system

Abstract

The system according to this embodiment aims to convert old language code into the latest programming language and to efficiently optimize and customize it. [Solution] The system according to the embodiment comprises an analysis unit, a conversion unit, an optimization unit, a customization unit, an integration unit, and a generation unit. The analysis unit analyzes the old language code. The conversion unit converts the information analyzed by the analysis unit into the latest programming language. The optimization unit optimizes the code converted by the conversion unit. The customization unit interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding functions into the code. The integration unit automates the integration and operational testing of the code customized by the customization unit. The generation unit automatically generates design documents based on the code integrated by the integration unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that manual conversion from an old language to the latest programming language wastes time and cost, and requires a large amount of engineering resources to meet efficient optimization and customization requirements.

[0005] The system according to the embodiment aims to convert old language code into the latest programming language and optimize and customize it efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a conversion unit, an optimization unit, a customization unit, an integration unit, and a generation unit. The analysis unit analyzes the old language code. The conversion unit converts the information analyzed by the analysis unit into the latest programming language. The optimization unit optimizes the code converted by the conversion unit. The customization unit interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding functions into the code. The integration unit automates the integration and operational testing of the code customized by the customization unit. The generation unit automatically generates design documents based on the code integrated by the integration unit. [Effects of the Invention]

[0007] The system according to this embodiment can convert old language code to the latest programming language and efficiently optimize and customize it. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (for example, a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The code conversion system according to an embodiment of the present invention is a system that uses AI to automatically convert code from an older language to the latest programming language, optimize the code, perform interactive customization by generating AI, seamless integration and testing, and automatically generate design documents. First, the code conversion system uses AI to analyze the old language code and convert it to the latest programming language. Next, it simplifies, integrates, and improves the flow of processing to design an efficient program. Furthermore, the generating AI interacts with the user to directly reflect requests for externalizing specific parameters or adding functions into the code. The system automates the integration and operational testing of the converted code to support a smooth implementation. Finally, it automatically generates a design document based on the completed code, and allows for the addition of other functions to the design document and subsequent regeneration. For example, the code conversion system includes an analysis unit that analyzes the old language code. The analysis unit analyzes the old language code and provides information for conversion to the latest programming language. Next, the code conversion system includes a conversion unit that converts the code to the latest programming language based on the information provided by the analysis unit. The conversion unit converts the code to the latest programming language based on the information provided by the analysis unit. Furthermore, the code conversion system includes an optimization unit that optimizes the code converted by the conversion unit. The optimization unit optimizes the code converted by the conversion unit. Next, the code conversion system includes a customization unit that interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding functions into the code. The customization unit uses generation AI to interact with the user and directly reflect requests for externalizing specific parameters or adding functions into the code. Furthermore, the code conversion system includes an integration unit that automates the integration and operational testing of the code customized by the customization unit. The integration unit automates the integration and operational testing of the code customized by the customization unit. Finally, the code conversion system includes a generation unit that automatically generates design documents based on the code integrated by the integration unit. The generation unit automatically generates design documents based on the code integrated by the integration unit. As a result, the code conversion system can significantly reduce the time required for program conversion, improve the quality and efficiency of the converted code, and respond to business needs with flexible customization.This allows the code conversion system to efficiently analyze, convert, optimize, customize, integrate, and generate design documents for older language codes.

[0029] The code conversion system according to the embodiment comprises an analysis unit, a conversion unit, an optimization unit, a customization unit, an integration unit, and a generation unit. The analysis unit analyzes the old language code. The analysis unit, for example, analyzes the old language code and provides information for conversion to the latest programming language. The analysis unit performs syntactic analysis of the old language code and understands the structure of the code. The analysis unit, for example, analyzes each element of the old language code and generates information that maps it to the corresponding element of the latest programming language. The analysis unit analyzes the dependencies of the old language code and provides information necessary for conversion. The conversion unit converts to the latest programming language based on the information provided by the analysis unit. The conversion unit, for example, converts the syntax of the old language code to the syntax of the latest programming language. The conversion unit replaces libraries of the old language code with libraries of the latest programming language. The conversion unit converts functions and methods of the old language code to functions and methods of the latest programming language. The optimization unit optimizes the code converted by the conversion unit. The optimization unit, for example, removes unnecessary processing to improve the execution speed of the code. The optimization unit optimizes data structures to reduce the memory usage of the code. The optimization unit changes the order of processing to improve the flow of the code. The customization unit interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding features into the code. The customization unit uses generative AI to interact with the user and move specific parameters to configuration files. The customization unit uses generative AI to add new features based on user requests. The customization unit uses generative AI to extend existing features. The integration unit automates the integration and operational testing of the code customized by the customization unit. The integration unit, for example, merges code and resolves dependencies. The integration unit automates unit tests, integration tests, and system tests. The integration unit generates test results as reports and provides them to the user. The generation unit automatically generates design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams. The generation unit can also add other features to the design documents and regenerate them.As a result, the code conversion system according to the embodiment can efficiently perform analysis, conversion, optimization, customization, integration, and design document generation of old language codes.

[0030] The analysis unit analyzes the old language code. For example, the analysis unit analyzes the old language code and provides information for conversion to the latest programming language. Specifically, the analysis unit performs syntactic analysis of the old language code to understand the code structure in detail. Syntactic analysis includes two stages: lexical analysis and syntactic analysis. In lexical analysis, the code is divided into tokens, and in syntactic analysis, the tokens are analyzed to generate a syntax tree. The analysis unit analyzes each element of the old language code and generates information that maps it to the corresponding element of the latest programming language. For example, it creates mapping information to convert function definitions and variable declarations in the old language to the corresponding syntax of the latest language. Furthermore, the analysis unit analyzes the dependencies of the old language code, clarifying the relationships between modules and library dependencies. This provides a foundation for the conversion unit to accurately convert the code. The analysis unit also analyzes code comments and documentation, collecting information to appropriately reflect them in the converted code. This helps maintain the readability and maintainability of the code.

[0031] The conversion unit converts the code to the latest programming language based on the information provided by the parsing unit. For example, the conversion unit converts the syntax of the old language code to the syntax of the latest programming language. Specifically, it generates code according to the syntax of the latest language based on the syntax tree provided by the parsing unit. The conversion unit replaces the libraries of the old language code with libraries of the latest programming language. For example, it maps the standard libraries and third-party libraries used in the old language to the corresponding libraries in the latest language and adds necessary import statements and dependencies. The conversion unit converts the functions and methods of the old language code to the functions and methods of the latest programming language. For example, it converts the function signatures and method calls of the old language to the format of the latest language and sets the appropriate argument and return types. The conversion unit detects any errors or warnings that may occur during the code conversion process and makes appropriate corrections. This ensures that the converted code works correctly.

[0032] The optimization unit optimizes the code transformed by the transformation unit. For example, the optimization unit removes unnecessary processing to improve the execution speed of the code. Specifically, it removes redundant loops and conditional branches and replaces them with more efficient algorithms. The optimization unit optimizes data structures to reduce the memory usage of the code. For example, it appropriately adjusts the size of arrays and lists to prevent wasting memory. The optimization unit changes the order of processing to improve the flow of the code. For example, it moves frequently used processes to the front to improve the cache hit rate. The optimization unit uses profiling tools to identify bottlenecks and perform optimizations to improve the performance of the code. In this way, the optimization unit ensures that the transformed code operates efficiently.

[0033] The customization unit, based on the code optimized by the optimization unit, interacts with the user to directly reflect requests for externalizing specific parameters or adding features into the code. The customization unit uses generative AI to interact with the user and move specific parameters to a configuration file. Specifically, the generative AI receives input from the user in natural language and converts it into the appropriate configuration file format. The customization unit uses generative AI to add new features based on user requests. For example, if a user wants to add a specific feature, the generative AI analyzes the request, generates the necessary code, and integrates it into the existing code. The customization unit uses generative AI to extend existing features. For example, it can add new arguments to an existing function or change the behavior of an existing method. Through interaction with the user, the customization unit can customize the code quickly and accurately. This enables flexible code customization to meet user requests.

[0034] The Integration Department automates the integration and functional testing of code customized by the Customization Department. For example, the Integration Department performs code merging and resolves dependencies. Specifically, it integrates code written by multiple developers into a single repository and automatically resolves dependencies. The Integration Department automates unit tests, integration tests, and system tests. For example, it runs unit tests to ensure that code changes do not affect other parts, and integration and system tests to verify the overall functionality of the code. The Integration Department generates test results as reports and provides them to users, allowing them to review the code quality and make necessary corrections. The Integration Department uses continuous integration (CI) tools to automate code integration and testing, improving the efficiency of the development process.

[0035] The generation unit automatically generates design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams. Specifically, it uses UML (Unified Modeling Language) to visually represent the structure and operation of the code. The generation unit can also add other functions to the design document and regenerate it. For example, if a new function is added, it regenerates the design document to reflect that function, providing the latest design information. During the design document generation process, the generation unit refers to the code change history and reflects the changes in the design document. This ensures that the design document is always up-to-date, making it easy for developers to understand the structure and operation of the code. By automating the generation of design documents, the generation unit improves the efficiency of the development process and guarantees the quality of the design documents.

[0036] The analysis unit can analyze old language code and provide information for conversion to the latest programming language. For example, the analysis unit can perform syntactic analysis of the old language code to understand the code structure. For example, the analysis unit can analyze each element of the old language code and generate information that maps it to the corresponding element of the latest programming language. For example, the analysis unit can analyze the dependencies of the old language code and provide the information necessary for conversion. This improves the accuracy of the conversion by analyzing the old language code and providing the necessary information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the old language code into AI, which can analyze the code structure and generate the information necessary for conversion.

[0037] The conversion unit can convert to the latest programming language based on the information provided by the analysis unit. For example, the conversion unit can convert the syntax of the old language code to the syntax of the latest programming language. For example, the conversion unit can replace libraries of the old language code with libraries of the latest programming language. For example, the conversion unit can convert functions and methods of the old language code to functions and methods of the latest programming language. This improves the accuracy of the conversion by performing the conversion based on the information from the analysis unit. Some or all of the above processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the information provided by the analysis unit into the AI, and the AI ​​can convert the old language code to the latest programming language.

[0038] The optimization unit can optimize the code converted by the conversion unit. For example, the optimization unit may remove unnecessary processing to improve the execution speed of the code. For example, the optimization unit may optimize the data structure to reduce the memory usage of the code. For example, the optimization unit may change the order of processing to improve the flow of the code. In this way, the efficiency of the code is improved by optimizing the converted code. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the code converted by the conversion unit into AI, and the AI ​​can perform the code optimization.

[0039] The customization unit, based on the code optimized by the optimization unit, can interact with the user and directly reflect requests for externalizing specific parameters or adding features into the code. The customization unit uses a generation AI to interact with the user and move specific parameters to a configuration file. The customization unit uses a generation AI to add new features based on user requests. The customization unit uses a generation AI to extend existing features. This allows for flexible customization by directly reflecting user requests into the code. Some or all of the above processes in the customization unit may be performed using a generation AI, for example, or without a generation AI. For example, the customization unit can input user requests into a generation AI, which can then externalize specific parameters or add features.

[0040] The integration unit can automate the integration and functional testing of code customized by the customization unit. For example, the integration unit can merge code and resolve dependencies. It can also automate unit tests, integration tests, and system tests. The integration unit generates test results as reports and provides them to the user. This automates the integration and functional testing of customized code, resulting in a smoother deployment. Some or all of the processes described above in the integration unit may be performed using AI, for example, or not. For instance, the integration unit can input customized code into an AI, which can then perform code integration and functional testing.

[0041] The generation unit can automatically generate design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams. The generation unit can also add other functions to the design documents and regenerate them. This streamlines the creation of design documents by automatically generating them based on the integrated code. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the integrated code into AI, and the AI ​​can automatically generate the design documents.

[0042] The analysis unit can change its analysis method according to the complexity of the code when analyzing old language code. For example, the analysis unit can apply a detailed analysis method to complex code to clarify the dependencies between each part. For example, the analysis unit can apply a rapid analysis method to simple code to grasp the overall structure. For example, the analysis unit can apply a balanced analysis method to code of moderate complexity to perform analysis efficiently. This allows for efficient analysis by changing the analysis method according to the complexity of the code. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the old language code into AI, which can evaluate the complexity of the code and select an appropriate analysis method.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the code's version history when analyzing old language code. For example, the analysis unit can analyze the changes in each version and track the evolution of the code. For example, the analysis unit can identify bugs and problems in a particular version based on the version history. For example, the analysis unit can perform the analysis based on the most stable version by referring to the version history. This improves the accuracy of the analysis by referring to the code's version history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the version history of the old language code into AI, which can then analyze the version history and improve the accuracy of the analysis.

[0044] The analysis unit can perform analysis of old language code while taking into account the code's comment information. For example, the analysis unit can understand the intent and purpose of the code based on the comment information. For example, the analysis unit can reflect the comment information in the analysis to provide more accurate analysis results. For example, the analysis unit can focus on specific parts of the analysis based on the comment information. This improves the accuracy of the analysis by considering the code's comment information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the comment information of the old language code into AI, which can analyze the comment information and reflect it in the analysis results.

[0045] The analysis unit can perform analysis of old language codes while considering the dependencies between the codes. For example, the analysis unit can analyze dependencies and grasp the overall structure of the code. For example, the analysis unit can prioritize the analysis of specific parts based on the dependencies. For example, the analysis unit can select an efficient analysis method while considering the dependencies. This improves the accuracy of the analysis by considering the dependencies between the codes. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the dependencies of the old language codes into the AI, which can analyze the dependencies and reflect them in the analysis results.

[0046] The conversion unit can apply different conversion algorithms to each function of the code during conversion. For example, the conversion unit applies a dedicated conversion algorithm to database-related code. For example, the conversion unit applies a dedicated conversion algorithm to user interface-related code. For example, the conversion unit applies a dedicated conversion algorithm to network-related code. By applying different conversion algorithms to each function of the code, the accuracy of the conversion is improved. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input different conversion algorithms for each function of the code into the AI, and the AI ​​can apply the appropriate conversion algorithm.

[0047] The conversion unit can select the optimal conversion method while considering the performance of the code during the conversion process. For example, the conversion unit applies a high-speed conversion algorithm to parts where performance is critical. For example, the conversion unit applies a general-purpose conversion algorithm to parts where performance is less critical. For example, the conversion unit selects the optimal conversion method while considering a balance of performance. This improves the efficiency of the converted code by considering the performance of the code. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the code performance into AI, and the AI ​​can select the optimal conversion method.

[0048] The conversion unit can perform the conversion while considering the security requirements of the code. For example, the conversion unit can apply a strict conversion algorithm to parts where security is critical, and a general-purpose conversion algorithm to parts where security is less critical. For example, the conversion unit can select the optimal conversion method while considering a balance of security. This improves the security of the converted code by considering the security requirements of the code. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the security requirements of the code into AI, and the AI ​​can select the optimal conversion method.

[0049] The conversion unit can perform conversions while considering code compatibility. For example, the conversion unit applies a dedicated conversion algorithm to parts where compatibility is important. For example, the conversion unit applies a general-purpose conversion algorithm to parts where compatibility is not so important. For example, the conversion unit selects the optimal conversion method while considering a balance of compatibility. This improves the compatibility of the converted code by considering code compatibility. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input code compatibility into AI, and the AI ​​can select the optimal conversion method.

[0050] The optimization unit can select an optimization method while considering the execution speed of the code. For example, the optimization unit applies a high-speed optimization method to parts where execution speed is important. For example, the optimization unit applies a general-purpose optimization method to parts where execution speed is not so important. For example, the optimization unit selects the optimal optimization method while considering a balance of execution speeds. This improves the efficiency of the optimized code by considering the execution speed of the code. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the execution speed of the code to the AI, and the AI ​​can select the optimal optimization method.

[0051] The optimization unit can select an optimization method while considering the memory usage of the code. For example, the optimization unit applies a memory-efficient optimization method to parts where memory usage is important. For example, the optimization unit applies a general-purpose optimization method to parts where memory usage is not so important. For example, the optimization unit selects the optimal optimization method while considering the balance of memory usage. As a result, the memory efficiency of the optimized code is improved by considering the memory usage of the code. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the memory usage of the code into the AI, and the AI ​​can select the optimal optimization method.

[0052] The optimization unit can perform optimization while considering code readability. For example, the optimization unit can format the code or add comments in parts where readability is important. For example, the optimization unit can apply a general optimization method to parts where readability is not so important. For example, the optimization unit can select the optimal optimization method while considering the balance of readability. By considering code readability, the maintainability of the optimized code is improved. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the code readability into AI, and the AI ​​can select the optimal optimization method.

[0053] The optimization unit can perform optimization while considering the maintainability of the code. For example, the optimization unit can format the code and add comments to parts where maintainability is important. For example, the optimization unit can apply a general optimization method to parts where maintainability is not so important. For example, the optimization unit can select the optimal optimization method by considering the balance of maintainability. This makes it easier to maintain the optimized code by considering the maintainability of the code. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the maintainability of the code into AI, and the AI ​​can select the optimal optimization method.

[0054] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit can propose the optimal customization method based on the user's past customizations. For example, the customization unit can extract specific patterns from the user's past customization history and perform efficient customization. For example, the customization unit can analyze the user's past customization history and select the most effective customization method. This allows the optimal customization method to be selected by referring to the user's past customization history. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input the user's past customization history into a generative AI, which can then select the optimal customization method.

[0055] The customization unit can perform customization while considering the user's current project status. For example, the customization unit can propose the optimal customization method based on the user's current project progress. For example, the customization unit can determine the priority of customization according to the user's project priority. For example, the customization unit can perform efficient customization while considering the user's project schedule. This makes it possible to perform optimal customization by considering the user's current project status. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input the user's current project status into a generative AI, and the generative AI can select the optimal customization method.

[0056] The customization unit can perform customization while considering the user's industry-specific requirements. For example, the customization unit can perform customization based on the user's industry-specific regulations and standards. For example, the customization unit can propose the optimal customization method according to the user's industry-specific needs. For example, the customization unit can perform efficient customization while considering the user's industry-specific requirements. This makes optimal customization possible by considering the user's industry-specific requirements. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the customization unit can input the user's industry-specific requirements into a generative AI, and the generative AI can select the optimal customization method.

[0057] The customization unit can perform customization while considering its integration with other tools used by the user. For example, the customization unit can perform customization while considering data integration with other tools used by the user. For example, the customization unit can propose the optimal customization method to match the user's tool environment. For example, the customization unit can perform efficient customization while considering compatibility with the tools used by the user. This makes it possible to perform optimal customization by considering integration with other tools used by the user. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the customization unit can input the integration with other tools used by the user into a generative AI, and the generative AI can select the optimal customization method.

[0058] The integration unit can select an integration method while considering code dependencies during integration. For example, the integration unit analyzes dependencies and understands the overall structure of the code. For example, the integration unit prioritizes the integration of specific parts based on dependencies. For example, the integration unit selects an efficient integration method while considering dependencies. This improves the accuracy of integration by considering code dependencies. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input code dependencies into AI, which can analyze the dependencies and select an integration method.

[0059] The integration unit can select an integration method during integration, taking into account the test coverage of the code. For example, the integration unit may apply a strict integration method to parts where test coverage is critical. For example, the integration unit may apply a general-purpose integration method to parts where test coverage is less critical. For example, the integration unit may select the optimal integration method by considering the balance of test coverage. This improves the quality of the integrated code by considering the test coverage of the code. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the test coverage of the code into AI, and the AI ​​can select the optimal integration method.

[0060] The integration unit can perform integration while considering coordination with the code version control system. For example, the integration unit can select an efficient integration method while considering coordination with the version control system. For example, the integration unit can propose the optimal integration method based on the history of the version control system. For example, the integration unit can improve the accuracy of integration by strengthening coordination with the version control system. As a result, the accuracy of integration is improved by considering coordination with the code version control system. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data from the version control system into the AI, and the AI ​​can select the optimal integration method.

[0061] The integration unit can perform integration while considering the code deployment environment. For example, the integration unit can select the optimal integration method considering the deployment environment. For example, the integration unit can propose an efficient integration method according to the characteristics of the deployment environment. For example, the integration unit can improve the accuracy of the integration by considering compatibility with the deployment environment. This makes the deployment of the integrated code smoother by considering the code deployment environment. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input information about the deployment environment into the AI, and the AI ​​can select the optimal integration method.

[0062] The generation unit can update the contents of the design document by referring to the code change history when generating the design document. For example, the generation unit can automatically update the contents of the design document based on the code change history. For example, the generation unit can focus on updating specific parts of the design document by referring to the change history. For example, the generation unit can review the overall structure of the design document based on the change history. This ensures that the contents of the design document are kept up-to-date by referring to the code change history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the code change history into AI, and the AI ​​can update the contents of the design document.

[0063] The generation unit can optimize the content of the design document by considering code dependencies when generating the design document. For example, the generation unit can analyze dependencies and optimize the overall structure of the design document. For example, the generation unit can optimize specific parts of the design document based on dependencies. For example, the generation unit can select an efficient method for generating the design document by considering dependencies. This optimizes the content of the design document by considering code dependencies. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input code dependencies into AI, and the AI ​​can optimize the content of the design document.

[0064] The generation unit can generate design documents while considering the user's industry-specific requirements. For example, the generation unit generates design documents based on the user's industry-specific regulations and standards. For example, the generation unit proposes the optimal method for generating design documents according to the user's industry-specific needs. For example, the generation unit generates efficient design documents while considering the user's industry-specific requirements. As a result, the optimal design document is generated by considering the user's industry-specific requirements. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's industry-specific requirements into AI, and the AI ​​can select the optimal method for generating design documents.

[0065] The generation unit can generate design documents while considering their integration with other document tools used by the user. For example, the generation unit generates design documents while considering data integration with other document tools used by the user. For example, the generation unit proposes the optimal design document generation method according to the user's tool environment. For example, the generation unit generates efficient design documents while considering compatibility with the tools used by the user. This makes the generation of design documents more efficient by considering integration with other document tools used by the user. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the integration with other document tools used by the user into the AI, and the AI ​​can select the optimal design document generation method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The code conversion system can further improve the accuracy of its analysis by referring to the code's version history during the analysis of the old language code. For example, it can analyze the changes in each version and track the evolution of the code. It can also identify bugs and problems in a particular version based on the version history. Furthermore, by referring to the version history, it can perform the analysis based on the most stable version. As a result, by referring to the code's version history, the accuracy of the analysis is improved, enabling more accurate conversion. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0068] The code conversion system can further apply different conversion algorithms to each function of the code in its conversion unit. For example, a dedicated conversion algorithm can be applied to database-related codes, and another dedicated conversion algorithm can be applied to user interface-related codes. Furthermore, yet another dedicated conversion algorithm can be applied to network-related codes. By applying different conversion algorithms to each function of the code, the accuracy of the conversion is improved, enabling optimal conversion for each function. Some or all of the above-described processing in the conversion unit may be performed using AI, or it may be performed without AI.

[0069] The code conversion system can further select an integration method in its integration unit, taking into account the dependencies between the codes. For example, it can analyze dependencies and grasp the overall structure of the code. It can also prioritize the integration of specific parts based on the dependencies. Furthermore, it can select an efficient integration method, taking dependencies into account. This improves the accuracy of the integration, enabling more precise integration. Some or all of the above-described processes in the integration unit may be performed using AI or not.

[0070] The code conversion system can further perform analysis in its analysis unit, taking into account the comment information of the code when analyzing the old language code. For example, the intent and purpose of the code can be understood based on the comment information. Furthermore, by reflecting the comment information in the analysis, more accurate analysis results can be provided. In addition, the analysis can be performed with emphasis on specific parts based on the comment information. As a result, by considering the comment information of the code, the accuracy of the analysis is improved, and more accurate conversion becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0071] The code conversion system can further perform conversions in the conversion unit while considering the security requirements of the code. For example, a strict conversion algorithm can be applied to parts where security is critical. Conversely, a general-purpose conversion algorithm can be applied to parts where security is less critical. Furthermore, the system can select the optimal conversion method while considering a balance of security. This improves the security of the converted code by considering the security requirements of the code, enabling safer conversions. Some or all of the above-described processes in the conversion unit may be performed using AI or not.

[0072] The code conversion system can further optimize the code in its optimization unit, taking readability into consideration. For example, code formatting and commenting can be performed on parts where readability is important. General-purpose optimization methods can be applied to parts where readability is less important. Furthermore, the optimal optimization method can be selected considering a balance of readability. As a result, by considering code readability, the maintainability of the optimized code is improved, and more maintainable code is generated. Some or all of the above processing in the optimization unit may be performed using AI, or it may be performed without using AI.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The analysis unit analyzes the old language code. The analysis unit performs syntactic analysis of the old language code, understands the code structure, analyzes each element, and generates information to map it to the corresponding elements of the latest programming language. It also analyzes the dependencies of the old language code and provides the information necessary for conversion. Step 2: The conversion unit converts the old language code to the modern programming language based on the information provided by the analysis unit. The conversion unit converts the syntax of the old language code to the syntax of the modern programming language and replaces libraries, functions, and methods with those of the corresponding modern programming language. Step 3: The optimization unit optimizes the code transformed by the transformation unit. The optimization unit removes unnecessary processes to improve the execution speed of the code, optimizes data structures to reduce memory usage, and changes the order of processes to improve the flow of the code. Step 4: The customization unit interacts with the user based on the code optimized by the optimization unit, directly reflecting requests for externalizing specific parameters or adding features into the code. The customization unit interacts with the user using generative AI, moves specific parameters to configuration files, adds new features, and extends existing features. Step 5: The Integration Department automates the integration and functional testing of the code customized by the Customization Department. The Integration Department merges the code, resolves dependencies, automates unit tests, integration tests, and system tests, and generates test results as reports to provide to the user. Step 6: The generation unit automatically generates design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams, and can also add other functions to the design documents and regenerate them.

[0075] (Example of form 2) The code conversion system according to an embodiment of the present invention is a system that uses AI to automatically convert code from an older language to the latest programming language, optimize the code, perform interactive customization by generating AI, seamless integration and testing, and automatically generate design documents. First, the code conversion system uses AI to analyze the old language code and convert it to the latest programming language. Next, it simplifies, integrates, and improves the flow of processing to design an efficient program. Furthermore, the generating AI interacts with the user to directly reflect requests for externalizing specific parameters or adding functions into the code. The system automates the integration and operational testing of the converted code to support a smooth implementation. Finally, it automatically generates a design document based on the completed code, and allows for the addition of other functions to the design document and subsequent regeneration. For example, the code conversion system includes an analysis unit that analyzes the old language code. The analysis unit analyzes the old language code and provides information for conversion to the latest programming language. Next, the code conversion system includes a conversion unit that converts the code to the latest programming language based on the information provided by the analysis unit. The conversion unit converts the code to the latest programming language based on the information provided by the analysis unit. Furthermore, the code conversion system includes an optimization unit that optimizes the code converted by the conversion unit. The optimization unit optimizes the code converted by the conversion unit. Next, the code conversion system includes a customization unit that interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding functions into the code. The customization unit uses generation AI to interact with the user and directly reflect requests for externalizing specific parameters or adding functions into the code. Furthermore, the code conversion system includes an integration unit that automates the integration and operational testing of the code customized by the customization unit. The integration unit automates the integration and operational testing of the code customized by the customization unit. Finally, the code conversion system includes a generation unit that automatically generates design documents based on the code integrated by the integration unit. The generation unit automatically generates design documents based on the code integrated by the integration unit. As a result, the code conversion system can significantly reduce the time required for program conversion, improve the quality and efficiency of the converted code, and respond to business needs with flexible customization.This allows the code conversion system to efficiently analyze, convert, optimize, customize, integrate, and generate design documents for older language codes.

[0076] The code conversion system according to the embodiment comprises an analysis unit, a conversion unit, an optimization unit, a customization unit, an integration unit, and a generation unit. The analysis unit analyzes the old language code. The analysis unit, for example, analyzes the old language code and provides information for conversion to the latest programming language. The analysis unit performs syntactic analysis of the old language code and understands the structure of the code. The analysis unit, for example, analyzes each element of the old language code and generates information that maps it to the corresponding element of the latest programming language. The analysis unit analyzes the dependencies of the old language code and provides information necessary for conversion. The conversion unit converts to the latest programming language based on the information provided by the analysis unit. The conversion unit, for example, converts the syntax of the old language code to the syntax of the latest programming language. The conversion unit replaces libraries of the old language code with libraries of the latest programming language. The conversion unit converts functions and methods of the old language code to functions and methods of the latest programming language. The optimization unit optimizes the code converted by the conversion unit. The optimization unit, for example, removes unnecessary processing to improve the execution speed of the code. The optimization unit optimizes data structures to reduce the memory usage of the code. The optimization unit changes the order of processing to improve the flow of the code. The customization unit interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding features into the code. The customization unit uses generative AI to interact with the user and move specific parameters to configuration files. The customization unit uses generative AI to add new features based on user requests. The customization unit uses generative AI to extend existing features. The integration unit automates the integration and operational testing of the code customized by the customization unit. The integration unit, for example, merges code and resolves dependencies. The integration unit automates unit tests, integration tests, and system tests. The integration unit generates test results as reports and provides them to the user. The generation unit automatically generates design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams. The generation unit can also add other features to the design documents and regenerate them.As a result, the code conversion system according to the embodiment can efficiently perform analysis, conversion, optimization, customization, integration, and design document generation of old language codes.

[0077] The analysis unit analyzes the old language code. For example, the analysis unit analyzes the old language code and provides information for conversion to the latest programming language. Specifically, the analysis unit performs syntactic analysis of the old language code to understand the code structure in detail. Syntactic analysis includes two stages: lexical analysis and syntactic analysis. In lexical analysis, the code is divided into tokens, and in syntactic analysis, the tokens are analyzed to generate a syntax tree. The analysis unit analyzes each element of the old language code and generates information that maps it to the corresponding element of the latest programming language. For example, it creates mapping information to convert function definitions and variable declarations in the old language to the corresponding syntax of the latest language. Furthermore, the analysis unit analyzes the dependencies of the old language code, clarifying the relationships between modules and library dependencies. This provides a foundation for the conversion unit to accurately convert the code. The analysis unit also analyzes code comments and documentation, collecting information to appropriately reflect them in the converted code. This helps maintain the readability and maintainability of the code.

[0078] The conversion unit converts the code to the latest programming language based on the information provided by the parsing unit. For example, the conversion unit converts the syntax of the old language code to the syntax of the latest programming language. Specifically, it generates code according to the syntax of the latest language based on the syntax tree provided by the parsing unit. The conversion unit replaces the libraries of the old language code with libraries of the latest programming language. For example, it maps the standard libraries and third-party libraries used in the old language to the corresponding libraries in the latest language and adds necessary import statements and dependencies. The conversion unit converts the functions and methods of the old language code to the functions and methods of the latest programming language. For example, it converts the function signatures and method calls of the old language to the format of the latest language and sets the appropriate argument and return types. The conversion unit detects any errors or warnings that may occur during the code conversion process and makes appropriate corrections. This ensures that the converted code works correctly.

[0079] The optimization unit optimizes the code transformed by the transformation unit. For example, the optimization unit removes unnecessary processing to improve the execution speed of the code. Specifically, it removes redundant loops and conditional branches and replaces them with more efficient algorithms. The optimization unit optimizes data structures to reduce the memory usage of the code. For example, it appropriately adjusts the size of arrays and lists to prevent wasting memory. The optimization unit changes the order of processing to improve the flow of the code. For example, it moves frequently used processes to the front to improve the cache hit rate. The optimization unit uses profiling tools to identify bottlenecks and perform optimizations to improve the performance of the code. In this way, the optimization unit ensures that the transformed code operates efficiently.

[0080] The customization unit, based on the code optimized by the optimization unit, interacts with the user to directly reflect requests for externalizing specific parameters or adding features into the code. The customization unit uses generative AI to interact with the user and move specific parameters to a configuration file. Specifically, the generative AI receives input from the user in natural language and converts it into the appropriate configuration file format. The customization unit uses generative AI to add new features based on user requests. For example, if a user wants to add a specific feature, the generative AI analyzes the request, generates the necessary code, and integrates it into the existing code. The customization unit uses generative AI to extend existing features. For example, it can add new arguments to an existing function or change the behavior of an existing method. Through interaction with the user, the customization unit can customize the code quickly and accurately. This enables flexible code customization to meet user requests.

[0081] The Integration Department automates the integration and functional testing of code customized by the Customization Department. For example, the Integration Department performs code merging and resolves dependencies. Specifically, it integrates code written by multiple developers into a single repository and automatically resolves dependencies. The Integration Department automates unit tests, integration tests, and system tests. For example, it runs unit tests to ensure that code changes do not affect other parts, and integration and system tests to verify the overall functionality of the code. The Integration Department generates test results as reports and provides them to users, allowing them to review the code quality and make necessary corrections. The Integration Department uses continuous integration (CI) tools to automate code integration and testing, improving the efficiency of the development process.

[0082] The generation unit automatically generates design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams. Specifically, it uses UML (Unified Modeling Language) to visually represent the structure and operation of the code. The generation unit can also add other functions to the design document and regenerate it. For example, if a new function is added, it regenerates the design document to reflect that function, providing the latest design information. During the design document generation process, the generation unit refers to the code change history and reflects the changes in the design document. This ensures that the design document is always up-to-date, making it easy for developers to understand the structure and operation of the code. By automating the generation of design documents, the generation unit improves the efficiency of the development process and guarantees the quality of the design documents.

[0083] The analysis unit can analyze old language code and provide information for conversion to the latest programming language. For example, the analysis unit can perform syntactic analysis of the old language code to understand the code structure. For example, the analysis unit can analyze each element of the old language code and generate information that maps it to the corresponding element of the latest programming language. For example, the analysis unit can analyze the dependencies of the old language code and provide the information necessary for conversion. This improves the accuracy of the conversion by analyzing the old language code and providing the necessary information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the old language code into AI, which can analyze the code structure and generate the information necessary for conversion.

[0084] The conversion unit can convert to the latest programming language based on the information provided by the analysis unit. For example, the conversion unit can convert the syntax of the old language code to the syntax of the latest programming language. For example, the conversion unit can replace libraries of the old language code with libraries of the latest programming language. For example, the conversion unit can convert functions and methods of the old language code to functions and methods of the latest programming language. This improves the accuracy of the conversion by performing the conversion based on the information from the analysis unit. Some or all of the above processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the information provided by the analysis unit into the AI, and the AI ​​can convert the old language code to the latest programming language.

[0085] The optimization unit can optimize the code converted by the conversion unit. For example, the optimization unit may remove unnecessary processing to improve the execution speed of the code. For example, the optimization unit may optimize the data structure to reduce the memory usage of the code. For example, the optimization unit may change the order of processing to improve the flow of the code. In this way, the efficiency of the code is improved by optimizing the converted code. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the code converted by the conversion unit into AI, and the AI ​​can perform the code optimization.

[0086] The customization unit, based on the code optimized by the optimization unit, can interact with the user and directly reflect requests for externalizing specific parameters or adding features into the code. The customization unit uses a generation AI to interact with the user and move specific parameters to a configuration file. The customization unit uses a generation AI to add new features based on user requests. The customization unit uses a generation AI to extend existing features. This allows for flexible customization by directly reflecting user requests into the code. Some or all of the above processes in the customization unit may be performed using a generation AI, for example, or without a generation AI. For example, the customization unit can input user requests into a generation AI, which can then externalize specific parameters or add features.

[0087] The integration unit can automate the integration and functional testing of code customized by the customization unit. For example, the integration unit can merge code and resolve dependencies. It can also automate unit tests, integration tests, and system tests. The integration unit generates test results as reports and provides them to the user. This automates the integration and functional testing of customized code, resulting in a smoother deployment. Some or all of the processes described above in the integration unit may be performed using AI, for example, or not. For instance, the integration unit can input customized code into an AI, which can then perform code integration and functional testing.

[0088] The generation unit can automatically generate design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams. The generation unit can also add other functions to the design documents and regenerate them. This streamlines the creation of design documents by automatically generating them based on the integrated code. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the integrated code into AI, and the AI ​​can automatically generate the design documents.

[0089] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing the most important parts. For example, if the user is relaxed, the analysis unit will perform an overall analysis and provide a detailed report. For example, if the user is in a hurry, the analysis unit will prioritize analyzing the most time-consuming parts. This allows for analysis tailored to the user's needs by determining the priority of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of the analysis.

[0090] The analysis unit can change its analysis method according to the complexity of the code when analyzing old language code. For example, the analysis unit can apply a detailed analysis method to complex code to clarify the dependencies between each part. For example, the analysis unit can apply a rapid analysis method to simple code to grasp the overall structure. For example, the analysis unit can apply a balanced analysis method to code of moderate complexity to perform analysis efficiently. This allows for efficient analysis by changing the analysis method according to the complexity of the code. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the old language code into AI, which can evaluate the complexity of the code and select an appropriate analysis method.

[0091] The analysis unit can improve the accuracy of its analysis by referring to the code's version history when analyzing old language code. For example, the analysis unit can analyze the changes in each version and track the evolution of the code. For example, the analysis unit can identify bugs and problems in a particular version based on the version history. For example, the analysis unit can perform the analysis based on the most stable version by referring to the version history. This improves the accuracy of the analysis by referring to the code's version history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the version history of the old language code into AI, which can then analyze the version history and improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the analysis results can be adjusted.

[0093] The analysis unit can perform analysis of old language code while taking into account the code's comment information. For example, the analysis unit can understand the intent and purpose of the code based on the comment information. For example, the analysis unit can reflect the comment information in the analysis to provide more accurate analysis results. For example, the analysis unit can focus on specific parts of the analysis based on the comment information. This improves the accuracy of the analysis by considering the code's comment information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the comment information of the old language code into AI, which can analyze the comment information and reflect it in the analysis results.

[0094] The analysis unit can perform analysis of old language codes while considering the dependencies between the codes. For example, the analysis unit can analyze dependencies and grasp the overall structure of the code. For example, the analysis unit can prioritize the analysis of specific parts based on the dependencies. For example, the analysis unit can select an efficient analysis method while considering the dependencies. This improves the accuracy of the analysis by considering the dependencies between the codes. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the dependencies of the old language codes into the AI, which can analyze the dependencies and reflect them in the analysis results.

[0095] The transformation unit can estimate the user's emotions and determine the priority of the transformation based on the estimated emotions. For example, if the user is stressed, the transformation unit will prioritize transforming the most important parts. If the user is relaxed, the transformation unit will perform a full transformation and provide a detailed report. If the user is in a hurry, the transformation unit will prioritize transforming the most time-consuming parts. This allows for transformations tailored to the user's needs by determining the priority of the transformation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transformation unit may be performed using AI or not. For example, the transformation unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of the transformation.

[0096] The conversion unit can apply different conversion algorithms to each function of the code during conversion. For example, the conversion unit applies a dedicated conversion algorithm to database-related code. For example, the conversion unit applies a dedicated conversion algorithm to user interface-related code. For example, the conversion unit applies a dedicated conversion algorithm to network-related code. By applying different conversion algorithms to each function of the code, the accuracy of the conversion is improved. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input different conversion algorithms for each function of the code into the AI, and the AI ​​can apply the appropriate conversion algorithm.

[0097] The conversion unit can select the optimal conversion method while considering the performance of the code during the conversion process. For example, the conversion unit applies a high-speed conversion algorithm to parts where performance is critical. For example, the conversion unit applies a general-purpose conversion algorithm to parts where performance is less critical. For example, the conversion unit selects the optimal conversion method while considering a balance of performance. This improves the efficiency of the converted code by considering the performance of the code. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the code performance into AI, and the AI ​​can select the optimal conversion method.

[0098] The conversion unit can estimate the user's emotions and adjust the display method of the conversion results based on the estimated emotions. For example, if the user is nervous, the conversion unit provides a simple and highly visible display method. For example, if the user is relaxed, the conversion unit provides a display method that includes detailed information. For example, if the user is in a hurry, the conversion unit provides a display method that gets straight to the point. By adjusting the display method of the conversion results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the conversion results can be adjusted.

[0099] The conversion unit can perform the conversion while considering the security requirements of the code. For example, the conversion unit can apply a strict conversion algorithm to parts where security is critical, and a general-purpose conversion algorithm to parts where security is less critical. For example, the conversion unit can select the optimal conversion method while considering a balance of security. This improves the security of the converted code by considering the security requirements of the code. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the security requirements of the code into AI, and the AI ​​can select the optimal conversion method.

[0100] The conversion unit can perform conversions while considering code compatibility. For example, the conversion unit applies a dedicated conversion algorithm to parts where compatibility is important. For example, the conversion unit applies a general-purpose conversion algorithm to parts where compatibility is not so important. For example, the conversion unit selects the optimal conversion method while considering a balance of compatibility. This improves the compatibility of the converted code by considering code compatibility. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input code compatibility into AI, and the AI ​​can select the optimal conversion method.

[0101] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing the most important parts. For example, if the user is relaxed, the optimization unit will perform overall optimization and provide a detailed report. For example, if the user is in a hurry, the optimization unit will prioritize optimizing the most time-consuming parts. This allows for optimization tailored to the user's needs by determining optimization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into an AI, which can estimate the emotions and determine optimization priorities.

[0102] The optimization unit can select an optimization method while considering the execution speed of the code. For example, the optimization unit applies a high-speed optimization method to parts where execution speed is important. For example, the optimization unit applies a general-purpose optimization method to parts where execution speed is not so important. For example, the optimization unit selects the optimal optimization method while considering a balance of execution speeds. This improves the efficiency of the optimized code by considering the execution speed of the code. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the execution speed of the code to the AI, and the AI ​​can select the optimal optimization method.

[0103] The optimization unit can select an optimization method while considering the memory usage of the code. For example, the optimization unit applies a memory-efficient optimization method to parts where memory usage is important. For example, the optimization unit applies a general-purpose optimization method to parts where memory usage is not so important. For example, the optimization unit selects the optimal optimization method while considering the balance of memory usage. As a result, the memory efficiency of the optimized code is improved by considering the memory usage of the code. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the memory usage of the code into the AI, and the AI ​​can select the optimal optimization method.

[0104] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user emotions. For example, if the user is nervous, the optimization unit provides a simple and highly visible display method. For example, if the user is relaxed, the optimization unit provides a display method that includes detailed information. For example, if the user is in a hurry, the optimization unit provides a display method that gets straight to the point. By adjusting the display method of the optimization results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the optimization results can be adjusted.

[0105] The optimization unit can perform optimization while considering code readability. For example, the optimization unit can format the code or add comments in parts where readability is important. For example, the optimization unit can apply a general optimization method to parts where readability is not so important. For example, the optimization unit can select the optimal optimization method while considering the balance of readability. By considering code readability, the maintainability of the optimized code is improved. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the code readability into AI, and the AI ​​can select the optimal optimization method.

[0106] The optimization unit can perform optimization while considering the maintainability of the code. For example, the optimization unit can format the code and add comments to parts where maintainability is important. For example, the optimization unit can apply a general optimization method to parts where maintainability is not so important. For example, the optimization unit can select the optimal optimization method by considering the balance of maintainability. This makes it easier to maintain the optimized code by considering the maintainability of the code. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the maintainability of the code into AI, and the AI ​​can select the optimal optimization method.

[0107] The customization unit can estimate the user's emotions and determine the priority of customizations based on the estimated emotions. For example, if the user is stressed, the customization unit will prioritize high-priority customizations. If the user is relaxed, the customization unit will perform overall customizations and provide a detailed report. If the user is in a hurry, the customization unit will prioritize the most time-consuming customizations. This allows for customizations tailored to the user's needs by determining the priority of customizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the above-described processes in the customization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the customization unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of customizations.

[0108] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit can propose the optimal customization method based on the user's past customizations. For example, the customization unit can extract specific patterns from the user's past customization history and perform efficient customization. For example, the customization unit can analyze the user's past customization history and select the most effective customization method. This allows the optimal customization method to be selected by referring to the user's past customization history. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input the user's past customization history into a generative AI, which can then select the optimal customization method.

[0109] The customization unit can perform customization while considering the user's current project status. For example, the customization unit can propose the optimal customization method based on the user's current project progress. For example, the customization unit can determine the priority of customization according to the user's project priority. For example, the customization unit can perform efficient customization while considering the user's project schedule. This makes it possible to perform optimal customization by considering the user's current project status. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the customization unit can input the user's current project status into a generative AI, and the generative AI can select the optimal customization method.

[0110] The customization unit can estimate the user's emotions and adjust the display method of the customization results based on the estimated emotions. For example, if the user is nervous, the customization unit provides a simple and highly visible display method. For example, if the user is relaxed, the customization unit provides a display method that includes detailed information. For example, if the user is in a hurry, the customization unit provides a display method that gets straight to the point. By adjusting the display method of the customization results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the customization unit may be performed using a generative AI, or not using a generative AI. For example, the customization unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the display method of the customization results can be adjusted.

[0111] The customization unit can perform customization while considering the user's industry-specific requirements. For example, the customization unit can perform customization based on the user's industry-specific regulations and standards. For example, the customization unit can propose the optimal customization method according to the user's industry-specific needs. For example, the customization unit can perform efficient customization while considering the user's industry-specific requirements. This makes optimal customization possible by considering the user's industry-specific requirements. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the customization unit can input the user's industry-specific requirements into a generative AI, and the generative AI can select the optimal customization method.

[0112] The customization unit can perform customization while considering its integration with other tools used by the user. For example, the customization unit can perform customization while considering data integration with other tools used by the user. For example, the customization unit can propose the optimal customization method to match the user's tool environment. For example, the customization unit can perform efficient customization while considering compatibility with the tools used by the user. This makes it possible to perform optimal customization by considering integration with other tools used by the user. Some or all of the above processes in the customization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the customization unit can input the integration with other tools used by the user into a generative AI, and the generative AI can select the optimal customization method.

[0113] The integration unit can estimate the user's emotions and determine integration priorities based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating the most important parts. If the user is relaxed, the integration unit will perform a comprehensive integration and provide a detailed report. If the user is in a hurry, the integration unit will prioritize integrating the most time-consuming parts. This allows for integration tailored to the user's needs by determining integration priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into an AI, which can estimate the emotions and determine integration priorities.

[0114] The integration unit can select an integration method while considering code dependencies during integration. For example, the integration unit analyzes dependencies and understands the overall structure of the code. For example, the integration unit prioritizes the integration of specific parts based on dependencies. For example, the integration unit selects an efficient integration method while considering dependencies. This improves the accuracy of integration by considering code dependencies. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input code dependencies into AI, which can analyze the dependencies and select an integration method.

[0115] The integration unit can select an integration method during integration, taking into account the test coverage of the code. For example, the integration unit may apply a strict integration method to parts where test coverage is critical. For example, the integration unit may apply a general-purpose integration method to parts where test coverage is less critical. For example, the integration unit may select the optimal integration method by considering the balance of test coverage. This improves the quality of the integrated code by considering the test coverage of the code. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the test coverage of the code into AI, and the AI ​​can select the optimal integration method.

[0116] The integration unit can estimate the user's emotions and adjust the display method of the integration results based on the estimated user emotions. For example, if the user is tense, the integration unit provides a simple and highly visible display method. For example, if the user is relaxed, the integration unit provides a display method that includes detailed information. For example, if the user is in a hurry, the integration unit provides a display method that gets straight to the point. By adjusting the display method of the integration results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the integration results can be adjusted.

[0117] The integration unit can perform integration while considering coordination with the code version control system. For example, the integration unit can select an efficient integration method while considering coordination with the version control system. For example, the integration unit can propose the optimal integration method based on the history of the version control system. For example, the integration unit can improve the accuracy of integration by strengthening coordination with the version control system. As a result, the accuracy of integration is improved by considering coordination with the code version control system. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data from the version control system into the AI, and the AI ​​can select the optimal integration method.

[0118] The integration unit can perform integration while considering the code deployment environment. For example, the integration unit can select the optimal integration method considering the deployment environment. For example, the integration unit can propose an efficient integration method according to the characteristics of the deployment environment. For example, the integration unit can improve the accuracy of the integration by considering compatibility with the deployment environment. This makes the deployment of the integrated code smoother by considering the code deployment environment. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input information about the deployment environment into the AI, and the AI ​​can select the optimal integration method.

[0119] The generation unit can estimate the user's emotions and adjust the method of generating the design document based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed design document. If the user is in a hurry, the generation unit generates a concise design document that gets straight to the point. If the user is excited, the generation unit generates a design document with visually stimulating effects. In this way, by adjusting the method of generating the design document based on the user's emotions, a design document that meets the user's needs is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the method of generating the design document can be adjusted.

[0120] The generation unit can update the contents of the design document by referring to the code change history when generating the design document. For example, the generation unit can automatically update the contents of the design document based on the code change history. For example, the generation unit can focus on updating specific parts of the design document by referring to the change history. For example, the generation unit can review the overall structure of the design document based on the change history. This ensures that the contents of the design document are kept up-to-date by referring to the code change history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the code change history into AI, and the AI ​​can update the contents of the design document.

[0121] The generation unit can optimize the content of the design document by considering code dependencies when generating the design document. For example, the generation unit can analyze dependencies and optimize the overall structure of the design document. For example, the generation unit can optimize specific parts of the design document based on dependencies. For example, the generation unit can select an efficient method for generating the design document by considering dependencies. This optimizes the content of the design document by considering code dependencies. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input code dependencies into AI, and the AI ​​can optimize the content of the design document.

[0122] The generation unit can estimate the user's emotions and adjust the display method of the design document based on the estimated user emotions. For example, if the user is tense, the generation unit provides a simple and highly visible display method. For example, if the user is relaxed, the generation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the generation unit provides a display method that gets straight to the point. By adjusting the display method of the design document according to the user's emotions, it becomes possible to provide a display that is easy for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and adjust the display method of the design document.

[0123] The generation unit can generate design documents while considering the user's industry-specific requirements. For example, the generation unit generates design documents based on the user's industry-specific regulations and standards. For example, the generation unit proposes the optimal method for generating design documents according to the user's industry-specific needs. For example, the generation unit generates efficient design documents while considering the user's industry-specific requirements. As a result, the optimal design document is generated by considering the user's industry-specific requirements. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's industry-specific requirements into AI, and the AI ​​can select the optimal method for generating design documents.

[0124] The generation unit can generate design documents while considering their integration with other document tools used by the user. For example, the generation unit generates design documents while considering data integration with other document tools used by the user. For example, the generation unit proposes the optimal design document generation method according to the user's tool environment. For example, the generation unit generates efficient design documents while considering compatibility with the tools used by the user. This makes the generation of design documents more efficient by considering integration with other document tools used by the user. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the integration with other document tools used by the user into the AI, and the AI ​​can select the optimal design document generation method.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The code conversion system can further estimate the user's emotions and adjust the code conversion process based on those emotions. For example, if the user is stressed, the system can prioritize converting the most important parts and provide results quickly. If the user is relaxed, it can perform a comprehensive conversion and provide a detailed report. Furthermore, if the user is in a hurry, it can prioritize converting the most time-consuming parts for efficient conversion. This allows for flexible responses tailored to the user's needs by adjusting the conversion process based on their emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0127] The code conversion system can further improve the accuracy of its analysis by referring to the code's version history during the analysis of the old language code. For example, it can analyze the changes in each version and track the evolution of the code. It can also identify bugs and problems in a particular version based on the version history. Furthermore, by referring to the version history, it can perform the analysis based on the most stable version. As a result, by referring to the code's version history, the accuracy of the analysis is improved, enabling more accurate conversion. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0128] The code conversion system can further apply different conversion algorithms to each function of the code in its conversion unit. For example, a dedicated conversion algorithm can be applied to database-related codes, and another dedicated conversion algorithm can be applied to user interface-related codes. Furthermore, yet another dedicated conversion algorithm can be applied to network-related codes. By applying different conversion algorithms to each function of the code, the accuracy of the conversion is improved, enabling optimal conversion for each function. Some or all of the above-described processing in the conversion unit may be performed using AI, or it may be performed without AI.

[0129] The code conversion system can further estimate the user's emotions in its optimization section and determine optimization priorities based on those emotions. For example, if the user is stressed, the system can prioritize optimizing the most important parts. If the user is relaxed, it can perform overall optimization and provide a detailed report. Furthermore, if the user is in a hurry, it can prioritize optimizing the most time-consuming parts. This allows for flexible responses tailored to the user's needs by prioritizing optimization based on their emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0130] The code conversion system can further estimate the user's emotions in the customization section and determine the priority of customizations based on those emotions. For example, if the user is stressed, high-priority customizations can be prioritized. If the user is relaxed, overall customizations can be performed, and a detailed report can be provided. Furthermore, if the user is in a hurry, the most time-consuming customizations can be prioritized. This allows for flexible responses tailored to the user's needs by prioritizing customizations based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0131] The code conversion system can further select an integration method in its integration unit, taking into account the dependencies between the codes. For example, it can analyze dependencies and grasp the overall structure of the code. It can also prioritize the integration of specific parts based on the dependencies. Furthermore, it can select an efficient integration method, taking dependencies into account. This improves the accuracy of the integration, enabling more precise integration. Some or all of the above-described processes in the integration unit may be performed using AI or not.

[0132] The code conversion system can further estimate the user's emotions in its generation unit and adjust the design document generation method based on the estimated emotions. For example, if the user is relaxed, a detailed design document can be generated. If the user is in a hurry, a concise design document focusing on the essentials can be generated. Furthermore, if the user is excited, a design document with visually stimulating effects can be generated. In this way, by adjusting the design document generation method based on the user's emotions, a design document that meets the user's needs is generated. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI.

[0133] The code conversion system can further perform analysis in its analysis unit, taking into account the comment information of the code when analyzing the old language code. For example, the intent and purpose of the code can be understood based on the comment information. Furthermore, by reflecting the comment information in the analysis, more accurate analysis results can be provided. In addition, the analysis can be performed with emphasis on specific parts based on the comment information. As a result, by considering the comment information of the code, the accuracy of the analysis is improved, and more accurate conversion becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0134] The code conversion system can further perform conversions in the conversion unit while considering the security requirements of the code. For example, a strict conversion algorithm can be applied to parts where security is critical. Conversely, a general-purpose conversion algorithm can be applied to parts where security is less critical. Furthermore, the system can select the optimal conversion method while considering a balance of security. This improves the security of the converted code by considering the security requirements of the code, enabling safer conversions. Some or all of the above-described processes in the conversion unit may be performed using AI or not.

[0135] The code conversion system can further optimize the code in its optimization unit, taking readability into consideration. For example, code formatting and commenting can be performed on parts where readability is important. General-purpose optimization methods can be applied to parts where readability is less important. Furthermore, the optimal optimization method can be selected considering a balance of readability. As a result, by considering code readability, the maintainability of the optimized code is improved, and more maintainable code is generated. Some or all of the above processing in the optimization unit may be performed using AI, or it may be performed without using AI.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The analysis unit analyzes the old language code. The analysis unit performs syntactic analysis of the old language code, understands the code structure, analyzes each element, and generates information to map it to the corresponding elements of the latest programming language. It also analyzes the dependencies of the old language code and provides the information necessary for conversion. Step 2: The conversion unit converts the old language code to the modern programming language based on the information provided by the analysis unit. The conversion unit converts the syntax of the old language code to the syntax of the modern programming language and replaces libraries, functions, and methods with those of the corresponding modern programming language. Step 3: The optimization unit optimizes the code transformed by the transformation unit. The optimization unit removes unnecessary processes to improve the execution speed of the code, optimizes data structures to reduce memory usage, and changes the order of processes to improve the flow of the code. Step 4: The customization unit interacts with the user based on the code optimized by the optimization unit, directly reflecting requests for externalizing specific parameters or adding features into the code. The customization unit interacts with the user using generative AI, moves specific parameters to configuration files, adds new features, and extends existing features. Step 5: The Integration Department automates the integration and functional testing of the code customized by the Customization Department. The Integration Department merges the code, resolves dependencies, automates unit tests, integration tests, and system tests, and generates test results as reports to provide to the user. Step 6: The generation unit automatically generates design documents based on the code integrated by the integration unit. The generation unit generates design documents such as class diagrams, sequence diagrams, and data flow diagrams, and can also add other functions to the design documents and regenerate them.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the analysis unit, conversion unit, optimization unit, customization unit, integration unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes old language code and provides information for conversion to the latest programming language. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts to the latest programming language based on the information provided by the analysis unit. The optimization unit is implemented by the control unit 46A of the smart device 14 and optimizes the code converted by the conversion unit. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and interacts with the user to directly reflect requests for externalizing specific parameters or adding functions in the code. The integration unit is implemented by the control unit 46A of the smart device 14 and automates the integration and operational testing of the code customized by the customization unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates design documents based on the code integrated by the integration unit. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the analysis unit, conversion unit, optimization unit, customization unit, integration unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes old language code and provides information for conversion to the latest programming language. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts to the latest programming language based on the information provided by the analysis unit. The optimization unit is implemented by the control unit 46A of the smart glasses 214 and optimizes the code converted by the conversion unit. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and interacts with the user to directly reflect requests for externalizing specific parameters or adding functions in the code. The integration unit is implemented by the control unit 46A of the smart glasses 214 and automates the integration and operational testing of the code customized by the customization unit. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates design documents based on the code integrated by the integration unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the analysis unit, conversion unit, optimization unit, customization unit, integration unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes old language code and provides information for conversion to the latest programming language. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts to the latest programming language based on the information provided by the analysis unit. The optimization unit is implemented by the control unit 46A of the headset terminal 314 and optimizes the code converted by the conversion unit. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and interacts with the user to directly reflect requests for externalizing specific parameters or adding functions in the code. The integration unit is implemented by the control unit 46A of the headset terminal 314 and automates the integration and operational testing of the code customized by the customization unit. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates design documents based on the code integrated by the integration unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the analysis unit, conversion unit, optimization unit, customization unit, integration unit, and generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes old language code and provides information for conversion to the latest programming language. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts to the latest programming language based on the information provided by the analysis unit. The optimization unit is implemented by the control unit 46A of the robot 414 and optimizes the code converted by the conversion unit. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and interacts with the user to directly reflect requests for externalizing specific parameters or adding functions in the code. The integration unit is implemented by the control unit 46A of the robot 414 and automates the integration and operational testing of the code customized by the customization unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates design documents based on the code integrated by the integration unit. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] 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.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) An analysis unit that analyzes old language codes, A conversion unit that converts information analyzed by the aforementioned analysis unit into the latest programming language, An optimization unit that optimizes the code converted by the conversion unit, A customization unit interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding functions into the code. An integration unit that automates the integration and operational testing of the code customized by the aforementioned customization unit, The system comprises a generation unit that automatically generates design documents based on the code integrated by the aforementioned integration unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It provides information for analyzing old language code and converting it to the latest programming language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is Based on the information provided by the analysis unit, convert to the latest programming language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, The code converted by the aforementioned conversion unit is optimized. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Based on the code optimized by the aforementioned optimization unit, the system interacts with the user and directly reflects requests for externalizing specific parameters or adding new features into the code. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned integration unit is The aforementioned customization unit automates the integration and operational testing of the customized code. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The aforementioned integration unit automatically generates design documents based on the integrated code. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing old language codes, the analysis method is changed according to the complexity of the code. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing older language codes, referencing the code's version history improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing old language code, the analysis takes into account the code's comment information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing old language code, the analysis takes into account the dependencies between the codes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is It estimates the user's emotions and determines the priority of conversions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is During conversion, different conversion algorithms are applied for each function of the code. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is During conversion, the optimal conversion method is selected while considering the code's performance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is It estimates the user's emotions and adjusts how the conversion results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During the conversion process, the code's security requirements will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The conversion unit is During conversion, the conversion is performed while considering code compatibility. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, the optimization method is selected considering the execution speed of the code. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the optimization method is selected considering the memory usage of the code. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, It estimates the user's emotions and adjusts how the optimization results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, the readability of the code is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, the maintainability of the code is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past customization history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is When customizing, the user's current project status is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is It estimates the user's emotions and adjusts how the customized results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned customization unit is During customization, we take into account the user's industry-specific requirements. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is When customizing, consider how it will work with other tools the user uses. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned integration unit is It estimates user sentiment and determines integration priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned integration unit is During integration, select an integration method that takes code dependencies into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned integration unit is When integrating, select an integration method that takes into account the test coverage of the code. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned integration unit is It estimates the user's emotions and adjusts how the integrated results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned integration unit is When integrating, consider the integration process with the code version control system. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned integration unit is When integrating, the code deployment environment should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 38) The generating unit is We estimate the user's emotions and adjust the design document generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The generating unit is When generating a design document, update the document's contents by referring to the code's change history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The generating unit is When generating design documents, optimize the content of the design documents by taking code dependencies into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 41) The generating unit is It estimates the user's emotions and adjusts how the design document is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The generating unit is When generating design documents, the system takes into account the user's industry-specific requirements. The system described in Appendix 1, characterized by the features described herein. (Note 43) The generating unit is When generating design documents, the system should consider compatibility with other document tools used by the user. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes old language codes, A conversion unit that converts information analyzed by the aforementioned analysis unit into the latest programming language, An optimization unit that optimizes the code converted by the conversion unit, A customization unit interacts with the user based on the code optimized by the optimization unit and directly reflects requests for externalizing specific parameters or adding functions into the code. An integration unit that automates the integration and operational testing of the code customized by the aforementioned customization unit, The system comprises a generation unit that automatically generates design documents based on the code integrated by the aforementioned integration unit. A system characterized by the following features.

2. The aforementioned analysis unit, It provides information for analyzing old language code and converting it to the latest programming language. The system according to feature 1.

3. The conversion unit is Based on the information provided by the analysis unit, convert to the latest programming language. The system according to feature 1.

4. The optimization unit, The code converted by the aforementioned conversion unit is optimized. The system according to feature 1.

5. The aforementioned customization unit is Based on the code optimized by the aforementioned optimization unit, the system interacts with the user and directly reflects requests for externalizing specific parameters or adding new features into the code. The system according to feature 1.

6. The aforementioned integration unit is The aforementioned customization unit automates the integration and operational testing of the customized code. The system according to feature 1.

7. The generating unit is The aforementioned integration unit automatically generates design documents based on the integrated code. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system according to feature 1.

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