System

Abstract

An object of a system according to an embodiment is to enable a user to easily create a automatic trading program based on his / her own transaction logic.SOLUTION: A system according to an embodiment includes a transaction logic input unit, a programming instruction unit, and a compilation unit. The transaction logic input unit inputs a transaction logic of a user. The programming instruction unit generates a program based on the transaction logic input by the transaction logic input unit. The compiling unit compiles and installs the program generated by the programming instructing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for users to create automated trading programs based on their own trading logic, and specialized knowledge is required.

[0005] The system according to the embodiment aims to enable a user to easily create an automatic trading program based on his or her own trading logic. [Means for solving the problem]

[0006] The system according to the embodiment includes a trading logic input unit, a programming instruction unit, and a compilation unit. The trading logic input unit inputs a user's trading logic. The programming instruction unit generates a program based on the trading logic input by the trading logic input unit. The compilation unit compiles and installs the program generated by the programming instruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily create an automatic trading program based on his / her own trading logic. [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) The automated trading software creation platform according to an embodiment of the present invention is a system that allows users to create automated trading software based on their own trading logic. As a result, the automated trading software creation platform allows users to easily create and use automated trading software based on their own trading logic.

[0029] An automated trading software creation platform according to an embodiment includes a trading logic input unit, a programming instruction unit, and a compilation unit. The trading logic input unit inputs a user's trading logic. For example, the user inputs specific trading conditions, such as placing a buy order when a specific currency pair reaches a certain price. The programming instruction unit generates a program based on the trading logic input by the trading logic input unit. For example, the generation AI creates a program that automatically places buy orders or sell orders based on conditions specified by the user. The generation AI generates the program using a text generation AI (e.g., LLM) or a multimodal generation AI. The compilation unit compiles and installs the program generated by the programming instruction unit. For example, the compilation unit compiles the program generated by the generation AI and installs it on the user's PC. As a result, the automated trading software creation platform according to an embodiment allows users to easily create and use automated trading software based on their own trading logic.

[0030] The trading logic input unit can analyze trading history and propose optimal trading logic based on past successful patterns. In the trading logic input unit, for example, the generation AI analyzes the user's past trading history and extracts successful trading patterns. For example, it finds patterns with a high success rate for specific currency pairs or trading time periods and proposes optimal trading logic based on those. The generation AI also analyzes the user's trading history and proposes optimal trading logic from a risk management perspective. For example, it proposes logic to avoid large losses in past trading. The generation AI also analyzes the user's trading history and proposes optimal trading logic based on trading frequency and transaction amount. For example, it proposes logic for short-term trading for users who trade frequently. This makes it possible to propose optimal trading logic based on the user's trading history.

[0031] The trading logic input unit can explain the trading logic in natural language and provide feedback to the user in a form that is easy to understand. For example, the generation AI in the trading logic input unit analyzes the user's trading logic and explains its contents in natural language. For example, it provides an explanation such as, "This logic places a buy order when a specific currency pair reaches a certain price." The generation AI also analyzes the user's trading logic and provides feedback in natural language on the logic's strengths and weaknesses. For example, it provides feedback such as, "This logic has a high success rate, but also includes high-risk trades." The generation AI also analyzes the user's trading logic and suggests improvements to the logic in natural language. For example, it provides suggestions such as, "Adding risk management elements to this logic will enable more stable trading." This makes it easier for the user to understand the trading logic.

[0032] The trading logic input unit can anonymize and share the trading logic of other users so that it can be used as a reference. The trading logic input unit, for example, provides a function for the generation AI to anonymize and share the trading logic of other users. For example, it allows other users to refer to trading logic with a high success rate. The trading logic of other users can also be anonymized and shared, allowing users to use it as a reference for improving their own trading logic. For example, users can adjust their own logic based on the anonymized trading logic. The generation AI can also anonymize and share the trading logic of other users, allowing users to gain new trading ideas. For example, users can refer to the successful trading logic of other users. This allows users to refer to the trading logic of other users.

[0033] The trading logic input unit allows the input of trading logic for different markets, enabling automated cross-market trading. The trading logic input unit allows the generation AI to input trading logic for the stock market and the cryptocurrency market, for example, enabling automated cross-market trading. For example, trading in the cryptocurrency market is performed based on trading logic for the stock market. The input of trading logic for different markets also allows users to perform automated trading in multiple markets. For example, combining trading logic for the foreign exchange market and the stock market. The generation AI also analyzes the trading logic for different markets and optimizes automated cross-market trading. For example, trading in the foreign exchange market is performed based on trading logic for the stock market. This enables automated trading in different markets.

[0034] The programming instruction unit can make the trading logic compatible with multiple programming languages. For example, the programming instruction unit uses a generation AI to analyze the user's trading logic and make it compatible with multiple programming languages, such as Python and JavaScript. For example, the trading logic is generated as a Python script. In addition, the generation AI automatically performs code conversion to make the user's trading logic compatible with multiple programming languages. For example, it converts trading logic in MT4 language into JavaScript. In addition, the generation AI analyzes the user's trading logic and provides templates to make it compatible with multiple programming languages. For example, it generates code templates for Python and JavaScript. This makes it possible to support multiple programming languages.

[0035] The programming instruction unit can provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, the programming instruction unit allows the generation AI to provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, it simulates trading in the virtual currency market. The generation AI also provides a simulation environment for the user to test the trading logic. For example, it tests the trading logic based on past market data. The generation AI also provides a test environment for the trading logic, allowing the user to evaluate the performance of the trading logic. For example, it simulates the success rate and risk of the trading logic. This allows the trading logic to be simulated in advance.

[0036] The programming instruction unit can also support different trading platforms. For example, the programming instruction unit allows the generation AI to analyze the user's trading logic and enable it to support different trading platforms such as NinjaTrader and TradeStation. For example, the trading logic is converted into the language of each platform. In addition, the generation AI automatically performs code conversion to support different trading platforms. For example, it converts trading logic written in MT4 language into NinjaTrader language. In addition, the generation AI analyzes the user's trading logic and provides templates to support different trading platforms. For example, it generates code templates for NinjaTrader and TradeStation. This allows it to support different trading platforms.

[0037] The programming instruction unit can provide a function for sharing trading logic with other users and for collaborative programming. For example, the programming instruction unit provides a function for the generation AI to share a user's trading logic with other users and for collaborative programming. For example, it allows multiple users to edit the same trading logic. The generation AI also provides a collaboration function for sharing trading logic with other users and for collaborative programming. For example, it edits trading logic in real time. The generation AI also provides a platform for sharing a user's trading logic with other users and for collaborative programming. For example, it provides a version management function for trading logic. This allows collaborative programming with other users.

[0038] The compilation unit can automatically detect program compilation errors and suggest corrections. For example, the generation AI in the compilation unit automatically detects program compilation errors and suggests corrections. For example, it analyzes error messages and suggests specific correction methods. The generation AI also provides an automatic debugging function for detecting program compilation errors and suggesting corrections. For example, it identifies the location of the error and generates correction code. The generation AI also provides an interface for detecting program compilation errors and suggesting corrections. For example, it explains the cause of the error and suggests a correction method. This allows compilation errors to be automatically corrected.

[0039] The compilation unit can optimize program performance and improve execution speed. For example, the generation AI analyzes program performance and makes suggestions for optimization. For example, it can remove unnecessary code and improve execution speed. The generation AI can also automatically refactor code to optimize program performance. For example, it can replace it with an efficient algorithm. The generation AI can also provide tools to analyze and optimize program performance. For example, it can identify bottlenecks and suggest ways to improve them. This can improve program execution speed.

[0040] The compilation unit can install the program in a cloud environment and make it accessible from multiple devices. For example, the generation AI in the compilation unit installs the program in the cloud environment so that users can access it from multiple devices. For example, the compilation unit deploys the program to a cloud server. The generation AI also provides an automatic deployment function for installing the program in the cloud environment. For example, this is compatible with cloud platforms such as AWS and Azure. The generation AI also installs the program in the cloud environment so that users can access it remotely. For example, the program can be operated through a web interface. This makes it accessible from multiple devices.

[0041] The compilation unit manages program versions and allows users to easily revert to past versions. In the compilation unit, for example, the generation AI manages program versions and allows users to easily revert to past versions. For example, it uses a version control system such as Git. The generation AI also provides functions to automate program version management. For example, it records the change history of each version and allows users to revert to any version. The generation AI also manages program versions and provides an interface for users to revert to past versions. For example, it displays the differences between versions and allows users to easily revert. This makes it easy to revert to past versions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The trading logic input unit can support voice input when a user inputs trading logic. For example, a user can input trading logic by saying, "Place a buy order when a specific currency pair reaches a certain price." Supporting voice input also allows a user to input trading logic without using their hands. For example, trading logic can be input while driving or performing other tasks. Supporting voice input also allows a user to input trading logic more intuitively. For example, instructions in natural language can be reflected directly as trading logic. This makes it possible to input trading logic using voice input.

[0044] The transaction logic input unit can provide a visual interface when the user inputs transaction logic. For example, transaction conditions can be set by drag and drop. Providing a visual interface also makes it easier for the user to intuitively understand the transaction logic. For example, transaction conditions can be displayed using icons or graphs. Providing a visual interface also makes it easier for the user to edit the transaction logic. For example, transaction conditions can be changed by clicking on them. This makes it possible to input transaction logic using a visual interface.

[0045] The trading logic input unit can display market data in real time when the user is entering trading logic. For example, it displays the current price and trading volume of a currency pair. In addition, displaying market data in real time can be used as a reference when the user is entering trading logic. For example, trading conditions can be set based on current market conditions. In addition, displaying market data in real time provides the user with information to help them make decisions when entering trading logic. For example, trading logic can be entered while checking price fluctuations of a specific currency pair. This allows the user to enter trading logic while displaying market data in real time.

[0046] The trading logic input unit can provide prediction information based on past trading data when a user inputs trading logic. For example, it predicts how the price of a specific currency pair will fluctuate in the future. Furthermore, by providing prediction information based on past trading data, the user can use it as a reference when inputting trading logic. For example, trading logic can be input while displaying prediction information based on past trading data. Furthermore, by providing prediction information based on past trading data, the user can be provided with information to help them make decisions when inputting trading logic. For example, trading logic can be input while predicting price fluctuations of a specific currency pair. This allows trading logic to be input while providing prediction information based on past trading data.

[0047] The programming instruction unit can make trading logic compatible with multiple programming languages. For example, the generation AI analyzes the user's trading logic and makes it compatible with multiple programming languages, such as Python and JavaScript. For example, the trading logic is generated as a Python script. In addition, the generation AI automatically performs code conversion to make the user's trading logic compatible with multiple programming languages. For example, it converts trading logic in MT4 language into JavaScript. The generation AI also analyzes the user's trading logic and provides templates for making it compatible with multiple programming languages. For example, it generates code templates for Python and JavaScript. This makes it possible to support multiple programming languages.

[0048] The programming instruction unit can provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, the generation AI can provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, it can simulate transactions in the virtual currency market. The generation AI can also provide a simulation environment for the user to test the trading logic. For example, it can test the trading logic based on past market data. The generation AI can also provide a test environment for the trading logic, allowing the user to evaluate the performance of the trading logic. For example, it can simulate the success rate and risk of the trading logic. This allows the trading logic to be simulated in advance.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The trading logic input unit inputs the user's trading logic. For example, the user inputs specific trading conditions, such as placing a buy order when a specific currency pair reaches a certain price. Step 2: The programming instruction unit generates a program based on the trading logic input by the trading logic input unit. For example, the generation AI creates a program that automatically places buy orders or sell orders based on conditions specified by the user. The generation AI generates the program using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The compilation unit compiles and installs the program generated by the programming instruction unit. For example, the compilation unit compiles and installs the program generated by the generation AI on the user's PC.

[0051] (Example 2) The automated trading software creation platform according to an embodiment of the present invention is a system that allows users to create automated trading software based on their own trading logic. As a result, the automated trading software creation platform allows users to easily create and use automated trading software based on their own trading logic.

[0052] An automated trading software creation platform according to an embodiment includes a trading logic input unit, a programming instruction unit, and a compilation unit. The trading logic input unit inputs a user's trading logic. For example, the user inputs specific trading conditions, such as placing a buy order when a specific currency pair reaches a certain price. The programming instruction unit generates a program based on the trading logic input by the trading logic input unit. For example, the generation AI creates a program that automatically places buy orders or sell orders based on conditions specified by the user. The generation AI generates the program using a text generation AI (e.g., LLM) or a multimodal generation AI. The compilation unit compiles and installs the program generated by the programming instruction unit. For example, the compilation unit compiles the program generated by the generation AI and installs it on the user's PC. As a result, the automated trading software creation platform according to an embodiment allows users to easily create and use automated trading software based on their own trading logic.

[0053] The trading logic input unit can analyze trading history and propose optimal trading logic based on past successful patterns. In the trading logic input unit, for example, the generation AI analyzes the user's past trading history and extracts successful trading patterns. For example, it finds patterns with a high success rate for specific currency pairs or trading time periods and proposes optimal trading logic based on those. The generation AI also analyzes the user's trading history and proposes optimal trading logic from a risk management perspective. For example, it proposes logic to avoid large losses in past trading. The generation AI also analyzes the user's trading history and proposes optimal trading logic based on trading frequency and transaction amount. For example, it proposes logic for short-term trading for users who trade frequently. This makes it possible to propose optimal trading logic based on the user's trading history.

[0054] The trading logic input unit can explain the trading logic in natural language and provide feedback to the user in a form that is easy to understand. For example, the generation AI in the trading logic input unit analyzes the user's trading logic and explains its contents in natural language. For example, it provides an explanation such as, "This logic places a buy order when a specific currency pair reaches a certain price." The generation AI also analyzes the user's trading logic and provides feedback in natural language on the logic's strengths and weaknesses. For example, it provides feedback such as, "This logic has a high success rate, but also includes high-risk trades." The generation AI also analyzes the user's trading logic and suggests improvements to the logic in natural language. For example, it provides suggestions such as, "Adding risk management elements to this logic will enable more stable trading." This makes it easier for the user to understand the trading logic.

[0055] The trading logic input unit can use the emotion estimation function to analyze the user's emotional state and propose trading logic that reduces risk if stress or impatience is observed. For example, the trading logic input unit uses the generation AI to analyze the user's emotional state in real time and propose trading logic that reduces risk if stress or impatience is observed. For example, it proposes logic that reduces trading frequency and prioritizes low-risk transactions. The emotion estimation function can also be used to monitor the user's emotional state when entering trading logic and propose trading logic that reduces risk if negative emotions are present. For example, it proposes logic that reduces the trading amount and selects low-risk currency pairs. The generation AI can also analyze the user's emotional state and propose logic to temporarily suspend trading if stress or impatience is observed. For example, it proposes logic to refrain from trading until the user's emotional state stabilizes. This makes it possible to propose trading logic that corresponds to the user's emotional state.

[0056] The trading logic input unit can anonymize and share the trading logic of other users so that it can be used as a reference. The trading logic input unit, for example, provides a function for the generation AI to anonymize and share the trading logic of other users. For example, it allows other users to refer to trading logic with a high success rate. The trading logic of other users can also be anonymized and shared, allowing users to use it as a reference for improving their own trading logic. For example, users can adjust their own logic based on the anonymized trading logic. The generation AI can also anonymize and share the trading logic of other users, allowing users to gain new trading ideas. For example, users can refer to the successful trading logic of other users. This allows users to refer to the trading logic of other users.

[0057] The trading logic input unit allows the input of trading logic for different markets, enabling automated cross-market trading. The trading logic input unit allows the generation AI to input trading logic for the stock market and the cryptocurrency market, for example, enabling automated cross-market trading. For example, trading in the cryptocurrency market is performed based on trading logic for the stock market. The input of trading logic for different markets also allows users to perform automated trading in multiple markets. For example, combining trading logic for the foreign exchange market and the stock market. The generation AI also analyzes the trading logic for different markets and optimizes automated cross-market trading. For example, trading in the foreign exchange market is performed based on trading logic for the stock market. This enables automated trading in different markets.

[0058] The transaction logic input unit can use the emotion estimation function to monitor the emotions of the user when entering transaction logic in real time, and provide an interface that draws out positive emotions. The transaction logic input unit, for example, uses the emotion estimation function to monitor the emotions of the user when entering transaction logic in real time, and provide an interface that draws out positive emotions. For example, it displays music or messages that help the user to relax. It also monitors the emotions of the user when entering transaction logic, and provides feedback to draw out positive emotions. For example, it displays success stories or encouraging messages. It also uses the emotion estimation function to analyze the emotions of the user when entering transaction logic in real time, and provides an interface that draws out positive emotions. For example, it provides an environment that allows the user to relax. This draws out positive emotions from the user.

[0059] The programming instruction unit can make the trading logic compatible with multiple programming languages. For example, the programming instruction unit uses a generation AI to analyze the user's trading logic and make it compatible with multiple programming languages, such as Python and JavaScript. For example, the trading logic is generated as a Python script. In addition, the generation AI automatically performs code conversion to make the user's trading logic compatible with multiple programming languages. For example, it converts trading logic in MT4 language into JavaScript. In addition, the generation AI analyzes the user's trading logic and provides templates to make it compatible with multiple programming languages. For example, it generates code templates for Python and JavaScript. This makes it possible to support multiple programming languages.

[0060] The programming instruction unit can provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, the programming instruction unit allows the generation AI to provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, it simulates trading in the virtual currency market. The generation AI also provides a simulation environment for the user to test the trading logic. For example, it tests the trading logic based on past market data. The generation AI also provides a test environment for the trading logic, allowing the user to evaluate the performance of the trading logic. For example, it simulates the success rate and risk of the trading logic. This allows the trading logic to be simulated in advance.

[0061] The programming instruction unit can use the emotion estimation function to analyze the emotion a user has when issuing programming instructions and provide instructions that are easy to understand. The programming instruction unit can, for example, use the emotion estimation function to analyze the emotion a user has when issuing programming instructions in real time and provide instructions that are easy to understand. For example, it can provide instructions that allow the user to relax. It can also monitor the emotion a user has when issuing programming instructions and provide feedback to provide instructions that are easy to understand. For example, it can provide concise instructions if the user is impatient. It can also use the emotion estimation function to analyze the emotion a user has when issuing programming instructions in real time and provide instructions that are easy to understand. For example, it can provide an environment that allows the user to relax. This makes it possible to provide instructions that are easy for the user to understand.

[0062] The programming instruction unit can also support different trading platforms. For example, the programming instruction unit allows the generation AI to analyze the user's trading logic and enable it to support different trading platforms such as NinjaTrader and TradeStation. For example, the trading logic is converted into the language of each platform. In addition, the generation AI automatically performs code conversion to support different trading platforms. For example, it converts trading logic written in MT4 language into NinjaTrader language. In addition, the generation AI analyzes the user's trading logic and provides templates to support different trading platforms. For example, it generates code templates for NinjaTrader and TradeStation. This allows it to support different trading platforms.

[0063] The programming instruction unit can provide a function for sharing trading logic with other users and for collaborative programming. For example, the programming instruction unit provides a function for the generation AI to share a user's trading logic with other users and for collaborative programming. For example, it allows multiple users to edit the same trading logic. The generation AI also provides a collaboration function for sharing trading logic with other users and for collaborative programming. For example, it edits trading logic in real time. The generation AI also provides a platform for sharing a user's trading logic with other users and for collaborative programming. For example, it provides a version management function for trading logic. This allows collaborative programming with other users.

[0064] The programming instruction unit can use the emotion estimation function to monitor the emotions of the user when giving programming instructions in real time, and provide an interface that draws out positive emotions. The programming instruction unit can, for example, use the emotion estimation function to monitor the emotions of the user when giving programming instructions in real time, and provide an interface that draws out positive emotions. For example, music or messages that help the user to relax can be displayed. The programming instruction unit can also monitor the emotions of the user when giving programming instructions, and provide feedback to draw out positive emotions. For example, success stories or encouraging messages can be displayed. The emotion estimation function can also be used to analyze the emotions of the user when giving programming instructions in real time, and provide an interface that draws out positive emotions. For example, an environment that allows the user to relax can be provided. This can draw out positive emotions from the user.

[0065] The compilation unit can automatically detect program compilation errors and suggest corrections. For example, the generation AI in the compilation unit automatically detects program compilation errors and suggests corrections. For example, it analyzes error messages and suggests specific correction methods. The generation AI also provides an automatic debugging function for detecting program compilation errors and suggesting corrections. For example, it identifies the location of the error and generates correction code. The generation AI also provides an interface for detecting program compilation errors and suggesting corrections. For example, it explains the cause of the error and suggests a correction method. This allows compilation errors to be automatically corrected.

[0066] The compilation unit can optimize program performance and improve execution speed. For example, the generation AI analyzes program performance and makes suggestions for optimization. For example, it can remove unnecessary code and improve execution speed. The generation AI can also automatically refactor code to optimize program performance. For example, it can replace it with an efficient algorithm. The generation AI can also provide tools to analyze and optimize program performance. For example, it can identify bottlenecks and suggest ways to improve them. This can improve program execution speed.

[0067] The compilation unit can use the emotion estimation function to analyze the emotions of the user when compiling and installing, and provide support for reducing stress. The compilation unit, for example, uses the emotion estimation function to analyze the emotions of the user when compiling and installing in real time, and provide support for reducing stress. For example, the compilation unit displays music or a message that helps the user to relax. The compilation unit also monitors the emotions of the user when compiling and installing, and provides feedback for reducing stress. For example, the compilation unit displays success stories or encouraging messages. The compilation unit also uses the emotion estimation function to analyze the emotions of the user when compiling and installing in real time, and provides an interface for reducing stress. For example, the compilation unit provides an environment that allows the user to relax. This reduces the user's stress.

[0068] The compilation unit can install the program in a cloud environment and make it accessible from multiple devices. For example, the generation AI in the compilation unit installs the program in the cloud environment so that users can access it from multiple devices. For example, the compilation unit deploys the program to a cloud server. The generation AI also provides an automatic deployment function for installing the program in the cloud environment. For example, this is compatible with cloud platforms such as AWS and Azure. The generation AI also installs the program in the cloud environment so that users can access it remotely. For example, the program can be operated through a web interface. This makes it accessible from multiple devices.

[0069] The compilation unit manages program versions and allows users to easily revert to past versions. In the compilation unit, for example, the generation AI manages program versions and allows users to easily revert to past versions. For example, it uses a version control system such as Git. The generation AI also provides functions to automate program version management. For example, it records the change history of each version and allows users to revert to any version. The generation AI also manages program versions and provides an interface for users to revert to past versions. For example, it displays the differences between versions and allows users to easily revert. This makes it easy to revert to past versions.

[0070] The compilation unit can use the emotion estimation function to monitor the emotions of the user when compiling and installing in real time, and provide an interface that elicits positive emotions. The compilation unit, for example, uses the emotion estimation function to monitor the emotions of the user when compiling and installing in real time, and provide an interface that elicits positive emotions. For example, the compilation unit displays music or a message that helps the user to relax. The compilation unit also monitors the emotions of the user when compiling and installing, and provides feedback to elicit positive emotions. For example, the compilation unit displays success stories or encouraging messages. The compilation unit also uses the emotion estimation function to analyze the emotions of the user when compiling and installing in real time, and provides an interface that elicits positive emotions. For example, the compilation unit provides an environment that helps the user to relax. This makes it possible to elicit positive emotions from the user.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The trading logic input unit can support voice input when a user inputs trading logic. For example, a user can input trading logic by saying, "Place a buy order when a specific currency pair reaches a certain price." Supporting voice input also allows a user to input trading logic without using their hands. For example, trading logic can be input while driving or performing other tasks. Supporting voice input also allows a user to input trading logic more intuitively. For example, instructions in natural language can be reflected directly as trading logic. This makes it possible to input trading logic using voice input.

[0073] The transaction logic input unit can provide a visual interface when the user inputs transaction logic. For example, transaction conditions can be set by drag and drop. Providing a visual interface also makes it easier for the user to intuitively understand the transaction logic. For example, transaction conditions can be displayed using icons or graphs. Providing a visual interface also makes it easier for the user to edit the transaction logic. For example, transaction conditions can be changed by clicking on them. This makes it possible to input transaction logic using a visual interface.

[0074] The trading logic input unit can display market data in real time when the user is entering trading logic. For example, it displays the current price and trading volume of a currency pair. In addition, displaying market data in real time can be used as a reference when the user is entering trading logic. For example, trading conditions can be set based on current market conditions. In addition, displaying market data in real time provides the user with information to help them make decisions when entering trading logic. For example, trading logic can be entered while checking price fluctuations of a specific currency pair. This allows the user to enter trading logic while displaying market data in real time.

[0075] The transaction logic input unit uses the emotion estimation function to analyze the emotions of the user when entering transaction logic, and can provide a relaxing environment if stress or impatience is detected. For example, if the user is feeling stressed, relaxing music can be played. The emotion estimation function can also be used to monitor the emotions of the user when entering transaction logic, and display a message to elicit positive emotions. For example, a message such as "Please remain calm and enter your transaction logic" can be displayed. The emotion estimation function can also be used to analyze the emotions of the user when entering transaction logic in real time, and provide a relaxing environment. For example, a background image that helps the user relax can be displayed. This makes it possible to provide support according to the user's emotional state.

[0076] The transaction logic input unit uses an emotion estimation function to analyze the user's emotions when entering transaction logic, and can suspend the entry of transaction logic if negative emotions are detected. For example, if the user is feeling impatient or anxious, the transaction logic input unit can suspend the entry of transaction logic and provide time for the user to relax. The transaction logic input unit can also use the emotion estimation function to monitor the user's emotions when entering transaction logic, and if negative emotions are detected, can prompt the user to suspend the entry of transaction logic. For example, the transaction logic input unit can display a message such as "Take a break and then enter transaction logic again." The transaction logic input unit can also use the emotion estimation function to analyze the user's emotions in real time when entering transaction logic, and if negative emotions are detected, can suspend the entry of transaction logic. For example, the transaction logic input unit can provide an environment in which the user can relax. This can support the entry of transaction logic according to the user's emotional state.

[0077] The trading logic input unit can provide prediction information based on past trading data when a user inputs trading logic. For example, it predicts how the price of a specific currency pair will fluctuate in the future. Furthermore, by providing prediction information based on past trading data, the user can use it as a reference when inputting trading logic. For example, trading logic can be input while displaying prediction information based on past trading data. Furthermore, by providing prediction information based on past trading data, the user can be provided with information to help them make decisions when inputting trading logic. For example, trading logic can be input while predicting price fluctuations of a specific currency pair. This allows trading logic to be input while providing prediction information based on past trading data.

[0078] The trading logic input unit can use the emotion estimation function to analyze the emotions of the user when entering trading logic and provide an interface for eliciting positive emotions. For example, music or messages that help the user relax can be displayed. The emotion estimation function can also be used to monitor the emotions of the user when entering trading logic and provide feedback to elicit positive emotions. For example, success stories or encouraging messages can be displayed. The emotion estimation function can also be used to analyze the emotions of the user when entering trading logic in real time and provide an interface for eliciting positive emotions. For example, an environment that helps the user relax can be provided. This can elicit positive emotions from the user.

[0079] The programming instruction unit can make trading logic compatible with multiple programming languages. For example, the generation AI analyzes the user's trading logic and makes it compatible with multiple programming languages, such as Python and JavaScript. For example, the trading logic is generated as a Python script. In addition, the generation AI automatically performs code conversion to make the user's trading logic compatible with multiple programming languages. For example, it converts trading logic in MT4 language into JavaScript. The generation AI also analyzes the user's trading logic and provides templates for making it compatible with multiple programming languages. For example, it generates code templates for Python and JavaScript. This makes it possible to support multiple programming languages.

[0080] The programming instruction unit can provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, the generation AI can provide a test environment for the trading logic, allowing the user to perform a simulation before an actual transaction. For example, it can simulate transactions in the virtual currency market. The generation AI can also provide a simulation environment for the user to test the trading logic. For example, it can test the trading logic based on past market data. The generation AI can also provide a test environment for the trading logic, allowing the user to evaluate the performance of the trading logic. For example, it can simulate the success rate and risk of the trading logic. This allows the trading logic to be simulated in advance.

[0081] The programming instruction unit can use the emotion estimation function to analyze the emotion a user feels when giving programming instructions and provide instructions that are easy to understand. For example, the emotion estimation function can be used to analyze the emotion a user feels when giving programming instructions in real time and provide instructions that are easy to understand. For example, instructions that allow the user to relax can be provided. The emotion estimation function can also be used to monitor the emotion a user feels when giving programming instructions and provide feedback to provide instructions that are easy to understand. For example, if the user is impatient, concise instructions can be provided. The emotion estimation function can also be used to analyze the emotion a user feels when giving programming instructions in real time and provide instructions that are easy to understand. For example, an environment can be provided that allows the user to relax. This makes it possible to provide instructions that are easy for the user to understand.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The trading logic input unit inputs the user's trading logic. For example, the user inputs specific trading conditions, such as placing a buy order when a specific currency pair reaches a certain price. Step 2: The programming instruction unit generates a program based on the trading logic input by the trading logic input unit. For example, the generation AI creates a program that automatically places buy orders or sell orders based on conditions specified by the user. The generation AI generates the program using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The compilation unit compiles and installs the program generated by the programming instruction unit. For example, the compilation unit compiles and installs the program generated by the generation AI on the user's PC.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 robot 414 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 transaction logic input unit for inputting a user's transaction logic; a programming instruction unit that generates a program based on the transaction logic input by the transaction logic input unit; a compilation unit that compiles and installs the program generated by the programming instruction unit. A system characterized by:

2. The transaction logic input unit Analyzes trading history and proposes optimal trading logic based on past success patterns 2. The system of claim 1.

3. The transaction logic input unit Explain the transaction logic in natural language and provide feedback to users in a way that is easy to understand 2. The system of claim 1.

4. The transaction logic input unit Analyzes the user's emotional state and suggests trading logic that reduces risk if stress or impatience is detected 2. The system of claim 1.

5. The transaction logic input unit Share and anonymize other users' trading logic for reference purposes 2. The system of claim 1.

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