system

Abstract

The system according to this embodiment aims to optimize complex decision-making processes by utilizing quantum computing and AI. [Solution] The system according to the embodiment comprises a processing unit, an analysis unit, a correlation analysis unit, a simulation unit, and a security unit. The processing unit performs massively parallel processing using a quantum algorithm. The analysis unit performs personalized analysis based on the data obtained by the processing unit. The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. The simulation unit performs real-time simulation based on the data obtained by the correlation analysis unit. The security unit enhances security based on the data obtained by the simulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are limitations in accuracy and speed for optimizing a complex decision-making process, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize a complex decision-making process by utilizing quantum computing and AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a processing unit, an analysis unit, a correlation analysis unit, a simulation unit, and a security unit. The processing unit performs massively parallel processing using a quantum algorithm. The analysis unit performs personalized analysis based on the data obtained by the processing unit. The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. The simulation unit performs real-time simulation based on the data obtained by the correlation analysis unit. The security unit enhances security based on the data obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize complex decision-making processes by utilizing quantum computing and AI. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. <0000​​​​​​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 decision support system according to an embodiment of the present invention is a system that integrates quantum information science and AI. This decision support system optimizes the user's complex decision-making process with the power of quantum computing and combines it with advanced analysis by AI to present optimal options to individuals and companies with unprecedented accuracy and speed. Specifically, it consists of the following steps: First, it performs massively parallel processing using quantum algorithms to simultaneously explore a vast number of possibilities. Next, it performs AI-driven personalized analysis, analyzing the user's past decisions, values, and goals using deep learning to provide personalized suggestions. Furthermore, it performs correlation analysis mimicking quantum entanglement to analyze the interactions between intricately intertwined factors from a quantum perspective. Using real-time quantum simulation, it predicts and visualizes the outcome of the decision using quantum probability. Finally, it enhances security with quantum cryptography to protect the user's confidential information at the quantum level. This makes it possible to present optimal options to individuals and companies with unprecedented accuracy and speed. Furthermore, using generative AI, the system converts the user's natural language input into a format that quantum algorithms can understand, enabling visualization of quantum states, quantum-inspired machine learning, dynamic scenario generation, and optimization of quantum error correction. This allows the decision support system to optimize the user's complex decision-making process, improving both accuracy and speed.

[0029] The decision support system according to this embodiment comprises a processing unit, an analysis unit, a correlation analysis unit, a simulation unit, and a security unit. The processing unit performs massively parallel processing using quantum algorithms. For example, the processing unit simultaneously explores a vast number of possibilities using quantum algorithms. For example, the processing unit can perform prime factorization using Shor's algorithm. The processing unit can also perform database searches using Grover's algorithm. Furthermore, the processing unit can perform parallel computations using qubits. The analysis unit performs personalized analysis based on the data obtained by the processing unit. For example, the analysis unit analyzes the user's past decisions, values, and goals using deep learning. For example, the analysis unit learns the user's past data using a neural network and makes personalized suggestions. The analysis unit can also build a model using training data and predict the user's behavior patterns. Furthermore, the analysis unit can customize the suggestions based on the user's values ​​and goals. The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. For example, the correlation analysis unit performs correlation analysis that mimics quantum entanglement. The correlation analysis unit analyzes the interaction between complex factors, for example, using an entanglement generation method. The correlation analysis unit can also analyze correlations by adjusting the number of qubits used. Furthermore, the correlation analysis unit can evaluate the correlation between data using a correlation coefficient calculation method. The simulation unit performs real-time simulations based on the data obtained by the correlation analysis unit. The simulation unit predicts and visualizes the outcome of decisions using quantum probabilities, for example, using real-time quantum simulation. The simulation unit performs simulations using different types of probability distributions, for example. The simulation unit can also adjust the simulation update frequency using a computation algorithm. Furthermore, the simulation unit can display simulation results as graphs and charts. The security unit enhances security based on the data obtained by the simulation unit. The security unit protects user confidential information using quantum cryptography, for example.The security unit generates encryption keys, for example, using quantum key distribution (QKD). The security unit can also encrypt data using qubits. Furthermore, the security unit can protect data by setting security policies. As a result, the decision support system according to this embodiment can optimize the user's complex decision-making process, improving both accuracy and speed.

[0030] The processing unit performs massively parallel processing using quantum algorithms. Specifically, it simultaneously explores a vast number of possibilities using quantum algorithms. For example, it can perform prime factorization using Shor's algorithm. Shor's algorithm can dramatically improve the computation speed of prime factorization compared to conventional classical algorithms. This brings significant advantages in fields such as cryptography and security analysis. It can also perform database searches using Grover's algorithm. Grover's algorithm is a quantum algorithm for rapidly searching specific data, and can complete searches in the square root of the time compared to classical search algorithms. Furthermore, the processing unit can also perform parallel computation using qubits. Since qubits can simultaneously be in both 0 and 1 states, they greatly improve the efficiency of parallel computation. This makes it possible to solve complex computational problems in a short amount of time. By making full use of these quantum algorithms, the processing unit can process vast amounts of data quickly and efficiently, improving the overall performance of the decision support system.

[0031] The analysis unit performs personalized analysis based on data obtained by the processing unit. Specifically, it analyzes the user's past decisions, values, and goals using deep learning. Deep learning is a technology that uses multi-layered neural networks to learn data and extract complex patterns and relationships. For example, the analysis unit inputs the user's past data into the neural network to learn the user's behavioral patterns and tendencies. This allows it to provide suggestions optimized for the user. The analysis unit can also build models using training data to predict the user's behavioral patterns. For example, it can predict future actions and provide appropriate advice based on the user's past decisions. Furthermore, the analysis unit can customize the suggestions based on the user's values ​​and goals. This allows it to provide individualized suggestions tailored to the user's needs and improve the accuracy of decision-making. The analysis unit utilizes these technologies to provide users with advanced personalized analysis and maximize the effectiveness of the decision support system.

[0032] The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. Specifically, it performs correlation analysis that mimics quantum entanglement. Quantum entanglement is a phenomenon that shows strong correlations between qubits, and by mimicking it, it is possible to analyze the interactions between complex factors. For example, the correlation analysis unit analyzes the correlation between data using an entanglement generation method. This makes it possible to clarify how multiple factors interact. The correlation analysis unit can also analyze correlations by adjusting the number of qubits used. Increasing the number of qubits makes it possible to perform a more detailed correlation analysis. Furthermore, the correlation analysis unit can also evaluate the correlation between data using a correlation coefficient calculation method. The correlation coefficient is an indicator that shows the strength of the relationship between data, and by using it, the correlation between data can be quantitatively evaluated. By making full use of these techniques, the correlation analysis unit can analyze complex correlations between data and improve the accuracy of the decision support system.

[0033] The simulation unit performs real-time simulations based on data obtained by the correlation analysis unit. Specifically, it uses real-time quantum simulation to predict and visualize the outcome of decisions using quantum probability. Quantum simulation is a technology that simulates the behavior of a system using the principles of quantum mechanics, enabling highly accurate prediction of the behavior of complex systems. The simulation unit performs simulations using, for example, different types of probability distributions. Probability distributions are used to probabilistically represent the behavior of a system, allowing for detailed predictions of its behavior. The simulation unit can also adjust the simulation update frequency using computational algorithms. By adjusting the update frequency, a good balance between simulation accuracy and speed can be maintained. Furthermore, the simulation unit can display simulation results as graphs and charts. This allows users to intuitively understand the simulation results. By utilizing these technologies, the simulation unit can provide highly accurate, real-time simulations, maximizing the effectiveness of the decision support system.

[0034] The Security Department enhances security based on data obtained by the Simulation Department. Specifically, it protects users' confidential information using quantum cryptography. Quantum cryptography is an encryption technology that utilizes the principles of quantum mechanics and provides significantly higher security than conventional encryption technologies. For example, the Security Department generates encryption keys using quantum key distribution (QKD). QKD is a technology that securely distributes encryption keys using quantum bits, making eavesdropping and tampering extremely difficult. The Security Department can also encrypt data using quantum bits. Using quantum bits allows for extremely fast encryption and decryption of data. Furthermore, the Security Department can protect data by setting security policies. Security policies define data access rights and usage conditions, preventing unauthorized access and leakage of data. By utilizing these technologies, the Security Department can highly protect users' confidential information and improve the reliability and security of the decision support system.

[0035] The processing unit can perform massively parallel processing using quantum algorithms. For example, the processing unit can simultaneously explore a vast number of possibilities using quantum algorithms. For example, the processing unit can perform prime factorization using Shor's algorithm. The processing unit can also perform database searches using Grover's algorithm. Furthermore, the processing unit can perform parallel computations using qubits. Thus, massively parallel processing becomes possible by using quantum algorithms. Quantum algorithms include, but are not limited to, Shor's algorithm and Grover's algorithm. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the execution of a quantum algorithm into an AI model, and the AI ​​model can select the optimal parallel processing method.

[0036] The analysis unit can analyze a user's past decisions, values, and goals using deep learning to provide personalized recommendations. For example, the analysis unit can learn from the user's past data and provide personalized recommendations. For example, the analysis unit can use a neural network to learn from the user's past data and provide personalized recommendations. Furthermore, the analysis unit can build a model using training data to predict the user's behavioral patterns. In addition, the analysis unit can customize the recommendations based on the user's values ​​and goals. This improves the accuracy of decision-making by providing optimized recommendations based on the user's past data. Deep learning includes, but is not limited to, neural networks and training data. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input the user's past data into an AI model, which can then generate optimal recommendations.

[0037] The correlation analysis unit can perform correlation analysis that mimics quantum entanglement. For example, the correlation analysis unit analyzes the interaction between complex factors using an entanglement generation method. The correlation analysis unit can also analyze correlations by adjusting the number of qubits used. Furthermore, the correlation analysis unit can evaluate the correlation between data using a correlation coefficient calculation method. This allows for the analysis of complex factor interactions by mimicking quantum entanglement. Quantum entanglement includes, but is not limited to, an entanglement generation method and the number of qubits used. Some or all of the above-described processes in the correlation analysis unit may be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input an entanglement generation method into an AI model, and the AI ​​model can analyze the correlations.

[0038] The simulation unit can predict and visualize the outcome of a decision using quantum probability through real-time quantum simulation. For example, the simulation unit can predict and visualize the outcome of a decision using quantum probability through real-time quantum simulation. The simulation unit can perform simulations using, for example, a type of probability distribution. Furthermore, the simulation unit can adjust the update frequency of the simulation using a computational algorithm. In addition, the simulation unit can display the simulation results as graphs or charts. This enables support for decision-making by performing simulations in real time and visualizing the results. Quantum probability includes, but is not limited to, types of probability distributions and computational algorithms. Some or all of the above-described processes in the simulation unit may be performed using, for example, AI, or without AI. For example, the simulation unit can input the simulation execution into an AI model, which can then visualize the simulation results.

[0039] The security unit can protect users' confidential information using quantum cryptography. For example, the security unit can protect users' confidential information using quantum cryptography. For example, the security unit can generate encryption keys using quantum key distribution (QKD). The security unit can also encrypt data using qubits. Furthermore, the security unit can protect data by setting security policies. This allows for high-security protection of users' confidential information using quantum cryptography. Quantum cryptography includes, but is not limited to, quantum key distribution (QKD) and the use of qubits. Some or all of the above-described processes in the security unit may be performed using, for example, AI, or not. For example, the security unit can input the execution of quantum cryptography into an AI model, which can then select the optimal encryption method.

[0040] The processing unit can use generative AI to convert the user's natural language input into a format that a quantum algorithm can understand. For example, the processing unit can use generative AI to convert the user's natural language input into a format that a quantum algorithm can understand. For example, the processing unit can use generative AI to analyze the user's natural language input and convert it into a format that a quantum algorithm can understand. The processing unit can also use generative AI to summarize the user's input and convert it into a format suitable for a quantum algorithm. Furthermore, the processing unit can use generative AI to understand the user's intent and select an appropriate quantum algorithm. In this way, by using generative AI, the user's natural language input can be converted into a format that a quantum algorithm can understand. Generative AI includes, but is not limited to, text generation AI and natural language processing techniques. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the execution of the generative AI into an AI model, and the AI ​​model can perform the optimal format conversion.

[0041] The simulation unit can visualize quantum states using generative AI. For example, the simulation unit visualizes quantum states using generative AI. For example, the simulation unit has the generative AI analyze and visualize quantum state data. The simulation unit can also have the generative AI display changes in quantum states in real time. Furthermore, the simulation unit can have the generative AI display the simulation results of quantum states as graphs or charts. Thus, the visualization of quantum states becomes possible by using generative AI. Visualization of quantum states includes, but is not limited to, visualization tools and the types of information to be displayed. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the execution of generative AI into an AI model, and the AI ​​model can visualize quantum states.

[0042] The analysis unit can perform quantum-inspired machine learning using generative AI. For example, the analysis unit can perform quantum-inspired machine learning using generative AI. For example, the analysis unit can have the generative AI execute a quantum-inspired machine learning algorithm and analyze the data. The analysis unit can also have the generative AI build a model using training data and perform quantum-inspired machine learning. Furthermore, the analysis unit can have the generative AI select the optimal machine learning model based on user data. This makes quantum-inspired machine learning possible by using generative AI. Quantum-inspired machine learning includes, but is not limited to, the algorithms used and training data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the execution of the generative AI into an AI model, and the AI ​​model can perform quantum-inspired machine learning.

[0043] The simulation unit can generate dynamic scenarios using generative AI. For example, the simulation unit generates dynamic scenarios using generative AI. For example, the simulation unit generates dynamic scenarios by having the generative AI execute a scenario generation algorithm. The simulation unit can also generate scenarios based on the data used by the generative AI. Furthermore, the simulation unit can display the changes in the scenarios generated by the generative AI in real time. This makes dynamic scenario generation possible by using generative AI. Dynamic scenario generation includes, but is not limited to, a scenario generation algorithm and the data used. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the execution of the generative AI into an AI model, and the AI ​​model can perform dynamic scenario generation.

[0044] The security unit can optimize quantum error correction using generative AI. For example, the security unit optimizes quantum error correction using generative AI. For example, the security unit optimizes quantum error correction by having the generative AI execute an algorithm for error correction code. The security unit can also have the generative AI perform error correction using an error detection method. Furthermore, the security unit can display the results of the error correction performed by the generative AI in real time. This makes it possible to optimize quantum error correction by using generative AI. Quantum error correction includes, but is not limited to, error correction codes and error detection methods. Some or all of the above processing in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the execution of the generative AI into an AI model, and the AI ​​model can optimize quantum error correction.

[0045] The processing unit can select the optimal parallel processing method by referring to the user's past decision-making history when executing a quantum algorithm. For example, the processing unit can select the optimal method based on the parallel processing method previously selected by the user. For example, the processing unit can also propose an efficient parallel processing method from the user's past decision-making history. Furthermore, the processing unit can analyze the user's past decision-making history and select the most effective parallel processing method. This allows the optimal parallel processing method to be selected by referring to the user's past decision-making history. Past decision-making history includes, but is not limited to, examples such as how historical data is stored and the reference algorithm. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal parallel processing method.

[0046] The processing unit can determine processing priorities based on the user's current situation and goals when executing quantum algorithms. For example, the processing unit can determine processing priorities based on the user's current project progress. The processing unit can also adjust processing priorities based on the user's short-term goals. Furthermore, the processing unit can set processing priorities based on the user's long-term goals. This enables efficient processing by determining processing priorities based on the user's current situation and goals. Current situation and goals include, but are not limited to, the method of collecting situation data and the criteria for setting goals. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's current situation and goals into an AI model, which can then determine the optimal processing priorities.

[0047] The processing unit can prioritize processing highly relevant data by considering the user's geographical location information when executing a quantum algorithm. For example, if the user is in a specific region, the processing unit can prioritize processing data related to that region. For example, if the user is on the move, the processing unit can also prioritize processing highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the processing unit can also prioritize processing data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, the method of obtaining location information and the criteria for evaluating relevance. Some or all of the processing described above in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the user's geographical location information into an AI model, which can then prioritize processing highly relevant data.

[0048] The processing unit can analyze the user's social media activity and process the relevant data when executing a quantum algorithm. For example, the processing unit can prioritize processing data related to the user's current interests from the user's social media activity. The processing unit can also analyze the user's social media activity and process the data based on relevant topics. Furthermore, the processing unit can extract important information from the user's social media activity and process it with priority. This allows for the priority processing of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity into an AI model, and the AI ​​model can process the relevant data.

[0049] The analysis unit can select the optimal analysis method by referring to the user's past decision history during analysis. For example, the analysis unit can select the optimal method based on the analysis methods the user has used in the past. For example, the analysis unit can also suggest an efficient analysis method from the user's past decision history. Furthermore, the analysis unit can analyze the user's past decision history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past decision history. The optimal analysis method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past decision history into an AI model, and the AI ​​model can select the optimal analysis method.

[0050] The analysis unit can adjust the level of detail of the analysis based on the user's values ​​and goals. For example, the analysis unit can prioritize analyzing important data based on the user's values. The analysis unit can also perform a detailed analysis based on the user's short-term goals. Furthermore, the analysis unit can perform a holistic analysis based on the user's long-term goals. By adjusting the level of detail of the analysis based on the user's values ​​and goals, more appropriate analysis results can be provided. The level of detail of the analysis includes, but is not limited to, the criteria for setting the level of detail and the types of information to display. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's values ​​and goals into an AI model, which can then adjust the optimal level of detail of the analysis.

[0051] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can prioritize the analysis of data related to that region. For example, if the user is on the move, the analysis unit can also prioritize the analysis of highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the analysis unit can also prioritize the analysis of data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's geographical location information into an AI model, and the AI ​​model can prioritize the analysis of highly relevant data.

[0052] The analysis unit can analyze users' social media activity and related data during the analysis process. For example, the analysis unit can prioritize the analysis of data related to users' current interests from their social media activity. The analysis unit can also analyze data based on relevant topics, for example, by analyzing users' social media activity. Furthermore, the analysis unit can extract important information from users' social media activity and prioritize its analysis. This allows for the priority analysis of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input users' social media activity into an AI model, and the AI ​​model can analyze the relevant data.

[0053] The correlation analysis unit can select the optimal correlation analysis method by referring to the user's past decision-making history during correlation analysis. For example, the correlation analysis unit can select the optimal method based on the correlation analysis methods the user has used in the past. For example, the correlation analysis unit can also propose an efficient correlation analysis method from the user's past decision-making history. Furthermore, the correlation analysis unit can analyze the user's past decision-making history and select the most effective correlation analysis method. This allows the optimal correlation analysis method to be selected by referring to the user's past decision-making history. The optimal correlation analysis method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or without AI. For example, the correlation analysis unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal correlation analysis method.

[0054] The correlation analysis unit can adjust the level of detail of correlations based on the user's values ​​and goals during correlation analysis. For example, the correlation analysis unit prioritizes analyzing important correlations based on the user's values. The correlation analysis unit can also perform detailed correlation analysis based on the user's short-term goals. Furthermore, the correlation analysis unit can perform overall correlation analysis based on the user's long-term goals. By adjusting the level of detail of correlations based on the user's values ​​and goals, a more appropriate correlation analysis becomes possible. The level of detail of correlations includes, but is not limited to, the criteria for setting the level of detail and the type of information to display. Some or all of the above processing in the correlation analysis unit may be performed using, for example, AI, or not using AI. For example, the correlation analysis unit can input the user's values ​​and goals into an AI model, and the AI ​​model can adjust the optimal level of detail of correlations.

[0055] The correlation analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location information during correlation analysis. For example, if the user is in a specific region, the correlation analysis unit can prioritize the analysis of data related to that region. For example, if the user is on the move, the correlation analysis unit can also prioritize the analysis of highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the correlation analysis unit can also prioritize the analysis of data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location information. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or without AI. For example, the correlation analysis unit can input the user's geographical location information into an AI model, which can then prioritize the analysis of highly relevant data.

[0056] The correlation analysis unit can analyze users' social media activity and analyze relevant data during correlation analysis. For example, the correlation analysis unit can prioritize the analysis of data related to users' current interests from their social media activity. The correlation analysis unit can also analyze data based on relevant topics, for example, by analyzing users' social media activity. Furthermore, the correlation analysis unit can extract important information from users' social media activity and prioritize its analysis. This allows for the priority analysis of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, the types of data used and analysis algorithms. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or without AI. For example, the correlation analysis unit can input users' social media activity into an AI model, and the AI ​​model can analyze the relevant data.

[0057] The simulation unit can select the optimal simulation method by referring to the user's past decision-making history during simulation. For example, the simulation unit can select the optimal method based on simulation methods previously used by the user. For example, the simulation unit can also propose an efficient simulation method based on the user's past decision-making history. Furthermore, the simulation unit can analyze the user's past decision-making history and select the most effective simulation method. In this way, the optimal simulation method can be selected by referring to the user's past decision-making history. The optimal simulation method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal simulation method.

[0058] The simulation unit can adjust the level of detail of the simulation based on the user's values ​​and goals during the simulation. For example, the simulation unit can prioritize simulating important data based on the user's values. The simulation unit can also perform detailed simulations based on the user's short-term goals. Furthermore, the simulation unit can perform overall simulations based on the user's long-term goals. By adjusting the level of detail of the simulation based on the user's values ​​and goals, a more appropriate simulation becomes possible. The level of detail of the simulation includes, but is not limited to, the criteria for setting the level of detail and the types of information to display. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's values ​​and goals into an AI model, and the AI ​​model can adjust the optimal level of detail of the simulation.

[0059] The simulation unit can prioritize simulating highly relevant data by considering the user's geographical location information during the simulation. For example, if the user is in a specific region, the simulation unit will prioritize simulating data related to that region. For example, if the user is on the move, the simulation unit can also prioritize simulating highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the simulation unit can also prioritize simulating data related to that location. This allows for the priority of simulating highly relevant data by considering the user's geographical location information. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information into an AI model, and the AI ​​model can prioritize simulating highly relevant data.

[0060] The simulation unit can analyze a user's social media activity and simulate relevant data during the simulation. For example, the simulation unit can prioritize simulating data related to the user's current interests from their social media activity. The simulation unit can also analyze a user's social media activity and simulate data based on relevant topics. Furthermore, the simulation unit can extract important information from a user's social media activity and prioritize simulating it. This allows for the priority simulation of relevant data by analyzing a user's social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input a user's social media activity into an AI model, and the AI ​​model can simulate relevant data.

[0061] The security department can select the optimal security enhancement method by referring to the user's past security history when enhancing security. For example, the security department can select the optimal method based on the security enhancement methods the user has used in the past. For example, the security department can also propose an efficient security enhancement method based on the user's past security history. Furthermore, the security department can analyze the user's past security history and select the most effective security enhancement method. In this way, the optimal security enhancement method can be selected by referring to the user's past security history. The optimal security enhancement method includes, but is not limited to, the algorithms used and the types of data used. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's past security history into an AI model, and the AI ​​model can select the optimal security enhancement method.

[0062] The security department can adjust the level of security detail based on the user's values ​​and goals when enhancing security. For example, the security department can prioritize the protection of critical data based on the user's values. The security department can also perform detailed security enhancements based on the user's short-term goals. Furthermore, the security department can perform overall security enhancements based on the user's long-term goals. This allows for more appropriate security enhancements by adjusting the level of security detail based on the user's values ​​and goals. Security detail includes, but is not limited to, the criteria for setting the level of detail and the types of information to display. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input the user's values ​​and goals into an AI model, which can then adjust the optimal level of security detail.

[0063] The security department can prioritize the protection of highly relevant data by considering the user's geographical location when enhancing security. For example, if the user is in a specific region, the security department can prioritize the protection of data related to that region. For example, if the user is on the move, the security department can also prioritize the protection of highly relevant data based on the user's current location. Furthermore, if the security department is in a specific location, the security department can also prioritize the protection of data related to that location. This allows for the priority protection of highly relevant data by considering the user's geographical location. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the security department may be performed using AI, for example, or not using AI. For example, the security department can input the user's geographical location information into an AI model, and the AI ​​model can prioritize the protection of highly relevant data.

[0064] The security department can analyze users' social media activity and protect related data when enhancing security. For example, the security department can prioritize protecting data related to users' current interests from their social media activity. The security department can also analyze users' social media activity and protect data based on relevant topics. Furthermore, the security department can extract important information from users' social media activity and prioritize its protection. This allows for the priority protection of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the above processing by the security department may be performed using, for example, AI, or not using AI. For example, the security department can input users' social media activity into an AI model, and the AI ​​model can protect related data.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The decision support system can further prioritize the processing of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the processing of data related to that region. If the user is on the move, it can also prioritize the processing of highly relevant data based on their current location. Furthermore, if the user is in a specific location, it can also prioritize the processing of data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, the method of acquiring location information and the criteria for evaluating relevance. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location information into an AI model, which can then prioritize the processing of highly relevant data.

[0067] The decision support system can further analyze the user's social media activity and process the relevant data. For example, it can prioritize processing data related to the user's current interests from the user's social media activity. It can also analyze the user's social media activity and process the data based on relevant topics. Furthermore, it can extract important information from the user's social media activity and process it with priority. This allows for the priority processing of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity into an AI model, and the AI ​​model can process the relevant data.

[0068] The decision support system can further select the optimal parallel processing method by referring to the user's past decision-making history. For example, it can select the optimal method based on the parallel processing methods the user has previously selected. It can also suggest an efficient parallel processing method based on the user's past decision-making history. Furthermore, it can analyze the user's past decision-making history and select the most effective parallel processing method. In this way, the optimal parallel processing method can be selected by referring to the user's past decision-making history. Past decision-making history includes, but is not limited to, examples such as the method of storing historical data and the reference algorithm. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal parallel processing method.

[0069] The decision support system can further determine processing priorities based on the user's values ​​and goals. For example, it can determine processing priorities based on the user's current project progress. It can also adjust processing priorities based on the user's short-term goals. Furthermore, it can set processing priorities based on the user's long-term goals. This enables efficient processing by determining processing priorities based on the user's current situation and goals. Current situation and goals include, but are not limited to, the method of collecting situation data and the criteria for setting goals. Some or all of the processing described above in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input the user's current situation and goals into an AI model, and the AI ​​model can determine the optimal processing priorities.

[0070] The decision support system can further select the optimal analysis method by referring to the user's past decision history. For example, it can select the optimal method based on analysis methods the user has used in the past. It can also suggest an efficient analysis method from the user's past decision history. Furthermore, it can analyze the user's past decision history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past decision history. The optimal analysis method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past decision history into an AI model, and the AI ​​model can select the optimal analysis method.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The processing unit performs massively parallel processing using quantum algorithms. For example, it can perform prime factorization using Shor's algorithm or database searches using Grover's algorithm. It can also perform parallel computation using qubits. Step 2: The analysis unit performs personalized analysis based on the data obtained by the processing unit. For example, it analyzes the user's past decisions, values, and goals using deep learning, learns the user's past data using a neural network, and provides personalized recommendations. It can also build a model using training data to predict the user's behavior patterns. Furthermore, it can customize the recommendations based on the user's values ​​and goals. Step 3: The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. For example, it performs correlation analysis mimicking quantum entanglement and analyzes the interaction between complex factors using an entanglement generation method. It can also analyze correlations by adjusting the number of qubits used. Furthermore, it can evaluate the correlation between data using a correlation coefficient calculation method. Step 4: The simulation unit performs real-time simulations based on the data obtained by the correlation analysis unit. For example, it uses real-time quantum simulation to predict and visualize the outcome of decisions using quantum probabilities. It is also possible to perform simulations using different probability distributions and adjust the simulation update frequency using computational algorithms. Furthermore, the simulation results can be displayed as graphs or charts. Step 5: The security unit enhances security based on the data obtained by the simulation unit. For example, it protects users' confidential information using quantum cryptography and generates encryption keys using quantum key distribution (QKD). It can also encrypt data using qubits. Furthermore, it can protect data by setting security policies.

[0073] (Example of form 2) The decision support system according to an embodiment of the present invention is a system that integrates quantum information science and AI. This decision support system optimizes the user's complex decision-making process with the power of quantum computing and combines it with advanced analysis by AI to present optimal options to individuals and companies with unprecedented accuracy and speed. Specifically, it consists of the following steps: First, it performs massively parallel processing using quantum algorithms to simultaneously explore a vast number of possibilities. Next, it performs AI-driven personalized analysis, analyzing the user's past decisions, values, and goals using deep learning to provide personalized suggestions. Furthermore, it performs correlation analysis mimicking quantum entanglement to analyze the interactions between intricately intertwined factors from a quantum perspective. Using real-time quantum simulation, it predicts and visualizes the outcome of the decision using quantum probability. Finally, it enhances security with quantum cryptography to protect the user's confidential information at the quantum level. This makes it possible to present optimal options to individuals and companies with unprecedented accuracy and speed. Furthermore, using generative AI, the system converts the user's natural language input into a format that quantum algorithms can understand, enabling visualization of quantum states, quantum-inspired machine learning, dynamic scenario generation, and optimization of quantum error correction. This allows the decision support system to optimize the user's complex decision-making process, improving both accuracy and speed.

[0074] The decision support system according to this embodiment comprises a processing unit, an analysis unit, a correlation analysis unit, a simulation unit, and a security unit. The processing unit performs massively parallel processing using quantum algorithms. For example, the processing unit simultaneously explores a vast number of possibilities using quantum algorithms. For example, the processing unit can perform prime factorization using Shor's algorithm. The processing unit can also perform database searches using Grover's algorithm. Furthermore, the processing unit can perform parallel computations using qubits. The analysis unit performs personalized analysis based on the data obtained by the processing unit. For example, the analysis unit analyzes the user's past decisions, values, and goals using deep learning. For example, the analysis unit learns the user's past data using a neural network and makes personalized suggestions. The analysis unit can also build a model using training data and predict the user's behavior patterns. Furthermore, the analysis unit can customize the suggestions based on the user's values ​​and goals. The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. For example, the correlation analysis unit performs correlation analysis that mimics quantum entanglement. The correlation analysis unit analyzes the interaction between complex factors, for example, using an entanglement generation method. The correlation analysis unit can also analyze correlations by adjusting the number of qubits used. Furthermore, the correlation analysis unit can evaluate the correlation between data using a correlation coefficient calculation method. The simulation unit performs real-time simulations based on the data obtained by the correlation analysis unit. The simulation unit predicts and visualizes the outcome of decisions using quantum probabilities, for example, using real-time quantum simulation. The simulation unit performs simulations using different types of probability distributions, for example. The simulation unit can also adjust the simulation update frequency using a computation algorithm. Furthermore, the simulation unit can display simulation results as graphs and charts. The security unit enhances security based on the data obtained by the simulation unit. The security unit protects user confidential information using quantum cryptography, for example.The security unit generates encryption keys, for example, using quantum key distribution (QKD). The security unit can also encrypt data using qubits. Furthermore, the security unit can protect data by setting security policies. As a result, the decision support system according to this embodiment can optimize the user's complex decision-making process, improving both accuracy and speed.

[0075] The processing unit performs massively parallel processing using quantum algorithms. Specifically, it simultaneously explores a vast number of possibilities using quantum algorithms. For example, it can perform prime factorization using Shor's algorithm. Shor's algorithm can dramatically improve the computation speed of prime factorization compared to conventional classical algorithms. This brings significant advantages in fields such as cryptography and security analysis. It can also perform database searches using Grover's algorithm. Grover's algorithm is a quantum algorithm for rapidly searching specific data, and can complete searches in the square root of the time compared to classical search algorithms. Furthermore, the processing unit can also perform parallel computation using qubits. Since qubits can simultaneously be in both 0 and 1 states, they greatly improve the efficiency of parallel computation. This makes it possible to solve complex computational problems in a short amount of time. By making full use of these quantum algorithms, the processing unit can process vast amounts of data quickly and efficiently, improving the overall performance of the decision support system.

[0076] The analysis unit performs personalized analysis based on data obtained by the processing unit. Specifically, it analyzes the user's past decisions, values, and goals using deep learning. Deep learning is a technology that uses multi-layered neural networks to learn data and extract complex patterns and relationships. For example, the analysis unit inputs the user's past data into the neural network to learn the user's behavioral patterns and tendencies. This allows it to provide suggestions optimized for the user. The analysis unit can also build models using training data to predict the user's behavioral patterns. For example, it can predict future actions and provide appropriate advice based on the user's past decisions. Furthermore, the analysis unit can customize the suggestions based on the user's values ​​and goals. This allows it to provide individualized suggestions tailored to the user's needs and improve the accuracy of decision-making. The analysis unit utilizes these technologies to provide users with advanced personalized analysis and maximize the effectiveness of the decision support system.

[0077] The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. Specifically, it performs correlation analysis that mimics quantum entanglement. Quantum entanglement is a phenomenon that shows strong correlations between qubits, and by mimicking it, it is possible to analyze the interactions between complex factors. For example, the correlation analysis unit analyzes the correlation between data using an entanglement generation method. This makes it possible to clarify how multiple factors interact. The correlation analysis unit can also analyze correlations by adjusting the number of qubits used. Increasing the number of qubits makes it possible to perform a more detailed correlation analysis. Furthermore, the correlation analysis unit can also evaluate the correlation between data using a correlation coefficient calculation method. The correlation coefficient is an indicator that shows the strength of the relationship between data, and by using it, the correlation between data can be quantitatively evaluated. By making full use of these techniques, the correlation analysis unit can analyze complex correlations between data and improve the accuracy of the decision support system.

[0078] The simulation unit performs real-time simulations based on data obtained by the correlation analysis unit. Specifically, it uses real-time quantum simulation to predict and visualize the outcome of decisions using quantum probability. Quantum simulation is a technology that simulates the behavior of a system using the principles of quantum mechanics, enabling highly accurate prediction of the behavior of complex systems. The simulation unit performs simulations using, for example, different types of probability distributions. Probability distributions are used to probabilistically represent the behavior of a system, allowing for detailed predictions of its behavior. The simulation unit can also adjust the simulation update frequency using computational algorithms. By adjusting the update frequency, a good balance between simulation accuracy and speed can be maintained. Furthermore, the simulation unit can display simulation results as graphs and charts. This allows users to intuitively understand the simulation results. By utilizing these technologies, the simulation unit can provide highly accurate, real-time simulations, maximizing the effectiveness of the decision support system.

[0079] The Security Department enhances security based on data obtained by the Simulation Department. Specifically, it protects users' confidential information using quantum cryptography. Quantum cryptography is an encryption technology that utilizes the principles of quantum mechanics and provides significantly higher security than conventional encryption technologies. For example, the Security Department generates encryption keys using quantum key distribution (QKD). QKD is a technology that securely distributes encryption keys using quantum bits, making eavesdropping and tampering extremely difficult. The Security Department can also encrypt data using quantum bits. Using quantum bits allows for extremely fast encryption and decryption of data. Furthermore, the Security Department can protect data by setting security policies. Security policies define data access rights and usage conditions, preventing unauthorized access and leakage of data. By utilizing these technologies, the Security Department can highly protect users' confidential information and improve the reliability and security of the decision support system.

[0080] The processing unit can perform massively parallel processing using quantum algorithms. For example, the processing unit can simultaneously explore a vast number of possibilities using quantum algorithms. For example, the processing unit can perform prime factorization using Shor's algorithm. The processing unit can also perform database searches using Grover's algorithm. Furthermore, the processing unit can perform parallel computations using qubits. Thus, massively parallel processing becomes possible by using quantum algorithms. Quantum algorithms include, but are not limited to, Shor's algorithm and Grover's algorithm. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the execution of a quantum algorithm into an AI model, and the AI ​​model can select the optimal parallel processing method.

[0081] The analysis unit can analyze a user's past decisions, values, and goals using deep learning to provide personalized recommendations. For example, the analysis unit can learn from the user's past data and provide personalized recommendations. For example, the analysis unit can use a neural network to learn from the user's past data and provide personalized recommendations. Furthermore, the analysis unit can build a model using training data to predict the user's behavioral patterns. In addition, the analysis unit can customize the recommendations based on the user's values ​​and goals. This improves the accuracy of decision-making by providing optimized recommendations based on the user's past data. Deep learning includes, but is not limited to, neural networks and training data. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input the user's past data into an AI model, which can then generate optimal recommendations.

[0082] The correlation analysis unit can perform correlation analysis that mimics quantum entanglement. For example, the correlation analysis unit analyzes the interaction between complex factors using an entanglement generation method. The correlation analysis unit can also analyze correlations by adjusting the number of qubits used. Furthermore, the correlation analysis unit can evaluate the correlation between data using a correlation coefficient calculation method. This allows for the analysis of complex factor interactions by mimicking quantum entanglement. Quantum entanglement includes, but is not limited to, an entanglement generation method and the number of qubits used. Some or all of the above-described processes in the correlation analysis unit may be performed using, for example, AI, or without AI. For example, the correlation analysis unit can input an entanglement generation method into an AI model, and the AI ​​model can analyze the correlations.

[0083] The simulation unit can predict and visualize the outcome of a decision using quantum probability through real-time quantum simulation. For example, the simulation unit can predict and visualize the outcome of a decision using quantum probability through real-time quantum simulation. The simulation unit can perform simulations using, for example, a type of probability distribution. Furthermore, the simulation unit can adjust the update frequency of the simulation using a computational algorithm. In addition, the simulation unit can display the simulation results as graphs or charts. This enables support for decision-making by performing simulations in real time and visualizing the results. Quantum probability includes, but is not limited to, types of probability distributions and computational algorithms. Some or all of the above-described processes in the simulation unit may be performed using, for example, AI, or without AI. For example, the simulation unit can input the simulation execution into an AI model, which can then visualize the simulation results.

[0084] The security unit can protect users' confidential information using quantum cryptography. For example, the security unit can protect users' confidential information using quantum cryptography. For example, the security unit can generate encryption keys using quantum key distribution (QKD). The security unit can also encrypt data using qubits. Furthermore, the security unit can protect data by setting security policies. This allows for high-security protection of users' confidential information using quantum cryptography. Quantum cryptography includes, but is not limited to, quantum key distribution (QKD) and the use of qubits. Some or all of the above-described processes in the security unit may be performed using, for example, AI, or not. For example, the security unit can input the execution of quantum cryptography into an AI model, which can then select the optimal encryption method.

[0085] The processing unit can use generative AI to convert the user's natural language input into a format that a quantum algorithm can understand. For example, the processing unit can use generative AI to convert the user's natural language input into a format that a quantum algorithm can understand. For example, the processing unit can use generative AI to analyze the user's natural language input and convert it into a format that a quantum algorithm can understand. The processing unit can also use generative AI to summarize the user's input and convert it into a format suitable for a quantum algorithm. Furthermore, the processing unit can use generative AI to understand the user's intent and select an appropriate quantum algorithm. In this way, by using generative AI, the user's natural language input can be converted into a format that a quantum algorithm can understand. Generative AI includes, but is not limited to, text generation AI and natural language processing techniques. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the execution of the generative AI into an AI model, and the AI ​​model can perform the optimal format conversion.

[0086] The simulation unit can visualize quantum states using generative AI. For example, the simulation unit visualizes quantum states using generative AI. For example, the simulation unit has the generative AI analyze and visualize quantum state data. The simulation unit can also have the generative AI display changes in quantum states in real time. Furthermore, the simulation unit can have the generative AI display the simulation results of quantum states as graphs or charts. Thus, the visualization of quantum states becomes possible by using generative AI. Visualization of quantum states includes, but is not limited to, visualization tools and the types of information to be displayed. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the execution of generative AI into an AI model, and the AI ​​model can visualize quantum states.

[0087] The analysis unit can perform quantum-inspired machine learning using generative AI. For example, the analysis unit can perform quantum-inspired machine learning using generative AI. For example, the analysis unit can have the generative AI execute a quantum-inspired machine learning algorithm and analyze the data. The analysis unit can also have the generative AI build a model using training data and perform quantum-inspired machine learning. Furthermore, the analysis unit can have the generative AI select the optimal machine learning model based on user data. This makes quantum-inspired machine learning possible by using generative AI. Quantum-inspired machine learning includes, but is not limited to, the algorithms used and training data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the execution of the generative AI into an AI model, and the AI ​​model can perform quantum-inspired machine learning.

[0088] The simulation unit can generate dynamic scenarios using generative AI. For example, the simulation unit generates dynamic scenarios using generative AI. For example, the simulation unit generates dynamic scenarios by having the generative AI execute a scenario generation algorithm. The simulation unit can also generate scenarios based on the data used by the generative AI. Furthermore, the simulation unit can display the changes in the scenarios generated by the generative AI in real time. This makes dynamic scenario generation possible by using generative AI. Dynamic scenario generation includes, but is not limited to, a scenario generation algorithm and the data used. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the execution of the generative AI into an AI model, and the AI ​​model can perform dynamic scenario generation.

[0089] The security unit can optimize quantum error correction using generative AI. For example, the security unit optimizes quantum error correction using generative AI. For example, the security unit optimizes quantum error correction by having the generative AI execute an algorithm for error correction code. The security unit can also have the generative AI perform error correction using an error detection method. Furthermore, the security unit can display the results of the error correction performed by the generative AI in real time. This makes it possible to optimize quantum error correction by using generative AI. Quantum error correction includes, but is not limited to, error correction codes and error detection methods. Some or all of the above processing in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input the execution of the generative AI into an AI model, and the AI ​​model can optimize quantum error correction.

[0090] The processing unit can estimate the user's emotions and adjust the parameters of the quantum algorithm based on the estimated emotions. For example, if the user is stressed, the processing unit can adjust the parameters of the quantum algorithm to reduce the computational load. For example, if the user is relaxed, the processing unit can also adjust the parameters of the quantum algorithm to perform a more detailed analysis. Furthermore, if the user is in a hurry, the processing unit can adjust the parameters of the quantum algorithm to provide a quick result. This allows for more appropriate processing by adjusting the parameters of the quantum algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user emotion data into a generative AI, which can then adjust the parameters of the quantum algorithm.

[0091] The processing unit can select the optimal parallel processing method by referring to the user's past decision-making history when executing a quantum algorithm. For example, the processing unit can select the optimal method based on the parallel processing method previously selected by the user. For example, the processing unit can also propose an efficient parallel processing method from the user's past decision-making history. Furthermore, the processing unit can analyze the user's past decision-making history and select the most effective parallel processing method. This allows the optimal parallel processing method to be selected by referring to the user's past decision-making history. Past decision-making history includes, but is not limited to, examples such as how historical data is stored and the reference algorithm. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal parallel processing method.

[0092] The processing unit can determine processing priorities based on the user's current situation and goals when executing quantum algorithms. For example, the processing unit can determine processing priorities based on the user's current project progress. The processing unit can also adjust processing priorities based on the user's short-term goals. Furthermore, the processing unit can set processing priorities based on the user's long-term goals. This enables efficient processing by determining processing priorities based on the user's current situation and goals. Current situation and goals include, but are not limited to, the method of collecting situation data and the criteria for setting goals. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's current situation and goals into an AI model, which can then determine the optimal processing priorities.

[0093] The processing unit can estimate the user's emotions and adjust the execution order of quantum algorithms based on the estimated emotions. For example, if the user is nervous, the processing unit can adjust the execution order of quantum algorithms to execute simpler tasks first. If the user is relaxed, the processing unit can also adjust the execution order of quantum algorithms to prioritize complex tasks. Furthermore, if the user is in a hurry, the processing unit can adjust the execution order of quantum algorithms to prioritize the most important tasks. This allows for more appropriate processing by adjusting the execution order of quantum algorithms based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the processing unit may be performed using AI or not using AI. For example, the processing unit can input user emotion data into a generative AI, which can then adjust the execution order of quantum algorithms.

[0094] The processing unit can prioritize processing highly relevant data by considering the user's geographical location information when executing a quantum algorithm. For example, if the user is in a specific region, the processing unit can prioritize processing data related to that region. For example, if the user is on the move, the processing unit can also prioritize processing highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the processing unit can also prioritize processing data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, the method of obtaining location information and the criteria for evaluating relevance. Some or all of the processing described above in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the user's geographical location information into an AI model, which can then prioritize processing highly relevant data.

[0095] The processing unit can analyze the user's social media activity and process the relevant data when executing a quantum algorithm. For example, the processing unit can prioritize processing data related to the user's current interests from the user's social media activity. The processing unit can also analyze the user's social media activity and process the data based on relevant topics. Furthermore, the processing unit can extract important information from the user's social media activity and process it with priority. This allows for the priority processing of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity into an AI model, and the AI ​​model can process the relevant data.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand presentation. For example, if the user is relaxed, the analysis unit can also provide a presentation that includes detailed data and graphs. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation that gets straight to the point. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0097] The analysis unit can select the optimal analysis method by referring to the user's past decision history during analysis. For example, the analysis unit can select the optimal method based on the analysis methods the user has used in the past. For example, the analysis unit can also suggest an efficient analysis method from the user's past decision history. Furthermore, the analysis unit can analyze the user's past decision history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past decision history. The optimal analysis method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past decision history into an AI model, and the AI ​​model can select the optimal analysis method.

[0098] The analysis unit can adjust the level of detail of the analysis based on the user's values ​​and goals. For example, the analysis unit can prioritize analyzing important data based on the user's values. The analysis unit can also perform a detailed analysis based on the user's short-term goals. Furthermore, the analysis unit can perform a holistic analysis based on the user's long-term goals. By adjusting the level of detail of the analysis based on the user's values ​​and goals, more appropriate analysis results can be provided. The level of detail of the analysis includes, but is not limited to, the criteria for setting the level of detail and the types of information to display. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's values ​​and goals into an AI model, which can then adjust the optimal level of detail of the analysis.

[0099] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing important data. If the user is relaxed, the analysis unit may also prioritize analyzing detailed data. Furthermore, if the user is in a hurry, the analysis unit may prioritize analyzing important data to obtain results quickly. This allows for more appropriate analysis results by prioritizing the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then determine the priority of the analysis.

[0100] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can prioritize the analysis of data related to that region. For example, if the user is on the move, the analysis unit can also prioritize the analysis of highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the analysis unit can also prioritize the analysis of data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's geographical location information into an AI model, and the AI ​​model can prioritize the analysis of highly relevant data.

[0101] The analysis unit can analyze users' social media activity and related data during the analysis process. For example, the analysis unit can prioritize the analysis of data related to users' current interests from their social media activity. The analysis unit can also analyze data based on relevant topics, for example, by analyzing users' social media activity. Furthermore, the analysis unit can extract important information from users' social media activity and prioritize its analysis. This allows for the priority analysis of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input users' social media activity into an AI model, and the AI ​​model can analyze the relevant data.

[0102] The correlation analysis unit can estimate the user's emotions and adjust the correlation analysis criteria based on the estimated user emotions. For example, if the user is stressed, the correlation analysis unit can use simple correlation analysis criteria. For example, if the user is relaxed, the correlation analysis unit can also use detailed correlation analysis criteria. Furthermore, if the user is in a hurry, the correlation analysis unit can use simplified correlation analysis criteria to obtain quick results. This allows for more appropriate correlation analysis by adjusting the correlation analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or not using AI. For example, the correlation analysis unit can input user emotion data into a generative AI, which can then adjust the correlation analysis criteria.

[0103] The correlation analysis unit can select the optimal correlation analysis method by referring to the user's past decision-making history during correlation analysis. For example, the correlation analysis unit can select the optimal method based on the correlation analysis methods the user has used in the past. For example, the correlation analysis unit can also propose an efficient correlation analysis method from the user's past decision-making history. Furthermore, the correlation analysis unit can analyze the user's past decision-making history and select the most effective correlation analysis method. This allows the optimal correlation analysis method to be selected by referring to the user's past decision-making history. The optimal correlation analysis method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or without AI. For example, the correlation analysis unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal correlation analysis method.

[0104] The correlation analysis unit can adjust the level of detail of correlations based on the user's values ​​and goals during correlation analysis. For example, the correlation analysis unit prioritizes analyzing important correlations based on the user's values. The correlation analysis unit can also perform detailed correlation analysis based on the user's short-term goals. Furthermore, the correlation analysis unit can perform overall correlation analysis based on the user's long-term goals. By adjusting the level of detail of correlations based on the user's values ​​and goals, a more appropriate correlation analysis becomes possible. The level of detail of correlations includes, but is not limited to, the criteria for setting the level of detail and the type of information to display. Some or all of the above processing in the correlation analysis unit may be performed using, for example, AI, or not using AI. For example, the correlation analysis unit can input the user's values ​​and goals into an AI model, and the AI ​​model can adjust the optimal level of detail of correlations.

[0105] The correlation analysis unit can estimate the user's emotions and adjust the order in which the correlation analysis results are displayed based on the estimated user emotions. For example, if the user is tense, the correlation analysis unit may prioritize displaying important results. For example, if the user is relaxed, the correlation analysis unit may also prioritize displaying detailed results. Furthermore, if the user is in a hurry, the correlation analysis unit may prioritize displaying important results to obtain results quickly. This allows for more appropriate result display by adjusting the order in which the correlation analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the correlation analysis unit may be performed using AI or not using AI. For example, the correlation analysis unit can input user emotion data into the generative AI, which can then adjust the order in which the correlation analysis results are displayed.

[0106] The correlation analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location information during correlation analysis. For example, if the user is in a specific region, the correlation analysis unit can prioritize the analysis of data related to that region. For example, if the user is on the move, the correlation analysis unit can also prioritize the analysis of highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the correlation analysis unit can also prioritize the analysis of data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location information. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or without AI. For example, the correlation analysis unit can input the user's geographical location information into an AI model, which can then prioritize the analysis of highly relevant data.

[0107] The correlation analysis unit can analyze users' social media activity and analyze relevant data during correlation analysis. For example, the correlation analysis unit can prioritize the analysis of data related to users' current interests from their social media activity. The correlation analysis unit can also analyze data based on relevant topics, for example, by analyzing users' social media activity. Furthermore, the correlation analysis unit can extract important information from users' social media activity and prioritize its analysis. This allows for the priority analysis of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, the types of data used and analysis algorithms. Some or all of the above processing in the correlation analysis unit may be performed using AI, for example, or without AI. For example, the correlation analysis unit can input users' social media activity into an AI model, and the AI ​​model can analyze the relevant data.

[0108] The simulation unit can estimate the user's emotions and adjust the simulation parameters based on the estimated emotions. For example, if the user is stressed, the simulation unit can adjust the simulation parameters to reduce the computational load. For example, if the user is relaxed, the simulation unit can also adjust the simulation parameters to perform a more detailed analysis. Furthermore, if the user is in a hurry, the simulation unit can adjust the simulation parameters to provide quick results. This allows for more appropriate simulations by adjusting the simulation parameters based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into the generative AI, which can then adjust the simulation parameters.

[0109] The simulation unit can select the optimal simulation method by referring to the user's past decision-making history during simulation. For example, the simulation unit can select the optimal method based on simulation methods previously used by the user. For example, the simulation unit can also propose an efficient simulation method based on the user's past decision-making history. Furthermore, the simulation unit can analyze the user's past decision-making history and select the most effective simulation method. In this way, the optimal simulation method can be selected by referring to the user's past decision-making history. The optimal simulation method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal simulation method.

[0110] The simulation unit can adjust the level of detail of the simulation based on the user's values ​​and goals during the simulation. For example, the simulation unit can prioritize simulating important data based on the user's values. The simulation unit can also perform detailed simulations based on the user's short-term goals. Furthermore, the simulation unit can perform overall simulations based on the user's long-term goals. By adjusting the level of detail of the simulation based on the user's values ​​and goals, a more appropriate simulation becomes possible. The level of detail of the simulation includes, but is not limited to, the criteria for setting the level of detail and the types of information to display. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's values ​​and goals into an AI model, and the AI ​​model can adjust the optimal level of detail of the simulation.

[0111] The simulation unit can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated emotions. For example, if the user is tense, the simulation unit may prioritize displaying important results. If the user is relaxed, the simulation unit may also prioritize displaying detailed results. Furthermore, if the user is in a hurry, the simulation unit may prioritize displaying important results to obtain results quickly. This allows for more appropriate result display by adjusting the order in which the simulation results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into the generative AI, and the generative AI can adjust the order in which the simulation results are displayed.

[0112] The simulation unit can prioritize simulating highly relevant data by considering the user's geographical location information during the simulation. For example, if the user is in a specific region, the simulation unit will prioritize simulating data related to that region. For example, if the user is on the move, the simulation unit can also prioritize simulating highly relevant data based on the user's current location. Furthermore, if the user is in a specific location, the simulation unit can also prioritize simulating data related to that location. This allows for the priority of simulating highly relevant data by considering the user's geographical location information. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information into an AI model, and the AI ​​model can prioritize simulating highly relevant data.

[0113] The simulation unit can analyze a user's social media activity and simulate relevant data during the simulation. For example, the simulation unit can prioritize simulating data related to the user's current interests from their social media activity. The simulation unit can also analyze a user's social media activity and simulate data based on relevant topics. Furthermore, the simulation unit can extract important information from a user's social media activity and prioritize simulating it. This allows for the priority simulation of relevant data by analyzing a user's social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input a user's social media activity into an AI model, and the AI ​​model can simulate relevant data.

[0114] The security unit can estimate the user's emotions and adjust security enhancement methods based on the estimated emotions. For example, if the user is stressed, the security unit can provide a simple security enhancement method. If the user is relaxed, for example, the security unit can also provide a more detailed security enhancement method. Furthermore, if the user is in a hurry, the security unit can provide a rapid security enhancement method. This allows for more appropriate security enhancement by adjusting security enhancement methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI, for example, or not using AI. For example, the security unit can input user emotion data into a generative AI, which can then adjust security enhancement methods.

[0115] The security department can select the optimal security enhancement method by referring to the user's past security history when enhancing security. For example, the security department can select the optimal method based on the security enhancement methods the user has used in the past. For example, the security department can also propose an efficient security enhancement method based on the user's past security history. Furthermore, the security department can analyze the user's past security history and select the most effective security enhancement method. In this way, the optimal security enhancement method can be selected by referring to the user's past security history. The optimal security enhancement method includes, but is not limited to, the algorithms used and the types of data used. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's past security history into an AI model, and the AI ​​model can select the optimal security enhancement method.

[0116] The security department can adjust the level of security detail based on the user's values ​​and goals when enhancing security. For example, the security department can prioritize the protection of critical data based on the user's values. The security department can also perform detailed security enhancements based on the user's short-term goals. Furthermore, the security department can perform overall security enhancements based on the user's long-term goals. This allows for more appropriate security enhancements by adjusting the level of security detail based on the user's values ​​and goals. Security detail includes, but is not limited to, the criteria for setting the level of detail and the types of information to display. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input the user's values ​​and goals into an AI model, which can then adjust the optimal level of security detail.

[0117] The security unit can estimate the user's emotions and determine the priority of security enhancements based on the estimated emotions. For example, if the user is stressed, the security unit may prioritize protecting important data. If the user is relaxed, the security unit may also prioritize protecting detailed data. Furthermore, if the user is in a hurry, the security unit may prioritize protecting important data to obtain results quickly. This allows for more appropriate security enhancements by prioritizing security enhancements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not. For example, the security unit can input user emotion data into a generative AI, which can then determine the priority of security enhancements.

[0118] The security department can prioritize the protection of highly relevant data by considering the user's geographical location when enhancing security. For example, if the user is in a specific region, the security department can prioritize the protection of data related to that region. For example, if the user is on the move, the security department can also prioritize the protection of highly relevant data based on the user's current location. Furthermore, if the security department is in a specific location, the security department can also prioritize the protection of data related to that location. This allows for the priority protection of highly relevant data by considering the user's geographical location. Highly relevant data includes, but is not limited to, data selection criteria and relevance evaluation methods. Some or all of the above processing in the security department may be performed using AI, for example, or not using AI. For example, the security department can input the user's geographical location information into an AI model, and the AI ​​model can prioritize the protection of highly relevant data.

[0119] The security department can analyze users' social media activity and protect related data when enhancing security. For example, the security department can prioritize protecting data related to users' current interests from their social media activity. The security department can also analyze users' social media activity and protect data based on relevant topics. Furthermore, the security department can extract important information from users' social media activity and prioritize its protection. This allows for the priority protection of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the above processing by the security department may be performed using, for example, AI, or not using AI. For example, the security department can input users' social media activity into an AI model, and the AI ​​model can protect related data.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] Decision support systems can further estimate the user's emotions and adjust the format of feedback based on those emotions. For example, if the user is stressed, the system can provide simple and intuitive feedback. If the user is relaxed, it can provide feedback that includes detailed data and analysis. Furthermore, if the user is in a hurry, it can provide concise feedback that gets straight to the point. This allows for more appropriate decision support by adjusting the format of feedback based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The format of feedback includes, but is not limited to, text, graphs, charts, etc.

[0122] The decision support system can further prioritize the processing of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the processing of data related to that region. If the user is on the move, it can also prioritize the processing of highly relevant data based on their current location. Furthermore, if the user is in a specific location, it can also prioritize the processing of data related to that location. This allows for the prioritization of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, the method of acquiring location information and the criteria for evaluating relevance. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location information into an AI model, which can then prioritize the processing of highly relevant data.

[0123] The decision support system can further analyze the user's social media activity and process the relevant data. For example, it can prioritize processing data related to the user's current interests from the user's social media activity. It can also analyze the user's social media activity and process the data based on relevant topics. Furthermore, it can extract important information from the user's social media activity and process it with priority. This allows for the priority processing of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, the types of data used and the analysis algorithms. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity into an AI model, and the AI ​​model can process the relevant data.

[0124] The decision support system can further estimate the user's emotions and dynamically change the system's interface based on the estimated emotions. For example, if the user is stressed, the system can provide a simple and intuitive interface. If the user is relaxed, it can provide an interface with detailed data and analysis results. Furthermore, if the user is in a hurry, it can provide a concise interface that gets straight to the point. This allows for more appropriate decision support by dynamically changing the interface based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Interface formats include, but are not limited to, text, graphs, charts, etc.

[0125] The decision support system can further select the optimal parallel processing method by referring to the user's past decision-making history. For example, it can select the optimal method based on the parallel processing methods the user has previously selected. It can also suggest an efficient parallel processing method based on the user's past decision-making history. Furthermore, it can analyze the user's past decision-making history and select the most effective parallel processing method. In this way, the optimal parallel processing method can be selected by referring to the user's past decision-making history. Past decision-making history includes, but is not limited to, examples such as the method of storing historical data and the reference algorithm. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past decision-making history into an AI model, and the AI ​​model can select the optimal parallel processing method.

[0126] The decision support system can further estimate the user's emotions and adjust the parameters of the quantum algorithm based on the estimated emotions. For example, if the user is stressed, the parameters of the quantum algorithm can be adjusted to reduce the computational load. If the user is relaxed, the parameters of the quantum algorithm can also be adjusted to perform a more detailed analysis. Furthermore, if the user is in a hurry, the parameters of the quantum algorithm can be adjusted to provide a quick result. This allows for more appropriate processing by adjusting the parameters of the quantum algorithm based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using, for example, AI or not using AI. For example, the processing unit can input user emotion data into the generative AI, which can then adjust the parameters of the quantum algorithm.

[0127] The decision support system can further determine processing priorities based on the user's values ​​and goals. For example, it can determine processing priorities based on the user's current project progress. It can also adjust processing priorities based on the user's short-term goals. Furthermore, it can set processing priorities based on the user's long-term goals. This enables efficient processing by determining processing priorities based on the user's current situation and goals. Current situation and goals include, but are not limited to, the method of collecting situation data and the criteria for setting goals. Some or all of the processing described above in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input the user's current situation and goals into an AI model, and the AI ​​model can determine the optimal processing priorities.

[0128] The decision support system can further estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, it can provide a simple and visually easy-to-understand presentation. If the user is relaxed, it can also provide a presentation that includes detailed data and graphs. Furthermore, if the user is in a hurry, it can provide a concise presentation that gets straight to the point. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide more appropriate analysis results. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the presentation of the analysis.

[0129] The decision support system can further select the optimal analysis method by referring to the user's past decision history. For example, it can select the optimal method based on analysis methods the user has used in the past. It can also suggest an efficient analysis method from the user's past decision history. Furthermore, it can analyze the user's past decision history and select the most effective analysis method. In this way, the optimal analysis method can be selected by referring to the user's past decision history. The optimal analysis method includes, but is not limited to, the algorithm used and the type of data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past decision history into an AI model, and the AI ​​model can select the optimal analysis method.

[0130] The decision support system can further estimate the user's emotions and adjust the simulation parameters based on the estimated emotions. For example, if the user is stressed, the simulation parameters can be adjusted to reduce the computational load. If the user is relaxed, the simulation parameters can also be adjusted to perform a more detailed analysis. Furthermore, if the user is in a hurry, the simulation parameters can be adjusted to provide quick results. This allows for more appropriate simulations by adjusting the simulation parameters based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into the generative AI, which can then adjust the simulation parameters.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The processing unit performs massively parallel processing using quantum algorithms. For example, it can perform prime factorization using Shor's algorithm or database searches using Grover's algorithm. It can also perform parallel computation using qubits. Step 2: The analysis unit performs personalized analysis based on the data obtained by the processing unit. For example, it analyzes the user's past decisions, values, and goals using deep learning, learns the user's past data using a neural network, and provides personalized recommendations. It can also build a model using training data to predict the user's behavior patterns. Furthermore, it can customize the recommendations based on the user's values ​​and goals. Step 3: The correlation analysis unit performs correlation analysis based on the data obtained by the analysis unit. For example, it performs correlation analysis mimicking quantum entanglement and analyzes the interaction between complex factors using an entanglement generation method. It can also analyze correlations by adjusting the number of qubits used. Furthermore, it can evaluate the correlation between data using a correlation coefficient calculation method. Step 4: The simulation unit performs real-time simulations based on the data obtained by the correlation analysis unit. For example, it uses real-time quantum simulation to predict and visualize the outcome of decisions using quantum probabilities. It is also possible to perform simulations using different probability distributions and adjust the simulation update frequency using computational algorithms. Furthermore, the simulation results can be displayed as graphs or charts. Step 5: The security unit enhances security based on the data obtained by the simulation unit. For example, it protects users' confidential information using quantum cryptography and generates encryption keys using quantum key distribution (QKD). It can also encrypt data using qubits. Furthermore, it can protect data by setting security policies.

[0133] 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.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] 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.

[0136] Each of the multiple elements described above, including the processing unit, analysis unit, correlation analysis unit, simulation unit, and security unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the processing unit performs massively parallel processing using quantum algorithms by the processor 46 of the smart device 14 or the processor 28 of the data processing unit 12. The analysis unit performs personalized analysis by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correlation analysis unit performs correlation analysis mimicking quantum entanglement by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The simulation unit performs real-time quantum simulation by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The security unit enhances security using quantum cryptography by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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.

[0152] Each of the multiple elements described above, including the processing unit, analysis unit, correlation analysis unit, simulation unit, and security unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the processing unit performs massively parallel processing using quantum algorithms by the processor 46 of the smart glasses 214 or the processor 28 of the data processing unit 12. The analysis unit performs personalized analysis by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correlation analysis unit performs correlation analysis mimicking quantum entanglement by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The simulation unit performs real-time quantum simulation by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The security unit enhances security using quantum cryptography by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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.

[0168] Each of the multiple elements described above, including the processing unit, analysis unit, correlation analysis unit, simulation unit, and security unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the processing unit performs massively parallel processing using a quantum algorithm by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12. The analysis unit performs personalized analysis by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correlation analysis unit performs correlation analysis mimicking quantum entanglement by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The simulation unit performs real-time quantum simulation by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The security unit enhances security using quantum cryptography by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] 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.

[0185] Each of the multiple elements described above, including the processing unit, analysis unit, correlation analysis unit, simulation unit, and security unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the processing unit performs massively parallel processing using quantum algorithms by the processor 46 of the robot 414 or the processor 28 of the data processing unit 12. The analysis unit performs personalized analysis by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correlation analysis unit performs correlation analysis mimicking quantum entanglement by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The simulation unit performs real-time quantum simulation by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The security unit enhances security using quantum cryptography by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A processing unit that performs massively parallel processing using quantum algorithms, An analysis unit that performs personalized analysis based on the data obtained by the processing unit, A correlation analysis unit performs correlation analysis based on the data obtained by the aforementioned analysis unit, A simulation unit that performs real-time simulations based on the data obtained by the correlation analysis unit, The system includes a security unit that enhances security based on data obtained by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned processing unit, Performing massively parallel processing using quantum algorithms The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We use deep learning to analyze users' past decisions, values, and goals, and provide personalized recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The correlation analysis unit described above is: We will perform correlation analysis that mimics quantum entanglement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, Using real-time quantum simulations, we predict and visualize the results of decisions using quantum probabilities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned security unit is Protecting users' confidential information using quantum cryptography. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned processing unit, Using generative AI, we convert the user's natural language input into a format that quantum algorithms can understand. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned simulation unit, Visualizing quantum states using generative AI The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Performing quantum-inspired machine learning using generative AI The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned simulation unit, Dynamic scenario generation is performed using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned security unit is Optimizing quantum error correction using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned processing unit, It estimates the user's emotions and adjusts the parameters of the quantum algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned processing unit, When executing quantum algorithms, the system selects the optimal parallel processing method by referring to the user's past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned processing unit, When executing quantum algorithms, processing priorities are determined based on the user's current situation and goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned processing unit, It estimates the user's emotions and adjusts the execution order of quantum algorithms based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned processing unit, When executing quantum algorithms, the system prioritizes processing highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned processing unit, During the execution of the quantum algorithm, the system analyzes the user's social media activity and processes the relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, the system selects the optimal analysis method by referring to the user's past decision history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the level of detail based on the user's values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and analyze relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The correlation analysis unit described above is: We estimate the user's emotions and adjust the criteria for correlation analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The correlation analysis unit described above is: During correlation analysis, the optimal correlation analysis method is selected by referring to the user's past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The correlation analysis unit described above is: During correlation analysis, adjust the level of detail of the correlation based on the user's values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The correlation analysis unit described above is: It estimates the user's emotions and adjusts the order in which the correlation analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The correlation analysis unit described above is: During correlation analysis, the system prioritizes analyzing highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The correlation analysis unit described above is: During correlation analysis, we analyze users' social media activity and analyze relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation parameters based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned simulation unit, During simulation, the optimal simulation method is selected by referring to the user's past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned simulation unit, During the simulation, adjust the level of detail based on the user's values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned simulation unit, It estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned simulation unit, During simulation, the system prioritizes simulating highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned simulation unit, During the simulation, the user's social media activity is analyzed, and relevant data is simulated. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned security unit is It estimates user sentiment and adjusts security enhancement methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned security unit is When strengthening security, the system selects the most suitable security enhancement method by referring to the user's past security history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned security unit is When enhancing security, adjust the level of security detail based on the user's values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned security unit is It estimates user sentiment and determines security enhancement priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned security unit is When enhancing security, the system prioritizes protecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned security unit is When enhancing security, we analyze users' social media activity and protect related data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A processing unit that performs massively parallel processing using quantum algorithms, An analysis unit that performs personalized analysis based on the data obtained by the processing unit, A correlation analysis unit performs correlation analysis based on the data obtained by the aforementioned analysis unit, A simulation unit that performs real-time simulations based on the data obtained by the correlation analysis unit, The system includes a security unit that enhances security based on data obtained by the simulation unit. A system characterized by the following features.

2. The aforementioned processing unit, Performing massively parallel processing using quantum algorithms The system according to feature 1.

3. The aforementioned analysis unit is We use deep learning to analyze users' past decisions, values, and goals, and provide personalized recommendations. The system according to feature 1.

4. The correlation analysis unit described above is: We will perform correlation analysis that mimics quantum entanglement. The system according to feature 1.

5. The aforementioned simulation unit, Using real-time quantum simulations, we predict and visualize the results of decisions using quantum probabilities. The system according to feature 1.

6. The aforementioned security unit is Protecting users' confidential information using quantum cryptography. The system according to feature 1.

7. The aforementioned processing unit, Using generative AI, we convert the user's natural language input into a format that a quantum algorithm can understand. The system according to feature 1.

8. The aforementioned simulation unit, Visualizing quantum states using generative AI The system according to feature 1.

Images