system

Abstract

The system according to this embodiment aims to accurately predict and reconstruct the genetic information of dinosaurs. [Solution] The system according to the embodiment comprises an acquisition unit, a prediction unit, a growth prediction unit, a supplementation unit, and a verification unit. The acquisition unit acquires genetic information of dinosaurs. The prediction unit predicts genetic information using a generative AI based on the genetic information acquired by the acquisition unit. The growth prediction unit performs growth prediction using a generative AI based on the information predicted by the prediction unit. The supplementation unit supplements genetic information using CRISPR technology based on the information predicted by the growth prediction unit. The verification unit verifies the genetic information supplemented by the supplementation unit using a DNA language model.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to accurately predict and restore dinosaur gene information.

[0005] The system according to the embodiment aims to accurately predict and restore dinosaur gene information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a prediction unit, a growth prediction unit, a supplementation unit, and a verification unit. The acquisition unit acquires the genetic information of dinosaurs. The prediction unit predicts the genetic information using generative AI based on the genetic information acquired by the acquisition unit. The growth prediction unit performs growth prediction using generative AI based on the information predicted by the prediction unit. The supplementation unit supplements the genetic information using CRISPR technology based on the information predicted by the growth prediction unit. The verification unit verifies the genetic information supplemented by the supplementation unit using a DNA language model. [Effects of the Invention]

[0007] The system according to this embodiment can accurately predict and reconstruct the genetic information of dinosaurs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. [[ID=I6]]

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dinosaur restoration system according to an embodiment of the present invention is a system that revives dinosaurs in the modern age by combining generative AI and biotechnology. This dinosaur restoration system analyzes DNA sequences from dinosaur fossils to obtain dinosaur-specific genetic information. Next, generative AI is used to learn from large-scale genetic data of reptiles and birds closely related to dinosaurs and predict the genetic information of dinosaurs. Furthermore, diffusion modeling is used to predict growth over time and restore the genes of dinosaurs. The restored genetic information is supplemented using genome editing technologies such as CRISPR. Finally, accuracy is improved by having DNA language models debate to confirm that the appropriate bases are folded correctly. This technology makes it possible to revive dinosaurs in the modern age. For example, DNA sequences are analyzed from dinosaur fossils. In this process, DNA fragments extracted from the fossils are analyzed to obtain dinosaur-specific genetic information. For example, DNA fragments extracted from the fossils of a specific dinosaur species are analyzed to identify their gene sequences. Next, generative AI is used to learn from large-scale genetic data of reptiles and birds closely related to dinosaurs. Based on this genetic data, the generative AI predicts the genetic information of dinosaurs. For example, the generative AI learns from the genetic data of existing birds and reptiles to estimate the gene sequence of dinosaurs. Furthermore, it uses diffusion modeling to predict growth over time and reconstruct the dinosaur's genes. The generative AI simulates genetic changes over time and predicts the growth process of dinosaurs. This makes it possible to reconstruct the dinosaur's genetic information. The reconstructed genetic information is supplemented using genome editing technologies such as CRISPR. For example, missing gene portions are supplemented with CRISPR technology to reconstruct the complete gene sequence. Finally, accuracy is improved by having DNA language models debate to confirm that the appropriate bases are folded correctly. The generative AI uses DNA language models to verify the accuracy of the gene sequence. This makes it possible to improve the accuracy of the dinosaur's genetic information. This technology makes it possible to bring dinosaurs back to life in the modern age, differentiating oneself from other companies as part of space tourism. Moreover, by reviving them on extraterrestrial planets, it is possible to protect Earth's ecosystems and public safety.This will enable the dinosaur reconstruction system to efficiently acquire, predict, predict growth, supplement, and verify the genetic information of dinosaurs.

[0029] The dinosaur restoration system according to this embodiment comprises an acquisition unit, a prediction unit, a growth prediction unit, a supplementation unit, and a verification unit. The acquisition unit acquires the genetic information of dinosaurs. The acquisition unit, for example, analyzes DNA sequences from dinosaur fossils to acquire dinosaur-specific genetic information. For example, the acquisition unit uses next-generation sequencing technology to analyze DNA fragments extracted from dinosaur fossils and identifies the gene sequence of a specific dinosaur species. The acquisition unit can also use PCR technology to amplify DNA fragments extracted from fossils and acquire genetic information of a specific dinosaur species. Furthermore, the acquisition unit can also use microarray technology to analyze DNA fragments extracted from fossils and acquire genetic information of a specific dinosaur species. The prediction unit uses generative AI to predict genetic information based on the genetic information acquired by the acquisition unit. For example, the prediction unit uses generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and predicts the genetic information of dinosaurs. For example, the prediction unit learns genetic data of existing birds and reptiles and estimates the gene sequence of dinosaurs. Furthermore, the prediction unit can use generative AI to comprehensively learn avian and reptile genetic data and simultaneously predict the gene sequences of multiple dinosaur species. In addition, the prediction unit can use generative AI to predict gene mutations under specific environmental conditions. The growth prediction unit uses generative AI to predict growth based on the information predicted by the prediction unit. For example, the growth prediction unit uses generative AI to predict growth over time and reconstruct the dinosaur's genes. For example, the growth prediction unit uses generative AI to simulate the dinosaur's growth process and predict gene changes over time. The growth prediction unit can also use generative AI to predict growth under specific environmental conditions and reconstruct genetic information. Furthermore, the growth prediction unit can use generative AI to estimate the dinosaur's emotions and predict growth based on the estimated emotions. The completion unit uses CRISPR technology to complete the genetic information based on the information predicted by the growth prediction unit. For example, the completion unit uses CRISPR technology to complete missing gene portions. For example, the completion unit uses CRISPR technology to target and accurately complete missing gene portions of specific dinosaur species.Furthermore, the complementation unit can use CRISPR technology to complement gene mutations under specific environmental conditions. Additionally, the complementation unit can use CRISPR technology to estimate the emotions of dinosaurs and complement missing gene portions based on the estimated emotions. The validation unit uses a DNA language model to validate the gene information complemented by the complementation unit. For example, the validation unit uses the DNA language model to confirm that the appropriate bases are correctly folded. For instance, the validation unit can use the DNA language model to analyze the gene sequence of a specific dinosaur species and confirm that the appropriate bases are correctly folded. The validation unit can also use the DNA language model to simultaneously analyze the gene sequences of multiple dinosaur species and confirm that the appropriate bases are correctly folded. Furthermore, the validation unit can use the DNA language model to confirm that the appropriate bases are correctly folded under specific environmental conditions. This enables the dinosaur reconstruction system according to the embodiment to efficiently acquire, predict, predict growth, complement, and validate dinosaur gene information.

[0030] The acquisition unit can analyze DNA sequences from dinosaur fossils and obtain dinosaur-specific genetic information. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils to obtain genetic information for a specific dinosaur species. For example, the acquisition unit can use next-generation sequencing technology to analyze DNA fragments extracted from dinosaur fossils and identify the gene sequence of a specific dinosaur species. The acquisition unit can also use PCR technology to amplify DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. Furthermore, the acquisition unit can use microarray technology to analyze DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. This allows for the acquisition of accurate genetic information from dinosaur fossils. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of genetic information for a specific dinosaur species.

[0031] The prediction unit can use generative AI to learn large-scale genetic data of reptiles or birds closely related to dinosaurs and predict the genetic information of dinosaurs. For example, the prediction unit can use generative AI to learn large-scale genetic data of reptiles or birds closely related to dinosaurs and predict the genetic information of dinosaurs. For example, the prediction unit can learn the genetic data of existing birds and reptiles and estimate the gene sequences of dinosaurs. The prediction unit can also use generative AI to comprehensively learn the genetic data of birds and reptiles and simultaneously predict the gene sequences of multiple dinosaur species. Furthermore, the prediction unit can use generative AI to predict gene mutations under specific environmental conditions. This improves the accuracy of predicting dinosaur genetic information by using generative AI. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input large-scale genetic data of reptiles or birds closely related to dinosaurs into the generative AI and have the generative AI perform the prediction of dinosaur genetic information.

[0032] The growth prediction unit can use generative AI to predict growth over time and reconstruct the genes of a dinosaur. For example, the growth prediction unit can use generative AI to predict growth over time and reconstruct the genes of a dinosaur. For example, the growth prediction unit can use generative AI to simulate the growth process of a dinosaur and predict changes in genes over time. The growth prediction unit can also use generative AI to predict growth under specific environmental conditions and reconstruct genetic information. Furthermore, the growth prediction unit can use generative AI to estimate the emotions of a dinosaur and make growth predictions based on the estimated emotions of the dinosaur. In this way, the growth process of a dinosaur can be accurately predicted by using generative AI. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without using AI. For example, the growth prediction unit can input the growth process of a dinosaur into generative AI and have the generative AI perform the growth prediction.

[0033] The complementation unit can complement missing gene portions using CRISPR technology. For example, the complementation unit can complement missing gene portions using CRISPR technology. For example, the complementation unit can use CRISPR technology to target and precisely complement missing gene portions of a specific dinosaur species. The complementation unit can also use CRISPR technology to repair missing gene portions of a specific dinosaur species and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species and restore a functional gene. Thus, by using CRISPR technology, missing gene portions can be precisely complemented. Some or all of the above processes in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input the missing gene portion into a generating AI and have the generating AI perform the complementation.

[0034] The validation unit can use a DNA language model to verify that the bases are correctly folded. For example, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to analyze the gene sequence of a specific dinosaur species and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simultaneously analyze the gene sequences of multiple dinosaur species and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded under specific environmental conditions. This allows for increased accuracy of gene sequences by using a DNA language model. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input a gene sequence into a generating AI and have the generating AI perform the base folding verification.

[0035] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information for a specific dinosaur species. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information for a specific dinosaur species. For example, the acquisition unit can use next-generation sequencing technology to analyze DNA fragments extracted from dinosaur fossils and identify the gene sequence of a specific dinosaur species. The acquisition unit can also use PCR technology to amplify DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. Furthermore, the acquisition unit can use microarray technology to analyze DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. This allows for the accurate acquisition of genetic information for a specific dinosaur species. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of genetic information for a specific dinosaur species.

[0036] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and simultaneously acquire genetic information for multiple dinosaur species. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and simultaneously acquire genetic information for multiple dinosaur species. Alternatively, the acquisition unit can use metagenomic analysis technology to analyze DNA fragments extracted from fossils and simultaneously acquire genetic information for multiple dinosaur species. Furthermore, the acquisition unit can use barcode sequencing technology to analyze DNA fragments extracted from fossils and simultaneously acquire genetic information for multiple dinosaur species. In addition, the acquisition unit can use shotgun sequencing technology to analyze DNA fragments extracted from fossils and simultaneously acquire genetic information for multiple dinosaur species. This allows for the simultaneous acquisition of genetic information for multiple dinosaur species. Some or all of the above-described processes in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of genetic information for multiple dinosaur species.

[0037] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain gene mutations under specific environmental conditions. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain gene mutations under specific environmental conditions. For example, the acquisition unit can use environmental DNA analysis technology to analyze DNA fragments extracted from fossils and obtain gene mutations under specific environmental conditions. The acquisition unit can also use epigenetics analysis technology to analyze DNA fragments extracted from fossils and obtain gene mutations under specific environmental conditions. Furthermore, the acquisition unit can use methylation analysis technology to analyze DNA fragments extracted from fossils and obtain gene mutations under specific environmental conditions. This makes it possible to obtain gene mutations under specific environmental conditions. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of gene mutations under specific environmental conditions.

[0038] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's habitat information. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's habitat information. For example, the acquisition unit can analyze DNA fragments extracted from fossils and identify genetic markers related to the dinosaur's habitat. The acquisition unit can also obtain genetic information under specific habitat conditions based on genetic markers related to the dinosaur's habitat. Furthermore, the acquisition unit can analyze genetic markers related to the dinosaur's habitat and obtain genetic mutations according to habitat conditions. This allows for the acquisition of genetic information while considering the dinosaur's habitat information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the consideration of dinosaur habitat information and the acquisition of genetic information.

[0039] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's diet. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's diet. For example, the acquisition unit can analyze DNA fragments extracted from fossils and identify genetic markers related to the dinosaur's diet. The acquisition unit can also obtain genetic information under specific dietary conditions based on genetic markers related to the dinosaur's diet. Furthermore, the acquisition unit can analyze genetic markers related to the dinosaur's diet and obtain genetic mutations according to dietary conditions. This allows for the acquisition of genetic information while considering the dinosaur's diet. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the consideration of the dinosaur's diet and the acquisition of genetic information.

[0040] The prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and simultaneously predict the genetic information of multiple dinosaur species. For example, the prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and simultaneously predict the genetic information of multiple dinosaur species. For example, the prediction unit can learn the genetic data of existing birds and reptiles and simultaneously predict the genetic sequences of multiple dinosaur species. The prediction unit can also use generative AI to comprehensively learn the genetic data of birds and reptiles and simultaneously predict the genetic sequences of multiple dinosaur species. Furthermore, the prediction unit can use generative AI to predict genetic mutations under specific environmental conditions. This allows for the simultaneous prediction of genetic information of multiple dinosaur species. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input large-scale genetic data of reptiles and birds closely related to dinosaurs into the generative AI and have the generative AI perform the prediction of genetic information of multiple dinosaur species.

[0041] The prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and predict genetic mutations under specific environmental conditions. For example, the prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and predict genetic mutations under specific environmental conditions. For example, the prediction unit can learn genetic data of existing birds and reptiles and predict genetic mutations under specific environmental conditions. The prediction unit can also use generative AI to comprehensively learn genetic data of birds and reptiles and predict genetic mutations under specific environmental conditions. Furthermore, the prediction unit can use generative AI to develop algorithms for predicting genetic mutations under specific environmental conditions. This enables the prediction of genetic mutations under specific environmental conditions. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input large-scale genetic data of reptiles and birds closely related to dinosaurs into a generative AI and have the generative AI perform the prediction of genetic mutations under specific environmental conditions.

[0042] The prediction unit can predict genetic information while considering dinosaur habitat information using generative AI. For example, the prediction unit can learn gene markers related to dinosaur habitats using generative AI and predict genetic information under specific habitat conditions. The prediction unit can also learn gene markers related to dinosaur habitats using generative AI and predict genetic mutations according to habitat conditions. Furthermore, the prediction unit can learn gene markers related to dinosaur habitats using generative AI and predict gene sequences based on habitat conditions. This allows for the prediction of genetic information while considering dinosaur habitat information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input gene markers related to dinosaur habitats into the generative AI and have the generative AI perform predictions of genetic information based on habitat information.

[0043] The prediction unit can predict genetic information while considering the dietary information of dinosaurs using generative AI. For example, the prediction unit can learn gene markers related to the diet of dinosaurs using generative AI and predict genetic information under specific dietary conditions. The prediction unit can also learn gene markers related to the diet of dinosaurs using generative AI and predict gene mutations according to dietary conditions. Furthermore, the prediction unit can learn gene markers related to the diet of dinosaurs using generative AI and predict gene sequences based on dietary conditions. This allows for the prediction of genetic information while considering the dietary information of dinosaurs. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input gene markers related to the diet of dinosaurs into the generative AI and have the generative AI perform the prediction of genetic information based on dietary information.

[0044] The growth prediction unit can use generative AI to simultaneously predict the growth of multiple dinosaur species over time and reconstruct their genetic information. For example, the growth prediction unit can use generative AI to simultaneously predict the growth of multiple dinosaur species over time and reconstruct their genetic information. For example, the growth prediction unit can use generative AI to simulate the growth process of multiple dinosaur species and simultaneously predict genetic changes over time. The growth prediction unit can also use generative AI to simulate the growth process of multiple dinosaur species and simultaneously predict genetic mutations associated with growth. Furthermore, the growth prediction unit can use generative AI to simulate the growth process of multiple dinosaur species and simultaneously predict changes in gene sequences associated with growth. This allows for efficient reconstruction of genetic information by simultaneously predicting the growth process of multiple dinosaur species. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input the growth processes of multiple dinosaur species into the generative AI and have the generative AI perform the growth prediction.

[0045] The growth prediction unit can use generative AI to predict growth under specific environmental conditions and restore genetic information. For example, the growth prediction unit can use generative AI to predict growth under specific environmental conditions and restore genetic information. For example, the growth prediction unit can use generative AI to simulate the growth process of dinosaurs under specific environmental conditions and predict genetic changes. The growth prediction unit can also use generative AI to simulate the growth process of dinosaurs under specific environmental conditions and predict genetic mutations associated with growth. Furthermore, the growth prediction unit can use generative AI to simulate the growth process of dinosaurs under specific environmental conditions and predict changes in gene sequences associated with growth. In this way, by predicting the growth process under specific environmental conditions, genetic information adapted to the environment can be restored. Some or all of the above processing in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input the growth process under specific environmental conditions into the generative AI and have the generative AI perform the growth prediction.

[0046] The growth prediction unit can use generative AI to predict growth and reconstruct genetic information while considering dinosaur habitat information. For example, the growth prediction unit can use generative AI to learn genetic markers related to dinosaur habitats and simulate the growth process under specific habitat conditions. The growth prediction unit can also use generative AI to learn genetic markers related to dinosaur habitats and make growth predictions according to habitat conditions. Furthermore, the growth prediction unit can use generative AI to learn genetic markers related to dinosaur habitats and simulate the growth process based on habitat conditions. This allows for the reconstruction of genetic information adapted to the habitat by predicting the growth process while considering dinosaur habitat information. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input genetic markers related to dinosaur habitats into the generative AI and have the generative AI perform growth predictions based on habitat information.

[0047] The growth prediction unit can use generative AI to predict growth and reconstruct genetic information, taking into account the dinosaur's diet. For example, the growth prediction unit can use generative AI to learn genetic markers related to the dinosaur's diet and simulate the growth process under specific dietary conditions. The growth prediction unit can also use generative AI to learn genetic markers related to the dinosaur's diet and make growth predictions according to dietary conditions. Furthermore, the growth prediction unit can use generative AI to learn genetic markers related to the dinosaur's diet and simulate the growth process based on dietary conditions. This allows for the reconstruction of genetic information adapted to the diet by predicting the growth process while considering the dinosaur's diet. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input genetic markers related to the dinosaur's diet into the generative AI and have the generative AI perform growth predictions based on dietary information.

[0048] The complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species. For example, the complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species. For example, the complementation unit can use CRISPR technology to target and precisely complement missing gene portions of a specific dinosaur species. The complementation unit can also use CRISPR technology to repair missing gene portions of a specific dinosaur species and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species and restore a functional gene. This allows for the precise complementation of missing gene portions of a specific dinosaur species. Some or all of the above processes in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input the missing gene portion into a generating AI and have the generating AI perform the complementation.

[0049] The complementation unit can simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology. For example, the complementation unit can simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology. For example, the complementation unit can target and simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology. Furthermore, the complementation unit can repair missing gene regions of multiple dinosaur species and simultaneously reconstruct complete gene sequences using CRISPR technology. In addition, the complementation unit can simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology and restore functional genes. This allows for the simultaneous complementation of missing gene regions of multiple dinosaur species. Some or all of the above-described processes in the complementation unit may be performed using, for example, AI, or without AI. For example, the complementation unit can input the missing gene regions into a generating AI and have the generating AI perform the complementation.

[0050] The complementation unit can complement gene mutations under specific environmental conditions using CRISPR technology. For example, the complementation unit can complement gene mutations under specific environmental conditions using CRISPR technology. For example, the complementation unit can target and precisely complement gene mutations under specific environmental conditions using CRISPR technology. The complementation unit can also repair gene mutations under specific environmental conditions and reconstruct a complete gene sequence using CRISPR technology. Furthermore, the complementation unit can complement gene mutations under specific environmental conditions and restore a functional gene using CRISPR technology. This allows for precise complementation of gene mutations under specific environmental conditions. Some or all of the above processes in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene mutations under specific environmental conditions into a generating AI and have the generating AI perform the complementation.

[0051] The complementation unit can use CRISPR technology to complement missing gene portions while considering dinosaur habitat information. For example, the complementation unit can use CRISPR technology to complement missing gene portions while considering dinosaur habitat information. For example, the complementation unit can use CRISPR technology to target gene markers related to dinosaur habitats and complement missing gene portions. The complementation unit can also use CRISPR technology to repair gene markers related to dinosaur habitats and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement gene markers related to dinosaur habitats and restore functional genes. This allows for the complementation of missing gene portions while considering dinosaur habitat information. Some or all of the above processing in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene markers related to dinosaur habitats into a generating AI and have the generating AI perform complementation based on habitat information.

[0052] The complementation unit can use CRISPR technology to complement missing gene portions while considering the dietary information of dinosaurs. For example, the complementation unit can use CRISPR technology to complement missing gene portions while considering the dietary information of dinosaurs. For example, the complementation unit can use CRISPR technology to target gene markers related to the diet of dinosaurs and complement the missing gene portions. The complementation unit can also use CRISPR technology to repair gene markers related to the diet of dinosaurs and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement gene markers related to the diet of dinosaurs and restore functional genes. This allows for the complementation of missing gene portions while considering the dietary information of dinosaurs. Some or all of the above processing in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene markers related to the diet of dinosaurs into a generating AI and have the generating AI perform complementation based on dietary information.

[0053] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded for a specific dinosaur species. For example, the validation unit can use a DNA language model to analyze the gene sequence of a specific dinosaur species and verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to simulate the gene sequence of a specific dinosaur species and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to compare the gene sequences of a specific dinosaur species and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of the gene sequence of a specific dinosaur species. Some or all of the above processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequence of a specific dinosaur species into a generating AI and have the generating AI perform the base folding verification.

[0054] The validation unit can use a DNA language model to simultaneously verify that the appropriate bases are correctly folded for multiple dinosaur species. For example, the validation unit can use a DNA language model to analyze the gene sequences of multiple dinosaur species and simultaneously verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to simulate the gene sequences of multiple dinosaur species and simultaneously verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to compare the gene sequences of multiple dinosaur species and simultaneously verify that the appropriate bases are correctly folded. This allows for simultaneous verification of the accuracy of the gene sequences of multiple dinosaur species. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequences of multiple dinosaur species into a generating AI and have the generating AI perform the base folding verification.

[0055] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded under specific environmental conditions. For example, the validation unit can use a DNA language model to analyze the gene sequence under specific environmental conditions and verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to simulate the gene sequence under specific environmental conditions and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to compare the gene sequence under specific environmental conditions and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of the gene sequence under specific environmental conditions. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequence under specific environmental conditions into a generating AI and have the generating AI perform the base folding verification.

[0056] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account information about dinosaur habitats. For example, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account information about dinosaur habitats. For example, the validation unit can use a DNA language model to analyze gene sequences related to dinosaur habitats and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simulate gene sequences related to dinosaur habitats and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to compare gene sequences related to dinosaur habitats and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of gene sequences, taking into account information about dinosaur habitats. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input gene sequences related to dinosaur habitats into a generating AI and have the generating AI perform the base folding verification.

[0057] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account the dinosaur's dietary information. For example, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account the dinosaur's dietary information. For example, the validation unit can use a DNA language model to analyze gene sequences related to the dinosaur's diet and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simulate gene sequences related to the dinosaur's diet and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to compare gene sequences related to the dinosaur's diet and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of gene sequences, taking into account the dinosaur's dietary information. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input gene sequences related to the dinosaur's diet into a generating AI and have the generating AI perform the base folding verification.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The dinosaur reconstruction system can also be equipped with an environmental adaptation unit. This unit can introduce genetic mutations to adapt to modern environmental conditions, based on the reconstructed genetic information of the dinosaur. For example, it can introduce genetic mutations to respond to fluctuations in temperature, humidity, and food resources. It can also introduce genetic mutations to adapt to specific human activities, such as urban or rural environments. Furthermore, it can introduce genetic mutations to adapt to extraterrestrial environmental conditions. This would make the dinosaurs more adaptable to the diverse environmental conditions of today.

[0060] The dinosaur reconstruction system can also be equipped with a data sharing unit. This unit can share data such as the genetic information, growth process, and behavioral patterns of the reconstructed dinosaurs with other research and educational institutions. For example, the data sharing unit can share data in real time through a cloud-based platform. Furthermore, the data sharing unit can provide necessary data for specific research projects. In addition, the data sharing unit can provide data for educational purposes, spreading knowledge about dinosaurs. This will advance research on dinosaur reconstruction and improve the quality of education.

[0061] The dinosaur restoration system can also be equipped with an environmental monitoring unit. This unit can monitor changes in the dinosaur's habitat in real time and take appropriate action. For example, it can collect environmental data such as temperature, humidity, and precipitation, and issue alerts if anomalies are detected. It can also monitor changes in plants and animals in the dinosaur's habitat and take measures to maintain the balance of the ecosystem. Furthermore, it can simulate extraterrestrial environmental conditions and evaluate the dinosaur's adaptability. This allows for the optimal maintenance of the dinosaur's habitat.

[0062] The dinosaur restoration system can also be equipped with a gene editing history unit. This unit can meticulously record the history of gene editing in the restored dinosaur, which can be used for future research and improvements. For example, it can record the date, time, content, and technology used for each gene edit. It can also track changes in the gene sequence after editing and their effects. Furthermore, it can compare gene editing across different dinosaur species, providing data to find the optimal editing method. This improves the accuracy and efficiency of gene editing, increasing the success rate of dinosaur restoration.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The acquisition unit acquires the genetic information of dinosaurs. For example, the acquisition unit analyzes DNA sequences from dinosaur fossils to acquire dinosaur-specific genetic information. Next-generation sequencing technology, PCR technology, and microarray technology are used to analyze DNA fragments extracted from fossils and acquire genetic information for a specific dinosaur species. Step 2: The prediction unit uses generative AI to predict genetic information based on the genetic information acquired by the acquisition unit. The prediction unit learns from large-scale genetic data of reptiles and birds closely related to dinosaurs to predict dinosaur genetic information. It learns from the genetic data of existing birds and reptiles to estimate the genetic sequences of dinosaurs. It can also learn from the genetic data of birds and reptiles in an integrated manner to simultaneously predict the genetic sequences of multiple dinosaur species. Furthermore, it can predict genetic mutations under specific environmental conditions. Step 3: The growth prediction unit uses generative AI to predict growth based on the information predicted by the prediction unit. The growth prediction unit predicts growth over time and reconstructs the dinosaur's genes. Using generative AI, it simulates the dinosaur's growth process and predicts genetic changes over time. It can also predict growth under specific environmental conditions and reconstruct genetic information. Furthermore, it can estimate the dinosaur's emotions and predict growth based on the estimated emotions of the dinosaur. Step 4: The complementation unit uses CRISPR technology to complement the genetic information based on the information predicted by the growth prediction unit. The complementation unit complements the missing gene portions. It can target and precisely complement missing gene portions in specific dinosaur species. It can also complement gene mutations under specific environmental conditions. Furthermore, it can estimate the emotions of dinosaurs and complement missing gene portions based on the estimated emotions of the dinosaurs. Step 5: The validation unit uses a DNA language model to verify the genetic information complemented by the complementation unit. The validation unit confirms that the appropriate bases are correctly folded. It can analyze the gene sequence of a specific dinosaur species and confirm that the appropriate bases are correctly folded. It can also analyze the gene sequences of multiple dinosaur species simultaneously and confirm that the appropriate bases are correctly folded. Furthermore, it can confirm that the appropriate bases are correctly folded under specific environmental conditions.

[0065] (Example of form 2) The dinosaur restoration system according to an embodiment of the present invention is a system that revives dinosaurs in the modern age by combining generative AI and biotechnology. This dinosaur restoration system analyzes DNA sequences from dinosaur fossils to obtain dinosaur-specific genetic information. Next, generative AI is used to learn from large-scale genetic data of reptiles and birds closely related to dinosaurs and predict the genetic information of dinosaurs. Furthermore, diffusion modeling is used to predict growth over time and restore the genes of dinosaurs. The restored genetic information is supplemented using genome editing technologies such as CRISPR. Finally, accuracy is improved by having DNA language models debate to confirm that the appropriate bases are folded correctly. This technology makes it possible to revive dinosaurs in the modern age. For example, DNA sequences are analyzed from dinosaur fossils. In this process, DNA fragments extracted from the fossils are analyzed to obtain dinosaur-specific genetic information. For example, DNA fragments extracted from the fossils of a specific dinosaur species are analyzed to identify their gene sequences. Next, generative AI is used to learn from large-scale genetic data of reptiles and birds closely related to dinosaurs. Based on this genetic data, the generative AI predicts the genetic information of dinosaurs. For example, the generative AI learns from the genetic data of existing birds and reptiles to estimate the gene sequence of dinosaurs. Furthermore, it uses diffusion modeling to predict growth over time and reconstruct the dinosaur's genes. The generative AI simulates genetic changes over time and predicts the growth process of dinosaurs. This makes it possible to reconstruct the dinosaur's genetic information. The reconstructed genetic information is supplemented using genome editing technologies such as CRISPR. For example, missing gene portions are supplemented with CRISPR technology to reconstruct the complete gene sequence. Finally, accuracy is improved by having DNA language models debate to confirm that the appropriate bases are folded correctly. The generative AI uses DNA language models to verify the accuracy of the gene sequence. This makes it possible to improve the accuracy of the dinosaur's genetic information. This technology makes it possible to bring dinosaurs back to life in the modern age, differentiating oneself from other companies as part of space tourism. Moreover, by reviving them on extraterrestrial planets, it is possible to protect Earth's ecosystems and public safety.This will enable the dinosaur reconstruction system to efficiently acquire, predict, predict growth, supplement, and verify the genetic information of dinosaurs.

[0066] The dinosaur restoration system according to this embodiment comprises an acquisition unit, a prediction unit, a growth prediction unit, a supplementation unit, and a verification unit. The acquisition unit acquires the genetic information of dinosaurs. The acquisition unit, for example, analyzes DNA sequences from dinosaur fossils to acquire dinosaur-specific genetic information. For example, the acquisition unit uses next-generation sequencing technology to analyze DNA fragments extracted from dinosaur fossils and identifies the gene sequence of a specific dinosaur species. The acquisition unit can also use PCR technology to amplify DNA fragments extracted from fossils and acquire genetic information of a specific dinosaur species. Furthermore, the acquisition unit can also use microarray technology to analyze DNA fragments extracted from fossils and acquire genetic information of a specific dinosaur species. The prediction unit uses generative AI to predict genetic information based on the genetic information acquired by the acquisition unit. For example, the prediction unit uses generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and predicts the genetic information of dinosaurs. For example, the prediction unit learns genetic data of existing birds and reptiles and estimates the gene sequence of dinosaurs. Furthermore, the prediction unit can use generative AI to comprehensively learn avian and reptile genetic data and simultaneously predict the gene sequences of multiple dinosaur species. In addition, the prediction unit can use generative AI to predict gene mutations under specific environmental conditions. The growth prediction unit uses generative AI to predict growth based on the information predicted by the prediction unit. For example, the growth prediction unit uses generative AI to predict growth over time and reconstruct the dinosaur's genes. For example, the growth prediction unit uses generative AI to simulate the dinosaur's growth process and predict gene changes over time. The growth prediction unit can also use generative AI to predict growth under specific environmental conditions and reconstruct genetic information. Furthermore, the growth prediction unit can use generative AI to estimate the dinosaur's emotions and predict growth based on the estimated emotions. The completion unit uses CRISPR technology to complete the genetic information based on the information predicted by the growth prediction unit. For example, the completion unit uses CRISPR technology to complete missing gene portions. For example, the completion unit uses CRISPR technology to target and accurately complete missing gene portions of specific dinosaur species.Furthermore, the complementation unit can use CRISPR technology to complement gene mutations under specific environmental conditions. Additionally, the complementation unit can use CRISPR technology to estimate the emotions of dinosaurs and complement missing gene portions based on the estimated emotions. The validation unit uses a DNA language model to validate the gene information complemented by the complementation unit. For example, the validation unit uses the DNA language model to confirm that the appropriate bases are correctly folded. For instance, the validation unit can use the DNA language model to analyze the gene sequence of a specific dinosaur species and confirm that the appropriate bases are correctly folded. The validation unit can also use the DNA language model to simultaneously analyze the gene sequences of multiple dinosaur species and confirm that the appropriate bases are correctly folded. Furthermore, the validation unit can use the DNA language model to confirm that the appropriate bases are correctly folded under specific environmental conditions. This enables the dinosaur reconstruction system according to the embodiment to efficiently acquire, predict, predict growth, complement, and validate dinosaur gene information.

[0067] The acquisition unit can analyze DNA sequences from dinosaur fossils and obtain dinosaur-specific genetic information. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils to obtain genetic information for a specific dinosaur species. For example, the acquisition unit can use next-generation sequencing technology to analyze DNA fragments extracted from dinosaur fossils and identify the gene sequence of a specific dinosaur species. The acquisition unit can also use PCR technology to amplify DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. Furthermore, the acquisition unit can use microarray technology to analyze DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. This allows for the acquisition of accurate genetic information from dinosaur fossils. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of genetic information for a specific dinosaur species.

[0068] The prediction unit can use generative AI to learn large-scale genetic data of reptiles or birds closely related to dinosaurs and predict the genetic information of dinosaurs. For example, the prediction unit can use generative AI to learn large-scale genetic data of reptiles or birds closely related to dinosaurs and predict the genetic information of dinosaurs. For example, the prediction unit can learn the genetic data of existing birds and reptiles and estimate the gene sequences of dinosaurs. The prediction unit can also use generative AI to comprehensively learn the genetic data of birds and reptiles and simultaneously predict the gene sequences of multiple dinosaur species. Furthermore, the prediction unit can use generative AI to predict gene mutations under specific environmental conditions. This improves the accuracy of predicting dinosaur genetic information by using generative AI. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input large-scale genetic data of reptiles or birds closely related to dinosaurs into the generative AI and have the generative AI perform the prediction of dinosaur genetic information.

[0069] The growth prediction unit can use generative AI to predict growth over time and reconstruct the genes of a dinosaur. For example, the growth prediction unit can use generative AI to predict growth over time and reconstruct the genes of a dinosaur. For example, the growth prediction unit can use generative AI to simulate the growth process of a dinosaur and predict changes in genes over time. The growth prediction unit can also use generative AI to predict growth under specific environmental conditions and reconstruct genetic information. Furthermore, the growth prediction unit can use generative AI to estimate the emotions of a dinosaur and make growth predictions based on the estimated emotions of the dinosaur. In this way, the growth process of a dinosaur can be accurately predicted by using generative AI. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without using AI. For example, the growth prediction unit can input the growth process of a dinosaur into generative AI and have the generative AI perform the growth prediction.

[0070] The complementation unit can complement missing gene portions using CRISPR technology. For example, the complementation unit can complement missing gene portions using CRISPR technology. For example, the complementation unit can use CRISPR technology to target and precisely complement missing gene portions of a specific dinosaur species. The complementation unit can also use CRISPR technology to repair missing gene portions of a specific dinosaur species and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species and restore a functional gene. Thus, by using CRISPR technology, missing gene portions can be precisely complemented. Some or all of the above processes in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input the missing gene portion into a generating AI and have the generating AI perform the complementation.

[0071] The validation unit can use a DNA language model to verify that the bases are correctly folded. For example, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to analyze the gene sequence of a specific dinosaur species and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simultaneously analyze the gene sequences of multiple dinosaur species and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded under specific environmental conditions. This allows for increased accuracy of gene sequences by using a DNA language model. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input a gene sequence into a generating AI and have the generating AI perform the base folding verification.

[0072] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information for a specific dinosaur species. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information for a specific dinosaur species. For example, the acquisition unit can use next-generation sequencing technology to analyze DNA fragments extracted from dinosaur fossils and identify the gene sequence of a specific dinosaur species. The acquisition unit can also use PCR technology to amplify DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. Furthermore, the acquisition unit can use microarray technology to analyze DNA fragments extracted from fossils and obtain genetic information for a specific dinosaur species. This allows for the accurate acquisition of genetic information for a specific dinosaur species. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of genetic information for a specific dinosaur species.

[0073] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and simultaneously acquire genetic information for multiple dinosaur species. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and simultaneously acquire genetic information for multiple dinosaur species. Alternatively, the acquisition unit can use metagenomic analysis technology to analyze DNA fragments extracted from fossils and simultaneously acquire genetic information for multiple dinosaur species. Furthermore, the acquisition unit can use barcode sequencing technology to analyze DNA fragments extracted from fossils and simultaneously acquire genetic information for multiple dinosaur species. In addition, the acquisition unit can use shotgun sequencing technology to analyze DNA fragments extracted from fossils and simultaneously acquire genetic information for multiple dinosaur species. This allows for the simultaneous acquisition of genetic information for multiple dinosaur species. Some or all of the above-described processes in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of genetic information for multiple dinosaur species.

[0074] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain gene mutations under specific environmental conditions. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain gene mutations under specific environmental conditions. For example, the acquisition unit can use environmental DNA analysis technology to analyze DNA fragments extracted from fossils and obtain gene mutations under specific environmental conditions. The acquisition unit can also use epigenetics analysis technology to analyze DNA fragments extracted from fossils and obtain gene mutations under specific environmental conditions. Furthermore, the acquisition unit can use methylation analysis technology to analyze DNA fragments extracted from fossils and obtain gene mutations under specific environmental conditions. This makes it possible to obtain gene mutations under specific environmental conditions. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the acquisition of gene mutations under specific environmental conditions.

[0075] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils, estimate the emotions of the dinosaurs, and acquire genetic information based on the estimated emotions. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils, estimate the emotions of the dinosaurs, and acquire genetic information based on the estimated emotions. For example, the acquisition unit can analyze DNA fragments extracted from fossils and identify genetic markers related to the emotions of the dinosaurs. The acquisition unit can also acquire genetic information for specific emotional states based on genetic markers related to the emotions of the dinosaurs. Furthermore, the acquisition unit can analyze genetic markers related to the emotions of the dinosaurs and acquire genetic mutations corresponding to the emotional state. This allows for the acquisition of genetic information based on the emotions of the dinosaurs. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the estimation of dinosaur emotions and the acquisition of genetic information.

[0076] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's habitat information. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's habitat information. For example, the acquisition unit can analyze DNA fragments extracted from fossils and identify genetic markers related to the dinosaur's habitat. The acquisition unit can also obtain genetic information under specific habitat conditions based on genetic markers related to the dinosaur's habitat. Furthermore, the acquisition unit can analyze genetic markers related to the dinosaur's habitat and obtain genetic mutations according to habitat conditions. This allows for the acquisition of genetic information while considering the dinosaur's habitat information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the consideration of dinosaur habitat information and the acquisition of genetic information.

[0077] The acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's diet. For example, the acquisition unit can analyze DNA fragments extracted from dinosaur fossils and obtain genetic information while considering the dinosaur's diet. For example, the acquisition unit can analyze DNA fragments extracted from fossils and identify genetic markers related to the dinosaur's diet. The acquisition unit can also obtain genetic information under specific dietary conditions based on genetic markers related to the dinosaur's diet. Furthermore, the acquisition unit can analyze genetic markers related to the dinosaur's diet and obtain genetic mutations according to dietary conditions. This allows for the acquisition of genetic information while considering the dinosaur's diet. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input DNA fragments extracted from fossils into a generating AI and have the generating AI perform the consideration of the dinosaur's diet and the acquisition of genetic information.

[0078] The prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and simultaneously predict the genetic information of multiple dinosaur species. For example, the prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and simultaneously predict the genetic information of multiple dinosaur species. For example, the prediction unit can learn the genetic data of existing birds and reptiles and simultaneously predict the genetic sequences of multiple dinosaur species. The prediction unit can also use generative AI to comprehensively learn the genetic data of birds and reptiles and simultaneously predict the genetic sequences of multiple dinosaur species. Furthermore, the prediction unit can use generative AI to predict genetic mutations under specific environmental conditions. This allows for the simultaneous prediction of genetic information of multiple dinosaur species. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input large-scale genetic data of reptiles and birds closely related to dinosaurs into the generative AI and have the generative AI perform the prediction of genetic information of multiple dinosaur species.

[0079] The prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and predict genetic mutations under specific environmental conditions. For example, the prediction unit can use generative AI to learn large-scale genetic data of reptiles and birds closely related to dinosaurs and predict genetic mutations under specific environmental conditions. For example, the prediction unit can learn genetic data of existing birds and reptiles and predict genetic mutations under specific environmental conditions. The prediction unit can also use generative AI to comprehensively learn genetic data of birds and reptiles and predict genetic mutations under specific environmental conditions. Furthermore, the prediction unit can use generative AI to develop algorithms for predicting genetic mutations under specific environmental conditions. This enables the prediction of genetic mutations under specific environmental conditions. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input large-scale genetic data of reptiles and birds closely related to dinosaurs into a generative AI and have the generative AI perform the prediction of genetic mutations under specific environmental conditions.

[0080] The prediction unit can use generative AI to estimate the emotions of dinosaurs and predict genetic information based on the estimated emotions. For example, the prediction unit can use generative AI to estimate the emotions of dinosaurs and predict genetic information based on the estimated emotions. For example, the prediction unit can use generative AI to learn genetic markers related to the emotions of dinosaurs and predict genetic information in a specific emotional state. The prediction unit can also use generative AI to learn genetic markers related to the emotions of dinosaurs and predict genetic mutations corresponding to emotional states. Furthermore, the prediction unit can use generative AI to learn genetic markers related to the emotions of dinosaurs and predict gene sequences based on emotional states. This makes it possible to predict genetic information based on the emotions of dinosaurs. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input genetic markers related to the emotions of dinosaurs into the generative AI and have the generative AI perform prediction of genetic information based on emotions.

[0081] The prediction unit can predict genetic information while considering dinosaur habitat information using generative AI. For example, the prediction unit can learn gene markers related to dinosaur habitats using generative AI and predict genetic information under specific habitat conditions. The prediction unit can also learn gene markers related to dinosaur habitats using generative AI and predict genetic mutations according to habitat conditions. Furthermore, the prediction unit can learn gene markers related to dinosaur habitats using generative AI and predict gene sequences based on habitat conditions. This allows for the prediction of genetic information while considering dinosaur habitat information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input gene markers related to dinosaur habitats into the generative AI and have the generative AI perform predictions of genetic information based on habitat information.

[0082] The prediction unit can predict genetic information while considering the dietary information of dinosaurs using generative AI. For example, the prediction unit can learn gene markers related to the diet of dinosaurs using generative AI and predict genetic information under specific dietary conditions. The prediction unit can also learn gene markers related to the diet of dinosaurs using generative AI and predict gene mutations according to dietary conditions. Furthermore, the prediction unit can learn gene markers related to the diet of dinosaurs using generative AI and predict gene sequences based on dietary conditions. This allows for the prediction of genetic information while considering the dietary information of dinosaurs. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input gene markers related to the diet of dinosaurs into the generative AI and have the generative AI perform the prediction of genetic information based on dietary information.

[0083] The growth prediction unit can use generative AI to simultaneously predict the growth of multiple dinosaur species over time and reconstruct their genetic information. For example, the growth prediction unit can use generative AI to simultaneously predict the growth of multiple dinosaur species over time and reconstruct their genetic information. For example, the growth prediction unit can use generative AI to simulate the growth process of multiple dinosaur species and simultaneously predict genetic changes over time. The growth prediction unit can also use generative AI to simulate the growth process of multiple dinosaur species and simultaneously predict genetic mutations associated with growth. Furthermore, the growth prediction unit can use generative AI to simulate the growth process of multiple dinosaur species and simultaneously predict changes in gene sequences associated with growth. This allows for efficient reconstruction of genetic information by simultaneously predicting the growth process of multiple dinosaur species. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input the growth processes of multiple dinosaur species into the generative AI and have the generative AI perform the growth prediction.

[0084] The growth prediction unit can use generative AI to predict growth under specific environmental conditions and restore genetic information. For example, the growth prediction unit can use generative AI to predict growth under specific environmental conditions and restore genetic information. For example, the growth prediction unit can use generative AI to simulate the growth process of dinosaurs under specific environmental conditions and predict genetic changes. The growth prediction unit can also use generative AI to simulate the growth process of dinosaurs under specific environmental conditions and predict genetic mutations associated with growth. Furthermore, the growth prediction unit can use generative AI to simulate the growth process of dinosaurs under specific environmental conditions and predict changes in gene sequences associated with growth. In this way, by predicting the growth process under specific environmental conditions, genetic information adapted to the environment can be restored. Some or all of the above processing in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input the growth process under specific environmental conditions into the generative AI and have the generative AI perform the growth prediction.

[0085] The growth prediction unit can use generative AI to estimate the emotions of dinosaurs, predict their growth based on the estimated emotions, and reconstruct their genetic information. For example, the growth prediction unit can use generative AI to estimate the emotions of dinosaurs, predict their growth based on the estimated emotions, and reconstruct their genetic information. For example, the growth prediction unit can use generative AI to learn genetic markers related to the emotions of dinosaurs and simulate the growth process in a specific emotional state. The growth prediction unit can also use generative AI to learn genetic markers related to the emotions of dinosaurs and perform growth predictions according to the emotional state. Furthermore, the growth prediction unit can use generative AI to learn genetic markers related to the emotions of dinosaurs and simulate the growth process based on the emotional state. This allows for the reconstruction of genetic information adapted to emotions by predicting the growth process based on the emotions of dinosaurs. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input genetic markers related to the emotions of dinosaurs into the generative AI and have the generative AI perform growth predictions based on emotions.

[0086] The growth prediction unit can use generative AI to predict growth and reconstruct genetic information while considering dinosaur habitat information. For example, the growth prediction unit can use generative AI to learn genetic markers related to dinosaur habitats and simulate the growth process under specific habitat conditions. The growth prediction unit can also use generative AI to learn genetic markers related to dinosaur habitats and make growth predictions according to habitat conditions. Furthermore, the growth prediction unit can use generative AI to learn genetic markers related to dinosaur habitats and simulate the growth process based on habitat conditions. This allows for the reconstruction of genetic information adapted to the habitat by predicting the growth process while considering dinosaur habitat information. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input genetic markers related to dinosaur habitats into the generative AI and have the generative AI perform growth predictions based on habitat information.

[0087] The growth prediction unit can use generative AI to predict growth and reconstruct genetic information, taking into account the dinosaur's diet. For example, the growth prediction unit can use generative AI to learn genetic markers related to the dinosaur's diet and simulate the growth process under specific dietary conditions. The growth prediction unit can also use generative AI to learn genetic markers related to the dinosaur's diet and make growth predictions according to dietary conditions. Furthermore, the growth prediction unit can use generative AI to learn genetic markers related to the dinosaur's diet and simulate the growth process based on dietary conditions. This allows for the reconstruction of genetic information adapted to the diet by predicting the growth process while considering the dinosaur's diet. Some or all of the above-described processes in the growth prediction unit may be performed using AI, for example, or without AI. For example, the growth prediction unit can input genetic markers related to the dinosaur's diet into the generative AI and have the generative AI perform growth predictions based on dietary information.

[0088] The complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species. For example, the complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species. For example, the complementation unit can use CRISPR technology to target and precisely complement missing gene portions of a specific dinosaur species. The complementation unit can also use CRISPR technology to repair missing gene portions of a specific dinosaur species and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement missing gene portions of a specific dinosaur species and restore a functional gene. This allows for the precise complementation of missing gene portions of a specific dinosaur species. Some or all of the above processes in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input the missing gene portion into a generating AI and have the generating AI perform the complementation.

[0089] The complementation unit can simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology. For example, the complementation unit can simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology. For example, the complementation unit can target and simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology. Furthermore, the complementation unit can repair missing gene regions of multiple dinosaur species and simultaneously reconstruct complete gene sequences using CRISPR technology. In addition, the complementation unit can simultaneously complement missing gene regions of multiple dinosaur species using CRISPR technology and restore functional genes. This allows for the simultaneous complementation of missing gene regions of multiple dinosaur species. Some or all of the above-described processes in the complementation unit may be performed using, for example, AI, or without AI. For example, the complementation unit can input the missing gene regions into a generating AI and have the generating AI perform the complementation.

[0090] The complementation unit can complement gene mutations under specific environmental conditions using CRISPR technology. For example, the complementation unit can complement gene mutations under specific environmental conditions using CRISPR technology. For example, the complementation unit can target and precisely complement gene mutations under specific environmental conditions using CRISPR technology. The complementation unit can also repair gene mutations under specific environmental conditions and reconstruct a complete gene sequence using CRISPR technology. Furthermore, the complementation unit can complement gene mutations under specific environmental conditions and restore a functional gene using CRISPR technology. This allows for precise complementation of gene mutations under specific environmental conditions. Some or all of the above processes in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene mutations under specific environmental conditions into a generating AI and have the generating AI perform the complementation.

[0091] The complementation unit can use CRISPR technology to estimate the emotions of dinosaurs and complement missing gene portions based on the estimated emotions. For example, the complementation unit can use CRISPR technology to estimate the emotions of dinosaurs and complement missing gene portions based on the estimated emotions. For example, the complementation unit can use CRISPR technology to target gene markers related to dinosaur emotions and complement missing gene portions. The complementation unit can also use CRISPR technology to repair gene markers related to dinosaur emotions and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement gene markers related to dinosaur emotions and restore functional genes. This allows for the complementation of missing gene portions based on dinosaur emotions. Some or all of the above processing in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene markers related to dinosaur emotions into a generating AI and have the generating AI perform emotion-based complementation.

[0092] The complementation unit can use CRISPR technology to complement missing gene portions while considering dinosaur habitat information. For example, the complementation unit can use CRISPR technology to complement missing gene portions while considering dinosaur habitat information. For example, the complementation unit can use CRISPR technology to target gene markers related to dinosaur habitats and complement missing gene portions. The complementation unit can also use CRISPR technology to repair gene markers related to dinosaur habitats and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement gene markers related to dinosaur habitats and restore functional genes. This allows for the complementation of missing gene portions while considering dinosaur habitat information. Some or all of the above processing in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene markers related to dinosaur habitats into a generating AI and have the generating AI perform complementation based on habitat information.

[0093] The complementation unit can use CRISPR technology to complement missing gene portions while considering the dietary information of dinosaurs. For example, the complementation unit can use CRISPR technology to complement missing gene portions while considering the dietary information of dinosaurs. For example, the complementation unit can use CRISPR technology to target gene markers related to the diet of dinosaurs and complement the missing gene portions. The complementation unit can also use CRISPR technology to repair gene markers related to the diet of dinosaurs and reconstruct the complete gene sequence. Furthermore, the complementation unit can use CRISPR technology to complement gene markers related to the diet of dinosaurs and restore functional genes. This allows for the complementation of missing gene portions while considering the dietary information of dinosaurs. Some or all of the above processing in the complementation unit may be performed using AI, for example, or without AI. For example, the complementation unit can input gene markers related to the diet of dinosaurs into a generating AI and have the generating AI perform complementation based on dietary information.

[0094] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded for a specific dinosaur species. For example, the validation unit can use a DNA language model to analyze the gene sequence of a specific dinosaur species and verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to simulate the gene sequence of a specific dinosaur species and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to compare the gene sequences of a specific dinosaur species and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of the gene sequence of a specific dinosaur species. Some or all of the above processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequence of a specific dinosaur species into a generating AI and have the generating AI perform the base folding verification.

[0095] The validation unit can use a DNA language model to simultaneously verify that the appropriate bases are correctly folded for multiple dinosaur species. For example, the validation unit can use a DNA language model to analyze the gene sequences of multiple dinosaur species and simultaneously verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to simulate the gene sequences of multiple dinosaur species and simultaneously verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to compare the gene sequences of multiple dinosaur species and simultaneously verify that the appropriate bases are correctly folded. This allows for simultaneous verification of the accuracy of the gene sequences of multiple dinosaur species. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequences of multiple dinosaur species into a generating AI and have the generating AI perform the base folding verification.

[0096] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded under specific environmental conditions. For example, the validation unit can use a DNA language model to analyze the gene sequence under specific environmental conditions and verify that the appropriate bases are correctly folded. For example, the validation unit can use a DNA language model to simulate the gene sequence under specific environmental conditions and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to compare the gene sequence under specific environmental conditions and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of the gene sequence under specific environmental conditions. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequence under specific environmental conditions into a generating AI and have the generating AI perform the base folding verification.

[0097] The validation unit can use a DNA language model to estimate the emotions of dinosaurs and verify that the appropriate bases are correctly folded based on the estimated emotions of the dinosaurs. For example, the validation unit can use a DNA language model to estimate the emotions of dinosaurs and verify that the appropriate bases are correctly folded based on the estimated emotions of the dinosaurs. For example, the validation unit can use a DNA language model to analyze the gene sequences related to the emotions of dinosaurs and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simulate the gene sequences related to the emotions of dinosaurs and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to compare the gene sequences related to the emotions of dinosaurs and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of the gene sequences based on the emotions of dinosaurs. Some or all of the above processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input the gene sequences related to the emotions of dinosaurs into a generating AI and have the generating AI perform the base folding verification.

[0098] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account information about dinosaur habitats. For example, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account information about dinosaur habitats. For example, the validation unit can use a DNA language model to analyze gene sequences related to dinosaur habitats and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simulate gene sequences related to dinosaur habitats and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to compare gene sequences related to dinosaur habitats and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of gene sequences, taking into account information about dinosaur habitats. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input gene sequences related to dinosaur habitats into a generating AI and have the generating AI perform the base folding verification.

[0099] The validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account the dinosaur's dietary information. For example, the validation unit can use a DNA language model to verify that the appropriate bases are correctly folded, taking into account the dinosaur's dietary information. For example, the validation unit can use a DNA language model to analyze gene sequences related to the dinosaur's diet and verify that the appropriate bases are correctly folded. The validation unit can also use a DNA language model to simulate gene sequences related to the dinosaur's diet and verify that the appropriate bases are correctly folded. Furthermore, the validation unit can use a DNA language model to compare gene sequences related to the dinosaur's diet and verify that the appropriate bases are correctly folded. This allows for verification of the accuracy of gene sequences, taking into account the dinosaur's dietary information. Some or all of the above-described processes in the validation unit may be performed using AI, for example, or without AI. For example, the validation unit can input gene sequences related to the dinosaur's diet into a generating AI and have the generating AI perform the base folding verification. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, prediction unit, growth prediction unit, completion unit, and verification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 38B of the smart device 14 to analyze DNA sequences from dinosaur fossils and acquire dinosaur-specific genetic information. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generative AI to learn large amounts of genetic data from reptiles and birds similar to dinosaurs and predict the genetic information of dinosaurs. The growth prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generative AI to predict growth over time and reconstruct the genes of dinosaurs. The completion unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses CRISPR technology to complete missing gene portions. The verification unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses a DNA language model to confirm that the appropriate bases are correctly folded. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, prediction unit, growth prediction unit, completion unit, and verification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 238 of the smart glasses 214 to analyze DNA sequences from dinosaur fossils and acquire dinosaur-specific genetic information. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses generative AI to learn large amounts of genetic data from reptiles and birds similar to dinosaurs and predict the genetic information of dinosaurs. The growth prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses generative AI to predict growth over time and reconstruct the genes of dinosaurs. The completion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses CRISPR technology to complete missing gene portions. The verification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses a DNA language model to confirm that the appropriate bases are correctly folded. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, prediction unit, growth prediction unit, completion unit, and verification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 238 of the headset terminal 314 to analyze DNA sequences from dinosaur fossils and acquire dinosaur-specific genetic information. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses generative AI to learn large amounts of genetic data from reptiles and birds similar to dinosaurs and predict the genetic information of dinosaurs. The growth prediction unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses generative AI to predict growth over time and reconstruct the genes of dinosaurs. The completion unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses CRISPR technology to complete missing gene portions. The verification unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses a DNA language model to confirm that the appropriate bases are correctly folded. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, prediction unit, growth prediction unit, completion unit, and verification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 238 of the robot 414 to analyze DNA sequences from dinosaur fossils and acquire dinosaur-specific genetic information. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses generative AI to learn large amounts of genetic data from reptiles and birds similar to dinosaurs and predict the genetic information of dinosaurs. The growth prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses generative AI to predict growth over time and reconstruct the genes of dinosaurs. The completion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses CRISPR technology to complete missing gene portions. The verification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses a DNA language model to confirm that the appropriate bases are correctly folded.

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

[0101] The dinosaur reconstruction system can also be equipped with an environmental adaptation unit. This unit can introduce genetic mutations to adapt to modern environmental conditions, based on the reconstructed genetic information of the dinosaur. For example, it can introduce genetic mutations to respond to fluctuations in temperature, humidity, and food resources. It can also introduce genetic mutations to adapt to specific human activities, such as urban or rural environments. Furthermore, it can introduce genetic mutations to adapt to extraterrestrial environmental conditions. This would make the dinosaurs more adaptable to the diverse environmental conditions of today.

[0102] The dinosaur reconstruction system can also be equipped with a behavior prediction unit. This unit can predict the behavioral patterns of the reconstructed dinosaur based on its genetic information. For example, it can predict hunting, breeding, and social behaviors. It can also predict migration patterns and habitat selection. Furthermore, it can predict specific behaviors based on the emotional state of the dinosaur. This allows for a prior understanding of dinosaur behavior, enabling appropriate management and conservation.

[0103] The dinosaur restoration system could also be equipped with a health management unit. This unit could monitor the health of the restored dinosaur and provide medical treatment as needed. For example, it could regularly measure the dinosaur's body temperature, heart rate, and blood components, enabling early detection of any abnormalities. Furthermore, the health management unit could take preventative measures against specific diseases based on the dinosaur's genetic information. In addition, the health management unit could monitor the dinosaur's emotional state and take measures to reduce stress and anxiety. This would help maintain the dinosaur's health and ensure its long-term survival.

[0104] The dinosaur restoration system could also be equipped with a communication unit. This unit could provide a means to enable effective communication between the restored dinosaur and humans. For example, it could analyze the dinosaur's vocalizations and body language and convey their meaning to humans. It could also display the dinosaur's emotional state in real time, helping to understand its feelings. Furthermore, the communication unit could provide a means to convey specific instructions to the dinosaur. This would enable smooth communication between dinosaurs and humans.

[0105] The dinosaur restoration system may also be equipped with an energy management unit. This unit can provide means for optimizing the energy consumption of the restored dinosaur. For example, it can calculate an appropriate energy intake based on the dinosaur's activity level and diet. It can also optimize energy consumption to reduce stress and anxiety based on the dinosaur's emotional state. Furthermore, it can adjust energy consumption according to the dinosaur's growth stage. This allows for efficient management of the dinosaur's energy consumption and maintenance of its health.

[0106] The dinosaur reconstruction system can also be equipped with a data sharing unit. This unit can share data such as the genetic information, growth process, and behavioral patterns of the reconstructed dinosaurs with other research and educational institutions. For example, the data sharing unit can share data in real time through a cloud-based platform. Furthermore, the data sharing unit can provide necessary data for specific research projects. In addition, the data sharing unit can provide data for educational purposes, spreading knowledge about dinosaurs. This will advance research on dinosaur reconstruction and improve the quality of education.

[0107] The dinosaur restoration system can also be equipped with an environmental monitoring unit. This unit can monitor changes in the dinosaur's habitat in real time and take appropriate action. For example, it can collect environmental data such as temperature, humidity, and precipitation, and issue alerts if anomalies are detected. It can also monitor changes in plants and animals in the dinosaur's habitat and take measures to maintain the balance of the ecosystem. Furthermore, it can simulate extraterrestrial environmental conditions and evaluate the dinosaur's adaptability. This allows for the optimal maintenance of the dinosaur's habitat.

[0108] The dinosaur restoration system may also include a nutrition management unit. This unit can provide means to optimize the nutritional status of the restored dinosaur. For example, it can monitor the dinosaur's diet and intake, and supplement necessary nutrients. It can also adjust the nutritional balance according to the dinosaur's growth stage. Furthermore, it can provide nutritional support to reduce stress and anxiety based on the dinosaur's emotional state. This helps maintain the dinosaur's health and promotes its growth.

[0109] The dinosaur restoration system can also be equipped with a gene editing history unit. This unit can meticulously record the history of gene editing in the restored dinosaur, which can be used for future research and improvements. For example, it can record the date, time, content, and technology used for each gene edit. It can also track changes in the gene sequence after editing and their effects. Furthermore, it can compare gene editing across different dinosaur species, providing data to find the optimal editing method. This improves the accuracy and efficiency of gene editing, increasing the success rate of dinosaur restoration.

[0110] The dinosaur restoration system could also be equipped with a social adaptation unit. This unit could provide the means for the restored dinosaur to adapt to modern society. For example, it could analyze the dinosaur's behavioral patterns and teach it the skills necessary for coexisting with humans. It could also monitor the dinosaur's emotional state and create an environment that reduces stress and anxiety. Furthermore, it could provide training programs for the dinosaur to fulfill its role in human society. This would enable the dinosaur to smoothly adapt to modern society and coexist with humans.

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

[0112] Step 1: The acquisition unit acquires the genetic information of dinosaurs. For example, the acquisition unit analyzes DNA sequences from dinosaur fossils to acquire dinosaur-specific genetic information. Next-generation sequencing technology, PCR technology, and microarray technology are used to analyze DNA fragments extracted from fossils and acquire genetic information for a specific dinosaur species. Step 2: The prediction unit uses generative AI to predict genetic information based on the genetic information acquired by the acquisition unit. The prediction unit learns from large-scale genetic data of reptiles and birds closely related to dinosaurs to predict dinosaur genetic information. It learns from the genetic data of existing birds and reptiles to estimate the genetic sequences of dinosaurs. It can also learn from the genetic data of birds and reptiles in an integrated manner to simultaneously predict the genetic sequences of multiple dinosaur species. Furthermore, it can predict genetic mutations under specific environmental conditions. Step 3: The growth prediction unit uses generative AI to predict growth based on the information predicted by the prediction unit. The growth prediction unit predicts growth over time and reconstructs the dinosaur's genes. Using generative AI, it simulates the dinosaur's growth process and predicts genetic changes over time. It can also predict growth under specific environmental conditions and reconstruct genetic information. Furthermore, it can estimate the dinosaur's emotions and predict growth based on the estimated emotions of the dinosaur. Step 4: The complementation unit uses CRISPR technology to complement the genetic information based on the information predicted by the growth prediction unit. The complementation unit complements the missing gene portions. It can target and precisely complement missing gene portions in specific dinosaur species. It can also complement gene mutations under specific environmental conditions. Furthermore, it can estimate the emotions of dinosaurs and complement missing gene portions based on the estimated emotions of the dinosaurs. Step 5: The validation unit uses a DNA language model to verify the genetic information complemented by the complementation unit. The validation unit confirms that the appropriate bases are correctly folded. It can analyze the gene sequence of a specific dinosaur species and confirm that the appropriate bases are correctly folded. It can also analyze the gene sequences of multiple dinosaur species simultaneously and confirm that the appropriate bases are correctly folded. Furthermore, it can confirm that the appropriate bases are correctly folded under specific environmental conditions.

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

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

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

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0184] [Explanation of symbols]

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

Claims

1. An acquisition unit for acquiring dinosaur genetic information, A prediction unit predicts genetic information using generated AI based on the genetic information acquired by the acquisition unit, A growth prediction unit that uses generated AI to predict growth based on the information predicted by the prediction unit, A complementation unit that uses CRISPR technology to complement genetic information based on the information predicted by the growth prediction unit, The system includes a verification unit that verifies the gene information supplemented by the supplementary unit using a DNA language model. A system characterized by the following features.

2. The acquisition unit is, Analyzing DNA sequences from dinosaur fossils to obtain unique genetic information about dinosaurs. The system according to feature 1.

3. The prediction unit, Using generative AI, we will learn from large-scale genetic data of reptiles or birds closely related to dinosaurs and predict the genetic information of dinosaurs. The system according to feature 1.

4. The aforementioned growth forecasting unit, Using generative AI, we predict growth over time and reconstruct the genes of dinosaurs. The system according to feature 1.

5. The aforementioned supplementary unit is, Using CRISPR technology, the missing portion of the gene is replaced. The system according to feature 1.

6. The verification unit, We use a DNA language model to verify that the bases are folded correctly. The system according to feature 1.

7. The acquisition unit is, Analyzing DNA fragments extracted from dinosaur fossils to obtain genetic information for specific dinosaur species. The system according to feature 1.

8. The acquisition unit is, Analyzing DNA fragments extracted from dinosaur fossils to simultaneously obtain genetic information from multiple dinosaur species. The system according to feature 1.

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