthermore, by utilizing generative AI to reconstruct ancient creatures, the system aims to contribute to academic research. The generated models and simulation results are shared with researchers and experts, providing a research environment that generates new knowledge and discoveries. Furthermore, to provide experiences for the general public interested in geology and paleontology, tourist facilities utilizing generative AI are enhanced. For example, a user selects a specific ancient creature. In this case, the user inputs the name and characteristics of the selected creature into the generative AI. For example, if the user selects a Tyrannosaurus, a type of dinosaur, the generative AI provides information about the creature's characteristics and behavioral patterns. The generative AI then provides realistic information about the selected ancient creature's characteristics and behavioral patterns. The generative AI generates detailed information about the selected creature based on academic data and research results. For example, information about the habitat, diet, and behavioral patterns of a Tyrannosaurus is provided. Users can then converse and interact with the selected ancient creature. The generative AI simulates the behavior of the selected creature based on user input, providing a realistic experience. For example, if a user inputs "roar" to a Tyrannosaurus, the generative AI will simulate the Tyrannosaurus' roaring behavior and provide the user with that experience. Furthermore, using generative AI to reconstruct ancient creatures contributes to academic research. The generated models and simulation results are shared with researchers and experts, providing a research environment that generates new knowledge and discoveries. For example, a skeletal model and ecological simulation of a Tyrannosaurus can be generated, allowing researchers to make new discoveries. Furthermore, enhancing tourist facilities using generative AI will provide opportunities for the general public interested in geology and paleontology.Through a variety of exhibits and activities, including realistic reproductions of ancient creatures and virtual reality experiences, an environment will be created where visitors can gain a deeper understanding of and enjoy the world of ancient creatures. This allows the system to provide users with the experience of conversing and interacting with specific ancient creatures, contributing to academic research and enhancing tourist facilities. This allows the system to provide users with the experience of conversing and interacting with specific ancient creatures, contributing to academic research and enhancing tourist facilities.

[0029] An experience providing system according to an embodiment includes a selection unit, an information providing unit, a dialogue unit, a restoration unit, and a sharing unit. The selection unit allows a user to select a specific ancient creature. For example, the user can input the name and characteristics of the selected creature to the generation AI. The information providing unit uses the generation AI to provide detailed information about the characteristics and behavioral patterns of the selected ancient creature. For example, the generation AI generates detailed information about the selected creature based on academic data and research results. The dialogue unit simulates the behavior of the selected ancient creature in response to the user's input. For example, if a user inputs "roar" to a Tyrannosaurus, the generation AI simulates the Tyrannosaurus' roaring behavior and provides the user with that experience. The restoration unit uses the generation AI to restore the ancient creature. For example, the generation AI generates a Tyrannosaurus skeletal model and ecological simulation. The sharing unit shares the generated model and simulation results with researchers and experts. For example, researchers can use the generated Tyrannosaurus skeletal model and ecological simulation. As a result, the experience provision system according to the embodiment can provide users with the experience of having conversations and interactions with specific ancient creatures, contributing to academic research and enhancing tourist facilities.

[0030] The selection unit can analyze the user's past selection history and suggest appropriate ancient creature options. For example, the selection unit can suggest creatures with similar characteristics based on data on ancient creatures the user has previously selected. The selection unit can also analyze the behavioral patterns of creatures the user has previously selected and suggest creatures that the user may be interested in. The selection unit can also preferentially suggest creatures that belong to a specific category based on the user's past selection history. This makes it possible to provide an experience that matches the user's interests by suggesting the most appropriate ancient creature options based on the user's past selection history.

[0031] When selecting an ancient creature, the selection unit can filter the results based on the user's current interests. For example, the selection unit can filter and present ancient creatures related to a theme that the user is currently interested in. The selection unit can also filter related ancient creatures based on keywords recently searched by the user. The selection unit can also filter ancient creatures based on topics in communities or forums in which the user participates. This allows the user to have a more interesting experience by filtering ancient creatures based on the user's current interests.

[0032] When selecting an ancient creature, the selection unit can provide an appropriate selection means according to the user's input method. For example, when the user uses voice input, the selection unit presents options of ancient creatures using voice recognition technology. When the user uses text input, the selection unit can also present options of ancient creatures based on the input keywords. When the user uses image input, the selection unit can also present options of related ancient creatures using image recognition technology. This improves user convenience by providing an optimal selection means according to the user's input method.

[0033] The information provision unit can provide information about the characteristics and behavioral patterns of ancient organisms selected by the generation AI. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0034] The dialogue unit can simulate the behavior of a selected ancient creature in response to user input. For example, the dialogue unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The dialogue unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The dialogue unit also develops an algorithm for the generation AI to analyze the logical structure and development of arguments in an answer and generate a logical summary. For example, it evaluates the logical consistency and importance of arguments. This allows for a realistic experience by simulating the behavior of ancient creatures in response to user input.

[0035] The reconstruction unit can utilize the generative AI to generate skeletal models and ecological simulations of ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the reconstruction unit collects the latest news articles and social media posts from the Internet and uses them as information sources for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summaries. For example, the reconstruction unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0036] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0037] The information provision unit can generate detailed information about the selected organism based on academic data and research results. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit can automatically collect relevant information and reflect it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0038] The dialogue unit can provide users with the experience of having a dialogue or interaction with ancient organisms. For example, when the generation AI generates a summary, the dialogue unit can automatically collect relevant background information and refer to it to understand the context. For example, the dialogue unit collects related news articles and academic papers. The dialogue unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the dialogue unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The dialogue unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the dialogue unit can automatically collect relevant information and incorporate it into the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the dialogue unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the dialogue unit can collect the latest news articles and social media posts from the Internet and use them as sources of information to understand the context. The dialogue unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the dialogue unit can evaluate the quality of the generated summaries and update the topic model based on the evaluation results. This allows the dialogue unit to always understand the context based on the latest information and provide more accurate summaries.

[0039] The reconstruction unit can utilize the generative AI to reconstruct ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the reconstruction unit collects the latest news articles and social media posts on the Internet and uses them as sources of information for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summary. For example, the reconstruction unit evaluates the quality of the generated summary and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0040] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0041] The information provision unit can provide information about the characteristics and behavioral patterns of ancient organisms selected by the generation AI. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0042] The dialogue unit can simulate the behavior of a selected ancient organism in response to user input. For example, when the generation AI generates a summary, the dialogue unit can automatically collect relevant background information and refer to it to understand the context. For example, the dialogue unit collects related news articles and academic papers. The dialogue unit can also use topic models to understand the context when the generation AI generates a summary. For example, the dialogue unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The dialogue unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the dialogue unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the dialogue unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the dialogue unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The dialogue unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the dialogue unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the dialogue unit to always understand the context based on the latest information and provide more accurate summaries.

[0043] The reconstruction unit can utilize the generative AI to generate skeletal models and ecological simulations of ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the reconstruction unit collects the latest news articles and social media posts from the Internet and uses them as information sources for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summaries. For example, the reconstruction unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0044] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0045] The information provision unit can generate detailed information about the selected organism based on academic data and research results. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit can automatically collect relevant information and reflect it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0046] The dialogue unit can provide users with the experience of having a dialogue or interaction with ancient organisms. For example, when the generation AI generates a summary, the dialogue unit can automatically collect relevant background information and refer to it to understand the context. For example, the dialogue unit collects related news articles and academic papers. The dialogue unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the dialogue unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The dialogue unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the dialogue unit can automatically collect relevant information and incorporate it into the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the dialogue unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the dialogue unit can collect the latest news articles and social media posts from the Internet and use them as sources of information to understand the context. The dialogue unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the dialogue unit can evaluate the quality of the generated summaries and update the topic model based on the evaluation results. This allows the dialogue unit to always understand the context based on the latest information and provide more accurate summaries.

[0047] The reconstruction unit can utilize the generative AI to reconstruct ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the reconstruction unit collects the latest news articles and social media posts on the Internet and uses them as sources of information for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summary. For example, the reconstruction unit evaluates the quality of the generated summary and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0048] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0049] When providing information, the information providing unit can adjust the level of detail of the information based on the importance of the ancient organism. For example, the information providing unit provides detailed information for important ancient organisms. The information providing unit can also provide concise information for general ancient organisms. The information providing unit can also provide specialized information for ancient organisms that are academically important. In this way, by adjusting the level of detail of the information based on the importance of the ancient organism, more appropriate information can be provided.

[0050] The dialogue unit can adjust the level of detail of the dialogue based on the behavioral patterns of the ancient creatures during dialogue. For example, in the case of a carnivorous dinosaur, the dialogue unit can provide detailed dialogue about hunting behavior patterns. In the case of a herbivorous dinosaur, the dialogue unit can also provide detailed dialogue about feeding habits and habitats. In the case of a marine creature, the dialogue unit can also provide detailed dialogue about swimming styles and habitats. This allows for adjusting the level of detail of the dialogue based on the behavioral patterns of the ancient creatures to provide a more appropriate dialogue experience.

[0051] During restoration, the restoration unit can adjust the level of detail of the restoration based on the skeletal model of the ancient organism. For example, the restoration unit performs detailed restoration for skeletal models of important ancient organisms. The restoration unit can also perform simple restoration for skeletal models of general ancient organisms. The restoration unit can also perform specialized restoration for skeletal models of academically important ancient organisms. In this way, by adjusting the level of detail of the restoration based on the skeletal model of the ancient organism, a more appropriate restoration experience can be provided.

[0052] The sharing unit can adjust the level of detail of sharing based on the research data of ancient organisms when sharing. For example, the sharing unit shares detailed information for important research data of ancient organisms. The sharing unit can also share concise information for general research data of ancient organisms. The sharing unit can also share specialized information for academically important research data of ancient organisms. In this way, adjusting the level of detail of sharing based on the research data of ancient organisms enables more appropriate information sharing.

[0053] When providing information, the information providing unit can apply different information providing algorithms depending on the category of the ancient organism. For example, for carnivorous dinosaurs, the information providing unit can provide information on hunting behavior patterns. For herbivorous dinosaurs, the information providing unit can also provide information on feeding habits and habitats. For marine organisms, the information providing unit can also provide information on swimming styles and habitats. In this way, by applying information providing algorithms depending on the category of ancient organisms, more appropriate information can be provided.

[0054] The dialogue unit can apply different dialogue algorithms depending on the category of the ancient creature during dialogue. For example, in the case of a carnivorous dinosaur, the dialogue unit applies a dialogue algorithm related to hunting behavior patterns. In addition, in the case of a herbivorous dinosaur, the dialogue unit can also apply a dialogue algorithm related to feeding habits and habitats. In addition, in the case of a marine creature, the dialogue unit can also apply a dialogue algorithm related to swimming style and habitats. In this way, by applying a dialogue algorithm according to the category of the ancient creature, a more appropriate dialogue experience can be provided.

[0055] During restoration, the restoration unit can apply different restoration algorithms depending on the category of the ancient organism. For example, in the case of a carnivorous dinosaur, the restoration unit applies a restoration algorithm based on its hunting behavior pattern. In addition, in the case of a herbivorous dinosaur, the restoration unit can also apply a restoration algorithm based on its feeding habits and habitat. In addition, in the case of a marine organism, the restoration unit can also apply a restoration algorithm based on its swimming style and habitat. In this way, by applying a restoration algorithm according to the category of the ancient organism, a more appropriate restoration experience can be provided.

[0056] When sharing, the sharing unit can apply different sharing algorithms depending on the category of the ancient organism. For example, in the case of a carnivorous dinosaur, the sharing unit can share information about hunting behavior patterns. In the case of a herbivorous dinosaur, the sharing unit can also share information about its feeding habits and habitat. In the case of a marine organism, the sharing unit can also share information about its swimming style and habitat. This allows for more appropriate information sharing by applying a sharing algorithm depending on the category of the ancient organism.

[0057] When providing information, the information providing unit can improve the accuracy of the information by referring to the user's past information provision results. The information providing unit improves the accuracy of the information, for example, based on information provision methods that the user has given high ratings to in the past. The information providing unit can also improve the accuracy of the information by analyzing feedback provided by the user in the past. The information providing unit can also improve the accuracy of information belonging to a specific category based on the user's past information provision results. In this way, the accuracy of the information can be improved by referring to the user's past information provision results.

[0058] The dialogue unit can improve the accuracy of the dialogue by referring to the user's past dialogue results during the dialogue. For example, the dialogue unit improves the accuracy of the dialogue based on dialogue methods that the user has given high ratings to in the past. The dialogue unit can also improve the accuracy of the dialogue by analyzing feedback provided by the user in the past. The dialogue unit can also improve the accuracy of dialogue belonging to a specific category based on the user's past dialogue results. In this way, the accuracy of the dialogue can be improved by referring to the user's past dialogue results.

[0059] During restoration, the restoration unit can improve the accuracy of restoration by referring to the user's past restoration results. The restoration unit improves the accuracy of restoration, for example, based on restoration methods that the user has given high ratings to in the past. The restoration unit can also improve the accuracy of restoration by analyzing feedback provided by the user in the past. The restoration unit can also improve the accuracy of restoration belonging to a specific category based on the user's past restoration results. In this way, the accuracy of restoration can be improved by referring to the user's past restoration results.

[0060] When sharing, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing results. The sharing unit improves the accuracy of sharing, for example, based on sharing methods that the user has given high ratings to in the past. The sharing unit can also improve the accuracy of sharing by analyzing feedback that the user has provided in the past. The sharing unit can also improve the accuracy of sharing that belongs to a specific category based on the user's past sharing results. In this way, the accuracy of sharing can be improved by referring to the user's past sharing results.

[0061] When providing information, the information providing unit can determine the priority of information based on the time when the ancient organism was selected. For example, the information providing unit provides information preferentially for ancient organisms recently selected by the user. The information providing unit can also provide information secondarily for ancient organisms previously selected by the user. The information providing unit can also provide information last for ancient organisms that the user has not yet selected. This enables more appropriate information to be provided by determining the priority of information based on the time when the ancient organism was selected.

[0062] During dialogue, the dialogue unit can determine dialogue priorities based on when the ancient creature was selected. For example, the dialogue unit prioritizes dialogue with an ancient creature recently selected by the user. The dialogue unit can also prioritize dialogue with an ancient creature previously selected by the user. The dialogue unit can also prioritize dialogue with an ancient creature that has not yet been selected by the user last. In this way, by determining dialogue priorities based on when the ancient creature was selected, a more appropriate dialogue experience can be provided.

[0063] During restoration, the restoration unit can determine restoration priorities based on the time of selection of the ancient creature. For example, the restoration unit prioritizes restoration of ancient creatures recently selected by the user. The restoration unit can also prioritize restoration of ancient creatures previously selected by the user. The restoration unit can also restore ancient creatures that have not yet been selected by the user last. In this way, by determining restoration priorities based on the time of selection of the ancient creature, a more appropriate restoration experience can be provided.

[0064] When sharing, the sharing unit can determine the priority of sharing based on when the ancient creature was selected. For example, the sharing unit prioritizes sharing information about ancient creatures recently selected by the user. The sharing unit can also prioritize sharing information about ancient creatures previously selected by the user. The sharing unit can also share information about ancient creatures that the user has not yet selected last. This allows for more appropriate information sharing by determining the priority of sharing based on when the ancient creature was selected.

[0065] When providing information, the information providing unit can adjust the order of information based on the relevance of ancient organisms. For example, the information providing unit preferentially provides information related to the ancient organism selected by the user. The information providing unit can also provide related information that the user is likely to be interested in next. The information providing unit can also provide related information that the user is not yet aware of last. In this way, by adjusting the order of information based on the relevance of ancient organisms, more appropriate information can be provided.

[0066] During the dialogue, the dialogue unit can adjust the order of dialogues based on the relevance of the ancient creatures. For example, the dialogue unit prioritizes dialogues related to the ancient creature selected by the user. The dialogue unit can also next carry out related dialogues that the user is likely to be interested in. The dialogue unit can also carry out related dialogues that the user is not yet familiar with last. In this way, by adjusting the order of dialogues based on the relevance of the ancient creatures, a more appropriate dialogue experience can be provided.

[0067] During the restoration, the restoration unit can improve the accuracy of the restoration by referring to literature related to the ancient organism. For example, the restoration unit performs a detailed restoration based on literature related to important ancient organisms. The restoration unit can also perform a concise restoration based on literature related to general ancient organisms. The restoration unit can also perform a specialized restoration based on literature related to academically important ancient organisms. In this way, the accuracy of the restoration can be improved by referring to literature related to ancient organisms.

[0068] When sharing, the shared section can improve the accuracy of sharing by referring to literature related to ancient organisms. For example, the shared section shares detailed information based on literature related to important ancient organisms. The shared section can also share concise information based on literature related to general ancient organisms. The shared section can also share specialized information based on literature related to academically important ancient organisms. In this way, by referring to literature related to ancient organisms, the accuracy of sharing can be improved.

[0069] When providing information, the information providing unit can adjust the use of technical terms in the information according to the user's level of expertise. For example, if the user is an expert, the information providing unit can provide information that uses a lot of technical terms. Furthermore, if the user is a general public, the information providing unit can also provide information that avoids technical terms. Furthermore, if the user is a student, the information providing unit can also provide information that includes educational explanations. This makes it possible to provide more appropriate information by providing information according to the user's level of expertise.

[0070] During the dialogue, the dialogue unit can adjust the use of technical terms in the dialogue depending on the user's level of expertise. For example, if the user is an expert, the dialogue unit can use a lot of technical terms. Also, if the user is a general public, the dialogue unit can avoid using technical terms. Also, if the user is a student, the dialogue unit can include educational explanations. This allows for a more appropriate dialogue experience by conducting a dialogue that is appropriate for the user's level of expertise.

[0071] The restoration unit can take into account the market value of the ancient organisms during restoration. For example, the restoration unit can perform detailed restoration for ancient organisms with high market value. The restoration unit can also perform simple restoration for ancient organisms with low market value. The restoration unit can also adjust the level of detail of the restoration in response to fluctuations in market value. This allows for a more appropriate restoration experience by taking into account the market value of the ancient organisms.

[0072] When sharing, the common part can share information taking into account the market value of the ancient organisms. For example, the common part can share detailed information about ancient organisms with high market value. The common part can also share simple information about ancient organisms with low market value. The common part can also adjust the level of detail of the sharing in accordance with fluctuations in market value. This allows for more appropriate information sharing by taking into account the market value of the ancient organisms.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The selection unit can acquire the user's current location information and preferentially present ancient creatures related to that location. For example, if the user is in a specific area, ancient creatures discovered in that area are preferentially presented. The selection unit can also present ancient creatures related to museums or ruins the user is visiting. Furthermore, the selection unit can present ancient creatures related to events or tours the user is participating in. This makes it possible to provide a selection of ancient creatures that are more relevant to the user based on the user's current location information.

[0075] The information providing unit can analyze the user's past search history and provide information on related ancient organisms preferentially. For example, information related to ancient organisms that the user has searched for in the past can be provided preferentially. The information providing unit can also provide information on ancient organisms related to themes in which the user has shown interest in the past. Furthermore, the information providing unit can also provide information on ancient organisms related to events or tours that the user has participated in in the past. This makes it possible to provide more relevant information based on the user's past search history.

[0076] The selection unit can acquire the user's current weather information and preferentially present ancient creatures related to that weather. For example, on a rainy day, aquatic creatures or ancient creatures living in wetlands can be preferentially presented. On a sunny day, terrestrial creatures or ancient creatures living in dry areas can be preferentially presented. Furthermore, the selection unit can present ancient creatures according to the season. This makes it possible to provide a selection of ancient creatures that are more relevant to the user based on the current weather information.

[0077] The information providing unit can analyze the user's current learning progress and provide information of appropriate difficulty. For example, it can provide basic information to beginners and detailed information to advanced learners. The information providing unit can also provide information on what the user should learn next based on what the user has learned in the past. Furthermore, the information providing unit can also provide information according to the user's learning goals. This makes it possible to provide information according to the user's learning progress.

[0078] The dialogue unit can acquire the user's current activity status and conduct dialogue appropriate to that status. For example, if the user is exercising, the dialogue unit can conduct a short, to-the-point dialogue. If the user is relaxed, the dialogue unit can conduct a dialogue including detailed explanations. Furthermore, if the user is concentrating, the dialogue unit can conduct a dialogue including specialized information. This makes it possible to provide a dialogue experience appropriate to the user's current activity status.

[0079] The restoration unit can select the optimal restoration method taking into account the performance of the user's current device. For example, detailed restoration is performed on a high-performance device, and simple restoration is performed on a low-performance device. The restoration unit can also select a restoration method according to the screen size of the user's device. Furthermore, the restoration unit can select a restoration method according to the remaining battery power of the user's device. This makes it possible to provide an optimal restoration experience according to the performance of the user's device.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: In the selection section, the user selects a specific ancient creature. For example, the user can input the name and characteristics of the selected creature into the generation AI. Step 2: The information provider uses the generation AI to provide detailed information about the characteristics and behavioral patterns of the selected ancient organism. For example, the generation AI generates detailed information about the selected organism based on academic data and research results. Step 3: The dialogue unit simulates the behavior of the selected ancient creature according to the user's input. For example, if the user inputs "roar" to a Tyrannosaurus, the generation AI will simulate the Tyrannosaurus' roaring behavior and provide the user with that experience. Step 4: The reconstruction unit uses generative AI to reconstruct ancient organisms. For example, generative AI generates a skeletal model and ecological simulation of a Tyrannosaurus rex. Step 5: The sharing department shares the generated models and simulation results with researchers and experts. For example, researchers can use the generated Tyrannosaurus skeletal model and ecological simulation.

[0082] (Example 2) A system according to an embodiment of the present invention uses a generative AI to provide a user with an experience of conversing and interacting with a specific ancient creature. The system allows the user to select a specific ancient creature, and the generative AI provides realistic information about the creature's characteristics and behavioral patterns. The user can then converse and interact with the creature. This experience allows the user to enjoy being a part of the creature. Furthermore, by utilizing generative AI to reconstruct ancient creatures, the system aims to contribute to academic research. The generated models and simulation results are shared with researchers and experts, providing a research environment that generates new knowledge and discoveries. Furthermore, to provide experiences for the general public interested in geology and paleontology, tourist facilities utilizing generative AI are enhanced. For example, a user selects a specific ancient creature. In this case, the user inputs the name and characteristics of the selected creature into the generative AI. For example, if the user selects a Tyrannosaurus, a type of dinosaur, the generative AI provides information about the creature's characteristics and behavioral patterns. The generative AI then provides realistic information about the selected ancient creature's characteristics and behavioral patterns. The generative AI generates detailed information about the selected creature based on academic data and research results. For example, information about the habitat, diet, and behavioral patterns of a Tyrannosaurus is provided. Users can then converse and interact with the selected ancient creature. The generative AI simulates the behavior of the selected creature based on user input, providing a realistic experience. For example, if a user inputs "roar" to a Tyrannosaurus, the generative AI will simulate the Tyrannosaurus' roaring behavior and provide the user with that experience. Furthermore, using generative AI to reconstruct ancient creatures contributes to academic research. The generated models and simulation results are shared with researchers and experts, providing a research environment that generates new knowledge and discoveries. For example, a skeletal model and ecological simulation of a Tyrannosaurus can be generated, allowing researchers to make new discoveries. Furthermore, enhancing tourist facilities using generative AI will provide opportunities for the general public interested in geology and paleontology.Through a variety of exhibits and activities, including realistic reproductions of ancient creatures and virtual reality experiences, an environment will be created where visitors can gain a deeper understanding of and enjoy the world of ancient creatures. This allows the system to provide users with the experience of conversing and interacting with specific ancient creatures, contributing to academic research and enhancing tourist facilities. This allows the system to provide users with the experience of conversing and interacting with specific ancient creatures, contributing to academic research and enhancing tourist facilities.

[0083] An experience providing system according to an embodiment includes a selection unit, an information providing unit, a dialogue unit, a restoration unit, and a sharing unit. The selection unit allows a user to select a specific ancient creature. For example, the user can input the name and characteristics of the selected creature to the generation AI. The information providing unit uses the generation AI to provide detailed information about the characteristics and behavioral patterns of the selected ancient creature. For example, the generation AI generates detailed information about the selected creature based on academic data and research results. The dialogue unit simulates the behavior of the selected ancient creature in response to the user's input. For example, if a user inputs "roar" to a Tyrannosaurus, the generation AI simulates the Tyrannosaurus' roaring behavior and provides the user with that experience. The restoration unit uses the generation AI to restore the ancient creature. For example, the generation AI generates a Tyrannosaurus skeletal model and ecological simulation. The sharing unit shares the generated model and simulation results with researchers and experts. For example, researchers can use the generated Tyrannosaurus skeletal model and ecological simulation. As a result, the experience provision system according to the embodiment can provide users with the experience of having conversations and interactions with specific ancient creatures, contributing to academic research and enhancing tourist facilities.

[0084] The selection unit estimates the user's emotions and presents options for ancient creatures based on the estimated user emotions. For example, if the user is excited, the selection unit may preferentially present ancient creatures with active behavior. If the user is relaxed, the selection unit may also preferentially present ancient creatures with calm behavior. If the user is curious, the selection unit may also preferentially present ancient creatures with unusual characteristics. This allows for a more appropriate experience by presenting options for ancient creatures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The selection unit can analyze the user's past selection history and suggest appropriate ancient creature options. For example, the selection unit can suggest creatures with similar characteristics based on data on ancient creatures the user has previously selected. The selection unit can also analyze the behavioral patterns of creatures the user has previously selected and suggest creatures that the user may be interested in. The selection unit can also preferentially suggest creatures that belong to a specific category based on the user's past selection history. This makes it possible to provide an experience that matches the user's interests by suggesting the most appropriate ancient creature options based on the user's past selection history.

[0086] When selecting an ancient creature, the selection unit can filter the results based on the user's current interests. For example, the selection unit can filter and present ancient creatures related to a theme that the user is currently interested in. The selection unit can also filter related ancient creatures based on keywords recently searched by the user. The selection unit can also filter ancient creatures based on topics in communities or forums in which the user participates. This allows the user to have a more interesting experience by filtering ancient creatures based on the user's current interests.

[0087] When selecting an ancient creature, the selection unit can provide an appropriate selection means according to the user's input method. For example, when the user uses voice input, the selection unit presents options of ancient creatures using voice recognition technology. When the user uses text input, the selection unit can also present options of ancient creatures based on the input keywords. When the user uses image input, the selection unit can also present options of related ancient creatures using image recognition technology. This improves user convenience by providing an optimal selection means according to the user's input method.

[0088] The information provision unit can provide information about the characteristics and behavioral patterns of ancient organisms selected by the generation AI. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0089] The dialogue unit can simulate the behavior of a selected ancient creature in response to user input. For example, the dialogue unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The dialogue unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The dialogue unit also develops an algorithm for the generation AI to analyze the logical structure and development of arguments in an answer and generate a logical summary. For example, it evaluates the logical consistency and importance of arguments. This allows for a realistic experience by simulating the behavior of ancient creatures in response to user input.

[0090] The reconstruction unit can utilize the generative AI to generate skeletal models and ecological simulations of ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the reconstruction unit collects the latest news articles and social media posts from the Internet and uses them as information sources for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summaries. For example, the reconstruction unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0091] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0092] The information provision unit can generate detailed information about the selected organism based on academic data and research results. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit can automatically collect relevant information and reflect it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0093] The dialogue unit can provide users with the experience of having a dialogue or interaction with ancient organisms. For example, when the generation AI generates a summary, the dialogue unit can automatically collect relevant background information and refer to it to understand the context. For example, the dialogue unit collects related news articles and academic papers. The dialogue unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the dialogue unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The dialogue unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the dialogue unit can automatically collect relevant information and incorporate it into the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the dialogue unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the dialogue unit can collect the latest news articles and social media posts from the Internet and use them as sources of information to understand the context. The dialogue unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the dialogue unit can evaluate the quality of the generated summaries and update the topic model based on the evaluation results. This allows the dialogue unit to always understand the context based on the latest information and provide more accurate summaries.

[0094] The reconstruction unit can utilize the generative AI to reconstruct ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the reconstruction unit collects the latest news articles and social media posts on the Internet and uses them as sources of information for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summary. For example, the reconstruction unit evaluates the quality of the generated summary and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0095] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0096] The information provision unit can provide information about the characteristics and behavioral patterns of ancient organisms selected by the generation AI. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0097] The dialogue unit can simulate the behavior of a selected ancient organism in response to user input. For example, when the generation AI generates a summary, the dialogue unit can automatically collect relevant background information and refer to it to understand the context. For example, the dialogue unit collects related news articles and academic papers. The dialogue unit can also use topic models to understand the context when the generation AI generates a summary. For example, the dialogue unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The dialogue unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the dialogue unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the dialogue unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the dialogue unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The dialogue unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the dialogue unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the dialogue unit to always understand the context based on the latest information and provide more accurate summaries.

[0098] The reconstruction unit can utilize the generative AI to generate skeletal models and ecological simulations of ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the reconstruction unit collects the latest news articles and social media posts from the Internet and uses them as information sources for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summaries. For example, the reconstruction unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0099] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0100] The information provision unit can generate detailed information about the selected organism based on academic data and research results. For example, when the generation AI generates a summary, the information provision unit can automatically collect relevant background information and refer to it to understand the context. For example, the information provision unit collects related news articles and academic papers. The information provision unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the information provision unit extracts related keywords and phrases based on the topic model. The topic model can be realized using techniques such as LDA (Latent Dirichlet Allocation), non-negative matrix factorization, or topic clustering. The information provision unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the information provision unit can automatically collect relevant information and reflect it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the information provision unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the information provider collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The information provider can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the information provider evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the information provider to always understand the context based on the latest information and provide more accurate summaries.

[0101] The dialogue unit can provide users with the experience of having a dialogue or interaction with ancient organisms. For example, when the generation AI generates a summary, the dialogue unit can automatically collect relevant background information and refer to it to understand the context. For example, the dialogue unit collects related news articles and academic papers. The dialogue unit can also use a topic model to understand the context when the generation AI generates a summary. For example, the dialogue unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The dialogue unit can also build a system for understanding the context by referring to relevant background information and topic models when the generation AI generates a summary. For example, the dialogue unit can automatically collect relevant information and incorporate it into the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the dialogue unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the dialogue unit can collect the latest news articles and social media posts from the Internet and use them as sources of information to understand the context. The dialogue unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the dialogue unit can evaluate the quality of the generated summaries and update the topic model based on the evaluation results. This allows the dialogue unit to always understand the context based on the latest information and provide more accurate summaries.

[0102] The reconstruction unit can utilize the generative AI to reconstruct ancient organisms. For example, when the generative AI generates a summary, the reconstruction unit can automatically collect relevant background information and refer to it to understand the context. For example, the reconstruction unit collects related news articles and academic papers. The reconstruction unit can also use a topic model to understand the context when the generative AI generates a summary. For example, the reconstruction unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as LDA (Latent Dirichlet Allocation), nonnegative matrix factorization, or topic clustering. The reconstruction unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the reconstruction unit automatically collects relevant information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the reconstruction unit can collect relevant information in real time to understand the context and reflect it in the summary. For example, the reconstruction unit collects the latest news articles and social media posts on the Internet and uses them as sources of information for understanding the context. The reconstruction unit can also use the topic model to create a feedback loop to improve the accuracy of the summary. For example, the reconstruction unit evaluates the quality of the generated summary and updates the topic model based on the evaluation results. This allows the reconstruction unit to always understand the context based on the latest information and provide more accurate summaries.

[0103] The sharing unit can share the generated models and simulation results with researchers and experts. For example, when the generative AI generates a summary, the sharing unit can automatically collect relevant background information and refer to it to understand the context. For example, the sharing unit collects related news articles and academic papers. The sharing unit can also use topic models to understand the context when the generative AI generates a summary. For example, the sharing unit extracts related keywords and phrases based on the topic model. Topic models are realized using techniques such as latent Dirichlet allocation (LDA), nonnegative matrix factorization, or topic clustering. The sharing unit can also build a system for understanding the context by referring to relevant background information and topic models when the generative AI generates a summary. For example, the sharing unit automatically collects relevant information and incorporates it into the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. Furthermore, the sharing unit can collect relevant information in real time to understand the context and incorporate it into the summary. For example, the sharing unit collects the latest news articles and social media posts from the Internet and uses them as sources of information for understanding the context. The sharing unit can also use the topic model to create a feedback loop to improve the accuracy of summaries. For example, the sharing unit evaluates the quality of the generated summaries and updates the topic model based on the evaluation results. This allows the sharing unit to always understand the context based on the latest information and provide more accurate summaries.

[0104] The information providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is excited, the information providing unit can provide information with a visually stimulating effect. If the user is relaxed, the information providing unit can also provide information in a calm tone. If the user is curious, the information providing unit can also provide detailed information. This allows for more appropriate information provision by adjusting the way information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated user emotions. For example, if the user is excited, the dialogue unit can use a lively tone to communicate. If the user is relaxed, the dialogue unit can also use a calm tone to communicate. If the user is curious, the dialogue unit can also provide a dialogue that includes detailed information. This allows for adjusting the way the dialogue is expressed based on the user's emotions, thereby providing a more appropriate dialogue experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] The restoration unit can estimate the user's emotions and adjust the restoration method based on the estimated user emotions. For example, if the user is excited, the restoration unit can perform restoration with a visually stimulating effect. If the user is relaxed, the restoration unit can also perform restoration in a calm tone. If the user is curious, the restoration unit can also perform restoration including detailed information. This allows for adjusting the restoration method according to the user's emotions to provide a more appropriate restoration experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The sharing unit can estimate the user's emotions and adjust the way in which information to be shared is presented based on the estimated user's emotions. For example, if the user is excited, the sharing unit can share information with a visually stimulating effect. If the user is relaxed, the sharing unit can also share information in a calm tone. If the user is curious, the sharing unit can also share detailed information. This allows for more appropriate information sharing by adjusting the way in which information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] When providing information, the information providing unit can adjust the level of detail of the information based on the importance of the ancient organism. For example, the information providing unit provides detailed information for important ancient organisms. The information providing unit can also provide concise information for general ancient organisms. The information providing unit can also provide specialized information for ancient organisms that are academically important. In this way, by adjusting the level of detail of the information based on the importance of the ancient organism, more appropriate information can be provided.

[0109] The dialogue unit can adjust the level of detail of the dialogue based on the behavioral patterns of the ancient creatures during dialogue. For example, in the case of a carnivorous dinosaur, the dialogue unit can provide detailed dialogue about hunting behavior patterns. In the case of a herbivorous dinosaur, the dialogue unit can also provide detailed dialogue about feeding habits and habitats. In the case of a marine creature, the dialogue unit can also provide detailed dialogue about swimming styles and habitats. This allows for adjusting the level of detail of the dialogue based on the behavioral patterns of the ancient creatures to provide a more appropriate dialogue experience.

[0110] During restoration, the restoration unit can adjust the level of detail of the restoration based on the skeletal model of the ancient organism. For example, the restoration unit performs detailed restoration for skeletal models of important ancient organisms. The restoration unit can also perform simple restoration for skeletal models of general ancient organisms. The restoration unit can also perform specialized restoration for skeletal models of academically important ancient organisms. In this way, by adjusting the level of detail of the restoration based on the skeletal model of the ancient organism, a more appropriate restoration experience can be provided.

[0111] The sharing unit can adjust the level of detail of sharing based on the research data of ancient organisms when sharing. For example, the sharing unit shares detailed information for important research data of ancient organisms. The sharing unit can also share concise information for general research data of ancient organisms. The sharing unit can also share specialized information for academically important research data of ancient organisms. In this way, adjusting the level of detail of sharing based on the research data of ancient organisms enables more appropriate information sharing.

[0112] When providing information, the information providing unit can apply different information providing algorithms depending on the category of the ancient organism. For example, for carnivorous dinosaurs, the information providing unit can provide information on hunting behavior patterns. For herbivorous dinosaurs, the information providing unit can also provide information on feeding habits and habitats. For marine organisms, the information providing unit can also provide information on swimming styles and habitats. In this way, by applying information providing algorithms depending on the category of ancient organisms, more appropriate information can be provided.

[0113] The dialogue unit can apply different dialogue algorithms depending on the category of the ancient creature during dialogue. For example, in the case of a carnivorous dinosaur, the dialogue unit applies a dialogue algorithm related to hunting behavior patterns. In addition, in the case of a herbivorous dinosaur, the dialogue unit can also apply a dialogue algorithm related to feeding habits and habitats. In addition, in the case of a marine creature, the dialogue unit can also apply a dialogue algorithm related to swimming style and habitats. In this way, by applying a dialogue algorithm according to the category of the ancient creature, a more appropriate dialogue experience can be provided.

[0114] During restoration, the restoration unit can apply different restoration algorithms depending on the category of the ancient organism. For example, in the case of a carnivorous dinosaur, the restoration unit applies a restoration algorithm based on its hunting behavior pattern. In addition, in the case of a herbivorous dinosaur, the restoration unit can also apply a restoration algorithm based on its feeding habits and habitat. In addition, in the case of a marine organism, the restoration unit can also apply a restoration algorithm based on its swimming style and habitat. In this way, by applying a restoration algorithm according to the category of the ancient organism, a more appropriate restoration experience can be provided.

[0115] When sharing, the sharing unit can apply different sharing algorithms depending on the category of the ancient organism. For example, in the case of a carnivorous dinosaur, the sharing unit can share information about hunting behavior patterns. In the case of a herbivorous dinosaur, the sharing unit can also share information about its feeding habits and habitat. In the case of a marine organism, the sharing unit can also share information about its swimming style and habitat. This allows for more appropriate information sharing by applying a sharing algorithm depending on the category of the ancient organism.

[0116] When providing information, the information providing unit can improve the accuracy of the information by referring to the user's past information provision results. The information providing unit improves the accuracy of the information, for example, based on information provision methods that the user has given high ratings to in the past. The information providing unit can also improve the accuracy of the information by analyzing feedback provided by the user in the past. The information providing unit can also improve the accuracy of information belonging to a specific category based on the user's past information provision results. In this way, the accuracy of the information can be improved by referring to the user's past information provision results.

[0117] The dialogue unit can improve the accuracy of the dialogue by referring to the user's past dialogue results during the dialogue. For example, the dialogue unit improves the accuracy of the dialogue based on dialogue methods that the user has given high ratings to in the past. The dialogue unit can also improve the accuracy of the dialogue by analyzing feedback provided by the user in the past. The dialogue unit can also improve the accuracy of dialogue belonging to a specific category based on the user's past dialogue results. In this way, the accuracy of the dialogue can be improved by referring to the user's past dialogue results.

[0118] During restoration, the restoration unit can improve the accuracy of restoration by referring to the user's past restoration results. The restoration unit improves the accuracy of restoration, for example, based on restoration methods that the user has given high ratings to in the past. The restoration unit can also improve the accuracy of restoration by analyzing feedback provided by the user in the past. The restoration unit can also improve the accuracy of restoration belonging to a specific category based on the user's past restoration results. In this way, the accuracy of restoration can be improved by referring to the user's past restoration results.

[0119] When sharing, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing results. The sharing unit improves the accuracy of sharing, for example, based on sharing methods that the user has given high ratings to in the past. The sharing unit can also improve the accuracy of sharing by analyzing feedback that the user has provided in the past. The sharing unit can also improve the accuracy of sharing that belongs to a specific category based on the user's past sharing results. In this way, the accuracy of sharing can be improved by referring to the user's past sharing results.

[0120] The information providing unit can estimate the user's emotions and adjust the length of the information based on the estimated user's emotions. For example, if the user is in a hurry, the information providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the information providing unit can provide longer information with detailed explanations. Furthermore, if the user is excited, the information providing unit can provide information with visually stimulating effects. This allows for more appropriate information provision by adjusting the length of the information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated user emotions. For example, if the user is in a hurry, the dialogue unit can hold a short, to-the-point dialogue. If the user is relaxed, the dialogue unit can hold a longer dialogue including detailed explanations. If the user is excited, the dialogue unit can hold a dialogue that adds visually stimulating effects. This allows for adjusting the length of the dialogue according to the user's emotions, thereby providing a more appropriate dialogue experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0122] The restoration unit can estimate the user's emotions and determine restoration priorities based on the estimated user emotions. For example, if the user is excited, the restoration unit can prioritize visually stimulating restoration. Furthermore, if the user is relaxed, the restoration unit can prioritize calm restoration. Furthermore, if the user is curious, the restoration unit can prioritize detailed restoration. This allows for determining restoration priorities according to the user's emotions, thereby providing a more appropriate restoration experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The sharing unit can estimate the user's emotions and adjust the length of the information to be shared based on the estimated user emotions. For example, if the user is in a hurry, the sharing unit can share short, to-the-point information. If the user is relaxed, the sharing unit can also share longer information with detailed explanations. If the user is excited, the sharing unit can also share information with visually stimulating effects. This allows for more appropriate information sharing by adjusting the length of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] When providing information, the information providing unit can determine the priority of information based on the time when the ancient organism was selected. For example, the information providing unit provides information preferentially for ancient organisms recently selected by the user. The information providing unit can also provide information secondarily for ancient organisms previously selected by the user. The information providing unit can also provide information last for ancient organisms that the user has not yet selected. This enables more appropriate information to be provided by determining the priority of information based on the time when the ancient organism was selected.

[0125] During dialogue, the dialogue unit can determine dialogue priorities based on when the ancient creature was selected. For example, the dialogue unit prioritizes dialogue with an ancient creature recently selected by the user. The dialogue unit can also prioritize dialogue with an ancient creature previously selected by the user. The dialogue unit can also prioritize dialogue with an ancient creature that has not yet been selected by the user last. In this way, by determining dialogue priorities based on when the ancient creature was selected, a more appropriate dialogue experience can be provided.

[0126] During restoration, the restoration unit can determine restoration priorities based on the time of selection of the ancient creature. For example, the restoration unit prioritizes restoration of ancient creatures recently selected by the user. The restoration unit can also prioritize restoration of ancient creatures previously selected by the user. The restoration unit can also restore ancient creatures that have not yet been selected by the user last. In this way, by determining restoration priorities based on the time of selection of the ancient creature, a more appropriate restoration experience can be provided.

[0127] When sharing, the sharing unit can determine the priority of sharing based on when the ancient creature was selected. For example, the sharing unit prioritizes sharing information about ancient creatures recently selected by the user. The sharing unit can also prioritize sharing information about ancient creatures previously selected by the user. The sharing unit can also share information about ancient creatures that the user has not yet selected last. This allows for more appropriate information sharing by determining the priority of sharing based on when the ancient creature was selected.

[0128] When providing information, the information providing unit can adjust the order of information based on the relevance of ancient organisms. For example, the information providing unit preferentially provides information related to the ancient organism selected by the user. The information providing unit can also provide related information that the user is likely to be interested in next. The information providing unit can also provide related information that the user is not yet aware of last. In this way, by adjusting the order of information based on the relevance of ancient organisms, more appropriate information can be provided.

[0129] During the dialogue, the dialogue unit can adjust the order of dialogues based on the relevance of the ancient creatures. For example, the dialogue unit prioritizes dialogues related to the ancient creature selected by the user. The dialogue unit can also next carry out related dialogues that the user is likely to be interested in. The dialogue unit can also carry out related dialogues that the user is not yet familiar with last. In this way, by adjusting the order of dialogues based on the relevance of the ancient creatures, a more appropriate dialogue experience can be provided.

[0130] During the restoration, the restoration unit can improve the accuracy of the restoration by referring to literature related to the ancient organism. For example, the restoration unit performs a detailed restoration based on literature related to important ancient organisms. The restoration unit can also perform a concise restoration based on literature related to general ancient organisms. The restoration unit can also perform a specialized restoration based on literature related to academically important ancient organisms. In this way, the accuracy of the restoration can be improved by referring to literature related to ancient organisms.

[0131] When sharing, the shared section can improve the accuracy of sharing by referring to literature related to ancient organisms. For example, the shared section shares detailed information based on literature related to important ancient organisms. The shared section can also share concise information based on literature related to general ancient organisms. The shared section can also share specialized information based on literature related to academically important ancient organisms. In this way, by referring to literature related to ancient organisms, the accuracy of sharing can be improved.

[0132] When providing information, the information providing unit can adjust the use of technical terms in the information according to the user's level of expertise. For example, if the user is an expert, the information providing unit can provide information that uses a lot of technical terms. Furthermore, if the user is a general public, the information providing unit can also provide information that avoids technical terms. Furthermore, if the user is a student, the information providing unit can also provide information that includes educational explanations. This makes it possible to provide more appropriate information by providing information according to the user's level of expertise.

[0133] During the dialogue, the dialogue unit can adjust the use of technical terms in the dialogue depending on the user's level of expertise. For example, if the user is an expert, the dialogue unit can use a lot of technical terms. Also, if the user is a general public, the dialogue unit can avoid using technical terms. Also, if the user is a student, the dialogue unit can include educational explanations. This allows for a more appropriate dialogue experience by conducting a dialogue that is appropriate for the user's level of expertise.

[0134] The restoration unit can take into account the market value of the ancient organisms during restoration. For example, the restoration unit can perform detailed restoration for ancient organisms with high market value. The restoration unit can also perform simple restoration for ancient organisms with low market value. The restoration unit can also adjust the level of detail of the restoration in response to fluctuations in market value. This allows for a more appropriate restoration experience by taking into account the market value of the ancient organisms.

[0135] When sharing, the common part can share information taking into account the market value of the ancient organisms. For example, the common part can share detailed information about ancient organisms with high market value. The common part can also share simple information about ancient organisms with low market value. The common part can also adjust the level of detail of the sharing in accordance with fluctuations in market value. This allows for more appropriate information sharing by taking into account the market value of the ancient organisms. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, information providing unit, dialogue unit, restoration unit, and sharing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14, and when a user selects a specific ancient creature, the user can input the name and characteristics of the selected creature to the generation AI. The information providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides detailed information about the characteristics and behavioral patterns of the ancient creature selected using the generation AI. The dialogue unit is realized by the control unit 46A of the smart device 14, and simulates the behavior of the selected ancient creature in response to user input. The restoration unit is realized by the specific processing unit 290 of the data processing device 12, and restores the ancient creature using the generation AI. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the generated model and simulation results with researchers and experts. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, information providing unit, dialogue unit, restoration unit, and sharing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214, and when a user selects a specific ancient creature, the user can input the name and characteristics of the selected creature to the generation AI. The information providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides detailed information about the characteristics and behavioral patterns of the ancient creature selected using the generation AI. The dialogue unit is realized by the control unit 46A of the smart glasses 214, and simulates the behavior of the selected ancient creature in response to user input. The restoration unit is realized by the specific processing unit 290 of the data processing device 12, and utilizes the generation AI to restore the ancient creature. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the generated model and simulation results with researchers and experts. === Hard Collateral 1-3 === Each of the multiple elements, including the selection unit, information providing unit, dialogue unit, restoration unit, and sharing unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset-type terminal 314, and when a user selects a specific ancient creature, the user can input the name and characteristics of the selected creature to the generation AI. The information providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides detailed information about the characteristics and behavioral patterns of the ancient creature selected using the generation AI. The dialogue unit is realized by the control unit 46A of the headset-type terminal 314, and simulates the behavior of the selected ancient creature in response to user input. The restoration unit is realized by the specific processing unit 290 of the data processing device 12, and utilizes the generation AI to restore the ancient creature. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the generated model and simulation results with researchers and experts. === Hard Collateral 1-4 === Each of the multiple elements, including the selection unit, information provision unit, dialogue unit, restoration unit, and sharing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414, and when a user selects a specific ancient creature, the user can input the name and characteristics of the selected creature to the generation AI. The information provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides detailed information about the characteristics and behavioral patterns of the ancient creature selected using the generation AI. The dialogue unit is realized by the control unit 46A of the robot 414, and simulates the behavior of the selected ancient creature in response to user input. The restoration unit is realized by the specific processing unit 290 of the data processing device 12, and utilizes the generation AI to restore the ancient creature. The sharing unit is realized by the specific processing unit 290 of the data processing device 12, and shares the generated model and simulation results with researchers and experts.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The selection unit can acquire the user's current location information and preferentially present ancient creatures related to that location. For example, if the user is in a specific area, ancient creatures discovered in that area are preferentially presented. The selection unit can also present ancient creatures related to museums or ruins the user is visiting. Furthermore, the selection unit can present ancient creatures related to events or tours the user is participating in. This makes it possible to provide a selection of ancient creatures that are more relevant to the user based on the user's current location information.

[0138] The selection unit can estimate the user's emotions and present a selection of ancient creatures based on the estimated user's emotions. For example, if the user is excited, ancient creatures with active behaviors can be presented preferentially. Alternatively, if the user is relaxed, ancient creatures with calm behaviors can be presented preferentially. Furthermore, if the user is curious, ancient creatures with unusual characteristics can be presented preferentially. This makes it possible to provide a more appropriate experience by presenting a selection of ancient creatures according to the user's emotions.

[0139] The information providing unit can analyze the user's past search history and provide information on related ancient organisms preferentially. For example, information related to ancient organisms that the user has searched for in the past can be provided preferentially. The information providing unit can also provide information on ancient organisms related to themes in which the user has shown interest in the past. Furthermore, the information providing unit can also provide information on ancient organisms related to events or tours that the user has participated in in the past. This makes it possible to provide more relevant information based on the user's past search history.

[0140] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated user emotions. For example, if the user is excited, the dialogue can be conducted in a lively and energetic tone. If the user is relaxed, the dialogue can be conducted in a calm and relaxed tone. Furthermore, if the user is curious, the dialogue can be conducted with detailed information. This makes it possible to provide a dialogue experience that suits the user's emotions.

[0141] The restoration unit can estimate the user's emotions and adjust the restoration method and level of detail based on the estimated user's emotions. For example, if the user is excited, restoration can be performed with a visually stimulating effect. If the user is relaxed, restoration can be performed in a calm tone. Furthermore, if the user is curious, restoration can be performed with detailed information. This makes it possible to provide a restoration experience that suits the user's emotions.

[0142] The sharing unit can estimate the user's emotions and adjust the way the information to be shared is presented based on the estimated user's emotions. For example, if the user is excited, the sharing unit can share information with a visually stimulating effect. If the user is relaxed, the sharing unit can share information in a calm tone. Furthermore, if the user is curious, the sharing unit can share detailed information. This makes it possible to share information according to the user's emotions.

[0143] The selection unit can acquire the user's current weather information and preferentially present ancient creatures related to that weather. For example, on a rainy day, aquatic creatures or ancient creatures living in wetlands can be preferentially presented. On a sunny day, terrestrial creatures or ancient creatures living in dry areas can be preferentially presented. Furthermore, the selection unit can present ancient creatures according to the season. This makes it possible to provide a selection of ancient creatures that are more relevant to the user based on the current weather information.

[0144] The information providing unit can analyze the user's current learning progress and provide information of appropriate difficulty. For example, it can provide basic information to beginners and detailed information to advanced learners. The information providing unit can also provide information on what the user should learn next based on what the user has learned in the past. Furthermore, the information providing unit can also provide information according to the user's learning goals. This makes it possible to provide information according to the user's learning progress.

[0145] The dialogue unit can acquire the user's current activity status and conduct dialogue appropriate to that status. For example, if the user is exercising, the dialogue unit can conduct a short, to-the-point dialogue. If the user is relaxed, the dialogue unit can conduct a dialogue including detailed explanations. Furthermore, if the user is concentrating, the dialogue unit can conduct a dialogue including specialized information. This makes it possible to provide a dialogue experience appropriate to the user's current activity status.

[0146] The restoration unit can select the optimal restoration method taking into account the performance of the user's current device. For example, detailed restoration is performed on a high-performance device, and simple restoration is performed on a low-performance device. The restoration unit can also select a restoration method according to the screen size of the user's device. Furthermore, the restoration unit can select a restoration method according to the remaining battery power of the user's device. This makes it possible to provide an optimal restoration experience according to the performance of the user's device.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: In the selection section, the user selects a specific ancient creature. For example, the user can input the name and characteristics of the selected creature into the generation AI. Step 2: The information provider uses the generation AI to provide detailed information about the characteristics and behavioral patterns of the selected ancient organism. For example, the generation AI generates detailed information about the selected organism based on academic data and research results. Step 3: The dialogue unit simulates the behavior of the selected ancient creature according to the user's input. For example, if the user inputs "roar" to a Tyrannosaurus, the generation AI will simulate the Tyrannosaurus' roaring behavior and provide the user with that experience. Step 4: The reconstruction unit uses generative AI to reconstruct ancient organisms. For example, generative AI generates a skeletal model and ecological simulation of a Tyrannosaurus rex. Step 5: The sharing department shares the generated models and simulation results with researchers and experts. For example, researchers can use the generated Tyrannosaurus skeletal model and ecological simulation.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a 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.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] 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.

[0212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection section in which a user selects a particular ancient organism; an information providing unit that provides detailed information about the characteristics and behavioral patterns of the ancient organisms selected by the selection unit; a dialogue unit that allows a user to have a dialogue or interaction with an ancient organism based on the information provided by the information providing unit; A restoration department that uses generation AI to restore ancient organisms; a sharing unit that shares the model and simulation results generated by the restoration unit with researchers and experts. A system characterized by:

2. The selection unit Estimate the user's emotions and present a selection of ancient creatures based on the estimated user emotions.

2. The system of claim 1.

3. The selection unit Analyze the user's past selection history and suggest appropriate ancient creature options 2. The system of claim 1.

4. The selection unit Filter ancient creature selection based on the user's current interests 2. The system of claim 1.

5. The selection unit When selecting an ancient creature, provide an appropriate selection method depending on the user's input method.

2. The system of claim 1.

6. The information providing unit Provide information about the characteristics and behavioral patterns of ancient creatures selected by generative AI 2. The system of claim 1.

7. The dialogue unit Simulate the behavior of selected ancient creatures based on user input 2. The system of claim 1.

8. The restoration unit is Generating skeletal models and ecological simulations of ancient organisms using generative AI 2. The system of claim 1.

Images