system

Abstract

The system according to this embodiment aims to reduce the burden on the user by systematizing the interaction with the generating AI. [Solution] The system according to the embodiment comprises a reception unit, a request unit, a debugging unit, and a modification unit. The reception unit registers the completed specification document. The request unit analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The debugging unit debugs the program code requested by the request unit to be made to the generation AI. The modification unit makes modification requests based on the results of debugging by the debugging unit.

Description

Technical Field

[0006] , , , ,

[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 that responds 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 a large amount of user labor is generated in the interaction with the generative AI, and it is difficult to generate efficient program code.

[0005] The system according to the embodiment aims to systematize the interaction with the generative AI and reduce the burden on the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a request unit, a debugging unit, and a modification unit. The reception unit registers the completed specification document. The request unit analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The debugging unit debugs the program code requested by the request unit to be made to the generation AI. The modification unit makes modification requests based on the results of debugging by the debugging unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on the user by systematizing the interaction with the generating AI. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to the embodiment of the present invention is a system that eliminates the burden on the user by systematizing the interaction (trial and error) with the generation AI. In this system, the generation AI generates program code, but for difficult requests, it often generates hallucinations (a phenomenon that generates information not based on facts), and it takes time to get the desired result. In addition, code corrections require frequent interaction with the generation AI, but all of these responses must be handled by the user, resulting in a large amount of user effort. The present invention reduces the burden on the user by systematizing the interaction with the generation AI. For example, the user only needs to register a "final specification document," and the interaction with the generation AI is carried out by a dedicated system (Try & Ai). This system consists of the following steps: First, the user registers a final specification document in the system. Next, the system analyzes the specification document and sequentially makes requests to the generation AI. The system receives the program code generated by the generation AI and performs debugging. If corrections are needed as a result of debugging, the system automatically requests corrections from the generation AI. By repeating this process, the burden on the user is reduced and program code can be generated efficiently. For example, when a user requests a Google Forms or GAS code generation AI for slides, the system breaks down the request and sequentially sends it to the generation AI. The system then debugs the code generated by the generation AI and requests corrections as needed. In this way, users can easily make complex requests. This system streamlines interaction with the generation AI and reduces the user's workload. It also improves the quality of the program code generated by the generation AI, increasing user satisfaction. Furthermore, this system is not limited to program development; it is expected to have applications in various fields such as project planning and evaluation, climate change modeling, gene sequencing, life counseling, and space exploration. As a result, the system can reduce the burden on users and generate program code efficiently.

[0029] The system according to this embodiment comprises a reception unit, a request unit, a debugging unit, and a modification unit. The reception unit receives completed specification documents registered by the user. The reception unit allows, for example, the user to upload completed specification documents to the system. The reception unit also allows the user to manually input specification documents. Furthermore, the reception unit can automatically detect the format of the specification document and convert it to an appropriate format. For example, the reception unit can convert a specification document in PDF format to text format. The request unit analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The request unit analyzes the content of the specification document using text analysis technology. Furthermore, the request unit can analyze the structure of the specification document and make appropriate requests to the generation AI. Furthermore, the request unit can automatically determine the order of requests and make requests efficiently. For example, the request unit analyzes each section of the specification document and sequentially makes requests to the generation AI. The debugging unit debugs the program code requested by the request unit to the generation AI. The debugging unit checks the program code generated by the generation AI for errors. Furthermore, the debugging unit can check and optimize the performance of the program code. In addition, the debugging unit can perform security checks to ensure the safety of the program code. For example, the debugging unit analyzes the error log of the program code and identifies the cause of the error. The correction unit makes correction requests based on the results of debugging by the debugging unit. For example, the correction unit requests the generation AI to correct the errors identified by the debugging unit. The correction unit can also make correction requests to improve the performance of the program code. Furthermore, the correction unit can make requests to correct security issues. For example, the correction unit identifies the parts to be corrected based on the error log and requests the generation AI to make corrections. As a result, the system according to the embodiment can reduce the burden on the user and generate program code efficiently.

[0030] The reception desk accepts completed specification documents registered by users. For example, the reception desk allows users to upload completed specification documents to the system. Specifically, users can upload specification documents in various formats, such as PDF, Word, and Excel, through the system interface. The reception desk receives these files and stores them in its internal database. The reception desk also allows users to manually input specification documents. For example, users can directly enter the content of the specification document into the system's input form. This input form is designed for efficient user input and includes features such as automatic saving of input content and input assistance. Furthermore, the reception desk can automatically detect the format of the specification document and convert it to the appropriate format. For example, the reception desk can convert a PDF specification document to text format. This involves a process that uses optical character recognition (OCR) technology to extract text from the PDF and convert it into an editable text format. This allows the system to centrally manage specification documents in various formats and convert them to formats suitable for subsequent processing. Through these functions, the reception desk enables users to register specification documents easily and quickly, improving the overall efficiency of the system.

[0031] The requesting department analyzes the specifications registered by the receiving department and sequentially makes requests to the generation AI. For example, the requesting department analyzes the content of the specifications using text analysis technology. Specifically, it uses natural language processing (NLP) technology to extract each section and item of the specifications and understand their content. The requesting department can analyze the structure of the specifications and make appropriate requests to the generation AI. For example, it can extract descriptions of specific functions or modules from the specifications and request the generation AI to generate program code based on that. Furthermore, the requesting department can automatically determine the order of requests and make requests efficiently. For example, the requesting department analyzes each section of the specifications and sequentially makes requests to the generation AI. This allows the requesting department to efficiently analyze the content of the specifications and make appropriate requests to the generation AI. In addition, the requesting department can monitor the progress of requests and modify the request content as needed. For example, it can check the quality of the program code generated by the generation AI and make additional requests as needed. This allows the requesting department to optimize the output of the generation AI and improve the overall efficiency of the system.

[0032] The debugging unit debugs the program code requested by the requesting unit to be generated by the generation AI. For example, the debugging unit checks the program code generated by the generation AI for errors. Specifically, it uses static analysis tools to detect syntax errors and type errors in the program code. The debugging unit can also check the performance of the program code and perform optimization. For example, it monitors the execution time and memory usage of the program code, identifies bottlenecks, and performs optimization. Furthermore, the debugging unit can perform security checks to ensure the safety of the program code. For example, it can detect vulnerabilities in the program code and propose solutions for fixing them. Through these functions, the debugging unit can improve the quality of the program code generated by the generation AI. For example, the debugging unit analyzes the error logs of the program code and identifies the causes of errors. In this way, the debugging unit can improve the quality of the program code generated by the generation AI and improve the reliability of the entire system.

[0033] The correction unit requests corrections based on the results of debugging performed by the debugging unit. For example, the correction unit requests the generation AI to correct errors identified by the debugging unit. Specifically, it identifies the areas to be corrected based on the error log and requests the generation AI to make corrections. The correction unit can also request corrections to improve the performance of the program code. For example, it can request optimizations to reduce the execution time of the program code. Furthermore, the correction unit can request corrections to fix security issues. For example, it can request corrections to fix vulnerabilities contained in the program code. Through these functions, the correction unit can improve the quality of the program code generated by the generation AI. For example, the correction unit identifies the areas to be corrected based on the error log and requests the generation AI to make corrections. In this way, the correction unit can improve the quality of the program code generated by the generation AI and improve the reliability of the entire system.

[0034] The requesting unit can sequentially issue requests to the generation AI. For example, the requesting unit can analyze the contents of the specification document and issue appropriate requests to the generation AI. For example, the requesting unit can analyze each section of the specification document and issue sequential requests to the generation AI. For example, the requesting unit can automatically determine the order of requests and issue them efficiently. As a result, program code can be generated efficiently by sequentially issuing requests to the generation AI. Some or all of the above-described processes in the requesting unit may be performed using AI, for example, or without using AI. For example, the requesting unit can input the contents of the specification document into the generation AI and have the generation AI generate the request content.

[0035] The debugging unit can debug the program code generated by the generation AI. The debugging unit can, for example, perform error checking on the program code generated by the generation AI. The debugging unit can, for example, check the performance of the program code and perform optimization. The debugging unit can, for example, perform security checks to ensure the safety of the program code. In this way, the quality of the code can be improved by debugging the program code generated by the generation AI. Some or all of the above processes in the debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input the program code generated by the generation AI into the generation AI and have the generation AI perform debugging.

[0036] The correction unit can make correction requests based on the debugging results. For example, the correction unit can request the generation AI to correct errors identified by the debugging unit. For example, the correction unit can make correction requests to improve the performance of the program code. For example, the correction unit can make requests to correct security issues. This allows for efficient modification of the program code by making correction requests based on the debugging results. Some or all of the above processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input errors identified by the debugging unit to the generation AI and have the generation AI execute correction requests.

[0037] The reception unit can accept completed specification documents registered by users. The reception unit can, for example, allow users to upload completed specification documents to the system. The reception unit can also, for example, allow users to manually input specification documents. The reception unit can, for example, automatically detect the format of the specification document and convert it to an appropriate format. This allows for efficient generation of program code by accepting completed specification documents registered by users. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the specification document uploaded by the user into a generation AI and have the generation AI perform analysis of the specification document.

[0038] The requesting unit can analyze the specification document and make requests to the generation AI. For example, the requesting unit can analyze the contents of the specification document using text analysis technology. For example, the requesting unit can analyze the structure of the specification document and make appropriate requests to the generation AI. For example, the requesting unit can automatically determine the order of requests and make requests efficiently. In this way, program code can be efficiently generated by analyzing the specification document and making requests to the generation AI. Some or all of the above processing in the requesting unit may be performed using AI, for example, or without using AI. For example, the requesting unit can input the contents of the specification document into the generation AI and have the generation AI generate the request content.

[0039] The reception department can analyze the user's past specification submission history and select the optimal reception method. For example, the reception department can analyze the content of specifications previously submitted by the user and prioritize the acceptance of similar specifications. For example, the reception department can analyze the submission times of specifications previously submitted by the user and propose the optimal reception time. For example, the reception department can analyze the frequency of specification submissions previously submitted by the user and set an appropriate reception frequency. In this way, the optimal reception method can be selected by analyzing the user's past specification submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input the user's past specification submission history into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter specifications based on the user's current projects and areas of interest. For example, the reception unit may prioritize receiving specifications related to the user's current projects. For example, the reception unit may filter and receive specifications related to the user's areas of interest. For example, the reception unit may prioritize receiving specifications related to areas the user has shown interest in in the past. This allows for efficient receipt of specifications by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the specification filtering.

[0041] The reception desk can prioritize receiving highly relevant specifications based on the user's geographical location information. For example, the reception desk can prioritize receiving specifications related to the user's current location. For example, the reception desk can prioritize receiving region-specific specifications based on the user's geographical location information. For example, the reception desk can prioritize receiving specifications related to places the user has visited in the past. This allows for efficient receipt of specifications by prioritizing highly relevant specifications based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI select highly relevant specifications.

[0042] The reception department can analyze a user's social media activity and accept relevant specifications. For example, the reception department may prioritize accepting specifications related to projects mentioned by the user on social media. For example, the reception department may analyze a user's social media activity and accept specifications related to areas of interest. For example, the reception department may prioritize accepting specifications related to accounts that a user follows. This allows for the efficient acceptance of relevant specifications by analyzing a user's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the user's social media activity data into a generating AI and have the generating AI select relevant specifications.

[0043] The requesting department can adjust the level of detail of requests based on the importance of the specifications. For example, the requesting department will make detailed requests for high-importance specifications. For example, the requesting department will make concise requests for low-importance specifications. For example, the requesting department can adjust the level of detail of requests in stages according to importance. This allows for efficient requests by adjusting the level of detail of requests based on the importance of the specifications. Some or all of the above processing in the requesting department may be performed using AI, for example, or without AI. For example, the requesting department can input specification importance data into a generating AI and have the generating AI adjust the level of detail of the requests.

[0044] The requesting unit can apply different request algorithms depending on the category of the specification. For example, for a request to generate program code, the requesting unit applies an algorithm specialized in code generation. For example, for a request related to design, the requesting unit applies an algorithm specialized in design generation. For example, for a request for data analysis, the requesting unit applies an algorithm specialized in data analysis. This allows for efficient requests by applying different request algorithms depending on the category of the specification. Some or all of the above processing in the requesting unit may be performed using AI, for example, or without AI. For example, the requesting unit can input the category data of the specification into a generation AI and have the generation AI apply an appropriate request algorithm.

[0045] The requesting department can determine the priority of requests based on the submission timing of the specifications. For example, the requesting department may prioritize requests for specifications with approaching submission deadlines. For example, it may postpone requests for specifications with later submission deadlines. For example, the requesting department may adjust the priority of requests in stages according to the submission timing. This allows for efficient request processing by determining the priority of requests based on the submission timing of the specifications. Some or all of the above processing in the requesting department may be performed using AI, for example, or not using AI. For example, the requesting department can input specification submission timing data into a generating AI and have the generating AI determine the priority of requests.

[0046] The requesting department can adjust the order of requests based on the relevance of the specifications. For example, the requesting department may prioritize requests for highly relevant specifications. For example, the requesting department may postpone requests for less relevant specifications. For example, the requesting department may adjust the order of requests in stages according to their relevance. This allows for efficient requests by adjusting the order of requests based on the relevance of the specifications. Some or all of the above processing in the requesting department may be performed using AI, for example, or without AI. For example, the requesting department can input specification relevance data into a generating AI and have the generating AI adjust the order of requests.

[0047] The debugging unit can improve the accuracy of debugging by considering the interrelationships of program code. For example, the debugging unit can analyze the dependencies of program code and perform debugging while considering these interrelationships. For example, the debugging unit can perform debugging while considering the interactions between modules of program code. For example, the debugging unit can perform debugging while considering the interrelationships between functions of program code. In this way, the accuracy of debugging can be improved by considering the interrelationships of program code. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input interrelationship data of program code into a generating AI, and the generating AI can improve the accuracy of debugging.

[0048] The debugging unit can perform debugging while considering the attribute information of the program code submitter. For example, the debugging unit can perform debugging while considering the submitter's programming skills. For example, the debugging unit can perform debugging while referring to the submitter's past debugging history. For example, the debugging unit can perform debugging while considering the submitter's field of expertise. In this way, the accuracy of debugging can be improved by considering the attribute information of the program code submitter. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without using AI. For example, the debugging unit can input the submitter's attribute information data into a generating AI and have the generating AI perform debugging.

[0049] The debugging unit can perform debugging while considering the geographical distribution of the program code. For example, the debugging unit can perform debugging while considering program code that operates in geographically different locations. For example, the debugging unit can perform debugging while considering geographical constraints. For example, the debugging unit can perform debugging while considering geographical characteristics. By considering the geographical distribution of the program code, the accuracy of debugging can be improved. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input geographical distribution data of the program code into a generating AI and have the generating AI perform debugging.

[0050] The debugging unit can improve the accuracy of debugging by referring to relevant literature for the program code. For example, the debugging unit performs debugging by referring to academic papers related to the program code. For example, the debugging unit performs debugging by referring to technical documents related to the program code. For example, the debugging unit performs debugging by referring to patent documents related to the program code. In this way, the accuracy of debugging can be improved by referring to relevant literature for the program code. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without using AI. For example, the debugging unit can input relevant literature data for the program code into a generating AI and have the generating AI perform debugging.

[0051] The correction unit can adjust the level of detail in correction requests based on the importance of the debug results. For example, the correction unit will issue detailed correction requests for high-importance debug results. For example, the correction unit will issue concise correction requests for low-importance debug results. For example, the correction unit can adjust the level of detail in correction requests in stages according to importance. This allows for efficient correction requests by adjusting the level of detail in correction requests based on the importance of the debug results. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input importance data of the debug results into a generating AI and have the generating AI adjust the level of detail in correction requests.

[0052] The correction unit can apply different correction algorithms depending on the category of the debug result. For example, the correction unit applies a bug-fixing algorithm to fix code bugs. For example, the correction unit applies a performance-fixing algorithm to improve performance. For example, the correction unit applies a security-fixing algorithm to fix security bugs. This allows for efficient correction requests by applying different correction algorithms depending on the category of the debug result. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the category data of the debug result into a generating AI and cause the generating AI to apply an appropriate correction algorithm.

[0053] The correction unit can determine the priority of correction requests based on the submission timing of the debug results. For example, the correction unit prioritizes correction requests for debug results with approaching submission deadlines. For example, the correction unit postpones correction requests for debug results with later submission deadlines. For example, the correction unit adjusts the priority of correction requests in stages according to the submission timing. This allows for efficient correction requests by determining the priority of correction requests based on the submission timing of the debug results. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input debug result submission timing data into a generating AI and have the generating AI determine the priority of correction requests.

[0054] The correction unit can adjust the order of correction requests based on the relevance of the debug results. For example, the correction unit can prioritize correction requests for highly relevant debug results. For example, the correction unit can postpone correction requests for less relevant debug results. For example, the correction unit can adjust the order of correction requests in stages according to their relevance. This allows for efficient correction requests by adjusting the order of correction requests based on the relevance of the debug results. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input relevance data of the debug results into a generating AI and have the generating AI adjust the order of correction requests.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception department can analyze a user's past request history and understand trends in request content. For example, it can analyze the types and content of program code previously requested by the user and prioritize processing similar requests. It can also analyze the frequency and timing of user requests and suggest optimal request acceptance times. Furthermore, the reception department can automatically determine the priority of requests based on the user's request content. This allows for efficient request processing by leveraging the user's past request history.

[0057] The debugging unit can manage the version control of program code and analyze the differences between different versions. For example, the debugging unit can track the change history of program code and identify the changes. It can also analyze performance differences between different versions and select the optimal version. Furthermore, the debugging unit can evaluate the security risks between versions and recommend a secure version. In this way, version control of program code can improve the accuracy of debugging.

[0058] The reception desk can prioritize receiving highly relevant specifications based on the user's geographical location. For example, it can prioritize receiving specifications related to the user's current location. Furthermore, it can prioritize receiving region-specific specifications based on the user's geographical location. Additionally, it can prioritize receiving specifications related to places the user has visited in the past. This allows for efficient specification processing by prioritizing highly relevant specifications based on the user's geographical location.

[0059] The request department can analyze a user's social media activity and propose relevant requests. For example, the request department can prioritize requests related to projects the user has mentioned on social media. It can also analyze a user's social media activity and make requests related to areas of interest. Furthermore, it can prioritize requests related to accounts the user follows. This allows for efficient fulfillment of relevant requests by analyzing the user's social media activity.

[0060] The correction unit can adjust the level of detail in correction requests based on the importance of the debug results. For example, detailed correction requests can be made for highly important debug results, while concise requests can be made for less important ones. Furthermore, the level of detail in correction requests can be adjusted in stages according to the importance of the results. This allows for efficient correction requests by adjusting the level of detail based on the importance of the debug results.

[0061] The request department can apply different request algorithms depending on the category of the specification. For example, a request for program code generation can be handled with an algorithm specifically designed for code generation. Similarly, a request for design-related services can be handled with an algorithm specifically designed for design generation. Furthermore, a request for data analysis can be handled with an algorithm specifically designed for data analysis. This allows for efficient request processing by applying different request algorithms depending on the category of the specification.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives the completed specification document registered by the user. For example, it allows users to upload the completed specification document to the system, or users to manually enter the specification document. Furthermore, the reception desk can automatically detect the format of the specification document and convert it to the appropriate format. For example, it can convert a specification document in PDF format to text format. Step 2: The requesting unit analyzes the specifications registered by the receiving unit and sequentially sends requests to the generation AI. For example, it can analyze the content of the specifications using text analysis technology, analyze the structure of the specifications, and send appropriate requests to the generation AI. Furthermore, the requesting unit can automatically determine the order of requests and send them efficiently. Step 3: The debugging unit debugs the program code requested by the requesting unit to be generated by the AI. For example, it can perform error checking on the program code generated by the AI, check the performance of the program code, and optimize it. Furthermore, the debugging unit can perform security checks to ensure the safety of the program code. Step 4: The correction unit submits correction requests based on the results of debugging performed by the debugging unit. For example, it can submit requests to the generation AI to correct errors identified by the debugging unit, and it can also submit correction requests to improve the performance of the program code. Furthermore, the correction unit can submit requests to correct security issues.

[0064] (Example of form 2) The system according to the embodiment of the present invention is a system that eliminates the burden on the user by systematizing the interaction (trial and error) with the generation AI. In this system, the generation AI generates program code, but for difficult requests, it often generates hallucinations (a phenomenon that generates information not based on facts), and it takes time to get the desired result. In addition, code corrections require frequent interaction with the generation AI, but all of these responses must be handled by the user, resulting in a large amount of user effort. The present invention reduces the burden on the user by systematizing the interaction with the generation AI. For example, the user only needs to register a "final specification document," and the interaction with the generation AI is carried out by a dedicated system (Try & Ai). This system consists of the following steps: First, the user registers a final specification document in the system. Next, the system analyzes the specification document and sequentially makes requests to the generation AI. The system receives the program code generated by the generation AI and performs debugging. If corrections are needed as a result of debugging, the system automatically requests corrections from the generation AI. By repeating this process, the burden on the user is reduced and program code can be generated efficiently. For example, when a user requests a Google Forms or GAS code generation AI from a Google Slides generator, the system breaks down the request and sequentially sends it to the AI. The system then debugs the code generated by the AI ​​and requests corrections as needed. In this way, users can easily make complex requests. This system streamlines interaction with the AI ​​and reduces the user's workload. It also improves the quality of the program code generated by the AI, increasing user satisfaction. Furthermore, this system is not limited to program development; it is expected to have applications in various fields such as project planning and evaluation, climate change modeling, gene sequencing, life counseling, and space exploration. As a result, the system can reduce the burden on users and generate program code efficiently.

[0065] The system according to this embodiment comprises a reception unit, a request unit, a debugging unit, and a modification unit. The reception unit receives completed specification documents registered by the user. The reception unit allows, for example, the user to upload completed specification documents to the system. The reception unit also allows the user to manually input specification documents. Furthermore, the reception unit can automatically detect the format of the specification document and convert it to an appropriate format. For example, the reception unit can convert a specification document in PDF format to text format. The request unit analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The request unit analyzes the content of the specification document using text analysis technology. Furthermore, the request unit can analyze the structure of the specification document and make appropriate requests to the generation AI. Furthermore, the request unit can automatically determine the order of requests and make requests efficiently. For example, the request unit analyzes each section of the specification document and sequentially makes requests to the generation AI. The debugging unit debugs the program code requested by the request unit to the generation AI. The debugging unit checks the program code generated by the generation AI for errors. Furthermore, the debugging unit can check and optimize the performance of the program code. In addition, the debugging unit can perform security checks to ensure the safety of the program code. For example, the debugging unit analyzes the error log of the program code and identifies the cause of the error. The correction unit makes correction requests based on the results of debugging by the debugging unit. For example, the correction unit requests the generation AI to correct the errors identified by the debugging unit. The correction unit can also make correction requests to improve the performance of the program code. Furthermore, the correction unit can make requests to correct security issues. For example, the correction unit identifies the parts to be corrected based on the error log and requests the generation AI to make corrections. As a result, the system according to the embodiment can reduce the burden on the user and generate program code efficiently.

[0066] The reception desk accepts completed specification documents registered by users. For example, the reception desk allows users to upload completed specification documents to the system. Specifically, users can upload specification documents in various formats, such as PDF, Word, and Excel, through the system interface. The reception desk receives these files and stores them in its internal database. The reception desk also allows users to manually input specification documents. For example, users can directly enter the content of the specification document into the system's input form. This input form is designed for efficient user input and includes features such as automatic saving of input content and input assistance. Furthermore, the reception desk can automatically detect the format of the specification document and convert it to the appropriate format. For example, the reception desk can convert a PDF specification document to text format. This involves a process that uses optical character recognition (OCR) technology to extract text from the PDF and convert it into an editable text format. This allows the system to centrally manage specification documents in various formats and convert them to formats suitable for subsequent processing. Through these functions, the reception desk enables users to register specification documents easily and quickly, improving the overall efficiency of the system.

[0067] The requesting department analyzes the specifications registered by the receiving department and sequentially makes requests to the generation AI. For example, the requesting department analyzes the content of the specifications using text analysis technology. Specifically, it uses natural language processing (NLP) technology to extract each section and item of the specifications and understand their content. The requesting department can analyze the structure of the specifications and make appropriate requests to the generation AI. For example, it can extract descriptions of specific functions or modules from the specifications and request the generation AI to generate program code based on that. Furthermore, the requesting department can automatically determine the order of requests and make requests efficiently. For example, the requesting department analyzes each section of the specifications and sequentially makes requests to the generation AI. This allows the requesting department to efficiently analyze the content of the specifications and make appropriate requests to the generation AI. In addition, the requesting department can monitor the progress of requests and modify the request content as needed. For example, it can check the quality of the program code generated by the generation AI and make additional requests as needed. This allows the requesting department to optimize the output of the generation AI and improve the overall efficiency of the system.

[0068] The debugging unit debugs the program code requested by the requesting unit to be generated by the generation AI. For example, the debugging unit checks the program code generated by the generation AI for errors. Specifically, it uses static analysis tools to detect syntax errors and type errors in the program code. The debugging unit can also check the performance of the program code and perform optimization. For example, it monitors the execution time and memory usage of the program code, identifies bottlenecks, and performs optimization. Furthermore, the debugging unit can perform security checks to ensure the safety of the program code. For example, it can detect vulnerabilities in the program code and propose solutions for fixing them. Through these functions, the debugging unit can improve the quality of the program code generated by the generation AI. For example, the debugging unit analyzes the error logs of the program code and identifies the causes of errors. In this way, the debugging unit can improve the quality of the program code generated by the generation AI and improve the reliability of the entire system.

[0069] The correction unit requests corrections based on the results of debugging performed by the debugging unit. For example, the correction unit requests the generation AI to correct errors identified by the debugging unit. Specifically, it identifies the areas to be corrected based on the error log and requests the generation AI to make corrections. The correction unit can also request corrections to improve the performance of the program code. For example, it can request optimizations to reduce the execution time of the program code. Furthermore, the correction unit can request corrections to fix security issues. For example, it can request corrections to fix vulnerabilities contained in the program code. Through these functions, the correction unit can improve the quality of the program code generated by the generation AI. For example, the correction unit identifies the areas to be corrected based on the error log and requests the generation AI to make corrections. In this way, the correction unit can improve the quality of the program code generated by the generation AI and improve the reliability of the entire system.

[0070] The requesting unit can sequentially issue requests to the generation AI. For example, the requesting unit can analyze the contents of the specification document and issue appropriate requests to the generation AI. For example, the requesting unit can analyze each section of the specification document and issue sequential requests to the generation AI. For example, the requesting unit can automatically determine the order of requests and issue them efficiently. As a result, program code can be generated efficiently by sequentially issuing requests to the generation AI. Some or all of the above-described processes in the requesting unit may be performed using AI, for example, or without using AI. For example, the requesting unit can input the contents of the specification document into the generation AI and have the generation AI generate the request content.

[0071] The debugging unit can debug the program code generated by the generation AI. The debugging unit can, for example, perform error checking on the program code generated by the generation AI. The debugging unit can, for example, check the performance of the program code and perform optimization. The debugging unit can, for example, perform security checks to ensure the safety of the program code. In this way, the quality of the code can be improved by debugging the program code generated by the generation AI. Some or all of the above processes in the debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input the program code generated by the generation AI into the generation AI and have the generation AI perform debugging.

[0072] The correction unit can make correction requests based on the debugging results. For example, the correction unit can request the generation AI to correct errors identified by the debugging unit. For example, the correction unit can make correction requests to improve the performance of the program code. For example, the correction unit can make requests to correct security issues. This allows for efficient modification of the program code by making correction requests based on the debugging results. Some or all of the above processes in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input errors identified by the debugging unit to the generation AI and have the generation AI execute correction requests.

[0073] The reception unit can accept completed specification documents registered by users. The reception unit can, for example, allow users to upload completed specification documents to the system. The reception unit can also, for example, allow users to manually input specification documents. The reception unit can, for example, automatically detect the format of the specification document and convert it to an appropriate format. This allows for efficient generation of program code by accepting completed specification documents registered by users. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the specification document uploaded by the user into a generation AI and have the generation AI perform analysis of the specification document.

[0074] The requesting unit can analyze the specification document and make requests to the generation AI. For example, the requesting unit can analyze the contents of the specification document using text analysis technology. For example, the requesting unit can analyze the structure of the specification document and make appropriate requests to the generation AI. For example, the requesting unit can automatically determine the order of requests and make requests efficiently. In this way, program code can be efficiently generated by analyzing the specification document and making requests to the generation AI. Some or all of the above processing in the requesting unit may be performed using AI, for example, or without using AI. For example, the requesting unit can input the contents of the specification document into the generation AI and have the generation AI generate the request content.

[0075] The reception desk can estimate the user's emotions and adjust the timing of specification submission based on the estimated emotions. For example, if the user is stressed, the reception desk will accept the specification during a time when the user is relaxed. If the user is in a hurry, the reception desk will accept the specification immediately. If the user is focused, the reception desk will accept the specification during a time when their concentration is at its highest. By adjusting the timing of specification submission based on the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception department can analyze the user's past specification submission history and select the optimal reception method. For example, the reception department can analyze the content of specifications previously submitted by the user and prioritize the acceptance of similar specifications. For example, the reception department can analyze the submission times of specifications previously submitted by the user and propose the optimal reception time. For example, the reception department can analyze the frequency of specification submissions previously submitted by the user and set an appropriate reception frequency. In this way, the optimal reception method can be selected by analyzing the user's past specification submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input the user's past specification submission history into a generating AI and have the generating AI select the optimal reception method.

[0077] The reception unit can filter specifications based on the user's current projects and areas of interest. For example, the reception unit may prioritize receiving specifications related to the user's current projects. For example, the reception unit may filter and receive specifications related to the user's areas of interest. For example, the reception unit may prioritize receiving specifications related to areas the user has shown interest in in the past. This allows for efficient receipt of specifications by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the specification filtering.

[0078] The reception desk can estimate the user's emotions and determine the priority of the specifications to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone less important specifications. For example, if the user is relaxed, the reception desk will prioritize receiving more important specifications. For example, if the user is in a hurry, the reception desk will prioritize receiving more urgent specifications. This reduces the user's burden by determining the priority of specifications to be received based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can prioritize receiving highly relevant specifications based on the user's geographical location information. For example, the reception desk can prioritize receiving specifications related to the user's current location. For example, the reception desk can prioritize receiving region-specific specifications based on the user's geographical location information. For example, the reception desk can prioritize receiving specifications related to places the user has visited in the past. This allows for efficient receipt of specifications by prioritizing highly relevant specifications based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI select highly relevant specifications.

[0080] The reception department can analyze a user's social media activity and accept relevant specifications. For example, the reception department may prioritize accepting specifications related to projects mentioned by the user on social media. For example, the reception department may analyze a user's social media activity and accept specifications related to areas of interest. For example, the reception department may prioritize accepting specifications related to accounts that a user follows. This allows for the efficient acceptance of relevant specifications by analyzing a user's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the user's social media activity data into a generating AI and have the generating AI select relevant specifications.

[0081] The request unit can estimate the user's emotions and adjust the way the request is expressed based on the estimated emotions. For example, if the user is stressed, the request unit will make a concise and clear request. For example, if the user is relaxed, the request unit will make a detailed request. For example, if the user is in a hurry, the request unit will make a request that can be handled quickly. In this way, the burden on the user can be reduced by adjusting the way the request is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the request unit may be performed using AI, for example, or not using AI. For example, the request unit can input user emotion data into a generative AI and have the generative AI adjust the way the request is expressed.

[0082] The requesting department can adjust the level of detail of requests based on the importance of the specifications. For example, the requesting department will make detailed requests for high-importance specifications. For example, the requesting department will make concise requests for low-importance specifications. For example, the requesting department can adjust the level of detail of requests in stages according to importance. This allows for efficient requests by adjusting the level of detail of requests based on the importance of the specifications. Some or all of the above processing in the requesting department may be performed using AI, for example, or without AI. For example, the requesting department can input specification importance data into a generating AI and have the generating AI adjust the level of detail of the requests.

[0083] The requesting unit can apply different request algorithms depending on the category of the specification. For example, for a request to generate program code, the requesting unit applies an algorithm specialized in code generation. For example, for a request related to design, the requesting unit applies an algorithm specialized in design generation. For example, for a request for data analysis, the requesting unit applies an algorithm specialized in data analysis. This allows for efficient requests by applying different request algorithms depending on the category of the specification. Some or all of the above processing in the requesting unit may be performed using AI, for example, or without AI. For example, the requesting unit can input the category data of the specification into a generation AI and have the generation AI apply an appropriate request algorithm.

[0084] The request unit can estimate the user's emotions and adjust the length of the request based on the estimated emotions. For example, if the user is stressed, the request unit will make a short and concise request. For example, if the user is relaxed, the request unit will make a detailed request. For example, if the user is in a hurry, the request unit will make a short request that can be handled quickly. This reduces the burden on the user by adjusting the length of the request based on their 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 request unit may be performed using AI or not. For example, the request unit can input user emotion data into a generative AI and have the generative AI adjust the length of the request.

[0085] The requesting department can determine the priority of requests based on the submission timing of the specifications. For example, the requesting department may prioritize requests for specifications with approaching submission deadlines. For example, it may postpone requests for specifications with later submission deadlines. For example, the requesting department may adjust the priority of requests in stages according to the submission timing. This allows for efficient request processing by determining the priority of requests based on the submission timing of the specifications. Some or all of the above processing in the requesting department may be performed using AI, for example, or not using AI. For example, the requesting department can input specification submission timing data into a generating AI and have the generating AI determine the priority of requests.

[0086] The requesting department can adjust the order of requests based on the relevance of the specifications. For example, the requesting department may prioritize requests for highly relevant specifications. For example, the requesting department may postpone requests for less relevant specifications. For example, the requesting department may adjust the order of requests in stages according to their relevance. This allows for efficient requests by adjusting the order of requests based on the relevance of the specifications. Some or all of the above processing in the requesting department may be performed using AI, for example, or without AI. For example, the requesting department can input specification relevance data into a generating AI and have the generating AI adjust the order of requests.

[0087] The debugging unit can estimate the user's emotions and adjust the debugging criteria based on the estimated emotions. For example, if the user is stressed, the debugging unit provides concise and clear debugging results. For example, if the user is relaxed, the debugging unit provides detailed debugging results. For example, if the user is in a hurry, the debugging unit applies debugging criteria that allow for quick response. This reduces the user's burden by adjusting the debugging criteria 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 debugging unit may be performed using AI or not using AI. For example, the debugging unit can input user emotion data into a generative AI and have the generative AI adjust the debugging criteria.

[0088] The debugging unit can improve the accuracy of debugging by considering the interrelationships of program code. For example, the debugging unit can analyze the dependencies of program code and perform debugging while considering these interrelationships. For example, the debugging unit can perform debugging while considering the interactions between modules of program code. For example, the debugging unit can perform debugging while considering the interrelationships between functions of program code. In this way, the accuracy of debugging can be improved by considering the interrelationships of program code. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input interrelationship data of program code into a generating AI, and the generating AI can improve the accuracy of debugging.

[0089] The debugging unit can perform debugging while considering the attribute information of the program code submitter. For example, the debugging unit can perform debugging while considering the submitter's programming skills. For example, the debugging unit can perform debugging while referring to the submitter's past debugging history. For example, the debugging unit can perform debugging while considering the submitter's field of expertise. In this way, the accuracy of debugging can be improved by considering the attribute information of the program code submitter. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without using AI. For example, the debugging unit can input the submitter's attribute information data into a generating AI and have the generating AI perform debugging.

[0090] The debugging unit can estimate the user's emotions and adjust the order in which debugging results are displayed based on the estimated emotions. For example, if the user is stressed, the debugging unit will display important debugging results first. If the user is relaxed, the debugging unit will display detailed debugging results sequentially. If the user is in a hurry, the debugging unit will display debugging results that can be addressed quickly first. This reduces the user's burden by adjusting the order in which debugging results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input user emotion data into a generative AI and have the generative AI adjust the order in which debugging results are displayed.

[0091] The debugging unit can perform debugging while considering the geographical distribution of the program code. For example, the debugging unit can perform debugging while considering program code that operates in geographically different locations. For example, the debugging unit can perform debugging while considering geographical constraints. For example, the debugging unit can perform debugging while considering geographical characteristics. By considering the geographical distribution of the program code, the accuracy of debugging can be improved. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without AI. For example, the debugging unit can input geographical distribution data of the program code into a generating AI and have the generating AI perform debugging.

[0092] The debugging unit can improve the accuracy of debugging by referring to relevant literature for the program code. For example, the debugging unit performs debugging by referring to academic papers related to the program code. For example, the debugging unit performs debugging by referring to technical documents related to the program code. For example, the debugging unit performs debugging by referring to patent documents related to the program code. In this way, the accuracy of debugging can be improved by referring to relevant literature for the program code. Some or all of the above processing in the debugging unit may be performed using AI, for example, or without using AI. For example, the debugging unit can input relevant literature data for the program code into a generating AI and have the generating AI perform debugging.

[0093] The editing unit can estimate the user's emotions and adjust the method of requesting corrections based on the estimated emotions. For example, if the user is stressed, the editing unit will make a concise and clear correction request. For example, if the user is relaxed, the editing unit will make a detailed correction request. For example, if the user is in a hurry, the editing unit will make a correction request that can be addressed quickly. This reduces the burden on the user by adjusting the method of requesting corrections based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input user emotion data into a generative AI and have the generative AI adjust the method of requesting corrections.

[0094] The correction unit can adjust the level of detail in correction requests based on the importance of the debug results. For example, the correction unit will issue detailed correction requests for high-importance debug results. For example, the correction unit will issue concise correction requests for low-importance debug results. For example, the correction unit can adjust the level of detail in correction requests in stages according to importance. This allows for efficient correction requests by adjusting the level of detail in correction requests based on the importance of the debug results. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input importance data of the debug results into a generating AI and have the generating AI adjust the level of detail in correction requests.

[0095] The correction unit can apply different correction algorithms depending on the category of the debug result. For example, the correction unit applies a bug-fixing algorithm to fix code bugs. For example, the correction unit applies a performance-fixing algorithm to improve performance. For example, the correction unit applies a security-fixing algorithm to fix security bugs. This allows for efficient correction requests by applying different correction algorithms depending on the category of the debug result. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the category data of the debug result into a generating AI and cause the generating AI to apply an appropriate correction algorithm.

[0096] The editing unit can estimate the user's emotions and determine the priority of editing requests based on the estimated emotions. For example, if the user is stressed, the editing unit will postpone less important editing requests. For example, if the user is relaxed, the editing unit will prioritize more important editing requests. For example, if the user is in a hurry, the editing unit will prioritize more urgent editing requests. This reduces the user's burden by prioritizing editing requests based on their 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 editing unit may be performed using AI or not. For example, the editing unit can input user emotion data into a generative AI and have the generative AI determine the priority of editing requests.

[0097] The correction unit can determine the priority of correction requests based on the submission timing of the debug results. For example, the correction unit prioritizes correction requests for debug results with approaching submission deadlines. For example, the correction unit postpones correction requests for debug results with later submission deadlines. For example, the correction unit adjusts the priority of correction requests in stages according to the submission timing. This allows for efficient correction requests by determining the priority of correction requests based on the submission timing of the debug results. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input debug result submission timing data into a generating AI and have the generating AI determine the priority of correction requests.

[0098] The correction unit can adjust the order of correction requests based on the relevance of the debug results. For example, the correction unit can prioritize correction requests for highly relevant debug results. For example, the correction unit can postpone correction requests for less relevant debug results. For example, the correction unit can adjust the order of correction requests in stages according to their relevance. This allows for efficient correction requests by adjusting the order of correction requests based on the relevance of the debug results. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input relevance data of the debug results into a generating AI and have the generating AI adjust the order of correction requests.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception department can analyze a user's past request history and understand trends in request content. For example, it can analyze the types and content of program code previously requested by the user and prioritize processing similar requests. It can also analyze the frequency and timing of user requests and suggest optimal request acceptance times. Furthermore, the reception department can automatically determine the priority of requests based on the user's request content. This allows for efficient request processing by leveraging the user's past request history.

[0101] The requesting department can estimate the user's emotions and adjust the request based on those emotions. For example, if the user is stressed, the requesting department can make a concise and clear request. If the user is relaxed, the requesting department can make a detailed request. Furthermore, if the user is in a hurry, the requesting department can make a request that can be handled quickly. In this way, by adjusting the request based on the user's emotions, the burden on the user can be reduced.

[0102] The debugging unit can manage the version control of program code and analyze the differences between different versions. For example, the debugging unit can track the change history of program code and identify the changes. It can also analyze performance differences between different versions and select the optimal version. Furthermore, the debugging unit can evaluate the security risks between versions and recommend a secure version. In this way, version control of program code can improve the accuracy of debugging.

[0103] The correction unit can estimate the user's emotions and prioritize correction requests based on those emotions. For example, if the user is stressed, the correction unit can postpone less important correction requests. Conversely, if the user is relaxed, the correction unit can prioritize more important correction requests. Furthermore, if the user is in a hurry, the correction unit can prioritize urgent correction requests. By prioritizing correction requests based on the user's emotions, the burden on the user can be reduced.

[0104] The reception desk can prioritize receiving highly relevant specifications based on the user's geographical location. For example, it can prioritize receiving specifications related to the user's current location. Furthermore, it can prioritize receiving region-specific specifications based on the user's geographical location. Additionally, it can prioritize receiving specifications related to places the user has visited in the past. This allows for efficient specification processing by prioritizing highly relevant specifications based on the user's geographical location.

[0105] The request department can analyze a user's social media activity and propose relevant requests. For example, the request department can prioritize requests related to projects the user has mentioned on social media. It can also analyze a user's social media activity and make requests related to areas of interest. Furthermore, it can prioritize requests related to accounts the user follows. This allows for efficient fulfillment of relevant requests by analyzing the user's social media activity.

[0106] The debugging unit can estimate the user's emotions and adjust the debugging criteria based on those emotions. For example, if the user is stressed, the debugging unit can provide concise and clear debugging results. If the user is relaxed, the debugging unit can provide detailed debugging results. Furthermore, if the user is in a hurry, the debugging unit can apply debugging criteria that allow for quick response. In this way, adjusting the debugging criteria based on the user's emotions can reduce the burden on the user.

[0107] The correction unit can adjust the level of detail in correction requests based on the importance of the debug results. For example, detailed correction requests can be made for highly important debug results, while concise requests can be made for less important ones. Furthermore, the level of detail in correction requests can be adjusted in stages according to the importance of the results. This allows for efficient correction requests by adjusting the level of detail based on the importance of the debug results.

[0108] The request department can apply different request algorithms depending on the category of the specification. For example, a request for program code generation can be handled with an algorithm specifically designed for code generation. Similarly, a request for design-related services can be handled with an algorithm specifically designed for design generation. Furthermore, a request for data analysis can be handled with an algorithm specifically designed for data analysis. This allows for efficient request processing by applying different request algorithms depending on the category of the specification.

[0109] The correction unit can estimate the user's emotions and adjust the method of correction requests based on those emotions. For example, if the user is stressed, the correction unit can make a concise and clear correction request. If the user is relaxed, the correction unit can make a detailed correction request. Furthermore, if the user is in a hurry, the correction unit can make a correction request that can be addressed quickly. In this way, by adjusting the method of correction requests based on the user's emotions, the burden on the user can be reduced.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives the completed specification document registered by the user. For example, it allows users to upload the completed specification document to the system, or users to manually enter the specification document. Furthermore, the reception desk can automatically detect the format of the specification document and convert it to the appropriate format. For example, it can convert a specification document in PDF format to text format. Step 2: The requesting unit analyzes the specifications registered by the receiving unit and sequentially sends requests to the generation AI. For example, it can analyze the content of the specifications using text analysis technology, analyze the structure of the specifications, and send appropriate requests to the generation AI. Furthermore, the requesting unit can automatically determine the order of requests and send them efficiently. Step 3: The debugging unit debugs the program code requested by the requesting unit to be generated by the AI. For example, it can perform error checking on the program code generated by the AI, check the performance of the program code, and optimize it. Furthermore, the debugging unit can perform security checks to ensure the safety of the program code. Step 4: The correction unit submits correction requests based on the results of debugging performed by the debugging unit. For example, it can submit requests to the generation AI to correct errors identified by the debugging unit, and it can also submit correction requests to improve the performance of the program code. Furthermore, the correction unit can submit requests to correct security issues.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, request unit, debugging unit, and modification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the completed specification document registered by the user. The request unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The debugging unit is implemented by the specific processing unit 290 of the data processing unit 12 and debugs the program code generated by the generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes modification requests based on the results debugged by the debugging unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, request unit, debugging unit, and modification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the completed specification document registered by the user. The request unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The debugging unit is implemented by the specific processing unit 290 of the data processing unit 12 and debugs the program code generated by the generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes modification requests based on the results debugged by the debugging unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, request unit, debugging unit, and modification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the completed specification document registered by the user. The request unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the specification document registered by the reception unit and sequentially makes requests to the generation AI. The debugging unit is implemented by the specific processing unit 290 of the data processing unit 12 and debugs the program code generated by the generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes modification requests based on the results debugged by the debugging unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, request unit, debugging unit, and modification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the completed specification document registered by the user. The request unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the specification document registered by the reception unit and sequentially makes requests to the generating AI. The debugging unit is implemented by the specific processing unit 290 of the data processing unit 12 and debugs the program code generated by the generating AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes modification requests based on the results debugged by the debugging unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The reception department registers the completed specification document, The requesting unit analyzes the specifications registered by the aforementioned receiving unit and sequentially makes requests to the generation AI, A debugging unit that debugs the program code requested by the requesting unit to be generated by the AI, The system includes a correction unit that requests corrections based on the results of debugging performed by the aforementioned debugging unit. A system characterized by the following features. (Note 2) The aforementioned request unit, Requests are sent sequentially to the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The debugging unit described above, Debugging program code generated by a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned modification section is, We will submit a correction request based on the debugging results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We accept completed specification documents registered by users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned request unit, Analyze the specifications and make a request to the generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the timing of specification submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past specification submission history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Filter specifications based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the specifications to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Based on the user's geographical location, we will prioritize accepting specifications that are highly relevant. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is We analyze users' social media activity and accept relevant specifications. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned request unit, The system estimates the user's emotions and adjusts the way requests are phrased based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned request unit, Adjust the level of detail in the request based on the importance of the specifications. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned request unit, Apply different request algorithms depending on the category of the specification. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned request unit, It estimates the user's emotions and adjusts the length of the request based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned request unit, We will prioritize requests based on the submission date of the specifications. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned request unit, Adjust the order of requests based on the relevance of the specifications. The system described in Appendix 1, characterized by the features described herein. (Note 19) The debugging unit described above, We estimate the user's emotions and adjust the debugging criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The debugging unit described above, Improve debugging accuracy by considering the interrelationships of program code. The system described in Appendix 1, characterized by the features described herein. (Note 21) The debugging unit described above, Debugging is performed while considering the attribute information of the program code submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The debugging unit described above, It estimates the user's emotions and adjusts the order in which debug results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The debugging unit described above, Debugging is performed while considering the geographical distribution of the program code. The system described in Appendix 1, characterized by the features described herein. (Note 24) The debugging unit described above, Improve debugging accuracy by referring to relevant documentation for the program code. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned modification section is, We estimate the user's emotions and adjust the method of requesting corrections based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned modification section is, Adjust the level of detail in the fix request based on the importance of the debug results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned modification section is, Apply different correction algorithms depending on the category of the debug result. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned modification section is, The system estimates user sentiment and prioritizes correction requests based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned modification section is, Prioritize correction requests based on when debugging results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned modification section is, Adjust the order of correction requests based on the relevance of the debugging results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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. The reception department registers the completed specification document, The requesting unit analyzes the specifications registered by the aforementioned receiving unit and sequentially makes requests to the generation AI, A debugging unit that debugs the program code requested by the request unit to be generated by the AI, The system includes a correction unit that requests corrections based on the results of debugging performed by the aforementioned debugging unit. A system characterized by the following features.

2. The aforementioned request unit, Requests are sent sequentially to the generating AI. The system according to feature 1.

3. The debugging unit described above, Debugging program code generated by a generation AI. The system according to feature 1.

4. The aforementioned modification section is, We will submit a correction request based on the debugging results. The system according to feature 1.

5. The aforementioned reception unit is We accept completed specification documents registered by users. The system according to feature 1.

6. The aforementioned request unit, The specifications are analyzed, and requests are made to the generation AI. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the timing of specification submission based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past specification submission history and select the most suitable submission method. The system according to feature 1.

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