system

Abstract

The system according to the embodiment aims to improve the speed of access to the exchange and optimize the speed of investment. [Solution] A system according to an embodiment includes a network construction unit, a data processing unit, and an algorithm adjustment unit. The network construction unit constructs a network for accessing an exchange. The data processing unit processes trading data through the network constructed by the network construction unit. The algorithm adjustment unit adjusts the investment algorithm based on the data processed by the data processing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of slow access speeds to exchanges, which could lead to differences in investment speeds.

[0005] The system according to the embodiment aims to improve the speed of access to the exchange and optimize the speed of investment. [Means for solving the problem]

[0006] The system according to the embodiment includes a network construction unit, a data processing unit, and an algorithm adjustment unit. The network construction unit constructs a network for accessing an exchange. The data processing unit processes transaction data through the network constructed by the network construction unit. The algorithm adjustment unit adjusts the investment algorithm based on the data processed by the data processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the speed of access to the exchange and optimize the speed of investment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An ultra-low latency network system according to an embodiment of the present invention is a system for improving the trading speed of AI investment agents. The ultra-low latency network system builds an ultra-low latency network to an exchange, enabling the AI ​​investment agent to trade quickly. For example, the ultra-low latency network system builds a network for accessing an exchange. For example, the ultra-low latency network system establishes a direct connection to the exchange using fiber optic cables or dedicated lines. Next, the ultra-low latency network system installs hardware for the AI ​​investment agent to trade. For example, the ultra-low latency network system repurposes SuperPods purchased for LLM to perform high-speed data processing. Furthermore, the ultra-low latency network system optimizes the algorithms used by the AI ​​investment agent to trade. For example, the ultra-low latency network system uses machine learning and deep learning to learn from past trading data and predict future market trends. Based on this prediction, the AI ​​investment agent trades quickly and maximizes profits. This allows the ultra-low latency network system to outperform other investment agents in terms of trading speed. This allows the AI ​​investment agent to trade quickly and accurately, thereby improving the success rate of investments. For example, if the communication latency to the exchange is less than 0.001 seconds, you can trade faster than other investment agents. This improves your investment success rate and maximizes your profits. In addition, this ultra-low latency network system, provided by carriers with their own communications infrastructure, gives you an advantage over other investment agents.

[0029] An ultra-low latency network system according to an embodiment includes a network construction unit, a data processing unit, and an algorithm adjustment unit. The network construction unit constructs a network for accessing an exchange. The network construction unit establishes a direct connection with the exchange using, for example, an optical fiber cable or a dedicated line. The network construction unit can also select an optimal connection route to minimize communication delays. For example, the network construction unit selects the optimal connection route based on the geographic location of the exchange. The data processing unit processes trading data through the network constructed by the network construction unit. The data processing unit performs high-speed data processing, for example, by repurposing a SuperPod purchased for LLM. The data processing unit analyzes market data in real time and makes optimal investment decisions. The algorithm adjustment unit adjusts investment algorithms based on data processed by the data processing unit. The algorithm adjustment unit learns past trading data and predicts future market trends using, for example, machine learning or deep learning. This allows the algorithm adjustment unit to quickly execute transactions and maximize profits. This allows the ultra-low latency network system according to an embodiment to optimize access to exchanges and quickly process trading data and optimize investment algorithms.

[0030] The network construction unit can establish a direct connection with the exchange using an optical fiber cable or a dedicated line. The optical fiber cable or dedicated line may include, but is not limited to, communication speed, bandwidth, provider, etc. The network construction unit can establish a direct connection with the exchange using, for example, an optical fiber cable. The network construction unit can also establish a direct connection with the exchange using a dedicated line. This can minimize communication delays with the exchange. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can select a connection path using an AI model for optimizing a connection path with the exchange.

[0031] The data processing unit can perform high-speed data processing by repurposing a SuperPod purchased for LLM. The SuperPod purchased for LLM includes, but is not limited to, processing power, memory capacity, and software used. The data processing unit performs high-speed data processing, for example, using a SuperPod purchased for LLM. The data processing unit can also use the SuperPod to analyze market data in real time and make optimal investment decisions. This enables high-speed data processing and accelerates trading. Some or all of the above-described processing in the data processing unit may be performed using, for example, AI, or may be performed without AI. For example, the data processing unit can input market data acquired using the SuperPod into a generation AI and have the generation AI perform data analysis.

[0032] When constructing a network, the network construction unit can select an optimal connection route based on the geographic location of the exchange. For example, if the exchange is located nearby, the network construction unit selects the shortest connection route. Furthermore, if the exchange is located far away, the network construction unit can select an optimal connection route via multiple relay points. Furthermore, if the exchange is located in a different country, the network construction unit can select an optimal connection route using international communication infrastructure. By selecting an optimal connection route based on the geographic location of the exchange, communication efficiency can be improved. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input geographic location data of the exchange into the generation AI and cause the generation AI to select an optimal connection route.

[0033] When constructing a network, the network construction unit can configure optimal communication settings according to the exchange's communication protocol. For example, if the exchange uses the TCP / IP protocol, the network construction unit configures optimal TCP / IP settings. Furthermore, if the exchange uses the UDP protocol, the network construction unit can also configure optimal UDP settings. Furthermore, if the exchange uses its own proprietary communication protocol, the network construction unit can also configure settings compatible with that protocol. This allows for optimal communication settings according to the exchange's communication protocol, thereby improving communication stability and efficiency. Some or all of the above-described processing in the network construction unit may be performed using, or without, AI. For example, the network construction unit may input the exchange's communication protocol data into a generation AI and have the generation AI execute optimal communication settings.

[0034] When constructing the network, the network construction unit can adjust the network bandwidth based on the trading volume of the exchange. For example, when the trading volume is high, the network construction unit widens the bandwidth to ensure communication speed. Furthermore, when the trading volume is low, the network construction unit can narrow the bandwidth to save resources. Furthermore, the network construction unit can adjust the bandwidth in real time according to fluctuations in trading volume. This makes it possible to optimize communication speed and resources by adjusting the network bandwidth according to the trading volume. Some or all of the above-mentioned processing in the network construction unit may be performed using, or without, AI, for example. For example, the network construction unit can input trading volume data into the generation AI and have the generation AI adjust the bandwidth.

[0035] When constructing a network, the network construction unit can select a connection method according to the security level of the exchange. For example, if the security level of the exchange is high, the network construction unit connects using encrypted communication. Furthermore, if the security level of the exchange is medium, the network construction unit can also connect using an authentication protocol. Furthermore, if the security level of the exchange is low, the network construction unit can also use a simple connection method. In this way, by selecting a connection method according to the security level of the exchange, communication security can be ensured. Some or all of the above-mentioned processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input security level data of the exchange into the generation AI and have the generation AI select the optimal connection method.

[0036] When constructing the network, the network construction unit can adjust the connection schedule based on the operating hours of the exchange. For example, if the exchange operates 24 hours a day, the network construction unit maintains a constant connection. Furthermore, if the exchange operates only during specific hours, the network construction unit can adjust the connection schedule to match those hours. Furthermore, if the operating hours of the exchange fluctuate, the network construction unit can adjust the connection schedule in real time. As a result, by adjusting the connection schedule based on the operating hours of the exchange, efficient connections can be maintained. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input the operating hours data of the exchange into the generation AI and cause the generation AI to adjust the connection schedule.

[0037] When constructing a network, the network construction unit can select the optimal connection method based on the exchange's transaction fees. For example, if transaction fees are high, the network construction unit selects the optimal connection method to reduce costs. Furthermore, if transaction fees are low, the network construction unit can also select a connection method that prioritizes communication speed. Furthermore, the network construction unit can adjust the connection method in real time in response to fluctuations in transaction fees. This allows for improved cost efficiency by selecting the optimal connection method based on transaction fees. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input transaction fee data into a generation AI and have the generation AI select the optimal connection method.

[0038] The data processing unit can apply different processing algorithms depending on the type of transaction data when processing data. For example, the data processing unit applies a specific algorithm to stock transaction data. The data processing unit can also apply a different algorithm to foreign exchange transaction data. Furthermore, the data processing unit can also apply a dedicated algorithm to cryptocurrency transaction data. This allows for the application of an optimal processing algorithm depending on the type of transaction data, thereby improving the efficiency of data processing. Some or all of the above-mentioned processing in the data processing unit may be performed using AI, for example, or may be performed without using AI. For example, the data processing unit can input the type of transaction data into the generation AI and have the generation AI select the optimal processing algorithm.

[0039] The data processing unit can adjust the processing speed based on the amount of transaction data during data processing. For example, when the amount of transaction data is large, the data processing unit can set the processing speed to a high speed. Furthermore, when the amount of transaction data is small, the data processing unit can set the processing speed to a low speed. Furthermore, the data processing unit can adjust the processing speed in real time in accordance with fluctuations in the amount of transaction data. This makes it possible to optimize the efficiency of data processing by adjusting the processing speed in accordance with the amount of transaction data. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the amount of transaction data to the generation AI and have the generation AI adjust the processing speed.

[0040] During data processing, the data processing unit can select a processing method based on the reliability of the transaction data. For example, the data processing unit applies a rapid processing method to highly reliable data. The data processing unit can also apply a cautious processing method to less reliable data. Furthermore, the data processing unit can adjust the processing method in real time depending on the reliability of the data. This allows for improving the accuracy of data processing by selecting a processing method based on the reliability of the transaction data. Some or all of the above-described processing in the data processing unit can be performed using, or without, AI, for example. For example, the data processing unit can input the reliability of the transaction data into the generation AI and have the generation AI select the optimal processing method.

[0041] During data processing, the data processing unit can determine the processing priority based on the submission time of the transaction data. For example, the data processing unit prioritizes processing of data submitted earlier. The data processing unit can also postpone data submitted later. Furthermore, the data processing unit can adjust the processing priority in real time according to fluctuations in the submission time. This enables efficient data processing by determining the processing priority based on the submission time of the transaction data. Some or all of the above-described processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input the submission time of the transaction data to the generation AI and have the generation AI determine the processing priority.

[0042] During data processing, the data processing unit can adjust the processing order based on the relevance of the transaction data. For example, the data processing unit prioritizes processing of highly relevant data. The data processing unit can also postpone processing of less relevant data. Furthermore, the data processing unit can adjust the processing order in real time according to the relevance of the data. This allows for efficient data processing by adjusting the processing order based on the relevance of the transaction data. Some or all of the above-described processing in the data processing unit may be performed using, or without, AI, for example. For example, the data processing unit can input the relevance of the transaction data to a generation AI and have the generation AI adjust the processing order.

[0043] During data processing, the data processing unit can select a processing method based on the market value of the transaction data. For example, the data processing unit can apply a rapid processing method to data with a high market value. The data processing unit can also apply a cautious processing method to data with a low market value. Furthermore, the data processing unit can adjust the processing method in real time according to the market value of the data. This allows for improved efficiency and accuracy of data processing by selecting a processing method based on the market value of the transaction data. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the market value of the transaction data into the generation AI and have the generation AI select the optimal processing method.

[0044] When optimizing an algorithm, the algorithm optimization unit can select an optimized algorithm by referring to past trading data. For example, the algorithm optimization unit analyzes past trading data and selects an algorithm with the highest success rate. The algorithm optimization unit can also select an algorithm that minimizes risk from past trading data. Furthermore, the algorithm optimization unit can select the most efficient algorithm based on past trading data. In this way, by referring to past trading data, it is possible to select an optimal algorithm and improve the accuracy of investment decisions. Some or all of the above-mentioned processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input past trading data into a generation AI and have the generation AI select an optimal algorithm.

[0045] During algorithm optimization, the algorithm optimization unit can adjust optimization parameters based on market trends on the exchange. For example, when the market is trending upward, the algorithm optimization unit sets risk-taking parameters. Also, when the market is trending downward, the algorithm optimization unit can set risk-reducing parameters. Furthermore, the algorithm optimization unit can adjust parameters in real time according to market fluctuations. This allows for adjusting optimization parameters based on market trends, thereby improving the accuracy of investment decisions. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input market trend data into the generation AI and cause the generation AI to adjust the optimization parameters.

[0046] During algorithm optimization, the algorithm optimization unit can adjust the optimization frequency based on the trading volume of the exchange. For example, when the trading volume is high, the algorithm optimization unit sets the optimization frequency high. Also, when the trading volume is low, the algorithm optimization unit can set the optimization frequency low. Furthermore, the algorithm optimization unit can adjust the optimization frequency in real time according to fluctuations in trading volume. As a result, efficient algorithm optimization can be achieved by adjusting the optimization frequency based on the trading volume. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input trading volume data to the generation AI and cause the generation AI to adjust the optimization frequency.

[0047] During algorithm optimization, the algorithm optimization unit can determine the priority of optimization based on the submission time of the transaction data. For example, the algorithm optimization unit prioritizes optimization of data submitted earlier. The algorithm optimization unit can also postpone data submitted later. Furthermore, the algorithm optimization unit can adjust the priority of optimization in real time according to fluctuations in the submission time. As a result, efficient algorithm optimization can be achieved by determining the priority of optimization based on the submission time of the transaction data. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input the submission time of the transaction data to the generation AI and cause the generation AI to determine the optimization priority.

[0048] The algorithm optimization unit can adjust the optimization order based on the relevance of the transaction data when optimizing the algorithm. For example, the algorithm optimization unit prioritizes optimization of highly relevant data. The algorithm optimization unit can also postpone optimization of less relevant data. Furthermore, the algorithm optimization unit can adjust the optimization order in real time according to the data relevance. As a result, efficient algorithm optimization can be achieved by adjusting the optimization order based on the relevance of the transaction data. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input the relevance of the transaction data to the generation AI and cause the generation AI to adjust the optimization order.

[0049] During algorithm optimization, the algorithm optimization unit can select an optimization method based on the market value of the transaction data. For example, the algorithm optimization unit can apply a rapid optimization method to data with high market value. The algorithm optimization unit can also apply a conservative optimization method to data with low market value. Furthermore, the algorithm optimization unit can adjust the optimization method in real time according to the market value of the data. As a result, efficient algorithm optimization can be achieved by selecting an optimization method based on the market value of the transaction data. Some or all of the above-described processing in the algorithm optimization unit can be performed using, for example, AI, or can be performed without using AI. For example, the algorithm optimization unit can input the market value of the transaction data to the generation AI and cause the generation AI to select the optimal optimization method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The network construction unit can select the optimal connection route based on the geographic location of the exchange. For example, if the exchange is located nearby, the shortest connection route can be selected. Alternatively, if the exchange is located far away, the optimal connection route can be selected via multiple relay points. Furthermore, if the exchanges are located in different countries, the optimal connection route can be selected using international communication infrastructure. This allows for improved communication efficiency by selecting the optimal connection route based on the geographic location of the exchange. Some or all of the above-described processing in the network construction unit can be performed using, for example, AI, or without AI. For example, the network construction unit can input the geographic location data of the exchange into the generation AI and cause the generation AI to select the optimal connection route.

[0052] During algorithm optimization, the algorithm optimization unit can adjust optimization parameters based on market trends on the exchange. For example, if the market is trending upward, risk-taking parameters can be set. Conversely, if the market is trending downward, risk-reducing parameters can be set. Furthermore, parameters can be adjusted in real time according to market fluctuations. This allows for adjusting optimization parameters based on market trends, thereby improving the accuracy of investment decisions. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input market trend data into the generation AI and have the generation AI adjust the optimization parameters.

[0053] When processing data, the data processing unit can apply different processing algorithms depending on the type of transaction data. For example, a specific algorithm can be applied to stock transaction data. The data processing unit can also apply a different algorithm to foreign exchange transaction data. Furthermore, the data processing unit can also apply a dedicated algorithm to cryptocurrency transaction data. This allows for the application of an optimal processing algorithm depending on the type of transaction data, thereby improving the efficiency of data processing. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the type of transaction data into the generation AI and have the generation AI select the optimal processing algorithm.

[0054] When constructing a network, the network construction unit can configure optimal communication settings according to the exchange's communication protocol. For example, if the exchange uses the TCP / IP protocol, the network construction unit can configure optimal TCP / IP settings. Furthermore, if the exchange uses the UDP protocol, the network construction unit can configure optimal UDP settings. Furthermore, if the exchange uses its own proprietary communication protocol, the network construction unit can configure settings compatible with that protocol. This allows for optimal communication settings according to the exchange's communication protocol, thereby improving communication stability and efficiency. Some or all of the above-described processing in the network construction unit may be performed using, or without, AI. For example, the network construction unit can input the exchange's communication protocol data into a generation AI and have the generation AI execute optimal communication settings.

[0055] During algorithm optimization, the algorithm optimization unit can determine the priority of optimization based on the submission time of the transaction data. For example, data submitted earlier is given priority for optimization. The algorithm optimization unit can also postpone data submitted later. Furthermore, the algorithm optimization unit can adjust the priority of optimization in real time according to fluctuations in the submission time. In this way, by determining the priority of optimization based on the submission time of the transaction data, efficient algorithm optimization can be achieved. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input the submission time of the transaction data to the generation AI and have the generation AI determine the priority of optimization.

[0056] When constructing a network, the network construction unit can select a connection method according to the security level of the exchange. For example, if the security level of the exchange is high, the connection is made using encrypted communication. Furthermore, if the security level of the exchange is medium, the network construction unit can also connect using an authentication protocol. Furthermore, if the security level of the exchange is low, a simple connection method can also be used. Thus, by selecting a connection method according to the security level of the exchange, the security of communication can be ensured. Some or all of the above-described processing in the network construction unit may be performed using, or without, AI, for example. For example, the network construction unit can input the security level data of the exchange into the generation AI and have the generation AI select the optimal connection method.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The network construction department constructs a network for accessing the exchange. For example, the network construction department establishes a direct connection with the exchange using fiber optic cables or dedicated lines. The network construction department can also select the optimal connection route to minimize communication delays. For example, the optimal connection route can be selected based on the geographic location of the exchange. Step 2: The data processing unit processes the trading data through the network constructed by the network construction unit. For example, the data processing unit repurposes the SuperPod purchased for LLM to perform high-speed data processing. The data processing unit analyzes market data in real time and can make optimal investment decisions. Step 3: The algorithm adjustment unit adjusts the investment algorithm based on the data processed by the data processing unit. The algorithm adjustment unit uses, for example, machine learning or deep learning to learn from past trading data and predict future market trends. This allows for faster trading and maximized profits.

[0059] (Example 2) An ultra-low latency network system according to an embodiment of the present invention is a system for improving the trading speed of AI investment agents. The ultra-low latency network system builds an ultra-low latency network to an exchange, enabling the AI ​​investment agent to trade quickly. For example, the ultra-low latency network system builds a network for accessing an exchange. For example, the ultra-low latency network system establishes a direct connection to the exchange using fiber optic cables or dedicated lines. Next, the ultra-low latency network system installs hardware for the AI ​​investment agent to trade. For example, the ultra-low latency network system repurposes SuperPods purchased for LLM to perform high-speed data processing. Furthermore, the ultra-low latency network system optimizes the algorithms used by the AI ​​investment agent to trade. For example, the ultra-low latency network system uses machine learning and deep learning to learn from past trading data and predict future market trends. Based on this prediction, the AI ​​investment agent trades quickly and maximizes profits. This allows the ultra-low latency network system to outperform other investment agents in terms of trading speed. This allows the AI ​​investment agent to trade quickly and accurately, thereby improving the success rate of investments. For example, if the communication latency to the exchange is less than 0.001 seconds, you can trade faster than other investment agents. This improves your investment success rate and maximizes your profits. In addition, this ultra-low latency network system, provided by carriers with their own communications infrastructure, gives you an advantage over other investment agents.

[0060] An ultra-low latency network system according to an embodiment includes a network construction unit, a data processing unit, and an algorithm adjustment unit. The network construction unit constructs a network for accessing an exchange. The network construction unit establishes a direct connection with the exchange using, for example, an optical fiber cable or a dedicated line. The network construction unit can also select an optimal connection route to minimize communication delays. For example, the network construction unit selects the optimal connection route based on the geographic location of the exchange. The data processing unit processes trading data through the network constructed by the network construction unit. The data processing unit performs high-speed data processing, for example, by repurposing a SuperPod purchased for LLM. The data processing unit analyzes market data in real time and makes optimal investment decisions. The algorithm adjustment unit adjusts investment algorithms based on data processed by the data processing unit. The algorithm adjustment unit learns past trading data and predicts future market trends using, for example, machine learning or deep learning. This allows the algorithm adjustment unit to quickly execute transactions and maximize profits. This allows the ultra-low latency network system according to an embodiment to optimize access to exchanges and quickly process trading data and optimize investment algorithms.

[0061] The network construction unit can establish a direct connection with the exchange using an optical fiber cable or a dedicated line. The optical fiber cable or dedicated line may include, but is not limited to, communication speed, bandwidth, provider, etc. The network construction unit can establish a direct connection with the exchange using, for example, an optical fiber cable. The network construction unit can also establish a direct connection with the exchange using a dedicated line. This can minimize communication delays with the exchange. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can select a connection path using an AI model for optimizing a connection path with the exchange.

[0062] The data processing unit can perform high-speed data processing by repurposing a SuperPod purchased for LLM. The SuperPod purchased for LLM includes, but is not limited to, processing power, memory capacity, and software used. The data processing unit performs high-speed data processing, for example, using a SuperPod purchased for LLM. The data processing unit can also use the SuperPod to analyze market data in real time and make optimal investment decisions. This enables high-speed data processing and accelerates trading. Some or all of the above-described processing in the data processing unit may be performed using, for example, AI, or may be performed without AI. For example, the data processing unit can input market data acquired using the SuperPod into a generation AI and have the generation AI perform data analysis.

[0063] The network construction unit can analyze the user's emotions and adjust the timing of network construction based on the analyzed user's emotions. For example, if the user is nervous, the network construction unit can quickly build the network to provide a sense of security. Furthermore, if the user is relaxed, the network construction unit can slowly build the network to emphasize stability. Furthermore, if the user is in a hurry, the network construction unit can build the network in the shortest time possible to enable quick transactions. This allows the timing of network construction to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the network construction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the network construction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0064] When constructing a network, the network construction unit can select an optimal connection route based on the geographic location of the exchange. For example, if the exchange is located nearby, the network construction unit selects the shortest connection route. Furthermore, if the exchange is located far away, the network construction unit can select an optimal connection route via multiple relay points. Furthermore, if the exchange is located in a different country, the network construction unit can select an optimal connection route using international communication infrastructure. By selecting an optimal connection route based on the geographic location of the exchange, communication efficiency can be improved. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input geographic location data of the exchange into the generation AI and cause the generation AI to select an optimal connection route.

[0065] When constructing a network, the network construction unit can configure optimal communication settings according to the exchange's communication protocol. For example, if the exchange uses the TCP / IP protocol, the network construction unit configures optimal TCP / IP settings. Furthermore, if the exchange uses the UDP protocol, the network construction unit can also configure optimal UDP settings. Furthermore, if the exchange uses its own proprietary communication protocol, the network construction unit can also configure settings compatible with that protocol. This allows for optimal communication settings according to the exchange's communication protocol, thereby improving communication stability and efficiency. Some or all of the above-described processing in the network construction unit may be performed using, or without, AI. For example, the network construction unit may input the exchange's communication protocol data into a generation AI and have the generation AI execute optimal communication settings.

[0066] When constructing the network, the network construction unit can adjust the network bandwidth based on the trading volume of the exchange. For example, when the trading volume is high, the network construction unit widens the bandwidth to ensure communication speed. Furthermore, when the trading volume is low, the network construction unit can narrow the bandwidth to save resources. Furthermore, the network construction unit can adjust the bandwidth in real time according to fluctuations in trading volume. This makes it possible to optimize communication speed and resources by adjusting the network bandwidth according to the trading volume. Some or all of the above-mentioned processing in the network construction unit may be performed using, or without, AI, for example. For example, the network construction unit can input trading volume data into the generation AI and have the generation AI adjust the bandwidth.

[0067] The network construction unit can analyze the user's emotions and determine network priorities based on the analyzed user's emotions. For example, if the user is nervous, the network construction unit can set the network priority high to provide a quick connection. Alternatively, if the user is relaxed, the network construction unit can set the network priority low to prioritize stability. Furthermore, if the user is in a hurry, the network construction unit can set the priority high to establish a connection in the shortest time. This allows the network priority to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the network construction unit can be performed using AI, or without AI. For example, the network construction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0068] When constructing a network, the network construction unit can select a connection method according to the security level of the exchange. For example, if the security level of the exchange is high, the network construction unit connects using encrypted communication. Furthermore, if the security level of the exchange is medium, the network construction unit can also connect using an authentication protocol. Furthermore, if the security level of the exchange is low, the network construction unit can also use a simple connection method. In this way, by selecting a connection method according to the security level of the exchange, communication security can be ensured. Some or all of the above-mentioned processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input security level data of the exchange into the generation AI and have the generation AI select the optimal connection method.

[0069] When constructing the network, the network construction unit can adjust the connection schedule based on the operating hours of the exchange. For example, if the exchange operates 24 hours a day, the network construction unit maintains a constant connection. Furthermore, if the exchange operates only during specific hours, the network construction unit can adjust the connection schedule to match those hours. Furthermore, if the operating hours of the exchange fluctuate, the network construction unit can adjust the connection schedule in real time. As a result, by adjusting the connection schedule based on the operating hours of the exchange, efficient connections can be maintained. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input the operating hours data of the exchange into the generation AI and cause the generation AI to adjust the connection schedule.

[0070] When constructing a network, the network construction unit can select the optimal connection method based on the exchange's transaction fees. For example, if transaction fees are high, the network construction unit selects the optimal connection method to reduce costs. Furthermore, if transaction fees are low, the network construction unit can also select a connection method that prioritizes communication speed. Furthermore, the network construction unit can adjust the connection method in real time in response to fluctuations in transaction fees. This allows for improved cost efficiency by selecting the optimal connection method based on transaction fees. Some or all of the above-described processing in the network construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the network construction unit can input transaction fee data into a generation AI and have the generation AI select the optimal connection method.

[0071] The data processing unit can analyze the user's emotions and determine the priority of data processing based on the analyzed user's emotions. For example, if the user is nervous, the data processing unit prioritizes processing of important data. The data processing unit can also flexibly adjust the order of data processing when the user is relaxed. Furthermore, if the user is in a hurry, the data processing unit can prioritize processing of the most important data. This allows the data processing priority to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data processing unit can input the user's emotion data into the generative AI and have the generative AI perform emotion analysis.

[0072] The data processing unit can apply different processing algorithms depending on the type of transaction data when processing data. For example, the data processing unit applies a specific algorithm to stock transaction data. The data processing unit can also apply a different algorithm to foreign exchange transaction data. Furthermore, the data processing unit can also apply a dedicated algorithm to cryptocurrency transaction data. This allows for the application of an optimal processing algorithm depending on the type of transaction data, thereby improving the efficiency of data processing. Some or all of the above-mentioned processing in the data processing unit may be performed using AI, for example, or may be performed without using AI. For example, the data processing unit can input the type of transaction data into the generation AI and have the generation AI select the optimal processing algorithm.

[0073] The data processing unit can adjust the processing speed based on the amount of transaction data during data processing. For example, when the amount of transaction data is large, the data processing unit can set the processing speed to a high speed. Furthermore, when the amount of transaction data is small, the data processing unit can set the processing speed to a low speed. Furthermore, the data processing unit can adjust the processing speed in real time in accordance with fluctuations in the amount of transaction data. This makes it possible to optimize the efficiency of data processing by adjusting the processing speed in accordance with the amount of transaction data. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the amount of transaction data to the generation AI and have the generation AI adjust the processing speed.

[0074] During data processing, the data processing unit can select a processing method based on the reliability of the transaction data. For example, the data processing unit applies a rapid processing method to highly reliable data. The data processing unit can also apply a cautious processing method to less reliable data. Furthermore, the data processing unit can adjust the processing method in real time depending on the reliability of the data. This allows for improving the accuracy of data processing by selecting a processing method based on the reliability of the transaction data. Some or all of the above-described processing in the data processing unit can be performed using, or without, AI, for example. For example, the data processing unit can input the reliability of the transaction data into the generation AI and have the generation AI select the optimal processing method.

[0075] The data processing unit can analyze the user's emotions and adjust the display method of the data processing based on the analyzed user's emotions. For example, if the user is nervous, the data processing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the data processing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data processing unit can provide a display method that focuses on the main points. This allows the display method of the data processing to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data processing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the data processing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0076] During data processing, the data processing unit can determine the processing priority based on the submission time of the transaction data. For example, the data processing unit prioritizes processing of data submitted earlier. The data processing unit can also postpone data submitted later. Furthermore, the data processing unit can adjust the processing priority in real time according to fluctuations in the submission time. This enables efficient data processing by determining the processing priority based on the submission time of the transaction data. Some or all of the above-described processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input the submission time of the transaction data to the generation AI and have the generation AI determine the processing priority.

[0077] During data processing, the data processing unit can adjust the processing order based on the relevance of the transaction data. For example, the data processing unit prioritizes processing of highly relevant data. The data processing unit can also postpone processing of less relevant data. Furthermore, the data processing unit can adjust the processing order in real time according to the relevance of the data. This allows for efficient data processing by adjusting the processing order based on the relevance of the transaction data. Some or all of the above-described processing in the data processing unit may be performed using, or without, AI, for example. For example, the data processing unit can input the relevance of the transaction data to a generation AI and have the generation AI adjust the processing order.

[0078] During data processing, the data processing unit can select a processing method based on the market value of the transaction data. For example, the data processing unit can apply a rapid processing method to data with a high market value. The data processing unit can also apply a cautious processing method to data with a low market value. Furthermore, the data processing unit can adjust the processing method in real time according to the market value of the data. This allows for improved efficiency and accuracy of data processing by selecting a processing method based on the market value of the transaction data. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the market value of the transaction data into the generation AI and have the generation AI select the optimal processing method.

[0079] The algorithm optimization unit can analyze the user's emotions and adjust the algorithm optimization method based on the analyzed user's emotions. For example, if the user is nervous, the algorithm optimization unit can apply an algorithm that emphasizes stability. Furthermore, if the user is relaxed, the algorithm optimization unit can also apply an algorithm that takes risks. Furthermore, if the user is in a hurry, the algorithm optimization unit can also apply an algorithm that performs rapid optimization. This allows optimal investment decisions to be made by adjusting the algorithm optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the algorithm optimization unit can be performed using, for example, an AI, or without an AI. For example, the algorithm optimization unit can input the user's emotion data into the generation AI and have the generation AI adjust the optimization method.

[0080] When optimizing an algorithm, the algorithm optimization unit can select an optimized algorithm by referring to past trading data. For example, the algorithm optimization unit analyzes past trading data and selects an algorithm with the highest success rate. The algorithm optimization unit can also select an algorithm that minimizes risk from past trading data. Furthermore, the algorithm optimization unit can select the most efficient algorithm based on past trading data. In this way, by referring to past trading data, it is possible to select an optimal algorithm and improve the accuracy of investment decisions. Some or all of the above-mentioned processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input past trading data into a generation AI and have the generation AI select an optimal algorithm.

[0081] During algorithm optimization, the algorithm optimization unit can adjust optimization parameters based on market trends on the exchange. For example, when the market is trending upward, the algorithm optimization unit sets risk-taking parameters. Also, when the market is trending downward, the algorithm optimization unit can set risk-reducing parameters. Furthermore, the algorithm optimization unit can adjust parameters in real time according to market fluctuations. This allows for adjusting optimization parameters based on market trends, thereby improving the accuracy of investment decisions. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input market trend data into the generation AI and cause the generation AI to adjust the optimization parameters.

[0082] During algorithm optimization, the algorithm optimization unit can adjust the optimization frequency based on the trading volume of the exchange. For example, when the trading volume is high, the algorithm optimization unit sets the optimization frequency high. Also, when the trading volume is low, the algorithm optimization unit can set the optimization frequency low. Furthermore, the algorithm optimization unit can adjust the optimization frequency in real time according to fluctuations in trading volume. As a result, efficient algorithm optimization can be achieved by adjusting the optimization frequency based on the trading volume. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input trading volume data to the generation AI and cause the generation AI to adjust the optimization frequency.

[0083] The algorithm optimization unit can analyze the user's emotions and prioritize algorithms based on the analyzed user's emotions. For example, if the user is nervous, the algorithm optimization unit can prioritize algorithms that emphasize stability. Furthermore, if the user is relaxed, the algorithm optimization unit can prioritize algorithms that take risks. Furthermore, if the user is in a hurry, the algorithm optimization unit can prioritize algorithms that perform rapid optimization. By optimizing the priority of algorithms according to the user's emotions, optimal investment decisions can be made. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the algorithm optimization unit can be performed using, for example, an AI, or without an AI. For example, the algorithm optimization unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the algorithms.

[0084] During algorithm optimization, the algorithm optimization unit can determine the priority of optimization based on the submission time of the transaction data. For example, the algorithm optimization unit prioritizes optimization of data submitted earlier. The algorithm optimization unit can also postpone data submitted later. Furthermore, the algorithm optimization unit can adjust the priority of optimization in real time according to fluctuations in the submission time. As a result, efficient algorithm optimization can be achieved by determining the priority of optimization based on the submission time of the transaction data. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input the submission time of the transaction data to the generation AI and cause the generation AI to determine the optimization priority.

[0085] The algorithm optimization unit can adjust the optimization order based on the relevance of the transaction data when optimizing the algorithm. For example, the algorithm optimization unit prioritizes optimization of highly relevant data. The algorithm optimization unit can also postpone optimization of less relevant data. Furthermore, the algorithm optimization unit can adjust the optimization order in real time according to the data relevance. As a result, efficient algorithm optimization can be achieved by adjusting the optimization order based on the relevance of the transaction data. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input the relevance of the transaction data to the generation AI and cause the generation AI to adjust the optimization order.

[0086] During algorithm optimization, the algorithm optimization unit can select an optimization method based on the market value of the transaction data. For example, the algorithm optimization unit can apply a rapid optimization method to data with high market value. The algorithm optimization unit can also apply a conservative optimization method to data with low market value. Furthermore, the algorithm optimization unit can adjust the optimization method in real time according to the market value of the data. As a result, efficient algorithm optimization can be achieved by selecting an optimization method based on the market value of the transaction data. Some or all of the above-described processing in the algorithm optimization unit can be performed using, for example, AI, or can be performed without using AI. For example, the algorithm optimization unit can input the market value of the transaction data to the generation AI and cause the generation AI to select the optimal optimization method. === Hard Collateral 1-1 === Each of the multiple elements including the network construction unit, data processing unit, and algorithm adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the network construction unit establishes a connection with an exchange using the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing device 12. The data processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs high-speed data processing using a SuperPod. The algorithm adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the investment algorithm using machine learning or deep learning. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned network construction unit, data processing unit, and algorithm adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the network construction unit establishes a connection with an exchange using the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing device 12. The data processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs high-speed data processing using a SuperPod. The algorithm adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the investment algorithm using machine learning or deep learning. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned network construction unit, data processing unit, and algorithm adjustment unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the network construction unit establishes a connection with an exchange using the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing device 12. The data processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs high-speed data processing using a SuperPod. The algorithm adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the investment algorithm using machine learning or deep learning. === Hard Collateral 1-4 === Each of the multiple elements including the network construction unit, data processing unit, and algorithm adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the network construction unit establishes a connection with an exchange using the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing device 12. The data processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs high-speed data processing using a SuperPod. The algorithm adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the investment algorithm using machine learning or deep learning.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The network construction unit can select the optimal connection route based on the geographic location of the exchange. For example, if the exchange is located nearby, the shortest connection route can be selected. Alternatively, if the exchange is located far away, the optimal connection route can be selected via multiple relay points. Furthermore, if the exchanges are located in different countries, the optimal connection route can be selected using international communication infrastructure. This allows for improved communication efficiency by selecting the optimal connection route based on the geographic location of the exchange. Some or all of the above-described processing in the network construction unit can be performed using, for example, AI, or without AI. For example, the network construction unit can input the geographic location data of the exchange into the generation AI and cause the generation AI to select the optimal connection route.

[0089] The data processing unit can analyze the user's emotions and determine the priority of data processing based on the analyzed user's emotions. For example, if the user is nervous, important data can be processed first. The data processing order can also be flexibly adjusted if the user is relaxed. Furthermore, if the user is in a hurry, the most important data can be processed first. This allows the data processing priority to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or without AI. For example, the data processing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0090] During algorithm optimization, the algorithm optimization unit can adjust optimization parameters based on market trends on the exchange. For example, if the market is trending upward, risk-taking parameters can be set. Conversely, if the market is trending downward, risk-reducing parameters can be set. Furthermore, parameters can be adjusted in real time according to market fluctuations. This allows for adjusting optimization parameters based on market trends, thereby improving the accuracy of investment decisions. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input market trend data into the generation AI and have the generation AI adjust the optimization parameters.

[0091] The network construction unit can analyze the user's emotions and determine network priorities based on the analyzed user's emotions. For example, if the user is nervous, the network priority can be set high to provide a quick connection. Alternatively, if the user is relaxed, the network priority can be set low to emphasize stability. Furthermore, if the user is in a hurry, the priority can be set high to establish a connection in the shortest time. This allows the network priority to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the network construction unit can be performed using AI, for example, or without AI. For example, the network construction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0092] When processing data, the data processing unit can apply different processing algorithms depending on the type of transaction data. For example, a specific algorithm can be applied to stock transaction data. The data processing unit can also apply a different algorithm to foreign exchange transaction data. Furthermore, the data processing unit can also apply a dedicated algorithm to cryptocurrency transaction data. This allows for the application of an optimal processing algorithm depending on the type of transaction data, thereby improving the efficiency of data processing. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the type of transaction data into the generation AI and have the generation AI select the optimal processing algorithm.

[0093] The algorithm optimization unit can analyze the user's emotions and adjust the algorithm optimization method based on the analyzed user's emotions. For example, if the user is nervous, an algorithm that emphasizes stability can be applied. Also, if the user is relaxed, an algorithm that emphasizes risk can be applied. Furthermore, if the user is in a hurry, an algorithm that performs rapid optimization can be applied. By adjusting the algorithm optimization method according to the user's emotions, optimal investment decisions can be made. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the algorithm optimization unit can be performed using, for example, an AI, or without an AI. For example, the algorithm optimization unit can input the user's emotion data into the generation AI and have the generation AI adjust the optimization method.

[0094] When constructing a network, the network construction unit can configure optimal communication settings according to the exchange's communication protocol. For example, if the exchange uses the TCP / IP protocol, the network construction unit can configure optimal TCP / IP settings. Furthermore, if the exchange uses the UDP protocol, the network construction unit can configure optimal UDP settings. Furthermore, if the exchange uses its own proprietary communication protocol, the network construction unit can configure settings compatible with that protocol. This allows for optimal communication settings according to the exchange's communication protocol, thereby improving communication stability and efficiency. Some or all of the above-described processing in the network construction unit may be performed using, or without, AI. For example, the network construction unit can input the exchange's communication protocol data into a generation AI and have the generation AI execute optimal communication settings.

[0095] The data processing unit can analyze the user's emotions and adjust the display method of the data processing based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the data processing to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or without AI. For example, the data processing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0096] During algorithm optimization, the algorithm optimization unit can determine the priority of optimization based on the submission time of the transaction data. For example, data submitted earlier is given priority for optimization. The algorithm optimization unit can also postpone data submitted later. Furthermore, the algorithm optimization unit can adjust the priority of optimization in real time according to fluctuations in the submission time. In this way, by determining the priority of optimization based on the submission time of the transaction data, efficient algorithm optimization can be achieved. Some or all of the above-described processing in the algorithm optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the algorithm optimization unit can input the submission time of the transaction data to the generation AI and have the generation AI determine the priority of optimization.

[0097] When constructing a network, the network construction unit can select a connection method according to the security level of the exchange. For example, if the security level of the exchange is high, the connection is made using encrypted communication. Furthermore, if the security level of the exchange is medium, the network construction unit can also connect using an authentication protocol. Furthermore, if the security level of the exchange is low, a simple connection method can also be used. Thus, by selecting a connection method according to the security level of the exchange, the security of communication can be ensured. Some or all of the above-described processing in the network construction unit may be performed using, or without, AI, for example. For example, the network construction unit can input the security level data of the exchange into the generation AI and have the generation AI select the optimal connection method.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The network construction department constructs a network for accessing the exchange. For example, the network construction department establishes a direct connection with the exchange using fiber optic cables or dedicated lines. The network construction department can also select the optimal connection route to minimize communication delays. For example, the optimal connection route can be selected based on the geographic location of the exchange. Step 2: The data processing unit processes the trading data through the network constructed by the network construction unit. For example, the data processing unit repurposes the SuperPod purchased for LLM to perform high-speed data processing. The data processing unit analyzes market data in real time and can make optimal investment decisions. Step 3: The algorithm adjustment unit adjusts the investment algorithm based on the data processed by the data processing unit. The algorithm adjustment unit uses, for example, machine learning or deep learning to learn from past trading data and predict future market trends. This allows for faster trading and maximized profits.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A network construction department that builds a network for accessing the exchange; a data processing unit that processes transaction data through the network constructed by the network construction unit; an algorithm adjustment unit that adjusts an investment algorithm based on the data processed by the data processing unit; A system characterized by:

2. The network construction unit Establish a direct connection to the exchange using fiber optic cable or dedicated lines 2. The system of claim 1.

3. The data processing unit Repurpose the SuperPod purchased for LLM to perform high-speed data processing 2. The system of claim 1.

4. The network construction unit Analyze user sentiment and adjust the timing of network construction based on the analyzed user sentiment.

2. The system of claim 1.

5. The network construction unit When building the network, select efficient connection routes based on the geographic location of the exchanges.

2. The system of claim 1.

6. The network construction unit When building a network, efficient communication settings are made according to the exchange's communication protocol.

2. The system of claim 1.

7. The network construction unit During network construction, network bandwidth will be adjusted based on exchange trading volume.

2. The system of claim 1.

8. The network construction unit Analyze user sentiment and determine network priorities based on the analyzed user sentiment.

2. The system of claim 1.

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